diff --git a/.circleci/config.yml b/.circleci/config.yml index dbeb412506f..abcdbf45187 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -133,6 +133,26 @@ commands: done echo "record/replay proxy did not become ready" >&2 exit 1 + start_fake_openai_endpoint: + description: "Start the canned OpenAI mock (tests/_fake_openai_endpoint_server.py) on host port 8190 and wait until healthy. Models whose api_base points here (via FAKE_OPENAI_API_BASE) get well-formed chat/text/embedding responses with realistic usage, so the E2E run neither pays for nor depends on the live provider. A request whose model is '429' returns HTTP 429 for rate-limit/cooldown tests. Run after uv deps are synced." + steps: + - run: + name: Start fake OpenAI endpoint + background: true + command: | + uv run --no-sync python tests/_fake_openai_endpoint_server.py --host 0.0.0.0 --port 8190 + - run: + name: Wait for fake OpenAI endpoint + command: | + for i in $(seq 1 30); do + if curl -sf http://localhost:8190/health >/dev/null 2>&1; then + echo "fake OpenAI endpoint is up" + exit 0 + fi + sleep 1 + done + echo "fake OpenAI endpoint did not become ready" >&2 + exit 1 setup_litellm_enterprise_pip: steps: - run: @@ -168,6 +188,8 @@ jobs: name: win/default shell: powershell.exe working_directory: ~/project + environment: + UV_PYTHON: "3.11" steps: - checkout - run: @@ -200,7 +222,7 @@ jobs: if (-not (Select-String -Path $PROFILE -SimpleMatch $uvBin -Quiet)) { Add-Content -Path $PROFILE -Value "`$env:Path = `"$uvBin;`$env:Path`"" } - uv sync --frozen --group dev --python (Get-Command python).Source + uv sync --frozen --group dev --python 3.11 - run: name: Run Windows-specific test command: | @@ -594,6 +616,8 @@ jobs: working_directory: ~/project resource_class: large parallelism: 4 + environment: + FAKE_OPENAI_API_BASE: http://127.0.0.1:8190 steps: - checkout - setup_google_dns @@ -609,6 +633,7 @@ jobs: paths: - ~/.cache/uv key: v1-uv-cache-{{ checksum "uv.lock" }} + - start_fake_openai_endpoint # Run pytest and generate JUnit XML report - setup_litellm_enterprise_pip - run: @@ -1549,6 +1574,7 @@ jobs: name: Install Dependencies command: | uv sync --frozen --all-groups --all-extras --python 3.12 + - start_fake_openai_endpoint - start_postgres: db_name: litellm_test - attach_workspace: @@ -1586,6 +1612,7 @@ jobs: -e DATABASE_URL="postgresql://postgres:postgres@host.docker.internal:5432/litellm_test" \ -e DEFAULT_NUM_WORKERS_LITELLM_PROXY=1 \ -e DISABLE_SCHEMA_UPDATE="True" \ + -e FAKE_OPENAI_API_BASE=http://host.docker.internal:8190 \ --name my-app \ --add-host=host.docker.internal:host-gateway \ -v $(pwd)/litellm/proxy/example_config_yaml/bad_schema.prisma:/app/schema.prisma \ @@ -1648,6 +1675,7 @@ jobs: zstd -d litellm-docker-database.tar.zst --stdout | docker load docker tag litellm-docker-database:ci my-app:latest - start_openai_record_replay_proxy + - start_fake_openai_endpoint - run: name: Run Docker container command: | @@ -1655,6 +1683,7 @@ jobs: -p 4000:4000 \ -e DATABASE_URL=postgresql://postgres:postgres@host.docker.internal:5432/circle_test \ -e USE_PRISMA_MIGRATE=True \ + -e FAKE_OPENAI_API_BASE=http://host.docker.internal:8190 \ -e AZURE_API_KEY=$AZURE_API_KEY \ -e REDIS_HOST=$REDIS_HOST \ -e REDIS_PASSWORD=$REDIS_PASSWORD \ @@ -1817,6 +1846,7 @@ jobs: zstd -d litellm-docker-database.tar.zst --stdout | docker load docker images | grep litellm-docker-database - start_openai_record_replay_proxy + - start_fake_openai_endpoint - run: name: Run Docker container # intentionally give bad redis credentials here @@ -1830,6 +1860,7 @@ jobs: -e REDIS_PORT=$REDIS_PORT \ -e LITELLM_MASTER_KEY="sk-1234" \ -e OPENAI_API_KEY=$OPENAI_API_KEY \ + -e FAKE_OPENAI_API_BASE=http://host.docker.internal:8190 \ -e LITELLM_LICENSE=$LITELLM_LICENSE \ -e OTEL_EXPORTER="in_memory" \ -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \ @@ -1889,6 +1920,7 @@ jobs: -e REDIS_PORT=$REDIS_PORT \ -e LITELLM_MASTER_KEY="sk-1234" \ -e OPENAI_API_KEY=$OPENAI_API_KEY \ + -e FAKE_OPENAI_API_BASE=http://host.docker.internal:8190 \ -e LITELLM_LICENSE="bad-license" \ --add-host host.docker.internal:host-gateway \ --name my-app-3 \ @@ -1938,6 +1970,7 @@ jobs: uv sync --frozen --all-groups --all-extras --python 3.12 - start_postgres - start_redis + - start_fake_openai_endpoint - attach_workspace: at: ~/project - run: @@ -1961,6 +1994,7 @@ jobs: -e REDIS_PORT=6379 \ -e LITELLM_MASTER_KEY="sk-1234" \ -e OPENAI_API_KEY=$OPENAI_API_KEY \ + -e FAKE_OPENAI_API_BASE=http://host.docker.internal:8190 \ -e LITELLM_LICENSE=$LITELLM_LICENSE \ -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \ -e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \ @@ -2020,6 +2054,7 @@ jobs: command: | uv sync --frozen --all-groups --all-extras --python 3.12 - start_postgres + - start_fake_openai_endpoint - attach_workspace: at: ~/project - run: @@ -2039,6 +2074,7 @@ jobs: -e REDIS_PASSWORD=$REDIS_PASSWORD \ -e REDIS_PORT=$REDIS_PORT \ -e LITELLM_MASTER_KEY="sk-1234" \ + -e FAKE_OPENAI_API_BASE=http://host.docker.internal:8190 \ -e LITELLM_LICENSE=$LITELLM_LICENSE \ -e USE_DDTRACE=True \ -e DD_API_KEY=$DD_API_KEY \ @@ -2060,6 +2096,7 @@ jobs: -e REDIS_PASSWORD=$REDIS_PASSWORD \ -e REDIS_PORT=$REDIS_PORT \ -e LITELLM_MASTER_KEY="sk-1234" \ + -e FAKE_OPENAI_API_BASE=http://host.docker.internal:8190 \ -e LITELLM_LICENSE=$LITELLM_LICENSE \ -e USE_DDTRACE=True \ -e DD_API_KEY=$DD_API_KEY \ @@ -2112,6 +2149,7 @@ jobs: command: | uv sync --frozen --all-groups --all-extras --python 3.12 - start_postgres + - start_fake_openai_endpoint - attach_workspace: at: ~/project - run: @@ -2129,6 +2167,7 @@ jobs: -e DATABASE_URL=postgresql://postgres:postgres@host.docker.internal:5432/circle_test \ -e STORE_MODEL_IN_DB="True" \ -e LITELLM_MASTER_KEY="sk-1234" \ + -e FAKE_OPENAI_API_BASE=http://host.docker.internal:8190 \ -e LITELLM_LICENSE=$LITELLM_LICENSE \ --add-host host.docker.internal:host-gateway \ --name my-app \ @@ -2187,6 +2226,7 @@ jobs: command: | docker build -t my-app:latest -f docker/build_from_pip/Dockerfile.build_from_pip . - start_postgres + - start_fake_openai_endpoint - run: name: Run Docker container # intentionally give bad redis credentials here @@ -2200,6 +2240,7 @@ jobs: -e REDIS_PORT=$REDIS_PORT \ -e LITELLM_MASTER_KEY="sk-1234" \ -e OPENAI_API_KEY=$OPENAI_API_KEY \ + -e FAKE_OPENAI_API_BASE=http://host.docker.internal:8190 \ -e LITELLM_LICENSE=$LITELLM_LICENSE \ -e OTEL_EXPORTER="in_memory" \ -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \ diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md index 99f79c0b272..9658baeb89a 100644 --- a/.github/pull_request_template.md +++ b/.github/pull_request_template.md @@ -4,7 +4,7 @@ ## Linear ticket - + ## Pre-Submission checklist diff --git a/.github/scripts/_agent_shin_actions.py b/.github/scripts/_agent_shin_actions.py new file mode 100644 index 00000000000..b3d1ff055b3 --- /dev/null +++ b/.github/scripts/_agent_shin_actions.py @@ -0,0 +1,50 @@ +"""Dry-run wrapper(s) around Agent Shin GitHub mutations. + +The rollout scripts currently need only one mutation wrapped, so this module +exposes a single ``maybe_post_comment`` helper. It takes a ``dry_run: bool`` +keyword argument and the body is intentionally trivial: + + if dry_run: + print(...) # log what we would do, return + return + real_mutation(...) # otherwise, actually do it + +That shape means a dry-run preview differs from the real run in exactly one +line per side effect: the call site. So when you `python3 script.py` locally +without ``--close``, you can be confident the actions printed are the ones the +GitHub Action would have performed (modulo ordering on retry/error paths, +which are deliberately simple). Any further mutation a rollout script needs +should get the same ``maybe_*`` treatment instead of calling the raw +``triage_with_llm`` mutation directly. + +Importing from this module pulls in the real mutation from ``triage_with_llm`` +— call sites in the rollout scripts should NEVER import ``post_comment`` +directly; that would skip the dry-run gate and is the bug class this module +exists to prevent. +""" + +from __future__ import annotations + +import sys +import textwrap + +# Import the module itself rather than the bare names so monkeypatching +# `triage_with_llm.post_comment` (or any of the other mutations) in tests is +# reflected here — `from triage_with_llm import post_comment` would bind the +# original function to a local name and bypass the patch, defeating the whole +# point of these wrappers. +import triage_with_llm + + +def _log(line: str) -> None: + """Print a single dry-run line to stdout (one log statement per side effect).""" + print(line, file=sys.stdout, flush=True) + + +def maybe_post_comment(repo: str, number: int, body: str, *, dry_run: bool) -> None: + """Post a comment on ``repo#number`` — or, in dry-run, log what we would post.""" + if dry_run: + _log(f"[DRY RUN] comment {repo}#{number}:") + _log(textwrap.indent(body, " ")) + return + triage_with_llm.post_comment(repo, number, body) diff --git a/.github/scripts/agent_shin_shared.py b/.github/scripts/agent_shin_shared.py new file mode 100644 index 00000000000..8f3dc3c2322 --- /dev/null +++ b/.github/scripts/agent_shin_shared.py @@ -0,0 +1,211 @@ +"""Constants and helpers shared by Agent Shin's triage scripts. + +Both `triage_with_llm.py` (the LLM-judge entrypoint) and +`close_low_quality_prs.py` (the daily Greptile-score sweep) need to +agree on the same notions of: + + * What counts as a Greptile-authored review comment + (``GREPTILE_BOT_LOGINS``) and how to extract a confidence score from + its body (``SCORE_PATTERN`` / :func:`extract_greptile_score`). + * How long the 2-hour grace window is (``GRACE_PERIOD_SECONDS``) and + the HTML marker stamped into a grace-warning comment so the *other* + script can see "Agent Shin already warned" and behave accordingly + (``GRACE_COMMENT_MARKER``). + * Who Agent Shin is on GitHub (``AGENT_SHIN_DEFAULT_BOT_LOGIN``). + * How GitHub-style ISO-8601 timestamps round-trip into timezone-aware + :class:`datetime.datetime` (:func:`parse_iso8601`). + +Keeping these in one module means a future change (new Greptile output +format, a longer grace window, a new allowlisted account) is a single edit +instead of two — the original split version had to call out in comments +that the two copies "must stay in sync" precisely because nothing +enforced it. +""" + +from __future__ import annotations + +import datetime as dt +import json +import os +import re +import subprocess +from typing import Iterable + +GREPTILE_BOT_LOGINS = frozenset({"greptile-apps", "greptile-apps[bot]"}) + +SCORE_PATTERN = re.compile( + r"confidence\s*score\s*[:\-]?\s*(\d+)\s*/\s*5", + re.IGNORECASE, +) + +GRACE_COMMENT_MARKER = "" + +# Hidden HTML marker stamped on every Agent Shin auto-close comment (the LLM +# judge's grace/review-gate close and the daily Greptile sweep's close). +# `was_closed_by_agent_shin` requires this marker — not just the closing actor — +# before `@agent-shin reconsider` may reopen, because the `github-actions[bot]` +# identity is shared with every other workflow in the repo and is not unique to +# Agent Shin. Both close paths must stamp it or the reconsider path silently +# rejects the contributor. +AGENT_SHIN_CLOSE_MARKER = "" + +# 2 hours between the grace warning and the auto-close. Short enough to +# dogfood the "fix it before it closes" loop in one sitting; bump back up +# (e.g. 86400 for a day) for the public rollout. +GRACE_PERIOD_SECONDS = 7200 + +AGENT_SHIN_DEFAULT_BOT_LOGIN = "github-actions[bot]" + + +def _logins(*names: str) -> frozenset[str]: + """Build a login set normalized for case-insensitive membership checks. + + Callers compare via ``login.lower() in ``, so the stored values + must be lowercase. Normalizing here lets the literals keep each + account's canonical GitHub casing (e.g. ``SwiftWinds``) for + readability without breaking the lookup. + """ + return frozenset(name.lower() for name in names) + + +# Dogfood rollout gate. While this set is non-empty, Agent Shin acts ONLY on +# PRs/issues authored by these logins and skips everyone else. For an +# allowlisted author the usual internal/external classification is bypassed, so +# an internal account (e.g. a maintainer's own work login) still gets triaged +# while the bot is being tested on a small set of accounts. Empty the set to +# lift the restriction and restore full triage for the public rollout. Logins +# are compared case-insensitively. +ALLOWLIST_LOGINS = _logins("mateo-berri", "SwiftWinds") + +# `gh {pr,issue} list` has no "fetch everything" flag — `--limit` is the only +# control and it defaults to 30. Pass a ceiling far above any realistic open +# backlog (low thousands today) so gh paginates the API until the queue is +# exhausted rather than silently truncating. The bulk sweeps MUST see the whole +# backlog: gh lists newest-first, so a low cap drops the *oldest* PRs/issues — +# exactly the stale ones a low-quality sweep is meant to catch. +GH_LIST_ALL_LIMIT = 100_000 + + +def extract_greptile_score(comments: Iterable[dict]) -> tuple[int, dict] | None: + """Return (score, comment) for the most recent Greptile-authored comment + that contains a "Confidence Score: X/5". Returns None if no such comment. + + "Most recent" is determined by the comment's `updated_at` (falling back to + `created_at`), so re-reviews override earlier passes. + """ + candidates: list[tuple[str, int, dict]] = [] + for comment in comments: + user = (comment.get("user") or {}).get("login", "") + if user not in GREPTILE_BOT_LOGINS: + continue + body = comment.get("body") or "" + match = SCORE_PATTERN.search(body) + if not match: + continue + score = int(match.group(1)) + timestamp = comment.get("updated_at") or comment.get("created_at") or "" + candidates.append((timestamp, score, comment)) + + if not candidates: + return None + + candidates.sort(key=lambda triple: triple[0]) + _, score, comment = candidates[-1] + return score, comment + + +def parse_iso8601(value: str) -> dt.datetime: + """Parse a GitHub ISO-8601 timestamp into a timezone-aware datetime.""" + return dt.datetime.fromisoformat(value.replace("Z", "+00:00")) + + +def gh(*args: str) -> str: + """Run a `gh` CLI command and return stdout. Raises on non-zero exit. + + Shared by both Agent Shin entrypoints so a future change here + (timeout handling, logging, retry on transient failures) only needs + to be made once. + """ + result = subprocess.run( + ["gh", *args], + capture_output=True, + text=True, + check=True, + ) + return result.stdout + + +def list_open_items(kind: str, *, repo: str | None, fields: str) -> list[dict]: + """Return EVERY open PR (``kind="pr"``) or issue (``kind="issue"``) in ``repo``. + + Wraps ``gh {pr,issue} list`` with ``--limit GH_LIST_ALL_LIMIT`` so the full + backlog is fetched instead of the default 30 (or any other arbitrary cap). + Both bulk sweeps — the daily Greptile closer and the one-shot rollout + heads-up — rely on this seeing the whole queue, including the oldest items. + + ``fields`` is the comma-separated ``--json`` field list the caller needs + (e.g. ``"number"`` for the rollout, the full set for the closer). + """ + if kind not in ("pr", "issue"): + raise ValueError(f"kind must be 'pr' or 'issue', got {kind!r}") + repo_args = ["--repo", repo] if repo else [] + raw = gh( + kind, + "list", + "--state", + "open", + "--limit", + str(GH_LIST_ALL_LIMIT), + "--json", + fields, + *repo_args, + ) + return json.loads(raw) + + +def seconds_since_latest_marker_comment( + comments: Iterable[dict], + *, + marker: str, + bot_login: str | None = None, + now: dt.datetime | None = None, +) -> float | None: + """Return seconds since the bot's most recent comment containing ``marker``. + + Filters comments by author so a contributor who quotes the HTML + marker (e.g. via GitHub's "Quote reply" feature, which preserves + HTML comments in the raw markdown of the quoted text) is not + mistaken for a bot warning — that would silently reset cooldown + timers and suppress legitimate notifications. + + ``bot_login`` defaults to the `AGENT_SHIN_BOT_LOGIN` env override or + ``AGENT_SHIN_DEFAULT_BOT_LOGIN`` so callers normally don't need to + pass it. ``now`` is injectable for tests / callers (like the daily + sweep) that want every age calculation pinned to one snapshot. + """ + expected_login = ( + bot_login + or os.environ.get("AGENT_SHIN_BOT_LOGIN") + or AGENT_SHIN_DEFAULT_BOT_LOGIN + ).lower() + latest: dt.datetime | None = None + for comment in comments: + author = ((comment.get("user") or {}).get("login") or "").lower() + if author != expected_login: + continue + body = comment.get("body") or "" + if marker not in body: + continue + created = comment.get("created_at") + if not created: + continue + try: + ts = parse_iso8601(created) + except ValueError: + continue + if latest is None or ts > latest: + latest = ts + if latest is None: + return None + reference = now if now is not None else dt.datetime.now(dt.timezone.utc) + return (reference - latest).total_seconds() diff --git a/.github/scripts/close_low_quality_prs.py b/.github/scripts/close_low_quality_prs.py new file mode 100644 index 00000000000..7b9bbb579e3 --- /dev/null +++ b/.github/scripts/close_low_quality_prs.py @@ -0,0 +1,573 @@ +#!/usr/bin/env python3 +""" +Auto-close low-quality pull requests. + +Closes open PRs (including drafts, regardless of age) that satisfy ALL of: + 1. Have a Greptile (`greptile-apps`) review comment whose latest + "Confidence Score: X/5" is below the configured threshold (default: 4). + 2. Are authored by an external OSS contributor (internal BerriAI + contributors are exempt). + 3. Do not carry an opt-out label (default: "do not close"). + +`--min-age-days` is retained as an opt-in safety net for one-off backfill +runs (default: 0). The team's intent is that the count of open PRs equals +the count of PRs internal collaborators need to action on, so neither age +nor draft status acts as a free pass. + +For each match, the script posts an explanatory comment and closes the PR. +Because OSS contributors *cannot* reopen a PR closed by the bot/maintainer +(GitHub limitation), the close-comment instructs them to push their fixes +and **open a fresh PR**, or to comment `@agent-shin reconsider` on the +closed PR to have the LLM judge re-evaluate (and reopen on pass). + +Requires the `gh` CLI to be authenticated. + +Usage examples: + # Dry run (default) - prints what would be closed + python3 close_low_quality_prs.py + + # Actually close matching PRs + python3 close_low_quality_prs.py --close + + # Restrict to PRs at least N days old (one-off backfill safety net) + python3 close_low_quality_prs.py --min-age-days 7 --min-score 4 --close +""" + +from __future__ import annotations + +import argparse +import datetime as dt +import json +import os +import subprocess +import sys +from typing import Iterable + +# Add this script's directory to `sys.path` so the sibling +# `agent_shin_shared` module is importable when the script is invoked +# directly (e.g. `python3 .github/scripts/close_low_quality_prs.py ...`). +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) + +from agent_shin_shared import ( # noqa: E402 -- sys.path adjusted above + AGENT_SHIN_CLOSE_MARKER, + ALLOWLIST_LOGINS, + GRACE_COMMENT_MARKER, + GRACE_PERIOD_SECONDS, + GREPTILE_BOT_LOGINS, + SCORE_PATTERN, + extract_greptile_score, + gh, + list_open_items, + parse_iso8601, + seconds_since_latest_marker_comment, +) + +# `GREPTILE_BOT_LOGINS` and `SCORE_PATTERN` (Greptile's GitHub App login +# variants and the "Confidence Score: X/5" regex) are imported from +# `agent_shin_shared` so the LLM judge in `triage_with_llm.py` and this +# daily Greptile sweep read the score through the same set of logins +# and the same regex. + +# `author_association` values for internal BerriAI contributors who should be +# exempt from auto-triage. +INTERNAL_AUTHOR_ASSOCIATIONS = frozenset({"OWNER", "MEMBER", "COLLABORATOR"}) + +# Default labels that exempt a PR from auto-close. Defined at module scope (not +# as a mutable argparse default) so that `--optout-label foo` REPLACES the +# defaults instead of appending to them — the argparse `action="append"` + +# `default=[...]` combination silently mutates the shared default list. +DEFAULT_OPTOUT_LABELS = ("do not close", "keep open", "wip") + +# `GRACE_COMMENT_MARKER` (HTML marker appended to grace-period warning +# comments — used by either script to recognize that a warning was +# already posted) and `GRACE_PERIOD_SECONDS` (length of the grace +# period between the warning and the actual auto-close, 2 hours) are +# imported from `agent_shin_shared` so the Agent Shin LLM judge and +# this daily Greptile sweep agree on the same marker and duration. + + +def fetch_open_prs(repo: str | None) -> list[dict]: + """Fetch all open PRs (number, createdAt, isDraft, labels, author). + + Includes drafts: `gh pr list --state open` returns both ready-for-review + and draft PRs by default. This is the desired behavior — drafts are not + a free pass; the internal-collaborator open-PR queue should reflect every + PR that needs human attention regardless of draft status. + """ + fields = "number,title,createdAt,isDraft,labels,author,url" + return list_open_items("pr", repo=repo, fields=fields) + + +def fetch_pr_author_association(pr_number: int, repo: str | None) -> str: + """Return the GitHub `author_association` for a PR, uppercase. + + Values: OWNER, MEMBER, COLLABORATOR, CONTRIBUTOR, FIRST_TIME_CONTRIBUTOR, + FIRST_TIMER, MANNEQUIN, NONE. Returns "" on lookup failure. + """ + endpoint = ( + f"repos/{repo}/pulls/{pr_number}" + if repo + else f"repos/{{owner}}/{{repo}}/pulls/{pr_number}" + ) + try: + data = json.loads(gh("api", endpoint)) + except subprocess.CalledProcessError: + return "" + return (data.get("author_association") or "").upper() + + +def is_external_pr_author(pr: dict, repo: str | None) -> bool: + """Return True if the PR author is an external OSS contributor. + + Internal = `OWNER` / `MEMBER` / `COLLABORATOR` association, or a bot login. + """ + login = ((pr.get("author") or {}).get("login") or "").lower() + if login.endswith("[bot]") or login in {"dependabot", "github-actions"}: + return False + association = fetch_pr_author_association(pr["number"], repo) + # Fail-safe: if the API lookup failed (empty string), treat the author as + # internal so we don't auto-close their PR. Auto-close is destructive, so + # an unknown association should never make a PR eligible for closing. + if not association or association in INTERNAL_AUTHOR_ASSOCIATIONS: + return False + return True + + +def fetch_pr_comments(pr_number: int, repo: str | None) -> list[dict]: + """Fetch issue-level comments on a PR (where Greptile posts its summary).""" + endpoint = ( + f"repos/{repo}/issues/{pr_number}/comments?per_page=100" + if repo + else f"repos/{{owner}}/{{repo}}/issues/{pr_number}/comments?per_page=100" + ) + raw = gh("api", "--paginate", endpoint) + comments: list[dict] = [] + for line in raw.strip().splitlines(): + line = line.strip() + if not line: + continue + try: + parsed = json.loads(line) + except json.JSONDecodeError: + # A malformed line should not blow up the whole sweep. Skip and + # carry on so the remaining PRs in this run still get evaluated. + continue + if isinstance(parsed, list): + comments.extend(parsed) + else: + comments.append(parsed) + return comments + + +def has_optout_label(pr: dict, optout_labels: set[str]) -> bool: + labels = {label.get("name", "").lower() for label in pr.get("labels", [])} + return bool(labels & {lbl.lower() for lbl in optout_labels}) + + +def seconds_since_last_grace_warning( + comments: Iterable[dict], + *, + bot_login: str | None = None, + now: dt.datetime | None = None, +) -> float | None: + """Return seconds since the bot's most recent grace-period warning, or + None if no such warning has ever been posted on this PR. + + Thin wrapper over + `agent_shin_shared.seconds_since_latest_marker_comment` — the + centralized helper handles the bot-author filter, marker match, + timestamp parsing, and `now` injection. Keeping this wrapper + preserves the closer's "already-fetched comments + injectable now" + interface so callers (and tests) don't need to change. + """ + return seconds_since_latest_marker_comment( + comments, + marker=GRACE_COMMENT_MARKER, + bot_login=bot_login, + now=now, + ) + + +def format_grace_warning_comment(score: int, threshold: int) -> str: + """Comment posted on the FIRST low-Greptile-score detection — gives + the contributor a 2-hour grace window before the auto-close fires on + the next daily cron run. + + Mirrors `format_grace_warning_pr_comment` in + `triage_with_llm.py` in spirit (2-hour grace + escape hatches), but + framed around Greptile's confidence score instead of the LLM judge's + rubric since the close trigger here is the Greptile signal. + """ + return ( + "🚅 Hi, thanks for the PR! I'm **Agent Shin**, the automated triage bot for this " + "repository.\n" + "\n" + "Heads up: Greptile's most recent review scored this PR " + f"**{score}/5**, below our merge bar of **{threshold}/5**.\n" + "\n" + "If the score isn't lifted in the next **2 hours**, I'll auto-close this PR. That's " + "**not** us saying the change isn't worthwhile. We want the open-PR list to mirror " + "what a maintainer can act on *right now*, so contributors like you don't get lost in " + "a backlog. Take your time; everything below still works after the close.\n" + "\n" + "**During the grace period:** push fixes that address Greptile's feedback, then comment " + "`@greptileai` to request a fresh review. If " + f"the new score is **{threshold}/5 or higher**, the PR stays open and no further " + "action is needed on your side.\n" + "\n" + "**If the PR does get auto-closed in 2 hours, you still have an easy recovery path:**\n" + "\n" + "- Comment `@greptileai` to request a fresh review. **This still works even after " + f"the PR is closed**, and a score of {threshold}/5 or higher is one of the signals " + "that lifts the PR back into the review queue. A low Greptile score isn't a blocker.\n" + "- Comment `@agent-shin reconsider` after pushing fixes; I'll re-run the rubric and " + "reopen the PR if both gates (description rubric + Greptile score) now pass.\n" + "\n" + f"{GRACE_COMMENT_MARKER}" + ) + + +def post_grace_warning( + pr: dict, + score: int, + threshold: int, + repo: str | None, + dry_run: bool, +) -> None: + """Post the 2-hour grace-period warning comment on `pr`. + + The warning carries `GRACE_COMMENT_MARKER` so subsequent runs can + detect that the contributor has already been told about the + pending close. Does NOT close the PR — the close happens on the + next eligible run after `GRACE_PERIOD_SECONDS` elapses (handled + by `close_pr`). + """ + pr_number = pr["number"] + repo_args = ["--repo", repo] if repo else [] + + if dry_run: + print( + f" [DRY RUN] Would post grace warning to PR #{pr_number} " + f"(greptile={score}/5): {pr['title']}" + ) + return + + comment_body = format_grace_warning_comment(score, threshold) + gh("pr", "comment", str(pr_number), "--body", comment_body, *repo_args) + print(f" Posted grace warning on PR #{pr_number} (greptile={score}/5)") + + +def format_close_comment(score: int, threshold: int) -> str: + """Comment posted when a low-Greptile-score PR is auto-closed. + + Carries `AGENT_SHIN_CLOSE_MARKER` so the `@agent-shin reconsider` path + (guarded by `was_closed_by_agent_shin`) recognizes this as an Agent Shin + close and is allowed to reopen the PR once it passes again; without the + marker that recovery path the comment advertises silently rejects the + contributor. + """ + score_sentence = ( + f"Greptile's most recent review scored this PR **{score}/5**, below " + f"our merge bar of **{threshold}/5**, and the 2-hour grace period since " + "the warning has elapsed.\n\n" + ) + return ( + f"Closing as part of automated PR triage.\n\n" + f"{score_sentence}" + "We close low-confidence PRs aggressively to keep the review queue " + "manageable for maintainers and contributors alike. **This is not a " + "rejection of the idea.** To bring this back:\n\n" + "1. Push the fixes that address Greptile's feedback (continue using " + "your existing branch is fine).\n" + "2. **Open a new PR** with the updated branch. Greptile will review " + "it again, and if it scores " + f"**{threshold}/5 or higher** a maintainer will take another look.\n\n" + "_Why open a new PR instead of reopening this one?_ GitHub does not " + "let external contributors reopen a PR that was closed by a bot or " + "maintainer, so a fresh PR is the most reliable path forward. If you " + "would prefer this exact PR re-evaluated, comment " + "`@agent-shin reconsider` once you've pushed the fixes; Agent Shin " + "will re-run triage and reopen this PR if it now meets the bar. " + "You can also comment `@greptileai` to request a fresh Greptile " + "review; that works **even after the PR is closed**.\n\n" + "Thanks for contributing to LiteLLM. We know auto-closures can sting; " + "the goal is to keep the project healthy, not to dismiss your work." + f"\n\n{AGENT_SHIN_CLOSE_MARKER}" + ) + + +def close_pr( + pr: dict, + score: int, + threshold: int, + age_days: int, + repo: str | None, + dry_run: bool, + label: str | None, +) -> None: + """Post the explanatory comment and close the PR.""" + pr_number = pr["number"] + repo_args = ["--repo", repo] if repo else [] + + if dry_run: + print( + f" [DRY RUN] Would close PR #{pr_number} " + f"(age={age_days}d, greptile={score}/5): {pr['title']}" + ) + return + + comment_body = format_close_comment(score, threshold) + gh("pr", "comment", str(pr_number), "--body", comment_body, *repo_args) + + if label: + try: + gh("pr", "edit", str(pr_number), "--add-label", label, *repo_args) + except subprocess.CalledProcessError as exc: + stderr = (exc.stderr or "").strip() + print(f" warn: failed to add label '{label}' to #{pr_number}: {stderr}") + + gh("pr", "close", str(pr_number), *repo_args) + print(f" Closed PR #{pr_number} (greptile={score}/5, age={age_days}d)") + + +def evaluate_pr( + pr: dict, + now: dt.datetime, + min_age_days: int, + min_score: int, + repo: str | None, + optout_labels: set[str], + allowlist: frozenset[str] = ALLOWLIST_LOGINS, +) -> tuple[str, int | None, int | None]: + """Decide what to do with `pr` on this triage run. + + Returns (action, score_or_none, age_days_or_none) where action is one of: + "skip-too-young", "skip-optout-label", "skip-not-allowlisted", + "skip-internal", "skip-no-greptile-score", "skip-score-ok", + "warn-grace", "skip-in-grace-period", or "close". + + Drafts are NOT skipped — the goal is "open PR count == PRs internal + collaborators need to action on", and a draft that Greptile scored <4/5 + is still in that queue. Authors can opt out via the `wip` label (see + `DEFAULT_OPTOUT_LABELS`) if they need to keep a long-lived draft open. + + Grace-period semantics: the first time a PR fails the rubric, the + action is `warn-grace` — the caller should post a warning comment but + NOT close the PR. On a subsequent run, if the warning is still less + than `GRACE_PERIOD_SECONDS` old AND the PR still fails, the action is + `skip-in-grace-period`. Once the warning ages out and the rubric is + still failing, the action is `close`. + """ + if has_optout_label(pr, optout_labels): + return ("skip-optout-label", None, None) + + created = parse_iso8601(pr["createdAt"]) + age_days = (now - created).days + # `min_age_days` defaults to 0 (close as soon as Greptile scores low). + # Set a positive value via --min-age-days for one-off backfill runs that + # want to skip very-young PRs. + if min_age_days > 0 and age_days < min_age_days: + return ("skip-too-young", None, age_days) + + # While the allowlist is active it is the sole author gate: only those + # logins are acted on and the external-only restriction is bypassed for + # them. Otherwise auto-close only external OSS contributors — internal + # contributors (BerriAI org members) handle their own backlog. + login = ((pr.get("author") or {}).get("login") or "").lower() + if allowlist: + if login not in allowlist: + return ("skip-not-allowlisted", None, age_days) + elif not is_external_pr_author(pr, repo): + return ("skip-internal", None, age_days) + + comments = fetch_pr_comments(pr["number"], repo) + extraction = extract_greptile_score(comments) + if extraction is None: + return ("skip-no-greptile-score", None, age_days) + + score, _ = extraction + if score >= min_score: + return ("skip-score-ok", score, age_days) + + grace_age = seconds_since_last_grace_warning(comments, now=now) + if grace_age is None: + return ("warn-grace", score, age_days) + if grace_age < GRACE_PERIOD_SECONDS: + return ("skip-in-grace-period", score, age_days) + + return ("close", score, age_days) + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--repo", + type=str, + default=None, + help="Repository (owner/repo). Auto-detected if omitted.", + ) + parser.add_argument( + "--min-age-days", + type=int, + default=0, + help=( + "Minimum age (in days) before a PR is eligible. Default 0 = " + "close as soon as Greptile flags it. Set a positive value for " + "one-off backfill runs that want to spare very-young PRs." + ), + ) + parser.add_argument( + "--min-score", + type=int, + default=4, + choices=range(1, 6), + help="Greptile score below which a PR is closed (default: 4 -> closes <4/5).", + ) + parser.add_argument( + "--optout-label", + action="append", + default=None, + help=( + "Label(s) that exempt a PR from auto-close. Repeat to add more. " + "Case-insensitive. When omitted, defaults to " + f"{list(DEFAULT_OPTOUT_LABELS)!r}; passing this flag REPLACES the " + "defaults (argparse `append` with a mutable default would append " + "instead, which we explicitly avoid)." + ), + ) + parser.add_argument( + "--close-label", + type=str, + default=None, + help=( + "Optional label to add to PRs that get auto-closed " + "(e.g. 'auto-closed-low-quality'). Must already exist on the repo." + ), + ) + parser.add_argument( + "--close", + action="store_true", + help="Actually close matching PRs (default is dry-run).", + ) + parser.add_argument( + "--limit", + type=int, + default=None, + help="Maximum number of PRs to close in one run (safety net).", + ) + args = parser.parse_args() + + dry_run = not args.close + if dry_run: + print("=== DRY RUN MODE (pass --close to actually close PRs) ===\n") + + print("Fetching open PRs...") + prs = fetch_open_prs(args.repo) + print(f"Found {len(prs)} open PRs.\n") + + now = dt.datetime.now(dt.timezone.utc) + optout_labels = set(args.optout_label or DEFAULT_OPTOUT_LABELS) + + closed = 0 + summary = { + "close": 0, + "warn-grace": 0, + "skip-in-grace-period": 0, + "skip-too-young": 0, + "skip-optout-label": 0, + "skip-not-allowlisted": 0, + "skip-internal": 0, + "skip-no-greptile-score": 0, + "skip-score-ok": 0, + } + + # `warned` tracks grace-warning comments posted in this run so the + # `--limit` safety net bounds *all* destructive write actions, not + # just closures. Without this cap, a backlog of PRs failing the + # threshold simultaneously could flood contributors with comments. + warned = 0 + for pr in sorted(prs, key=lambda p: p["createdAt"]): + try: + action, score, age_days = evaluate_pr( + pr, + now, + args.min_age_days, + args.min_score, + args.repo, + optout_labels, + ) + summary[action] = summary.get(action, 0) + 1 + + if action == "warn-grace": + assert score is not None + print( + f"#{pr['number']}: \"{pr['title']}\" " + f"(age={age_days}d, greptile={score}/5) -> warn-grace" + ) + post_grace_warning( + pr, + score=score, + threshold=args.min_score, + repo=args.repo, + dry_run=dry_run, + ) + if not dry_run: + warned += 1 + if args.limit is not None and (warned + closed) >= args.limit: + print( + f"\nReached --limit={args.limit} " + f"(closed={closed}, warned={warned}); stopping." + ) + break + continue + + if action != "close": + continue + + assert score is not None and age_days is not None + print( + f"#{pr['number']}: \"{pr['title']}\" " + f"(age={age_days}d, greptile={score}/5) -> close" + ) + close_pr( + pr, + score=score, + threshold=args.min_score, + age_days=age_days, + repo=args.repo, + dry_run=dry_run, + label=args.close_label, + ) + + if not dry_run: + closed += 1 + if args.limit is not None and (warned + closed) >= args.limit: + print( + f"\nReached --limit={args.limit} " + f"(closed={closed}, warned={warned}); stopping." + ) + break + except Exception as exc: # noqa: BLE001 - per-PR errors don't abort the sweep + summary["error"] = summary.get("error", 0) + 1 + print( + f"!! PR #{pr.get('number')}: {exc}", + file=sys.stderr, + ) + continue + + print("\n=== Summary ===") + for key, value in summary.items(): + print(f" {key:28s} {value}") + if dry_run: + print(f"\nTotal would close: {summary['close']}") + else: + print(f"\nTotal closed: {closed}") + print( + f"Total {'would warn (grace)' if dry_run else 'warned (grace)'}: " + f"{summary['warn-grace']}" + ) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/.github/scripts/triage-requirements.txt b/.github/scripts/triage-requirements.txt new file mode 100644 index 00000000000..a18f05fbb95 --- /dev/null +++ b/.github/scripts/triage-requirements.txt @@ -0,0 +1,282 @@ +# Hash-pinned dependency set for the Agent Shin triage scripts. +# Installed in privileged triage workflows, so every package is pinned to an +# exact version with SHA-256 hashes and installed with pip --require-hashes. +# +# Regenerate after bumping openai: +# echo 'openai==' \ +# | uv pip compile - --generate-hashes --python-version 3.12 \ +# --no-annotate --no-header -o .github/scripts/triage-requirements.txt + +annotated-types==0.7.0 \ + --hash=sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53 \ + --hash=sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89 +anyio==4.14.0 \ + --hash=sha256:b47c1f9ccf73e67021df785332508f99379c68fa7d0684e8e3492cb1d4b23f89 \ + --hash=sha256:dd9b7a2a9799ed6552fde617b2c5df02b7fdd7d88392fc48101e51bae46164d9 +certifi==2026.6.17 \ + --hash=sha256:024c88eeec92ca068db80f02b8b07c9cef7b9fe261d1d535abfd5abd6f6af432 \ + --hash=sha256:2227dcbaafe0d2f59279d1762ddddc37783ed4354594f194ffc31d20f41fc3db +distro==1.9.0 \ + --hash=sha256:2fa77c6fd8940f116ee1d6b94a2f90b13b5ea8d019b98bc8bafdcabcdd9bdbed \ + --hash=sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2 +h11==0.16.0 \ + --hash=sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1 \ + --hash=sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86 +httpcore==1.0.9 \ + --hash=sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55 \ + --hash=sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8 +httpx==0.28.1 \ + --hash=sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc \ + --hash=sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad +idna==3.18 \ + --hash=sha256:7f952cbe720b688055e3f87de14f5c3e5fdaa8bc3928985c4077ca689de849a2 \ + --hash=sha256:ffb385a7e039654cef1ab9ef32c6fafe283c0c0467bba1d9029738ce4a14a848 +jiter==0.15.0 \ + --hash=sha256:01a8222cf05ab1128e239421156c207949808acaaea2bdfd33130ae666786e86 \ + --hash=sha256:032396229564bca02440396bd327710719f724f5e7b7e9f7a8eb3faa4a2c2281 \ + --hash=sha256:04b400bbf8c9efb03d9bdd976475c919c1d85593b04b9fff7ae234065daf87ae \ + --hash=sha256:05906b93d72f03339e6bb7cf8dc10ebda64a0266126eed6beba79e20abcf5fd4 \ + 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--git a/.github/scripts/triage_rollout_heads_up.py b/.github/scripts/triage_rollout_heads_up.py new file mode 100644 index 00000000000..a5dedb1c9e7 --- /dev/null +++ b/.github/scripts/triage_rollout_heads_up.py @@ -0,0 +1,557 @@ +#!/usr/bin/env python3 +"""One-shot 7-day heads-up sweep for the Agent Shin rollout. + +Posts a friendly "the OSS triage bot kicks in next Monday" comment on every +open external PR/issue that currently *would* fail the new rubric — i.e., +every PR/issue Agent Shin would close once the rollout completes. The point +is to give contributors a full week to fix their description before the bot +ever takes a destructive action, so nobody is surprised by an auto-close. + +The script is designed to run **exactly once** at rollout, fired by a manual +``workflow_dispatch`` (``dry_run=false``) on the heads-up workflow. Re-runs +are safe: every comment is stamped with the hidden ``HEADS_UP_MARKER`` and +PRs/issues that already carry the marker are skipped. + +Dry-run vs. real run +-------------------- +Defaults to dry-run. Passing ``--close`` flips into real mode. Every GitHub +mutation goes through ``_agent_shin_actions``, which has a one-line +``if dry_run: log else: do_it`` per call, so the only difference between a +dry-run preview and the real run is the call site that actually hits the +GitHub API. + +Local preview:: + + python3 .github/scripts/triage_rollout_heads_up.py --repo BerriAI/litellm + +Real run (the manual rollout dispatch uses this):: + + python3 .github/scripts/triage_rollout_heads_up.py --repo BerriAI/litellm --close +""" + +from __future__ import annotations + +import argparse +import datetime as dt +import json +import os +import sys +from pathlib import Path +from typing import Any + +# Make the sibling triage_with_llm + _agent_shin_actions importable when this +# script is invoked directly (the GitHub workflow does `python3 .github/scripts/...`). +_SCRIPTS_DIR = Path(__file__).resolve().parent +if str(_SCRIPTS_DIR) not in sys.path: + sys.path.insert(0, str(_SCRIPTS_DIR)) + +from _agent_shin_actions import maybe_post_comment # noqa: E402 +from agent_shin_shared import ( # noqa: E402 + AGENT_SHIN_DEFAULT_BOT_LOGIN, + ALLOWLIST_LOGINS, + list_open_items, +) +from triage_with_llm import ( # noqa: E402 + DEFAULT_MODEL, + call_llm_judge, + fetch_issue, + fetch_pr, + gh, + is_internal_contributor, + review_gate, + triage, +) + +# Hidden marker so re-runs skip PRs/issues we've already notified. Distinct from +# the within-grace / ready / regressed markers so it can't be confused with the +# steady-state lifecycle comments. +HEADS_UP_MARKER = "" + +# Placeholder until the litellm-docs PR ships. The rollout blog post explains +# the new rubric, the 7-day grace, and how to recover after an auto-close. +# TODO(docs): replace with the canonical URL once the litellm-docs PR merges. +ROLLOUT_BLOG_URL = "https://docs.litellm.ai/docs/agent_shin_triage_rollout" + +# Default cutoff is one week from "now". Computed at runtime so the wording +# stays correct even if the rollout is merged later than planned. The user can +# override with --close-on YYYY-MM-DD when running the script manually. +DEFAULT_GRACE_DAYS = 7 + +# The daily auto-close sweeps (close_low_quality_prs.yml at 09:00 UTC and +# review_gate.yml at 09:30 UTC) are what actually close a still-failing item, +# so the deadline we promise contributors has to name that wall-clock moment. +ACTIVATION_TIME_UTC = "09:00 UTC" + + +def _format_cutoff(cutoff: dt.date) -> str: + """Human-readable, timezone-explicit cutoff, e.g. ``Monday, June 1, 2026 + (09:00 UTC)`` — the moment a still-failing PR/issue gets closed.""" + return ( + f"{cutoff.strftime('%A, %B')} {cutoff.day}, {cutoff.year} " + f"({ACTIVATION_TIME_UTC})" + ) + + +def _rubric_section_pr() -> str: + return ( + "**Going forward, every external PR needs ONE of:**\n" + "\n" + "- A linked GitHub issue using a closing keyword: " + "`Fixes #1234`, `Closes #1234`, or `Resolves #1234`, OR\n" + "- All three of: a clear **problem description**, **expected vs. " + "actual behavior**, and **end-to-end QA proof** (at least one of a " + "short screen recording / video, before/after screenshots, or the " + "exact commands you ran with their real output; mocked or stubbed " + "runs don't count).\n" + "\n" + "PRs also need a **Greptile confidence score of 4/5 or higher** before " + "the bot will tag them `ready for review`. You can `@greptileai` to " + "request a fresh review at any time, including after the PR is closed." + ) + + +def _rubric_section_issue() -> str: + return ( + "**Going forward, every external issue needs:**\n" + "\n" + "- For **bug reports**: end-to-end evidence of the bug (at least one " + "of a screen recording / video, a screenshot, or the exact commands " + "you ran with their real output / traceback) plus expected vs. actual " + "behavior. Written steps with no run output don't count, and mocked " + "or stubbed runs don't count.\n" + "- For **feature requests**: a clear description of the proposed " + "feature plus a use case + concrete example (config, API call, UI " + "flow, or scenario showing what's blocked today)." + ) + + +def _description_only_note(kind: str) -> str: + noun = "PR" if kind == "pr" else "issue" + return ( + f"⚠️ **The requirements must live in the {noun} *description*, not in " + "comments.** Some PRs/issues collect 100+ comments from humans and " + "bots; reading the entire thread on every triage run would balloon " + "GitHub API usage (we'd start getting 429'd) and blow out the LLM " + "judge's context. The bot only reads the description, so anything " + "you add as a comment will be invisible to it." + ) + + +def _missing_section(verdict: dict, greptile_score: int | None) -> str: + """Bullet list of what's currently missing on this PR/issue. + + Combines the LLM judge's `missing` list (rubric items) with a Greptile + shortfall (for PRs) so the contributor sees one list of things to fix. + """ + missing = list(verdict.get("missing") or []) + if greptile_score is not None and greptile_score < 4: + missing.insert( + 0, + f"Greptile's most recent review scored this PR {greptile_score}/5 " + "(below the 4/5 bar Agent Shin will require).", + ) + if not missing: + return ( + "_The bot couldn't articulate a specific missing piece; see the " + "rubric link above and double-check the description includes all " + "of it before the rollout._" + ) + bullets = "\n".join(f"- {m}" for m in missing) + return f"**What this one is currently missing:**\n\n{bullets}" + + +def _recovery_section(kind: str) -> str: + if kind == "pr": + return ( + "**If the bot closes this PR after the rollout:** update the " + "description with the missing pieces, then either open a fresh " + "PR or comment `@agent-shin reconsider` on the closed PR. If " + "Greptile re-scores you at 4/5 or higher I'll reopen and tag " + "the PR `ready for review`. (`@greptileai` works on closed PRs " + "too; a fresh review is one of the signals that lifts you back " + "into the queue.) This is **not** us losing interest in your " + "change; far from it. We just need open PRs to be a list of " + "things a maintainer can act on, so we can get to yours faster." + ) + return ( + "**If the bot closes this issue after the rollout:** edit the issue " + "description to add the missing pieces, then comment `@agent-shin " + "reconsider` on the closed issue. I'll re-evaluate and, if the rubric " + "is met, reopen it. (GitHub doesn't let external authors reopen an " + "issue a maintainer or bot closed, so the comment is the reliable " + "path.) This is **not** us saying the bug isn't real or the request " + "isn't useful; it's so the remaining open issues are a list of things " + "a maintainer can act on." + ) + + +def format_heads_up_comment( + *, kind: str, verdict: dict, greptile_score: int | None, cutoff: dt.date +) -> str: + """Compose the friendly 7-day heads-up comment posted on a failing PR/issue.""" + noun = "PR" if kind == "pr" else "issue" + rubric = _rubric_section_pr() if kind == "pr" else _rubric_section_issue() + cutoff_str = _format_cutoff(cutoff) + explanation = (verdict.get("explanation") or "").strip() + explanation_block = ( + f"> _(The judge's note for this one: {explanation})_\n\n" if explanation else "" + ) + + return ( + "🚅 **Heads-up: we're turning on the OSS triage bot in " + f"{DEFAULT_GRACE_DAYS} days, on {cutoff_str}.**\n" + "\n" + "We're rolling out **Agent Shin**, an LLM-as-judge triage bot for " + f"external {noun}s. Once it's live, the bot reads each open " + f"{noun}'s description, scores it against a small rubric, and " + f"auto-closes any {noun} that's missing the basics, with a single " + f"comment explaining what's missing and how to recover. Full " + f"context: [Agent Shin rollout blog post]({ROLLOUT_BLOG_URL}).\n" + "\n" + f"{rubric}\n" + "\n" + f"{_description_only_note(kind)}\n" + "\n" + f"{_missing_section(verdict, greptile_score)}\n" + "\n" + f"{explanation_block}" + "**Timeline (you have a week):**\n" + "\n" + f"- We turn the bot on in {DEFAULT_GRACE_DAYS} days, on " + f"**{cutoff_str}**. You have until then to update this {noun}'s " + "description with the missing pieces above.\n" + f"- If this {noun} still fails the rubric at **{cutoff_str}**, " + "we'll close it.\n" + f"- From then on the bot runs daily, and every {noun} that fails " + "the rubric gets a **2-hour lifetime**: one warning comment, then " + "auto-close 2 hours later.\n" + "\n" + f"{_recovery_section(kind)}\n" + "\n" + f"{HEADS_UP_MARKER}" + ) + + +def _list_open_numbers(repo: str, kind: str) -> list[int]: + """Return every open PR or issue number in ``repo``. + + Delegates to ``list_open_items`` so the full backlog is fetched (no cap) + and the `gh {pr,issue} list` invocation stays in one shared place. ``gh + issue list`` would include PRs, but ``list_open_items`` uses the dedicated + command per kind, so the two never mix. + """ + return [ + item["number"] for item in list_open_items(kind, repo=repo, fields="number") + ] + + +def _has_heads_up_marker(item: dict) -> bool: + """Cheap fast-path: check the PR/issue body itself for the marker. + + The marker is appended to the *comment* we post, not the body, so this + will only fire if the body literally contains the marker text. We still + do the comment-marker check separately below; this body check just lets + us short-circuit for PRs/issues that quote the marker for any reason. + """ + body = item.get("body") or "" + return HEADS_UP_MARKER in body + + +def _comments_have_marker(repo: str, number: int) -> bool: + """True if the bot already posted a comment carrying the marker. + + Used for idempotency: a re-run skips items the previous run notified. + Filters by author (matching the sibling marker-checks in + ``triage_with_llm._has_marker`` and + ``agent_shin_shared.seconds_since_latest_marker_comment``) so a + contributor who quotes the heads-up via GitHub's "Quote reply" — which + preserves HTML comments in the raw markdown — can't trick the + idempotency check into silently skipping a real heads-up. + + Comments live on the unified issues endpoint regardless of whether the + item is a PR or an issue, so no ``kind`` argument is required here. + """ + expected_login = ( + os.environ.get("AGENT_SHIN_BOT_LOGIN") or AGENT_SHIN_DEFAULT_BOT_LOGIN + ).lower() + raw = gh( + "api", + "--paginate", + f"repos/{repo}/issues/{number}/comments?per_page=100", + ) + for line in raw.splitlines(): + line = line.strip() + if not line: + continue + try: + payload = json.loads(line) + except json.JSONDecodeError: + continue + comments = payload if isinstance(payload, list) else [payload] + for comment in comments: + author = ((comment.get("user") or {}).get("login") or "").lower() + if author != expected_login: + continue + if HEADS_UP_MARKER in (comment.get("body") or ""): + return True + return False + + +def _evaluate_pr(*, repo: str, number: int, model: str, judge: Any = None) -> dict: + """Run the future PR rubric (review_gate) in dry-run and return the result.""" + return review_gate( + repo=repo, + number=number, + close=False, # we only want the verdict, never act here + model=model, + judge=judge, + ) + + +def _evaluate_issue(*, repo: str, number: int, model: str, judge: Any = None) -> dict: + """Run the future issue rubric (triage kind='issue') in dry-run.""" + return triage( + repo=repo, + kind="issue", + number=number, + close=False, + model=model, + judge=judge, + ) + + +def _would_be_closed(kind: str, result: dict) -> bool: + """True if the future triage would auto-close this PR/issue based on the + rubric (regardless of grace-period gating). + + For PRs we trust ``review_gate``'s ``passing`` field — it combines the LLM + verdict and the Greptile score. For issues we read the LLM verdict + directly. Both fields are ``None``/missing on skip paths + (skip-internal-author, skip-llm-error, etc.) where the future bot would + NOT close the item — those return False. + """ + if kind == "pr": + passing = result.get("passing") + if passing is None: + return False # skipped — nothing for the heads-up to warn about + return passing is False + verdict = result.get("verdict") or {} + return (verdict.get("verdict") or "").lower() == "fail" + + +def _process_one( + *, + repo: str, + kind: str, + number: int, + model: str, + cutoff: dt.date, + dry_run: bool, + judge: Any = None, + skip_marker_check: bool = False, + allowlist: frozenset[str] = ALLOWLIST_LOGINS, +) -> dict: + """Evaluate one PR/issue and post a heads-up if it would be auto-closed. + + Returns a per-item dict for the summary table. + """ + base = {"kind": kind, "number": number} + fetcher = fetch_pr if kind == "pr" else fetch_issue + item = fetcher(repo, number) + + if (item.get("state") or "") != "open": + return {**base, "action": "skip-not-open"} + if allowlist: + login = (item.get("user") or {}).get("login") or "" + if login.lower() not in allowlist: + return {**base, "action": "skip-not-allowlisted"} + elif is_internal_contributor(item): + return {**base, "action": "skip-internal-author"} + if not skip_marker_check and _has_heads_up_marker(item): + return {**base, "action": "skip-already-marked-in-body"} + if not skip_marker_check and _comments_have_marker(repo, number): + return {**base, "action": "skip-already-notified"} + + if kind == "pr": + result = _evaluate_pr(repo=repo, number=number, model=model, judge=judge) + else: + result = _evaluate_issue(repo=repo, number=number, model=model, judge=judge) + + if not _would_be_closed(kind, result): + return {**base, "action": "skip-passing", "evaluator": result.get("action")} + + verdict = result.get("verdict") or {} + greptile_score = result.get("greptile_score") if kind == "pr" else None + comment = format_heads_up_comment( + kind=kind, verdict=verdict, greptile_score=greptile_score, cutoff=cutoff + ) + maybe_post_comment(repo, number, comment, dry_run=dry_run) + return { + **base, + "action": "heads-up-posted" if not dry_run else "would-post-heads-up", + "verdict": (verdict.get("verdict") or "").lower(), + "greptile_score": greptile_score, + } + + +def _print_summary(results: list[dict]) -> None: + """Tally per-action counts so a dry-run preview tells you at a glance how + many comments the real run would post.""" + counts: dict[str, int] = {} + for r in results: + counts[r["action"]] = counts.get(r["action"], 0) + 1 + print("\n=== rollout heads-up summary ===") + for action in sorted(counts): + print(f" {action:35s} {counts[action]}") + print(f" total {len(results)}") + + +def run( + *, + repo: str, + close: bool, + cutoff: dt.date, + model: str, + kinds: tuple[str, ...] = ("pr", "issue"), + judge: Any = None, + only_numbers: dict[str, list[int]] | None = None, + skip_marker_check: bool = False, +) -> list[dict]: + """Sweep ``repo`` and post heads-up comments. Returns the per-item results.""" + dry_run = not close + if dry_run: + print( + f"[DRY RUN] sweeping {repo}; --close not passed, no comments will be posted." + ) + else: + print(f"[REAL RUN] sweeping {repo}; comments WILL be posted.") + print(f"Cutoff date in comment body: {cutoff.isoformat()}") + + results: list[dict] = [] + for kind in kinds: + if only_numbers and kind in only_numbers: + numbers = list(only_numbers[kind]) + else: + numbers = _list_open_numbers(repo, kind) + print(f"\n--- {kind}s: {len(numbers)} open ---") + for n in numbers: + try: + result = _process_one( + repo=repo, + kind=kind, + number=n, + model=model, + cutoff=cutoff, + dry_run=dry_run, + judge=judge, + skip_marker_check=skip_marker_check, + ) + except ( + Exception + ) as exc: # noqa: BLE001 - per-item errors don't abort the sweep + result = { + "kind": kind, + "number": n, + "action": "error", + "error": str(exc), + } + print(f"!! {kind}#{n}: {exc}", file=sys.stderr) + print(f" {kind}#{n}: {result['action']}") + results.append(result) + _print_summary(results) + return results + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--repo", required=True, help="owner/repo") + parser.add_argument( + "--close", + action="store_true", + help=( + "Actually post comments. Without this flag the script is in " + "dry-run mode and only logs what it would do." + ), + ) + parser.add_argument( + "--close-on", + type=dt.date.fromisoformat, + default=None, + help=( + "Cutoff date shown in the heads-up comment as the rollout date " + f"(default: today + {DEFAULT_GRACE_DAYS} days)." + ), + ) + parser.add_argument( + "--model", + default=os.environ.get("TRIAGE_MODEL") or DEFAULT_MODEL, + help=f"Model for the rubric LLM judge (default: {DEFAULT_MODEL}).", + ) + parser.add_argument( + "--kind", + choices=("pr", "issue", "both"), + default="both", + help="Restrict the sweep to PRs or issues only (default: both).", + ) + parser.add_argument( + "--only-pr", + type=int, + action="append", + default=[], + help="Limit the PR sweep to these PR numbers (repeat for several).", + ) + parser.add_argument( + "--only-issue", + type=int, + action="append", + default=[], + help="Limit the issue sweep to these issue numbers (repeat for several).", + ) + parser.add_argument( + "--ignore-existing-marker", + action="store_true", + help=( + "Re-post on PRs/issues that already carry the heads-up marker. " + "Useful for testing the comment wording on a known PR." + ), + ) + args = parser.parse_args() + + cutoff = args.close_on or ( + dt.datetime.now(dt.timezone.utc).date() + dt.timedelta(days=DEFAULT_GRACE_DAYS) + ) + + kinds: tuple[str, ...] + if args.kind == "pr": + kinds = ("pr",) + elif args.kind == "issue": + kinds = ("issue",) + else: + kinds = ("pr", "issue") + + only: dict[str, list[int]] = {} + if args.only_pr: + only["pr"] = args.only_pr + if args.only_issue: + only["issue"] = args.only_issue + + # The script must NOT hit the LLM in dry-run if no key is set — we still + # want a useful preview that says "skip-no-llm-key" for items that would + # have been judged. Production runs require OPENAI_API_KEY. + if args.close and not os.environ.get("OPENAI_API_KEY"): + parser.error("OPENAI_API_KEY must be set for --close (real-run) mode.") + + run( + repo=args.repo, + close=args.close, + cutoff=cutoff, + model=args.model, + kinds=kinds, + only_numbers=only or None, + skip_marker_check=args.ignore_existing_marker, + ) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/.github/scripts/triage_with_llm.py b/.github/scripts/triage_with_llm.py new file mode 100644 index 00000000000..d2536058e01 --- /dev/null +++ b/.github/scripts/triage_with_llm.py @@ -0,0 +1,1778 @@ +#!/usr/bin/env python3 +""" +Agent Shin — LLM-as-judge triage for external OSS pull requests and issues. + +Evaluates a single PR or issue against the contribution rubric and, when the +LLM judge marks it as failing, posts an explanatory comment + closes the +PR/issue. Re-triggers on `reopened` so contributors can iterate back in by +filling in the missing pieces and reopening. + +Internal BerriAI contributors (`author_association` in {OWNER, MEMBER, +COLLABORATOR}) and bot accounts are skipped entirely. + +Usage: + triage_with_llm.py --repo owner/repo --pr 1234 + triage_with_llm.py --repo owner/repo --issue 5678 + triage_with_llm.py --repo owner/repo --pr 1234 --close # actually close + triage_with_llm.py --repo owner/repo --pr 1234 --print-prompt # show prompt + +Defaults are SAFE: without `--close` the script writes a verdict to stdout (and, +when running in GitHub Actions, to $GITHUB_STEP_SUMMARY) but takes no GitHub +write actions. + +Environment: + GH_TOKEN / GITHUB_TOKEN - for `gh` CLI auth (auto-set in Actions) + OPENAI_API_KEY - required when --close is passed + OPENAI_BASE_URL - optional (route to any OpenAI-compatible API) + TRIAGE_MODEL - optional model override (default: gpt-5.4-mini) +""" + +from __future__ import annotations + +import argparse +import datetime as dt +import json +import os +import re +import subprocess +import sys +import textwrap +import urllib.parse +from typing import Any, Iterable + +# Add this script's directory to `sys.path` so the sibling +# `agent_shin_shared` module is importable when the script is invoked +# directly (e.g. `python3 .github/scripts/triage_with_llm.py ...`) and +# also when the tests load this script via +# `importlib.util.spec_from_file_location`. +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) + +from agent_shin_shared import ( # noqa: E402 -- sys.path adjusted above + AGENT_SHIN_CLOSE_MARKER, + AGENT_SHIN_DEFAULT_BOT_LOGIN, + ALLOWLIST_LOGINS, + GRACE_COMMENT_MARKER, + GRACE_PERIOD_SECONDS, + GREPTILE_BOT_LOGINS, + SCORE_PATTERN, + extract_greptile_score, + gh, + parse_iso8601, + seconds_since_latest_marker_comment, +) + +DEFAULT_MODEL = "gpt-5.4-mini" + +INTERNAL_ASSOCIATIONS = frozenset({"OWNER", "MEMBER", "COLLABORATOR"}) + +# `AGENT_SHIN_DEFAULT_BOT_LOGIN` is imported from `agent_shin_shared`. +# When the workflow uses the default `secrets.GITHUB_TOKEN`, the +# closure / reopen event's `actor.login` is `github-actions[bot]`. The +# env override `AGENT_SHIN_BOT_LOGIN` exists for local debugging and for +# repos that wire Agent Shin to a PAT. + +# HTML marker appended to every reconsider verdict comment. We grep for this +# on subsequent reconsider triggers to enforce a short cooldown so that +# repeated `@agent-shin reconsider` comments don't burn CI/LLM budget. +# Using a unique HTML comment keeps the marker invisible to humans while +# being trivially greppable from a comments-list API response. +RECONSIDER_COMMENT_MARKER = "" + +# Minimum gap between two reconsider verdicts on the same PR/issue. Set to +# 10 minutes — long enough that a contributor can't trivially spam the +# trigger, short enough that a genuine "I just pushed a fix and reupdated +# the body" iteration loop isn't punished. +RECONSIDER_RATE_LIMIT_SECONDS = 600 + +# `GRACE_COMMENT_MARKER` (HTML marker on the grace-period warning comment +# posted on the first low-quality detection — used on subsequent triage +# runs to detect that a warning was already posted and measure how long +# ago it was posted) and `GRACE_PERIOD_SECONDS` (length of the grace +# period between the warning and the actual auto-close, 2 hours) are +# imported from `agent_shin_shared` so the daily Greptile sweep and the +# LLM judge agree on the same marker and duration. + +# --- Review-gate ("ready for review" label lifecycle) configuration ---------- +# The review gate keeps a single label in sync with whether a PR currently +# clears BOTH quality bars: the LLM rubric (clear problem + expected/actual + +# QA proof, or a linked issue) AND Greptile's most recent confidence score. +READY_FOR_REVIEW_LABEL = "ready for review" +DEFAULT_GRACE_DAYS = 1 # 24h before an un-passing, un-tagged PR is auto-closed +DEFAULT_MIN_GREPTILE_SCORE = 4 # Greptile < 4/5 counts as "not passing" + +# Hidden HTML-comment markers stamped into review-gate comments. They never +# render in the GitHub UI but let the gate detect its own prior actions so it +# (a) posts the within-grace "what's missing" notice at most once and (b) can +# tell a first-time pass ("ready for review") from a recovery after a +# regression ("all clear again"). +READY_MARKER = "" +REGRESSED_MARKER = "" +WITHIN_GRACE_MARKER = "" + +# `GREPTILE_BOT_LOGINS` (Greptile's GitHub App login variants — +# `greptile-apps[bot]` in REST API comments, `greptile-apps` in +# `gh pr view --json` output) and `SCORE_PATTERN` (regex matching lines +# like `Confidence Score: 3/5`) are imported from `agent_shin_shared` +# so the daily sweep and the review gate read the score through the +# same set of logins / patterns. + +# `AGENT_SHIN_CLOSE_MARKER` is imported from `agent_shin_shared` so this LLM +# judge and the daily Greptile sweep stamp the same marker on their close +# comments — `was_closed_by_agent_shin` keys the reconsider reopen path off it. + +# Model families that require `reasoning_effort` to be set, and that reject +# `temperature != 1` unless `reasoning_effort` is "none". For these models we +# pass `reasoning_effort="none"` so a `temperature=0` deterministic judgment +# is still accepted. See litellm/llms/openai/chat/gpt_5_transformation.py for +# the full set of constraints LiteLLM applies to these models. +GPT5_FAMILY_PREFIX = "gpt-5" + +# Regexes for picking off "obvious passes" without burning LLM tokens. +# +# Keep this list to GitHub's documented PR-closing keywords only +# (https://docs.github.com/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue). +# Casual mentions like "see #1234" or "ref #1234" are intentionally NOT +# auto-passed — they should fall through to the LLM judge, which has the +# stricter rubric "a bare issue number without a closing keyword counts only +# if it's clearly the related issue (not a passing mention)". +LINKED_ISSUE_PATTERN = re.compile( + r"\b(?:fixes|fix|fixed|closes|close|closed|resolves|resolve|resolved)\s+" + r"(?:#\d+|https?://github\.com/[\w.-]+/[\w.-]+/issues/\d+)", + re.IGNORECASE, +) +HTML_COMMENT_PATTERN = re.compile(r"", re.DOTALL) + + +# --------------------------------------------------------------------------- +# gh helpers +# +# `gh` is imported from `agent_shin_shared` so a future change (timeout, +# logging, retry) only needs to be made once. + + +def fetch_pr(repo: str, number: int) -> dict: + """Return the full GitHub REST representation of a PR.""" + return json.loads(gh("api", f"repos/{repo}/pulls/{number}")) + + +def fetch_issue(repo: str, number: int) -> dict: + """Return the full GitHub REST representation of an issue.""" + return json.loads(gh("api", f"repos/{repo}/issues/{number}")) + + +def post_comment(repo: str, number: int, body: str) -> None: + """Post an issue-style comment (works for both issues and PRs).""" + gh( + "api", + f"repos/{repo}/issues/{number}/comments", + "-X", + "POST", + "-f", + f"body={body}", + ) + + +def close_pr(repo: str, number: int) -> None: + """Close a pull request (state=closed).""" + gh( + "api", + f"repos/{repo}/pulls/{number}", + "-X", + "PATCH", + "-f", + "state=closed", + ) + + +def reopen_pr(repo: str, number: int) -> None: + """Reopen a previously-closed pull request (state=open). + + Used by the `@agent-shin reconsider` comment-trigger flow: the bot has + write access via GH_TOKEN, so it can reopen on the contributor's behalf + even though GitHub doesn't let the OSS author do it themselves. + """ + gh( + "api", + f"repos/{repo}/pulls/{number}", + "-X", + "PATCH", + "-f", + "state=open", + ) + + +def close_issue(repo: str, number: int, *, not_planned: bool = True) -> None: + """Close an issue, marking state_reason=not_planned by default.""" + args = [ + "api", + f"repos/{repo}/issues/{number}", + "-X", + "PATCH", + "-f", + "state=closed", + ] + if not_planned: + args.extend(["-f", "state_reason=not_planned"]) + gh(*args) + + +def reopen_issue(repo: str, number: int) -> None: + """Reopen a previously-closed issue (state=open, state_reason=reopened).""" + gh( + "api", + f"repos/{repo}/issues/{number}", + "-X", + "PATCH", + "-f", + "state=open", + "-f", + "state_reason=reopened", + ) + + +def add_label(repo: str, number: int, label: str) -> None: + """Add a label to a PR/issue (GitHub creates the label if it's missing).""" + gh( + "api", + f"repos/{repo}/issues/{number}/labels", + "-X", + "POST", + "-f", + f"labels[]={label}", + ) + + +def remove_label(repo: str, number: int, label: str) -> None: + """Remove a label from a PR/issue. A missing label (404) is not an error.""" + encoded = urllib.parse.quote(label, safe="") + try: + gh( + "api", + f"repos/{repo}/issues/{number}/labels/{encoded}", + "-X", + "DELETE", + ) + except subprocess.CalledProcessError as exc: + stderr = (exc.stderr or "").lower() + if "404" in stderr or "not found" in stderr: + return + raise + + +def _iter_paginated_json(*api_args: str) -> Any: + """Yield JSON objects from `gh api --paginate ... -q '.[]'`. + + `gh api --paginate` on a JSON-array endpoint concatenates pages into + one stream; `-q '.[]'` flattens that stream into newline-delimited + objects (jq-style). This keeps memory bounded for chatty endpoints + like issue events/comments on long-lived PRs. + """ + raw = gh("api", "--paginate", *api_args, "-q", ".[]") + for line in raw.splitlines(): + line = line.strip() + if not line: + continue + try: + yield json.loads(line) + except json.JSONDecodeError: + # A malformed line should not blow up the whole guard. Skip and + # carry on — at worst the guard fail-closes (returns False / + # None) and the caller treats it as "unknown". + continue + + +def fetch_last_close_event( + repo: str, number: int +) -> tuple[str | None, dt.datetime | None]: + """Return the actor login and timestamp of the most recent `closed` event. + + Either field may be None: actor when the events API returns nothing + (unusual for a closed item, but possible on transient errors), and + timestamp when the event lacks `created_at` or the value can't be + parsed. `was_closed_by_agent_shin` fail-closes on either. + """ + actor: str | None = None + closed_at: dt.datetime | None = None + for event in _iter_paginated_json(f"repos/{repo}/issues/{number}/events"): + if event.get("event") != "closed": + continue + actor = (event.get("actor") or {}).get("login") + created = event.get("created_at") + if not created: + closed_at = None + continue + try: + closed_at = parse_iso8601(created) + except ValueError: + closed_at = None + return actor, closed_at + + +# How much older than the latest `closed` event the Agent Shin marker +# comment is allowed to be while still counting as "this close was Agent +# Shin's". Agent Shin posts the close comment immediately before closing, +# so the marker timestamp is normally at most a few seconds before the +# close event; the buffer just absorbs clock skew between the comments +# API and the events API. +AGENT_SHIN_CLOSE_MARKER_SKEW_SECONDS = 300 + + +def was_closed_by_agent_shin( + repo: str, number: int, *, bot_login: str | None = None +) -> bool: + """Return True iff Agent Shin itself most-recently closed this PR/issue. + + This is the guard that stops `@agent-shin reconsider` from reopening an + item Agent Shin did not close — a maintainer closing for non-rubric + reasons (security, duplicate, design rejection), or a different workflow + (stale/duplicate sweeps) closing under the shared `github-actions[bot]` + identity. Three independent signals must all hold, because that identity + is not unique to Agent Shin and a marker comment from a prior + closed/reopened cycle would otherwise vouch for an unrelated close: + + 1. The most recent `closed` event's actor is the bot identity. + 2. Agent Shin left one of its auto-close comments, detected via + `AGENT_SHIN_CLOSE_MARKER`. The actor check alone can't tell an + Agent Shin close from any other `github-actions[bot]` close. + 3. That marker comment was posted at (or just before) the latest + close event, not on a previous close in an + Agent-Shin-close -> reconsider-reopen -> other-bot-reclose cycle. + + The check is intentionally fail-closed: any uncertainty about who closed + the item is treated as "not Agent Shin" so the destructive reopen path + stays gated. + """ + expected = ( + bot_login + or os.environ.get("AGENT_SHIN_BOT_LOGIN") + or AGENT_SHIN_DEFAULT_BOT_LOGIN + ).lower() + actor, closed_at = fetch_last_close_event(repo, number) + if not actor or actor.lower() != expected or closed_at is None: + return False + marker_seconds = seconds_since_last_agent_shin_close( + repo, number, bot_login=bot_login + ) + if marker_seconds is None: + return False + close_age_seconds = (dt.datetime.now(dt.timezone.utc) - closed_at).total_seconds() + return marker_seconds <= close_age_seconds + AGENT_SHIN_CLOSE_MARKER_SKEW_SECONDS + + +def _seconds_since_latest_marker_comment( + repo: str, + number: int, + *, + marker: str, + bot_login: str | None = None, +) -> float | None: + """Return seconds since the bot's most recent comment with ``marker``. + + Fetches comments via `_iter_paginated_json` and delegates the + iteration / author-filter / timestamp logic to + `agent_shin_shared.seconds_since_latest_marker_comment` so the daily + Greptile sweep and the LLM judge use one source of truth for the + "bot already posted X" detection. The wall-clock `now` is resolved + against this module's `dt` so tests that freeze time via + `monkeypatch.setattr(triage_module, "dt", ...)` still apply. + """ + return seconds_since_latest_marker_comment( + _iter_paginated_json(f"repos/{repo}/issues/{number}/comments"), + marker=marker, + bot_login=bot_login, + now=dt.datetime.now(dt.timezone.utc), + ) + + +def seconds_since_last_reconsider_verdict( + repo: str, number: int, *, bot_login: str | None = None +) -> float | None: + """Return seconds since the bot's most recent reconsider verdict comment. + + Detects comments by matching the HTML marker `RECONSIDER_COMMENT_MARKER` + appended by `format_reopen_comment` and + `format_reconsider_still_failing_comment`. Returns None when the bot + has never posted a reconsider verdict on this PR/issue (or when the + only matching comments are missing a `created_at` timestamp, which + shouldn't happen on a real GitHub response). + """ + return _seconds_since_latest_marker_comment( + repo, number, marker=RECONSIDER_COMMENT_MARKER, bot_login=bot_login + ) + + +def seconds_since_last_grace_warning( + repo: str, number: int, *, bot_login: str | None = None +) -> float | None: + """Return seconds since the bot's most recent grace-period warning. + + Detects warning comments by matching the HTML marker + `GRACE_COMMENT_MARKER` appended by `format_grace_warning_pr_comment` + and `format_grace_warning_issue_comment`. Returns None when no + grace warning has ever been posted on this PR/issue — that's the + "first low-quality detection" signal that drives the warning path. + """ + return _seconds_since_latest_marker_comment( + repo, number, marker=GRACE_COMMENT_MARKER, bot_login=bot_login + ) + + +def seconds_since_last_agent_shin_close( + repo: str, number: int, *, bot_login: str | None = None +) -> float | None: + """Return seconds since Agent Shin's most recent auto-close comment. + + Detects close comments by matching `AGENT_SHIN_CLOSE_MARKER` (stamped by + `format_pr_close_comment` / `format_issue_close_comment`). Returns None + when Agent Shin has never closed this PR/issue — the signal + `was_closed_by_agent_shin` uses to keep the reconsider reopen path gated + against closures performed by other workflows sharing the bot identity. + """ + return _seconds_since_latest_marker_comment( + repo, number, marker=AGENT_SHIN_CLOSE_MARKER, bot_login=bot_login + ) + + +# --------------------------------------------------------------------------- +# Author classification + + +def is_internal_contributor(item: dict) -> bool: + """Return True if the PR/issue author should be exempted from triage. + + Fail-safe: if `author_association` is missing or empty (which should never + happen on a successful GitHub REST response but is possible on schema + changes or partial responses), treat the author as INTERNAL so the + destructive close path never fires on an unknown contributor. This matches + the sibling `is_external_pr_author` in `close_low_quality_prs.py`. + """ + login = ((item.get("user") or {}).get("login") or "").lower() + if login.endswith("[bot]") or login in {"dependabot", "github-actions"}: + return True + association = (item.get("author_association") or "").upper() + if not association or association in INTERNAL_ASSOCIATIONS: + return True + return False + + +# --------------------------------------------------------------------------- +# Greptile score + age helpers (`extract_greptile_score`, `parse_iso8601`) +# live in `agent_shin_shared` — they're imported at the top of this module +# so both `triage_with_llm.py` and `close_low_quality_prs.py` share a +# single source of truth for the Confidence-Score regex and ISO-8601 +# parsing. + + +# --------------------------------------------------------------------------- +# Prompt construction + + +def strip_html_comments(text: str) -> str: + """Remove HTML comments — template placeholder text shouldn't fool the judge.""" + return HTML_COMMENT_PATTERN.sub("", text or "") + + +def has_linked_issue(text: str) -> bool: + """Heuristic: does this body link to an open issue (Fixes #123 etc.)?""" + return bool(LINKED_ISSUE_PATTERN.search(strip_html_comments(text or ""))) + + +def build_pr_prompt(*, title: str, body: str) -> str: + cleaned_body = strip_html_comments(body or "").strip() or "(empty)" + # Dedent the static template *before* interpolating dynamic fields so that + # multi-line bodies (whose 2nd+ lines start at column 0) don't defeat the + # common-indent computation in textwrap.dedent. + template = textwrap.dedent(""" + You are "Agent Shin", the OSS triage bot for the LiteLLM open-source + repository (BerriAI/litellm). Decide whether this external pull request + meets the project's contribution standards. + + A PR PASSES triage only if BOTH (1) AND (2) are satisfied. A linked + issue alone is NOT enough — it covers context, not proof. + + (1) CONTEXT — the PR provides AT LEAST ONE of: + (a) A link to a related GitHub issue. Acceptable forms: + "Fixes #1234", "Closes #1234", "Resolves #1234", + "Refs https://github.com/BerriAI/litellm/issues/1234". A + bare "#1234" without a closing keyword counts only if it + is clearly the related issue (not a passing mention). + (b) A clear problem description in the body (what bug or + missing feature this addresses, beyond the title) AND + expected vs. actual behavior (or, for features, "what's + possible now vs. with this PR"). + + (2) END-TO-END QA PROOF: the PR body contains AT LEAST ONE of: + (a) A screen recording / video showing the behavior before + and after the change (the bug reproducing, then the fix + working). For a brand-new feature with no meaningful + "before", a recording of it working end-to-end is fine. + (b) A screenshot (or before/after screenshots) showing the + fix or feature working. + (c) Specific commands that were actually run (curl, python, + a CLI invocation, etc.) PAIRED WITH their real + output, demonstrating the change works end-to-end against + the real system. Commands whose external dependencies + (LLM provider, DB, network) are mocked or stubbed do NOT + satisfy (2c); they are not end-to-end. + + `has_qa_proof` must be set to `true` only when (2a), (2b), + or a non-mocked (2c) is actually present in the body. If the + only "proof" is mocked tests, `has_qa_proof` is `false` and + the verdict is "fail". + + The following do NOT count as QA proof: + - Generic claims like "I tested it", "works locally", "all + tests pass", or a checked "I added tests" checkbox with no + output shown. + - A description of what tests exist or were added, without + their actual output in the PR body. + - `pytest` (or any test runner) executed against the + repository's own unit tests. Those mock the LLM provider, + DB, and network, so they are NOT end-to-end and never + satisfy (2), no matter how much passing output is pasted. + - A linked issue. The linked issue is context (1a), never + proof (2). + + FAIL the PR if EITHER (1) or (2) is missing. Do not bias toward PASS: + if QA proof is absent, the verdict is "fail" even when the rest of + the PR is well-written. + + Respond with a single JSON object, no prose: + + {{ + "verdict": "pass" | "fail", + "linked_issue": boolean, + "has_problem_description": boolean, + "has_expected_vs_actual": boolean, + "has_qa_proof": boolean, + "qa_proof_type": "video" | "screenshot" | "commands_with_output" | "none", + "missing": ["plain-english strings naming what is missing"], + "explanation": "1-2 sentence reasoning for the team to skim" + }} + + --- + PR title: {title} + + PR body: + --- + {cleaned_body} + --- + """).strip() + return template.format(title=title, cleaned_body=cleaned_body) + + +def build_issue_prompt(*, title: str, body: str) -> str: + cleaned_body = strip_html_comments(body or "").strip() or "(empty)" + # Dedent the static template *before* interpolating dynamic fields so that + # multi-line bodies (whose 2nd+ lines start at column 0) don't defeat the + # common-indent computation in textwrap.dedent. + template = textwrap.dedent(""" + You are "Agent Shin", the OSS triage bot for the LiteLLM open-source + repository (BerriAI/litellm). Decide whether this GitHub issue meets + the project's reporting standards. + + For a BUG REPORT the issue PASSES triage only when it contains BOTH: + (1) END-TO-END EVIDENCE OF THE BUG (the "before"; set + `has_repro=true` only when this is present): AT LEAST ONE of: + (a) A screen recording / video of the bug happening. + (b) A screenshot of the bug. + (c) The exact command(s) actually run (curl, python, a CLI + invocation, etc.) PAIRED WITH their real output, traceback, + or logs showing the failure against the real system. + Commands whose external dependencies (LLM provider, DB, + network) are mocked or stubbed do NOT count. + Prose-only "steps to reproduce" with no run output, video, or + screenshot do NOT satisfy (1). + (2) Expected vs. actual behavior (`has_expected_vs_actual`). + + FAIL the bug report if either (1) or (2) is missing. Do not bias + toward PASS: if the bug isn't demonstrated end-to-end, the verdict is + "fail" even when the report is well-written. + + For a FEATURE REQUEST the issue PASSES triage only when it contains + ALL of: + - A clear description of the proposed feature (what should LiteLLM do + that it does not today). + - Motivation / use case with a concrete example (config, API call, + UI flow, or scenario showing what's blocked today). + + For an issue that is neither a bug report nor a feature request (a + question, support request, or discussion), PASS as long as it has a + clear, specific ask and is not empty or template placeholder text. + + Respond with a single JSON object, no prose: + + {{ + "verdict": "pass" | "fail", + "kind": "bug" | "feature" | "other", + "has_repro": boolean, + "has_expected_vs_actual": boolean, + "has_motivation_example": boolean, + "missing": ["plain-english strings naming what is missing"], + "explanation": "1-2 sentence reasoning for the team to skim" + }} + + --- + Issue title: {title} + + Issue body: + --- + {cleaned_body} + --- + """).strip() + return template.format(title=title, cleaned_body=cleaned_body) + + +# --------------------------------------------------------------------------- +# LLM call + verdict parsing + + +def call_llm_judge( + prompt: str, *, model: str, api_key: str, base_url: str | None +) -> str: + """Call an OpenAI-compatible chat completions endpoint. Returns raw text.""" + # Import inside the function so unit tests that monkey-patch this never + # need the openai package installed. + from openai import OpenAI + + client = ( + OpenAI(api_key=api_key, base_url=base_url) + if base_url + else OpenAI(api_key=api_key) + ) + kwargs: dict[str, Any] = { + "model": model, + "messages": [{"role": "user", "content": prompt}], + "temperature": 0, + "response_format": {"type": "json_object"}, + } + # gpt-5.x reasoning models reject `temperature != 1` unless + # `reasoning_effort` is explicitly "none". Set it via `extra_body` so this + # works across openai SDK versions regardless of whether the SDK natively + # types `reasoning_effort` as a top-level chat-completions param yet. + if model.lower().startswith(GPT5_FAMILY_PREFIX): + kwargs["extra_body"] = {"reasoning_effort": "none"} + response = client.chat.completions.create(**kwargs) + return response.choices[0].message.content or "" + + +def parse_verdict(raw: str) -> dict: + """Parse the LLM's JSON response. Tolerates ```json fences and stray text.""" + if not raw: + raise ValueError("empty LLM response") + text = raw.strip() + if text.startswith("```"): + text = re.sub(r"^```(?:json)?\s*", "", text) + text = re.sub(r"\s*```$", "", text) + try: + return json.loads(text) + except json.JSONDecodeError: + match = re.search(r"\{.*\}", text, re.DOTALL) + if not match: + raise ValueError(f"could not extract JSON from LLM response: {raw[:200]}") + return json.loads(match.group(0)) + + +# --------------------------------------------------------------------------- +# Comment composition + + +def _format_missing(missing: list[str]) -> str: + if not missing: + return "- (see explanation below)" + return "\n".join(f"- {m}" for m in missing) + + +# Rubric items the judge can mark present. The first element of each tuple is +# the verdict-JSON boolean field, the second is the human-readable label we +# render in the "what you got right" section of close / grace-warning comments. +_PR_PRESENT_LABELS: tuple[tuple[str, str], ...] = ( + ("linked_issue", "Linked a related GitHub issue"), + ("has_problem_description", "Clear problem description"), + ("has_expected_vs_actual", "Expected vs. actual behavior"), + ("has_qa_proof", "End-to-end QA proof"), +) + +# Issue rubric labels grouped by `kind`. The judge sets `kind` to one of +# {"bug", "feature", "other"}; when "other" we render both groups so we don't +# silently drop a present-flag the judge actually set to True. +_ISSUE_BUG_LABELS: tuple[tuple[str, str], ...] = ( + ( + "has_repro", + "End-to-end evidence of the bug (video, screenshot, or command + real output)", + ), + ("has_expected_vs_actual", "Expected vs. actual behavior"), +) +_ISSUE_FEATURE_LABELS: tuple[tuple[str, str], ...] = ( + ("has_motivation_example", "Motivation and concrete example"), +) + + +def _format_present_for_pr(verdict: dict) -> list[str]: + """Human-readable rubric items the judge confirmed are present on a PR. + + Drives the "what you got right" section in close / grace-warning comments. + The user gave explicit feedback: contributors should see what they nailed + *before* the list of gaps, so the comment doesn't read as pure rejection. + """ + return [label for field, label in _PR_PRESENT_LABELS if verdict.get(field)] + + +def _format_present_for_issue(verdict: dict) -> list[str]: + """Human-readable rubric items the judge confirmed are present on an issue. + + Branches on the judge's `kind` field. For `"other"` (or missing kind) we + render the union so a present-flag isn't dropped just because the judge + couldn't classify the issue cleanly. + """ + kind = (verdict.get("kind") or "").lower() + groups: list[tuple[tuple[str, str], ...]] = [] + if kind in ("bug", "other", ""): + groups.append(_ISSUE_BUG_LABELS) + if kind in ("feature", "other", ""): + groups.append(_ISSUE_FEATURE_LABELS) + out: list[str] = [] + for group in groups: + for field, label in group: + if verdict.get(field) and label not in out: + out.append(label) + return out + + +def _format_present_block(items: list[str]) -> str: + """Render the optional "what you got right" block. Empty string when the + judge didn't confirm anything as present — better to omit the section + entirely than to show "What you got right: (nothing)". + """ + if not items: + return "" + bullets = "\n".join(f"- ✅ {item}" for item in items) + return f"**What you got right:**\n\n{bullets}\n\n" + + +def format_pr_close_comment(verdict: dict) -> str: + missing_lines = _format_missing(verdict.get("missing") or []) + present_block = _format_present_block(_format_present_for_pr(verdict)) + explanation = verdict.get("explanation") or "" + return ( + "🚅 Hi, thanks for the PR! I'm **Agent Shin**, the automated triage bot for this " + "repository. " + "[What's this and why am I getting it?](https://docs.litellm.ai/blog/agent-shin-triage)\n" + "\n" + "I read the description against our " + "[contribution rubric](https://github.com/BerriAI/litellm/blob/main/.github/pull_request_template.md). " + "Here's how it lined up:\n" + "\n" + f"{present_block}" + "**What's still missing:**\n" + "\n" + f"{missing_lines}\n" + "\n" + f"> {explanation}\n" + "\n" + "**Closing this PR isn't a rejection of the change.** We want the open-PR list to " + "mirror what a maintainer can act on *right now*, so contributors don't get lost in a " + 'backlog. A closed PR is a soft "park this for later"; your work is still here, ' + "the diff is still here, and getting it reopened is one comment away. Take your time.\n" + "\n" + "**To bring this PR back:**\n" + "\n" + "- Update the description with the missing pieces, then comment `@agent-shin reconsider` " + "on this PR. I'll re-evaluate and reopen if it now passes.\n" + "- Or **Open a new PR** with the same fix and the updated description. GitHub doesn't " + "always let external contributors reopen a bot-closed PR, so a fresh PR is the most " + "reliable path back into the review queue.\n" + "- If Greptile's most recent score on this PR was below 4/5, comment `@greptileai` to " + "request a fresh review; that **still works even after the PR is closed**, and a " + "stronger score is one of the signals that lifts the PR back into the queue. A low " + "Greptile score isn't a blocker.\n" + "\n" + '**What "end-to-end QA proof" means**, since it\'s the most common gap: at least one ' + "of a short before/after screen recording / video (the bug reproducing, then the fix " + "working; for a brand-new feature, a recording of it working end-to-end), a screenshot " + "(or before/after screenshots) of it working, or the exact commands you ran paired " + "with their **real output** against the real system. Running `pytest` on the repo's " + "unit tests doesn't count; those mock the LLM provider, DB, and network, so they " + "aren't end-to-end. Output from a real, no-mocks integration run is what we look " + "for. A linked issue alone isn't enough either: it covers context, not proof. See " + "[the full rubric](https://docs.litellm.ai/blog/agent-shin-triage#the-rubric-for-pull-requests).\n" + "\n" + "Internal BerriAI contributors: this rubric doesn't apply to you; ping a maintainer.\n" + "\n" + "_(I'm an LLM, so I'm not infallible. If you think I got this wrong, comment " + "`@agent-shin reconsider` or ping a maintainer; they'll override me.)_" + f"\n\n{AGENT_SHIN_CLOSE_MARKER}" + ) + + +def format_issue_close_comment(verdict: dict) -> str: + missing_lines = _format_missing(verdict.get("missing") or []) + present_block = _format_present_block(_format_present_for_issue(verdict)) + explanation = verdict.get("explanation") or "" + return ( + "🚅 Hi, thanks for filing this! I'm **Agent Shin**, the automated triage bot for this " + "repository. " + "[What's this and why am I getting it?](https://docs.litellm.ai/blog/agent-shin-triage)\n" + "\n" + "I read the issue against our reporting checklist. Here's how it lined up:\n" + "\n" + f"{present_block}" + "**What's still missing:**\n" + "\n" + f"{missing_lines}\n" + "\n" + f"> {explanation}\n" + "\n" + "**Closing this isn't us saying the bug isn't real or the request isn't useful.** We " + "want the open-issue list to mirror what a maintainer can act on *right now*, so " + "reports like yours don't get buried in a backlog. A closed issue is a soft \"park " + 'this for later"; your report is still here, and getting it reopened is one comment ' + "away. Take your time.\n" + "\n" + "**To bring this issue back:**\n" + "\n" + "1. Edit the issue description to add the missing pieces:\n" + " - For **bug reports**: end-to-end evidence of the bug (a screen recording / " + "video, a screenshot, or the exact commands you ran with their real output / " + "traceback) plus expected vs. actual behavior. Written steps with no run output, " + "video, or screenshot don't count, and mocked or stubbed runs don't count.\n" + " - For **feature requests**: a concrete description of what should change, plus a " + "use case and example (config / API call / UI flow).\n" + "2. Comment `@agent-shin reconsider`. I'll re-run triage and reopen the issue if it " + "now meets the bar. (GitHub doesn't let external authors reopen an issue a maintainer " + "or bot closed, so the comment-based reconsider is the reliable path.)\n" + "\n" + "Internal BerriAI contributors: this rubric doesn't apply to you; ping a maintainer.\n" + "\n" + "_(I'm an LLM, so I'm not infallible. If you think I got this wrong, comment " + "`@agent-shin reconsider` or ping a maintainer; they'll override me.)_" + f"\n\n{AGENT_SHIN_CLOSE_MARKER}" + ) + + +def format_grace_warning_pr_comment(verdict: dict) -> str: + """Comment posted on the FIRST low-quality detection — gives the + contributor a 2-hour grace window to fix the PR before the next + triage run actually closes it. + + This is the "before-close" warning. On the second triage run, if the + grace marker is older than `GRACE_PERIOD_SECONDS` AND the PR still + fails the rubric, the close path runs (which posts + `format_pr_close_comment` and closes the PR). + """ + missing_lines = _format_missing(verdict.get("missing") or []) + present_block = _format_present_block(_format_present_for_pr(verdict)) + explanation = verdict.get("explanation") or "" + return ( + "🚅 Hi, thanks for the PR! I'm **Agent Shin**, the automated triage bot for this " + "repository. " + "[What's this and why am I getting it?](https://docs.litellm.ai/blog/agent-shin-triage)\n" + "\n" + "I read the description against our " + "[contribution rubric](https://github.com/BerriAI/litellm/blob/main/.github/pull_request_template.md). " + "Here's how it lined up:\n" + "\n" + f"{present_block}" + "**What's still missing:**\n" + "\n" + f"{missing_lines}\n" + "\n" + f"> {explanation}\n" + "\n" + "If the description isn't updated in the next **2 hours**, I'll auto-close this PR. " + "That's **not** us saying we don't care about the change; we want the open-PR list to " + "mirror what a maintainer can act on *right now*, so contributors don't get lost in a " + 'backlog. A closed PR is a soft "park this for later," not a rejection. Take your ' + "time; everything below still works after the close.\n" + "\n" + "**During the grace period:** just update the PR description with the missing pieces. " + "No need to ping me; I'll re-check on the next sweep and skip the auto-close if it " + "now passes. See " + "[what counts as QA proof](https://docs.litellm.ai/blog/agent-shin-triage#the-rubric-for-pull-requests) " + "for the full rubric (a linked issue alone isn't enough; it covers context, not proof).\n" + "\n" + "**If the PR does get auto-closed in 2 hours, you still have easy recovery paths:**\n" + "\n" + "- Comment `@agent-shin reconsider` after updating the description. I'll re-evaluate " + "and reopen the PR if it now passes.\n" + "- Comment `@greptileai` to request a fresh Greptile review; that **still works even " + "after the PR is closed**, and a stronger score is one of the signals that lifts the " + "PR back into the queue. So a low Greptile score isn't a blocker either.\n" + "\n" + "Internal BerriAI contributors: this rubric doesn't apply to you; ping a maintainer.\n" + "\n" + "_(I'm an LLM, so I'm not infallible. If you think I got this wrong, ping a " + "maintainer; they'll override me.)_\n" + "\n" + f"{GRACE_COMMENT_MARKER}" + ) + + +def format_grace_warning_issue_comment(verdict: dict) -> str: + """Issue analogue of `format_grace_warning_pr_comment`.""" + missing_lines = _format_missing(verdict.get("missing") or []) + present_block = _format_present_block(_format_present_for_issue(verdict)) + explanation = verdict.get("explanation") or "" + return ( + "🚅 Hi, thanks for filing this! I'm **Agent Shin**, the automated triage bot for this " + "repository. " + "[What's this and why am I getting it?](https://docs.litellm.ai/blog/agent-shin-triage)\n" + "\n" + "I read the issue against our reporting checklist. Here's how it lined up:\n" + "\n" + f"{present_block}" + "**What's still missing:**\n" + "\n" + f"{missing_lines}\n" + "\n" + f"> {explanation}\n" + "\n" + "If the issue isn't updated in the next **2 hours**, I'll auto-close it. That's **not** us " + "saying the bug isn't real or the request isn't useful; we want the open-issue list " + "to mirror what a maintainer can act on *right now*, so reports like yours don't get " + 'buried in a backlog. A closed issue is a soft "park this for later," not a ' + "rejection. Take your time; reopening is one comment away.\n" + "\n" + "**During the grace period:** just edit the issue description with the missing " + "pieces. No need to ping me; I'll re-check on the next sweep and skip the auto-close " + "if it now passes.\n" + "\n" + "Missing pieces, depending on what this is:\n" + "\n" + "- For **bug reports**: end-to-end evidence of the bug (a screen recording / video, a " + "screenshot, or the exact commands you ran with their real output / traceback) plus " + "expected vs. actual behavior. Written steps with no run output don't count, and " + "mocked or stubbed runs don't count.\n" + "- For **feature requests**: a concrete description of what should change, plus a use " + "case and example (config / API call / UI flow).\n" + "\n" + "**If the issue does get auto-closed in 2 hours**, comment `@agent-shin reconsider` " + "and I'll re-evaluate. If it now meets the bar, I'll reopen the issue.\n" + "\n" + "Internal BerriAI contributors: this rubric doesn't apply to you; ping a maintainer.\n" + "\n" + "_(I'm an LLM, so I'm not infallible. If you think I got this wrong, ping a " + "maintainer; they'll override me.)_\n" + "\n" + f"{GRACE_COMMENT_MARKER}" + ) + + +# --------------------------------------------------------------------------- +# Step-summary helpers + + +def write_step_summary(content: str) -> None: + """When running inside GitHub Actions, append to the step summary file.""" + path = os.environ.get("GITHUB_STEP_SUMMARY") + if not path: + return + try: + with open(path, "a", encoding="utf-8") as handle: + handle.write(content) + if not content.endswith("\n"): + handle.write("\n") + except OSError as exc: + print(f"warn: failed to write step summary: {exc}", file=sys.stderr) + + +# --------------------------------------------------------------------------- +# Core orchestration + + +def format_reopen_comment(kind: str) -> str: + """Comment posted when Agent Shin reopens after a successful reconsider.""" + noun = "PR" if kind == "pr" else "issue" + # The trailing HTML marker is used by `seconds_since_last_reconsider_verdict` + # to enforce a cooldown between repeated `@agent-shin reconsider` triggers. + # Keep the marker on its own line so it doesn't disturb the rendered text. + return ( + f"♻️ **Re-evaluated and reopened.** Thanks for updating the {noun}!\n" + "\n" + "Agent Shin re-ran triage on the latest description and it now meets " + "the bar. A maintainer will take another look soon; please don't " + f"close this {noun} again unless asked to.\n" + "\n" + "_(If a maintainer ends up closing this for non-rubric reasons, that " + "decision stands; comment `@agent-shin reconsider` again only if you " + "have substantively new information.)_\n" + "\n" + f"{RECONSIDER_COMMENT_MARKER}" + ) + + +def format_reconsider_still_failing_comment(kind: str, verdict: dict) -> str: + """Comment posted when reconsider re-runs triage but the verdict is still fail.""" + missing_lines = _format_missing(verdict.get("missing") or []) + explanation = verdict.get("explanation") or "" + noun = "PR" if kind == "pr" else "issue" + # The trailing HTML marker is used by `seconds_since_last_reconsider_verdict` + # to enforce a cooldown between repeated `@agent-shin reconsider` triggers. + return ( + f"⏸️ **Re-evaluated; this {noun} still doesn't meet the rubric.**\n" + "\n" + "Agent Shin re-ran triage on the current description but is still " + "missing:\n" + "\n" + f"{missing_lines}\n" + "\n" + f"> {explanation}\n" + "\n" + "Update the description with the missing pieces and comment " + "`@agent-shin reconsider` again, or ping a maintainer if you think " + "I got this wrong.\n" + "\n" + "_(I'm an LLM and I'm not infallible.)_\n" + "\n" + f"{RECONSIDER_COMMENT_MARKER}" + ) + + +# --------------------------------------------------------------------------- +# Review gate — "ready for review" label lifecycle + +_UNSET = object() + + +def _combine_missing( + verdict: dict, greptile_score: int | None, min_score: int +) -> list[str]: + """Merge the LLM rubric's `missing` list with a Greptile-score shortfall.""" + missing = list(verdict.get("missing") or []) + if greptile_score is not None and greptile_score < min_score: + missing.insert( + 0, + f"Greptile's most recent review scored this PR {greptile_score}/5 " + f"(below the {min_score}/5 bar)", + ) + return missing or ["(see explanation below)"] + + +def _has_marker( + comments: Iterable[dict], marker: str, *, bot_login: str | None = None +) -> bool: + """Return True iff the bot itself posted a comment containing ``marker``. + + Filters by author so a contributor who quotes the marker (e.g. via + GitHub's "Quote reply" feature, which preserves HTML comments in + raw markdown) is not mistaken for a bot action — that would + silently suppress notifications or change which "recovered" wording + is selected. Matches the author-filter pattern used by the sibling + `_seconds_since_latest_marker_comment` helper. + """ + expected_login = ( + bot_login + or os.environ.get("AGENT_SHIN_BOT_LOGIN") + or AGENT_SHIN_DEFAULT_BOT_LOGIN + ).lower() + for comment in comments: + author = ((comment.get("user") or {}).get("login") or "").lower() + if author != expected_login: + continue + if marker in (comment.get("body") or ""): + return True + return False + + +def format_ready_for_review_comment( + verdict: dict, + greptile_score: int | None, + min_greptile_score: int = DEFAULT_MIN_GREPTILE_SCORE, +) -> str: + """Posted the first time a PR clears the bar (label added).""" + score_line = ( + f" Greptile scored it **{greptile_score}/5**." + if greptile_score is not None + else "" + ) + explanation = verdict.get("explanation") or "" + return ( + "✅ **Triage passed, tagging `ready for review`.**\n" + "\n" + "Agent Shin checked this PR against the " + "[contribution rubric](https://github.com/BerriAI/litellm/blob/main/.github/pull_request_template.md) " + "and it clears the bar (a linked issue, or a clear problem description " + f"+ expected vs. actual + QA proof).{score_line}\n" + "\n" + f"> {explanation}\n" + "\n" + "A maintainer will take it from here. If a later re-check finds the PR " + f"has regressed (Greptile drops below {min_greptile_score}/5, " + "the QA proof is removed, etc.) I'll pull the tag and comment with " + "what's missing; fix it and the tag comes back automatically.\n" + f"{READY_MARKER}" + ) + + +def format_all_clear_comment(verdict: dict, greptile_score: int | None) -> str: + """Posted when a PR recovers after a regression (label re-added).""" + score_line = ( + f" Greptile is back to **{greptile_score}/5**." + if greptile_score is not None + else "" + ) + explanation = verdict.get("explanation") or "" + return ( + "✅ **All clear again, re-adding `ready for review`.**\n" + "\n" + "Thanks for addressing the earlier feedback. On re-check this PR meets " + f"the contribution bar once more.{score_line}\n" + "\n" + f"> {explanation}\n" + "\n" + "A maintainer will take another look.\n" + f"{READY_MARKER}" + ) + + +def format_regression_comment( + missing: list[str], explanation: str, grace_days: int +) -> str: + """Posted when a previously-tagged PR regresses (label removed, PR stays open). + + Discloses the same ``grace_days`` deadline the state machine enforces: + once that window elapses with the PR still failing, the close path fires. + Hiding the deadline behind a bare "stays open" would surprise contributors + with an auto-close they were never warned about. + """ + window = "24 hours" if grace_days == 1 else f"{grace_days} days" + return ( + "⚠️ **Removing the `ready for review` tag.**\n" + "\n" + "On a re-check this PR no longer meets the contribution bar. What's " + "missing now:\n" + "\n" + f"{_format_missing(missing)}\n" + "\n" + f"> {explanation}\n" + "\n" + f"The PR stays open for ~{window}; address the points above and Agent " + 'Shin will post an "all clear" comment and re-add the tag ' + "automatically. If the points still aren't addressed after that " + "window, the PR is auto-closed; that's not a rejection, and you can " + "comment `@agent-shin reconsider` to have it re-evaluated and reopened " + "once it passes.\n" + f"{REGRESSED_MARKER}" + ) + + +def format_within_grace_comment( + missing: list[str], explanation: str, grace_days: int +) -> str: + """Posted once while a failing PR is still inside its grace window.""" + window = "24 hours" if grace_days == 1 else f"{grace_days} days" + return ( + "🚅 Hi, thanks for the PR! This is **Agent Shin**, the automated triage " + "bot. This PR doesn't quite meet the contribution bar yet:\n" + "\n" + f"{_format_missing(missing)}\n" + "\n" + f"> {explanation}\n" + "\n" + f"You have ~{window} from when this PR was opened to add the missing " + "pieces; just update the description and I'll re-check on the next " + "sweep. Once it passes I'll tag it `ready for review`. If it does get " + "auto-closed, that's not a rejection; comment `@agent-shin reconsider` " + "and I'll re-evaluate and reopen if it now passes.\n" + f"{WITHIN_GRACE_MARKER}" + ) + + +def review_gate( + *, + repo: str, + number: int, + close: bool, + model: str, + judge: Any = None, + greptile_score: Any = _UNSET, + comments: Any = _UNSET, + now: dt.datetime | None = None, + grace_days: int = DEFAULT_GRACE_DAYS, + min_greptile_score: int = DEFAULT_MIN_GREPTILE_SCORE, + label: str = READY_FOR_REVIEW_LABEL, + allowlist: frozenset[str] = ALLOWLIST_LOGINS, +) -> dict: + """Reconcile the `ready for review` label with a PR's current quality. + + A PR is *passing* when it clears BOTH gates: the LLM rubric (linked issue, + or problem description + expected/actual + QA proof) AND Greptile's most + recent confidence score (>= ``min_greptile_score``; absence of a score is + not held against the PR). The gate then drives a small state machine, using + the label itself as the persisted state so comments fire only on + transitions (never on every scheduled run): + + passing, untagged -> add label + "ready for review" / "all clear" + passing, tagged -> noop-passing + not passing, tagged -> remove label + regression comment (stays open) + not passing, untagged, old -> close + comment (past the grace window) + not passing, untagged, new -> one-time "what's missing" notice (within grace) + + ``close`` gates every destructive side effect: with ``close=False`` the + function returns a ``would-*`` preview and touches nothing, mirroring the + dry-run contract of :func:`triage`. ``judge``/``greptile_score``/ + ``comments``/``now`` are injectable for tests; in production they are + resolved from the OpenAI judge, the PR's Greptile comment, the live comment + list, and the wall clock respectively. + """ + item = fetch_pr(repo, number) + + title = item.get("title") or "" + body = item.get("body") or "" + login = (item.get("user") or {}).get("login") or "" + association = item.get("author_association") or "" + state = item.get("state") or "" + # GitHub label names are case-insensitive; compare lowercased so a repo + # that already has e.g. "Ready for Review" is recognized as the same + # label as our READY_FOR_REVIEW_LABEL constant ("ready for review"). + labels_now = {(lbl.get("name") or "").lower() for lbl in (item.get("labels") or [])} + label_key = label.lower() + created_raw = item.get("created_at") or "" + + base_result = { + "kind": "pr", + "number": number, + "title": title, + "author": login, + "author_association": association, + "state": state, + "labeled": label_key in labels_now, + "review_gate": True, + } + + if state != "open": + return {**base_result, "action": "skip-not-open"} + + if allowlist: + if login.lower() not in allowlist: + return {**base_result, "action": "skip-not-allowlisted"} + elif is_internal_contributor(item): + return {**base_result, "action": "skip-internal-author"} + + # Resolve the comment list once — used for both the Greptile score and the + # marker-based dedup below. + if comments is _UNSET: + comments = list(_iter_paginated_json(f"repos/{repo}/issues/{number}/comments")) + + # --- rubric verdict: linked-issue short-circuit, else the LLM judge ------- + if has_linked_issue(body): + verdict = { + "verdict": "pass", + "linked_issue": True, + "missing": [], + "explanation": "Linked-issue regex matched; LLM was not called.", + } + rubric_pass = True + else: + prompt = build_pr_prompt(title=title, body=body) + if judge is None: + api_key = os.environ.get("OPENAI_API_KEY") + if not api_key: + return {**base_result, "action": "skip-no-llm-key"} + base_url = os.environ.get("OPENAI_BASE_URL") or None + + def judge(p: str) -> str: + return call_llm_judge( + p, model=model, api_key=api_key, base_url=base_url + ) + + try: + verdict = parse_verdict(judge(prompt)) + except Exception as exc: # noqa: BLE001 - judge errors must never act + return {**base_result, "action": "skip-llm-error", "error": str(exc)} + rubric_pass = (verdict.get("verdict") or "").lower() == "pass" + + # --- Greptile score ------------------------------------------------------- + if greptile_score is _UNSET: + extraction = extract_greptile_score(comments) + greptile_score = extraction[0] if extraction else None + greptile_ok = greptile_score is None or greptile_score >= min_greptile_score + passing = rubric_pass and greptile_ok + + # --- age ------------------------------------------------------------------ + age_days = None + if created_raw: + reference = now or dt.datetime.now(dt.timezone.utc) + age_days = (reference - parse_iso8601(created_raw)).days + + label_present = label_key in labels_now + explanation = verdict.get("explanation") or "" + # When the rubric short-circuited to pass (linked-issue regex) but + # Greptile dragged the PR below the bar, the synthetic verdict's + # explanation ("LLM was not called") would mislead a contributor reading + # the regression / close comment. Surface the real reason instead. + if rubric_pass and not greptile_ok: + explanation = ( + f"Greptile's most recent review scored this PR " + f"{greptile_score}/5 (below the {min_greptile_score}/5 bar)." + ) + verdict = {**verdict, "explanation": explanation} + base_result = { + **base_result, + "verdict": verdict, + "greptile_score": greptile_score, + "passing": passing, + "age_days": age_days, + } + + if passing: + if label_present: + return {**base_result, "action": "noop-passing"} + recovered = _has_marker(comments, REGRESSED_MARKER) + comment = ( + format_all_clear_comment(verdict, greptile_score) + if recovered + else format_ready_for_review_comment( + verdict, greptile_score, min_greptile_score + ) + ) + if not close: + return {**base_result, "action": "would-label-ready", "comment": comment} + post_comment(repo, number, comment) + add_label(repo, number, label) + return {**base_result, "action": "labeled-ready", "comment": comment} + + missing = _combine_missing(verdict, greptile_score, min_greptile_score) + + if label_present: + comment = format_regression_comment(missing, explanation, grace_days) + if not close: + return {**base_result, "action": "would-remove-label", "comment": comment} + remove_label(repo, number, label) + post_comment(repo, number, comment) + return {**base_result, "action": "label-removed-regressed", "comment": comment} + + # Not passing and not tagged. If the PR was previously tagged and then + # regressed (we removed the label and posted REGRESSED_MARKER), honor the + # "PR stays open — fix it and the tag comes back" promise from + # `format_regression_comment` and skip the close path. Without this guard, + # any PR older than `grace_days` would be closed on the next evaluation, + # giving the contributor no realistic window to address the regression. + # + # The promise has a deliberate expiration: once `grace_days` have elapsed + # since the regression notice, fall through to the close path so a PR that + # was abandoned post-regression doesn't sit open forever. + if _has_marker(comments, REGRESSED_MARKER): + reference = now or dt.datetime.now(dt.timezone.utc) + seconds_since_regression = seconds_since_latest_marker_comment( + comments, marker=REGRESSED_MARKER, now=reference + ) + grace_seconds = grace_days * 86400 + if seconds_since_regression is None or seconds_since_regression < grace_seconds: + return {**base_result, "action": "regressed-already-notified"} + + # Not passing and not tagged: close if past the grace window, else notify once. + if age_days is not None and age_days >= grace_days: + comment = format_pr_close_comment({**verdict, "missing": missing}) + if not close: + return {**base_result, "action": "would-close", "comment": comment} + post_comment(repo, number, comment) + close_pr(repo, number) + return {**base_result, "action": "closed", "comment": comment} + + if _has_marker(comments, WITHIN_GRACE_MARKER): + return {**base_result, "action": "within-grace-already-notified"} + comment = format_within_grace_comment(missing, explanation, grace_days) + if not close: + return { + **base_result, + "action": "would-notify-within-grace", + "comment": comment, + } + post_comment(repo, number, comment) + return {**base_result, "action": "within-grace-notified", "comment": comment} + + +def triage( + *, + repo: str, + kind: str, + number: int, + close: bool, + model: str, + judge: Any = None, + print_prompt: bool = False, + reconsider: bool = False, + allowlist: frozenset[str] = ALLOWLIST_LOGINS, +) -> dict: + """Triage a single PR or issue. Returns a result dict for logging/tests. + + `judge` is an optional callable `(prompt) -> str` for tests / dry-run with + a stub. In production, leave it None and the script uses `call_llm_judge`. + + When `reconsider=True`, the closed-state guard is skipped and a + fail-but-no-comment is replaced with a "still failing" comment + leave + closed; a pass triggers `reopen_pr`/`reopen_issue` plus a reopen comment. + Reconsider mode is intended for the `@agent-shin reconsider` comment + trigger. Like regular triage, `close=False` keeps reconsider in dry-run + (returns `would-reopen` / `would-reconsider-still-failing` so a local + operator can preview without write side effects); the workflow only + passes `--close` when `AGENT_SHIN_ENABLED=true`. + + Reconsider mode adds two extra safety guards on top of the regular + triage skip-internal-author check: + + 1. **Bot-closed guard.** Only reopens if the most recent close was + performed by the bot identity (default `github-actions[bot]`). + This stops a contributor from using `@agent-shin reconsider` to + override a maintainer's close for non-rubric reasons. + 2. **Rate-limit guard.** If the bot has already posted a reconsider + verdict on this PR/issue within `RECONSIDER_RATE_LIMIT_SECONDS`, + skip — repeated triggers from the same contributor shouldn't burn + CI minutes or LLM budget. + """ + fetcher = {"pr": fetch_pr, "issue": fetch_issue}[kind] + item = fetcher(repo, number) + + title = item.get("title") or "" + body = item.get("body") or "" + login = (item.get("user") or {}).get("login") or "" + association = item.get("author_association") or "" + state = item.get("state") or "" + + base_result = { + "kind": kind, + "number": number, + "title": title, + "author": login, + "author_association": association, + "state": state, + "reconsider": reconsider, + } + + # Reconsider only makes sense on a closed PR/issue. A "reconsider on an + # open PR" is a no-op (the regular triage flow already evaluates open + # PRs); return a clear skip so the workflow can short-circuit. + if reconsider: + if state != "closed": + return {**base_result, "action": "skip-not-closed"} + else: + if state != "open": + return {**base_result, "action": "skip-not-open"} + + if allowlist: + if login.lower() not in allowlist: + return {**base_result, "action": "skip-not-allowlisted"} + elif is_internal_contributor(item): + return {**base_result, "action": "skip-internal-author"} + + # Reconsider-only guards — these run BEFORE the LLM call so a + # maintainer-closed PR / rate-limited trigger never spends LLM budget. + if reconsider: + if not was_closed_by_agent_shin(repo, number): + return {**base_result, "action": "skip-not-bot-closed"} + age = seconds_since_last_reconsider_verdict(repo, number) + if age is not None and age < RECONSIDER_RATE_LIMIT_SECONDS: + return { + **base_result, + "action": "skip-rate-limited", + "rate_limit_age_seconds": age, + "rate_limit_window_seconds": RECONSIDER_RATE_LIMIT_SECONDS, + } + + if kind == "pr": + # Short-circuit: if body very clearly links a related issue, just pass. + if has_linked_issue(body): + base = { + **base_result, + "action": "pass-linked-issue", + "verdict": { + "verdict": "pass", + "linked_issue": True, + "explanation": "Linked-issue regex matched; LLM was not called.", + }, + } + if reconsider: + # Pass-on-reconsider -> reopen the PR with a friendly comment. + reopen_body = format_reopen_comment(kind) + if not close: + return { + **base, + "action": "would-reopen", + "comment": reopen_body, + } + post_comment(repo, number, reopen_body) + reopen_pr(repo, number) + return { + **base, + "action": "reopened", + "comment": reopen_body, + } + return base + prompt = build_pr_prompt(title=title, body=body) + else: + prompt = build_issue_prompt(title=title, body=body) + + if print_prompt: + return {**base_result, "action": "print-prompt", "prompt": prompt} + + if judge is None: + api_key = os.environ.get("OPENAI_API_KEY") + if not api_key: + # No key configured — never take a destructive action. Report skip. + return { + **base_result, + "action": "skip-no-llm-key", + "prompt_preview": prompt[:200], + } + base_url = os.environ.get("OPENAI_BASE_URL") or None + + def judge(p: str) -> str: + return call_llm_judge(p, model=model, api_key=api_key, base_url=base_url) + + try: + raw = judge(prompt) + verdict = parse_verdict(raw) + except Exception as exc: # noqa: BLE001 - judge errors must never close PRs + return {**base_result, "action": "skip-llm-error", "error": str(exc)} + + decision = (verdict.get("verdict") or "").lower() + + if reconsider: + # Reconsider: an explicit `pass` -> reopen + post reopen comment; + # anything else (fail, missing/malformed verdict, typo) -> leave + # closed + post a "still failing" comment so the contributor can + # iterate again. Reopen is destructive, so a flaky/empty verdict + # must not satisfy the gate. + # In dry-run (`close=False`) we return `would-*` actions instead + # of touching GitHub state, mirroring the regular triage flow's + # `would-close`. This lets a local operator preview the outcome + # of `python triage_with_llm.py --reconsider --pr N` without + # risking accidental comments or reopens. + if decision == "pass": + reopen_body = format_reopen_comment(kind) + if not close: + return { + **base_result, + "action": "would-reopen", + "verdict": verdict, + "comment": reopen_body, + } + post_comment(repo, number, reopen_body) + if kind == "pr": + reopen_pr(repo, number) + else: + reopen_issue(repo, number) + return { + **base_result, + "action": "reopened", + "verdict": verdict, + "comment": reopen_body, + } + still_failing = format_reconsider_still_failing_comment(kind, verdict) + if not close: + return { + **base_result, + "action": "would-reconsider-still-failing", + "verdict": verdict, + "comment": still_failing, + } + post_comment(repo, number, still_failing) + return { + **base_result, + "action": "reconsider-still-failing", + "verdict": verdict, + "comment": still_failing, + } + + if decision != "fail": + return {**base_result, "action": "pass-llm", "verdict": verdict} + + # Grace-period flow: on the first low-quality detection, post a warning + # comment instead of closing immediately. On a subsequent triage run + # (manual re-trigger, or the daily `close_low_quality_prs.py` cron + # finding the same PR in its own pass), if `GRACE_PERIOD_SECONDS` has + # elapsed since the warning AND the PR still fails the rubric, close. + grace_age = seconds_since_last_grace_warning(repo, number) + if grace_age is None: + warning_body = ( + format_grace_warning_pr_comment(verdict) + if kind == "pr" + else format_grace_warning_issue_comment(verdict) + ) + if not close: + return { + **base_result, + "action": "would-warn-grace", + "verdict": verdict, + "comment": warning_body, + } + post_comment(repo, number, warning_body) + return { + **base_result, + "action": "warned-grace", + "verdict": verdict, + "comment": warning_body, + } + if grace_age < GRACE_PERIOD_SECONDS: + return { + **base_result, + "action": "skip-in-grace-period", + "verdict": verdict, + "grace_age_seconds": grace_age, + "grace_period_seconds": GRACE_PERIOD_SECONDS, + } + + # The grace window has elapsed. `--close` still gates the destructive + # write so a dry-run preview never posts or closes — the workflow only + # passes `--close` when `AGENT_SHIN_ENABLED=true`, which keeps the bot + # inert by default. + if not close: + return {**base_result, "action": "would-close", "verdict": verdict} + + comment_body = ( + format_pr_close_comment(verdict) + if kind == "pr" + else format_issue_close_comment(verdict) + ) + post_comment(repo, number, comment_body) + if kind == "pr": + close_pr(repo, number) + else: + close_issue(repo, number) + + return { + **base_result, + "action": "closed", + "verdict": verdict, + "comment": comment_body, + } + + +# --------------------------------------------------------------------------- +# CLI + + +def render_summary(result: dict) -> str: + """Render a human-readable summary block (used for stdout + step summary).""" + lines = ["## Agent Shin verdict", ""] + lines.append( + f"- **{result['kind'].upper()} #{result['number']}**: {result.get('title', '')}" + ) + lines.append( + f"- **Author**: `{result.get('author', '')}` ({result.get('author_association', '')})" + ) + lines.append(f"- **State**: {result.get('state', '')}") + lines.append(f"- **Action**: `{result['action']}`") + verdict = result.get("verdict") + if verdict: + lines.append("") + lines.append("```json") + lines.append(json.dumps(verdict, indent=2)) + lines.append("```") + error = result.get("error") + if error: + lines.append("") + lines.append(f"_LLM error: {error}_") + comment = result.get("comment") + if comment: + lines.append("") + lines.append("### Posted comment:") + lines.append("") + lines.append("> " + comment.replace("\n", "\n> ")) + return "\n".join(lines) + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--repo", required=True, help="Repository (owner/repo).") + target = parser.add_mutually_exclusive_group(required=True) + target.add_argument("--pr", type=int, help="Pull request number to triage.") + target.add_argument("--issue", type=int, help="Issue number to triage.") + parser.add_argument( + "--close", + action="store_true", + help="Actually post comment + close on fail (default: dry run).", + ) + parser.add_argument( + "--model", + # `os.environ.get("TRIAGE_MODEL", DEFAULT_MODEL)` would return "" when + # GitHub Actions exposes an unset repo variable as an empty-string env + # var, silently bypassing DEFAULT_MODEL and causing every call to fail + # as `skip-llm-error`. The `or` guard collapses empty -> default. + default=os.environ.get("TRIAGE_MODEL") or DEFAULT_MODEL, + help=f"OpenAI-compatible model name (default: {DEFAULT_MODEL}).", + ) + parser.add_argument( + "--print-prompt", + action="store_true", + help="Print the prompt that would be sent to the judge and exit.", + ) + parser.add_argument( + "--reconsider", + action="store_true", + help=( + "Re-run triage on a CLOSED PR/issue and reopen it on pass. " + "Used by the `@agent-shin reconsider` comment-trigger workflow. " + "Only invoke this from a workflow that has already gated on " + "AGENT_SHIN_ENABLED=true and verified the commenter is the " + "PR/issue author or an internal collaborator." + ), + ) + parser.add_argument( + "--review-gate", + action="store_true", + help=( + "Reconcile the `ready for review` label for an OPEN PR: tag on " + "pass, remove the tag + comment on regression, close after the " + "grace window if it never passed. PR-only." + ), + ) + parser.add_argument( + "--grace-days", + type=int, + default=DEFAULT_GRACE_DAYS, + help=( + "Review-gate only: hours/24 a failing, un-tagged PR may stay open " + f"before auto-close (default: {DEFAULT_GRACE_DAYS} = 24h)." + ), + ) + parser.add_argument( + "--min-greptile-score", + type=int, + default=DEFAULT_MIN_GREPTILE_SCORE, + choices=range(1, 6), + help=( + "Review-gate only: Greptile score below which a PR counts as not " + f"passing (default: {DEFAULT_MIN_GREPTILE_SCORE} -> <4/5 regresses)." + ), + ) + args = parser.parse_args() + + kind = "pr" if args.pr is not None else "issue" + number = args.pr if args.pr is not None else args.issue + + if args.review_gate: + if kind != "pr": + parser.error("--review-gate applies to pull requests only (use --pr).") + result = review_gate( + repo=args.repo, + number=number, + close=args.close, + model=args.model, + grace_days=args.grace_days, + min_greptile_score=args.min_greptile_score, + ) + else: + result = triage( + repo=args.repo, + kind=kind, + number=number, + close=args.close, + model=args.model, + print_prompt=args.print_prompt, + reconsider=args.reconsider, + ) + + if result.get("action") == "print-prompt": + print(result["prompt"]) + return 0 + + summary = render_summary(result) + print(summary) + write_step_summary(summary + "\n") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/.github/workflows/check-ui-api-types.yml b/.github/workflows/check-ui-api-types.yml index eeb5545b15e..d8053c15683 100644 --- a/.github/workflows/check-ui-api-types.yml +++ b/.github/workflows/check-ui-api-types.yml @@ -54,7 +54,7 @@ jobs: run: uv run --no-sync prisma generate --schema litellm/proxy/schema.prisma - name: Set up Node.js - uses: actions/setup-node@a0853c24544627f65ddf259abe73b1d18a591444 # v5.0 + uses: actions/setup-node@a0853c24544627f65ddf259abe73b1d18a591444 # v5.0.0 with: node-version: "20" cache: "npm" diff --git a/.github/workflows/close_low_quality_prs.yml b/.github/workflows/close_low_quality_prs.yml new file mode 100644 index 00000000000..2401be84000 --- /dev/null +++ b/.github/workflows/close_low_quality_prs.yml @@ -0,0 +1,92 @@ +name: Close Low-Quality PRs + +# Auto-close any open PR (including drafts, regardless of age) authored by an +# external OSS contributor that Greptile reviewed with a confidence score +# below 4/5. Closures are explained in a comment that tells the contributor +# to push fixes and open a fresh PR (since OSS authors cannot reopen a PR +# closed by a bot/maintainer) or comment `@agent-shin reconsider` to have +# Agent Shin re-evaluate. +# +# Manual one-off run: +# gh workflow run "Close Low-Quality PRs" -f close=true +# +# Dry-run preview (no PRs are touched): +# gh workflow run "Close Low-Quality PRs" -f close=false + +on: + schedule: + # Daily at 09:00 UTC. Pairs well with the stale-issue workflow at midnight. + - cron: "0 9 * * *" + workflow_dispatch: + inputs: + close: + description: "Actually close matching PRs (false = dry run)." + required: false + default: "false" + type: choice + options: + - "true" + - "false" + min_age_days: + description: "Minimum PR age in days (default 0 = no age filter)." + required: false + default: "0" + min_score: + description: "Greptile score below which a PR is closed (1-5)." + required: false + default: "4" + limit: + description: "Maximum number of PRs to close in a single run." + required: false + default: "25" + +permissions: + contents: read + pull-requests: write + issues: write + +jobs: + close-low-quality-prs: + if: github.repository == 'BerriAI/litellm' + runs-on: ubuntu-latest + steps: + - name: Checkout triage script + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + sparse-checkout: .github/scripts + persist-credentials: false + + - name: Set up Python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: "3.12" + + - name: Run low-quality PR closer + env: + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + # Scheduled runs are ALWAYS dry-run, even when AGENT_SHIN_ENABLED is + # "true", so the team can QA the closer's verdicts in step summaries + # before any contributor sees a PR closed. Real closures only happen + # on manual workflow_dispatch with close=true (and the variable set). + CLOSE_FLAG: ${{ github.event.inputs.close || 'false' }} + AGENT_SHIN_ENABLED: ${{ vars.AGENT_SHIN_ENABLED }} + MIN_AGE_DAYS: ${{ github.event.inputs.min_age_days || '0' }} + MIN_SCORE: ${{ github.event.inputs.min_score || '4' }} + LIMIT: ${{ github.event.inputs.limit || '25' }} + run: | + set -euo pipefail + ARGS=( + --repo "${{ github.repository }}" + --min-age-days "${MIN_AGE_DAYS}" + --min-score "${MIN_SCORE}" + --limit "${LIMIT}" + ) + if [ "${AGENT_SHIN_ENABLED:-false}" != "true" ]; then + echo "::notice::AGENT_SHIN_ENABLED is not 'true' -> forcing dry-run regardless of close input." + elif [ "${GITHUB_EVENT_NAME:-}" = "workflow_dispatch" ] && [ "${CLOSE_FLAG}" = "true" ]; then + ARGS+=(--close) + echo "::notice::Running in close-on-fail mode." + else + echo "::notice::AGENT_SHIN_ENABLED is true but this trigger is dry-run (scheduled event or close=false)." + fi + python3 .github/scripts/close_low_quality_prs.py "${ARGS[@]}" diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml index babe3b62933..d3a165a11da 100644 --- a/.github/workflows/codeql.yml +++ b/.github/workflows/codeql.yml @@ -43,14 +43,14 @@ jobs: persist-credentials: false - name: Initialize CodeQL - uses: github/codeql-action/init@ebcb5b36ded6beda4ceefea6a8bc4cc885255bb3 # v3 + uses: github/codeql-action/init@ebcb5b36ded6beda4ceefea6a8bc4cc885255bb3 # v3.34.1 with: languages: ${{ matrix.language }} build-mode: ${{ matrix.build-mode }} config-file: ./.github/codeql/codeql-config.yml - name: Perform CodeQL Analysis - uses: github/codeql-action/analyze@ebcb5b36ded6beda4ceefea6a8bc4cc885255bb3 # v3 + uses: github/codeql-action/analyze@ebcb5b36ded6beda4ceefea6a8bc4cc885255bb3 # v3.34.1 with: category: "/language:${{ matrix.language }}" output: sarif-results @@ -77,7 +77,7 @@ jobs: output: sarif-results/python.sarif - name: Upload SARIF - uses: github/codeql-action/upload-sarif@ebcb5b36ded6beda4ceefea6a8bc4cc885255bb3 # v3 + uses: github/codeql-action/upload-sarif@ebcb5b36ded6beda4ceefea6a8bc4cc885255bb3 # v3.34.1 with: sarif_file: sarif-results category: "/language:${{ matrix.language }}" diff --git a/.github/workflows/test-linting.yml b/.github/workflows/test-linting.yml index 0a80a65cbe6..de7e1b68346 100644 --- a/.github/workflows/test-linting.yml +++ b/.github/workflows/test-linting.yml @@ -87,14 +87,9 @@ jobs: run: | uv run --no-sync python -c "import openai; print(f'OpenAI version: {openai.__version__}')" - - name: Run MyPy type checking - run: | - cd litellm - (uv run --no-sync mypy . || true) | uv run --no-sync python ../scripts/type_check_gate.py --tool mypy - - name: Run basedpyright type checking run: | - (uv run --no-sync basedpyright --outputjson || true) | uv run --no-sync python scripts/type_check_gate.py --tool basedpyright + (uv run --no-sync basedpyright --outputjson || true) | uv run --no-sync python scripts/type_check_gate.py - name: Check for circular imports run: | @@ -133,56 +128,6 @@ jobs: run: | python scripts/budget_ratchet_check.py --base "$BASE_SHA" - any-discipline: - # Separate job: the first run cold-builds litellm's type cache (~2 min, ~3 GB), - # so keep it off the main lint job's time budget. Subsequent runs reuse the - # cached .mypy_cache_any and only re-type-check the changed files. - runs-on: ubuntu-latest - timeout-minutes: 10 - - steps: - - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 - # Check out the PR head, not the default refs/pull/N/merge: the merge ref - # folds in newer base commits, which the diff-based gates (ruff delta, - # Any-discipline) would otherwise blame on this branch. - with: - ref: ${{ github.event.pull_request.head.sha }} - fetch-depth: 0 - clean: true - persist-credentials: false - - - name: Set up Python - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 - with: - python-version: "3.12" - - - name: Set up uv - uses: astral-sh/setup-uv@37802adc94f370d6bfd71619e3f0bf239e1f3b78 # v7 - with: - version: "0.10.9" - - - name: Install dependencies - run: | - uv sync --frozen - - # Keyed on deps + mypy config (which fix the type cache's validity), not on - # source content, so changed files always differ from the restored cache. - # The gate also defensively invalidates each target's cache entry, so - # correctness never depends on cache freshness -- this is purely for speed. - - name: Restore Any-gate type cache - uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0 - with: - path: .mypy_cache_any - key: any-mypy-cache-${{ runner.os }}-py3.12-${{ hashFiles('uv.lock', 'litellm/mypy.ini') }} - restore-keys: | - any-mypy-cache-${{ runner.os }}-py3.12- - - - name: Check Any discipline (per-file budget on changed files) - env: - BASE_SHA: ${{ github.event.pull_request.base.sha }} - run: | - uv run --no-sync python scripts/check_any_discipline.py --changed --base "$BASE_SHA" - secret-scan: runs-on: ubuntu-latest timeout-minutes: 5 diff --git a/.github/workflows/test-litellm-ui-build.yml b/.github/workflows/test-litellm-ui-build.yml index 68497b10dbb..b83119712a7 100644 --- a/.github/workflows/test-litellm-ui-build.yml +++ b/.github/workflows/test-litellm-ui-build.yml @@ -25,7 +25,7 @@ jobs: persist-credentials: false - name: Setup Node.js - uses: actions/setup-node@a0853c24544627f65ddf259abe73b1d18a591444 # v5.0 + uses: actions/setup-node@a0853c24544627f65ddf259abe73b1d18a591444 # v5.0.0 with: node-version: "20" cache: "npm" @@ -77,7 +77,7 @@ jobs: - name: Setup Node.js if: steps.changed.outputs.has_files == 'true' - uses: actions/setup-node@a0853c24544627f65ddf259abe73b1d18a591444 # v5.0 + uses: actions/setup-node@a0853c24544627f65ddf259abe73b1d18a591444 # v5.0.0 with: node-version: "20" cache: "npm" diff --git a/.github/workflows/test-unit-proxy-endpoints.yml b/.github/workflows/test-unit-proxy-endpoints.yml index 0a9513ec024..d9b6a348b60 100644 --- a/.github/workflows/test-unit-proxy-endpoints.yml +++ b/.github/workflows/test-unit-proxy-endpoints.yml @@ -11,8 +11,6 @@ on: permissions: contents: read - id-token: write - pull-requests: write concurrency: group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} @@ -20,6 +18,10 @@ concurrency: jobs: proxy-endpoints: + permissions: + contents: read + id-token: write + pull-requests: write uses: ./.github/workflows/_test-unit-base.yml with: test-path: >- @@ -52,6 +54,10 @@ jobs: # is independent and its coverage artifact is uploaded separately. # See: https://www.notion.so/36c43b8acdab81ee845fd5365128a2fc proxy-server: + permissions: + contents: read + id-token: write + pull-requests: write uses: ./.github/workflows/_test-unit-base.yml with: test-path: tests/test_litellm/proxy/proxy_server diff --git a/.github/workflows/test_server_root_path.yml b/.github/workflows/test_server_root_path.yml index 57ff746c9c8..985653796c2 100644 --- a/.github/workflows/test_server_root_path.yml +++ b/.github/workflows/test_server_root_path.yml @@ -32,17 +32,16 @@ jobs: df -h / - name: Set up Docker Buildx - uses: docker/setup-buildx-action@8d2750c68a42422c14e847fe6c8ac0403b4cbd6f # v3.12 + uses: docker/setup-buildx-action@8d2750c68a42422c14e847fe6c8ac0403b4cbd6f # v3.12.0 - name: Build Docker image - uses: docker/build-push-action@0adf9959216b96bec444f325f1e493d4aa344497 #v6.14 + uses: docker/build-push-action@0adf9959216b96bec444f325f1e493d4aa344497 # v6.14.0 with: context: . file: ./docker/Dockerfile.non_root tags: litellm-test:${{ github.sha }} load: true - cache-from: type=gha - cache-to: type=gha,mode=max + push: false - name: Start LiteLLM container with SERVER_ROOT_PATH run: | diff --git a/.github/workflows/triage_issue_with_llm.yml b/.github/workflows/triage_issue_with_llm.yml new file mode 100644 index 00000000000..765453cf2c6 --- /dev/null +++ b/.github/workflows/triage_issue_with_llm.yml @@ -0,0 +1,96 @@ +name: Agent Shin — Issue triage + +# LLM-as-judge triage for external GitHub issues. +# +# DRY-RUN BY DEFAULT. See .github/workflows/triage_pr_with_llm.yml for the +# enablement procedure — same repo variable (`AGENT_SHIN_ENABLED=true`) +# unlocks the PR and issue triage flows together. + +on: + issues: + types: [opened, reopened] + workflow_dispatch: + inputs: + issue_number: + description: "Issue number to triage manually." + required: true + close: + description: "If true and AGENT_SHIN_ENABLED=true, actually close on fail." + required: false + default: "false" + type: choice + options: + - "true" + - "false" + +permissions: + contents: read + issues: write + +jobs: + triage: + if: github.repository == 'BerriAI/litellm' + runs-on: ubuntu-latest + steps: + - name: Checkout triage script + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + sparse-checkout: .github/scripts + persist-credentials: false + + - name: Set up Python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: "3.12" + + - name: Install LLM client + run: pip install --no-cache-dir --require-hashes -r .github/scripts/triage-requirements.txt + + - name: Run Agent Shin + env: + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + # Only expose the LLM key when the bot is enabled or a collaborator + # triggers it manually, so an external user can't force paid LLM + # calls by churning issues while the bot is still in dry-run. + # The Python script calls the LLM whenever this var is set + # (regardless of `--close`); stripping `--close` doesn't suppress + # the API call, only the destructive side effects. + OPENAI_API_KEY: ${{ (vars.AGENT_SHIN_ENABLED == 'true' || github.event_name == 'workflow_dispatch') && secrets.OPENAI_API_KEY || '' }} + OPENAI_BASE_URL: ${{ vars.OPENAI_BASE_URL }} + TRIAGE_MODEL: ${{ vars.TRIAGE_MODEL }} + AGENT_SHIN_ENABLED: ${{ vars.AGENT_SHIN_ENABLED }} + DISPATCH_CLOSE: ${{ github.event.inputs.close }} + ISSUE_NUMBER: ${{ github.event.issue.number || github.event.inputs.issue_number }} + run: | + set -euo pipefail + ARGS=(--repo "${{ github.repository }}" --issue "${ISSUE_NUMBER}") + # Fail-safe gating: only the EXACT string "true" enables the + # destructive --close path. The workflow_dispatch input is a + # `choice` dropdown of "true"/"false" so the UI is constrained, + # but the API (`gh workflow run -f close=...`) accepts any + # string, and a `!= "false"` check would treat "True", "yes", + # "1", "TRUE", typos, and accidental whitespace as enabling + # closure. Mirror the Greptile closer's `= "true"` pattern. + if [ "${AGENT_SHIN_ENABLED:-false}" = "true" ] && [ "${DISPATCH_CLOSE:-false}" = "true" ]; then + ARGS+=(--close) + echo "::notice::Agent Shin is ENABLED and running in close-on-fail mode." + elif [ "${AGENT_SHIN_ENABLED:-false}" = "true" ]; then + echo "::notice::Agent Shin is ENABLED but this trigger is dry-run (workflow_dispatch close != 'true')." + else + echo "::notice::Agent Shin is in DRY-RUN mode (AGENT_SHIN_ENABLED is not 'true'). No comments will be posted; no issues will be closed." + fi + # Automatic `issues` events stay dry-run regardless until the team + # explicitly invokes workflow_dispatch with close=true. + if [ "${GITHUB_EVENT_NAME:-}" = "issues" ]; then + # filter out --close rather than substituting to "" (which would + # leave an empty positional arg that argparse rejects) + FILTERED=() + for arg in "${ARGS[@]}"; do + if [ "${arg}" != "--close" ]; then + FILTERED+=("${arg}") + fi + done + ARGS=("${FILTERED[@]}") + echo "::notice::issues trigger -> forcing dry-run." + fi + python3 .github/scripts/triage_with_llm.py "${ARGS[@]}" diff --git a/.github/workflows/triage_reconsider.yml b/.github/workflows/triage_reconsider.yml new file mode 100644 index 00000000000..f35f681d09a --- /dev/null +++ b/.github/workflows/triage_reconsider.yml @@ -0,0 +1,172 @@ +name: Agent Shin — reconsider + +# Comment-trigger workflow: when the PR/issue author (or an internal +# collaborator) comments `@agent-shin reconsider` on a CLOSED PR/issue, +# Agent Shin re-runs LLM-judge triage on the current title+body and: +# +# - on PASS: posts a "re-evaluated and reopened" comment + reopens. +# - on FAIL: posts a "still missing X" comment and leaves it closed, +# so the contributor can iterate again. +# +# This exists because GitHub does NOT let an external (non-write-access) +# OSS contributor reopen a PR/issue closed by a bot or maintainer. Without +# this comment trigger, a contributor whose PR Agent Shin auto-closed +# would have no path back into the review queue except opening a fresh PR +# (which loses the original PR's history). The bot, on the other hand, +# has write access via GH_TOKEN and can reopen on their behalf. +# +# DRY-RUN BY DEFAULT — gated on `vars.AGENT_SHIN_ENABLED == 'true'` just +# like the other Agent Shin workflows. The workflow also gates on the +# commenter being either the PR/issue author or an internal collaborator +# (OWNER/MEMBER/COLLABORATOR) so random commenters cannot DOS the LLM +# judge or force a reopen. + +on: + issue_comment: + types: [created] + +permissions: + contents: read + issues: write + pull-requests: write + +jobs: + reconsider: + if: | + github.repository == 'BerriAI/litellm' + && contains(github.event.comment.body, '@agent-shin reconsider') + runs-on: ubuntu-latest + steps: + - name: Authorize commenter + # Only the PR/issue author OR an internal collaborator may trigger + # a reconsider. Outside random commenters could otherwise spam the + # phrase to burn LLM budget or, if a fail-open bug were ever + # introduced, force a reopen on someone else's behalf. + # + # We expose the authorization decision as a step output and gate + # every subsequent (potentially destructive) step on it. A `run:` + # step with `exit 0` would NOT stop the job — only `if:` gating + # on a known-true output is safe here. + id: auth + env: + COMMENTER: ${{ github.event.comment.user.login }} + AUTHOR: ${{ github.event.issue.user.login }} + ASSOCIATION: ${{ github.event.comment.author_association }} + run: | + set -euo pipefail + if [ "${COMMENTER}" = "${AUTHOR}" ]; then + echo "::notice::Authorized: commenter is the PR/issue author." + echo "authorized=true" >> "$GITHUB_OUTPUT" + exit 0 + fi + case "${ASSOCIATION}" in + OWNER|MEMBER|COLLABORATOR) + echo "::notice::Authorized: commenter is an internal collaborator (${ASSOCIATION})." + echo "authorized=true" >> "$GITHUB_OUTPUT" + ;; + *) + echo "::notice::Commenter '${COMMENTER}' (${ASSOCIATION}) is not authorized to trigger reconsider; skipping subsequent steps." + echo "authorized=false" >> "$GITHUB_OUTPUT" + ;; + esac + + - name: React 👀 to acknowledge the reconsider + # Add an eyes reaction to the triggering comment the moment we accept + # it, so the contributor gets instant feedback that the bot saw their + # `@agent-shin reconsider` before the slower triage steps run. Gated on + # AGENT_SHIN_ENABLED so dry-run leaves no visible trace. Best-effort: + # a reactions API hiccup must never fail the actual reconsider. + if: steps.auth.outputs.authorized == 'true' && vars.AGENT_SHIN_ENABLED == 'true' + env: + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + COMMENT_ID: ${{ github.event.comment.id }} + run: | + set -euo pipefail + gh api --method POST \ + -H "Accept: application/vnd.github+json" \ + "repos/${{ github.repository }}/issues/comments/${COMMENT_ID}/reactions" \ + -f content=eyes \ + || echo "::warning::failed to add 👀 reaction (non-fatal)" + + - name: Checkout triage script + if: steps.auth.outputs.authorized == 'true' + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + sparse-checkout: .github/scripts + persist-credentials: false + + - name: Set up Python + if: steps.auth.outputs.authorized == 'true' + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: "3.12" + + - name: Install LLM client + if: steps.auth.outputs.authorized == 'true' + run: pip install --no-cache-dir --require-hashes -r .github/scripts/triage-requirements.txt + + - name: Run Agent Shin reconsider + if: steps.auth.outputs.authorized == 'true' + env: + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + # Only expose the LLM key when the bot is enabled, so a PR/issue + # author can't force paid LLM calls by spamming `@agent-shin + # reconsider` while the bot is still in dry-run. The Python script + # calls the LLM whenever this var is set (regardless of `--close`); + # stripping `--close` doesn't suppress the API call, only the + # destructive side effects. Mirror the gating used by every other + # Agent Shin workflow (triage_pr_with_llm.yml, review_gate.yml, ...). + OPENAI_API_KEY: ${{ vars.AGENT_SHIN_ENABLED == 'true' && secrets.OPENAI_API_KEY || '' }} + OPENAI_BASE_URL: ${{ vars.OPENAI_BASE_URL }} + TRIAGE_MODEL: ${{ vars.TRIAGE_MODEL }} + AGENT_SHIN_ENABLED: ${{ vars.AGENT_SHIN_ENABLED }} + # `issue_comment` events fire for both issues and PR comments. + # `issue.pull_request` is set iff this is a PR comment, so we use + # its presence to decide whether to invoke `--pr N` or `--issue N`. + IS_PR: ${{ github.event.issue.pull_request != null }} + NUMBER: ${{ github.event.issue.number }} + run: | + set -euo pipefail + if [ "${IS_PR}" = "true" ]; then + ARGS=(--repo "${{ github.repository }}" --pr "${NUMBER}" --reconsider) + else + ARGS=(--repo "${{ github.repository }}" --issue "${NUMBER}" --reconsider) + fi + # Reconsider's destructive actions (post comment + reopen) are + # gated on `--close`, mirroring the regular triage workflows. + # When AGENT_SHIN_ENABLED is not the EXACT string "true", we + # still run the script so its verdict + would-X action lands in + # the step summary for QA — but without `--close`, the script + # returns `would-reopen` / `would-reconsider-still-failing` + # instead of touching GitHub state. + # + # Use the positive `= "true"` gate (not `!= "true" -> exit`) so + # the workflow guardrails in + # tests/test_litellm/test_github_triage_workflows.py see the + # canonical fail-safe enable pattern. Unknown values like + # "True", "yes", "1", or typos fall through to the dry-run + # branch, which is the safe default. + if [ "${AGENT_SHIN_ENABLED:-false}" = "true" ]; then + ARGS+=(--close) + echo "::notice::Agent Shin reconsider ENABLED — running real triage (close=true)." + else + echo "::notice::AGENT_SHIN_ENABLED is not 'true' -> reconsider stays in dry-run (no comment, no reopen)." + fi + python3 .github/scripts/triage_with_llm.py "${ARGS[@]}" + + - name: React 👍 when the reconsider finishes + # Once the reconsider run has completed successfully, add a thumbs-up so + # the contributor sees the bot is done (the 👀 stays, signalling + # seen -> handled). `success()` keeps this from firing if the run + # errored, and the AGENT_SHIN_ENABLED gate keeps dry-run inert. + if: success() && steps.auth.outputs.authorized == 'true' && vars.AGENT_SHIN_ENABLED == 'true' + env: + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + COMMENT_ID: ${{ github.event.comment.id }} + run: | + set -euo pipefail + gh api --method POST \ + -H "Accept: application/vnd.github+json" \ + "repos/${{ github.repository }}/issues/comments/${COMMENT_ID}/reactions" \ + -f content=+1 \ + || echo "::warning::failed to add 👍 reaction (non-fatal)" diff --git a/.github/workflows/triage_rollout_heads_up.yml b/.github/workflows/triage_rollout_heads_up.yml new file mode 100644 index 00000000000..903960151e2 --- /dev/null +++ b/.github/workflows/triage_rollout_heads_up.yml @@ -0,0 +1,92 @@ +name: Agent Shin — rollout heads-up (one-shot) + +# Fires the 7-day heads-up comment on every open external PR/issue that the +# new triage bot would auto-close. The real sweep is a deliberate one-shot: +# trigger it at rollout via a manual `workflow_dispatch` with `dry_run=false`. +# The script is idempotent (skips items that already carry the +# `` marker), so a re-run is harmless. +# +# The automatic push trigger runs DRY-RUN only, so merging the script to +# `litellm_internal_staging` never posts a comment; it just confirms the +# workflow is wired up. Posting real comments requires the manual dispatch, +# which is also the only trigger that exposes `OPENAI_API_KEY`. The heads-up +# is intentionally NOT gated on `AGENT_SHIN_ENABLED`: it has to warn +# contributors while that flag is still off, ahead of the flip that turns on +# auto-closing. +# +# The workflow is a thin shell over `.github/scripts/triage_rollout_heads_up.py`. +# Dry-run vs. real run differ in EXACTLY one CLI flag (`--close`), added only +# on a manual dispatch with `dry_run=false`. + +on: + push: + branches: + - litellm_internal_staging + paths: + # The presence of this script on staging IS the rollout merge marker. + # Editing the file later would re-fire the workflow; that's safe because + # the script skips PRs/issues that already have the heads-up marker. + - ".github/scripts/triage_rollout_heads_up.py" + workflow_dispatch: + inputs: + dry_run: + description: "Dry run (true = preview only, false = actually post comments)." + required: false + default: "true" + type: choice + options: + - "true" + - "false" + +permissions: + contents: read + issues: write + pull-requests: write + +jobs: + heads-up: + if: github.repository == 'BerriAI/litellm' + runs-on: ubuntu-latest + steps: + - name: Checkout triage scripts + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + sparse-checkout: .github/scripts + persist-credentials: false + + - name: Set up Python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: "3.12" + + - name: Install LLM client + run: pip install --no-cache-dir --require-hashes -r .github/scripts/triage-requirements.txt + + - name: Run heads-up sweep + env: + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + # Only the manual dispatch (the real-run trigger) needs the LLM key. + # The automatic push trigger runs dry-run and never posts, so it gets + # no key. Mirrors the sibling triage workflows, which expose the key + # only on an enabled/dispatched run rather than unconditionally. + OPENAI_API_KEY: ${{ github.event_name == 'workflow_dispatch' && secrets.OPENAI_API_KEY || '' }} + OPENAI_BASE_URL: ${{ vars.OPENAI_BASE_URL }} + TRIAGE_MODEL: ${{ vars.TRIAGE_MODEL }} + # The real run is a deliberate manual dispatch with dry_run=false. + # Use the EXACT "false" comparison so any unexpected input value + # fail-closes to dry-run (mirrors the AGENT_SHIN_ENABLED pattern in + # the sibling workflows). The automatic push trigger always stays + # dry-run, so merging the script never posts. + DRY_RUN_INPUT: ${{ github.event.inputs.dry_run }} + run: | + set -euo pipefail + ARGS=(--repo "${{ github.repository }}") + if [ "${GITHUB_EVENT_NAME:-}" = "workflow_dispatch" ] && [ "${DRY_RUN_INPUT:-true}" = "false" ]; then + ARGS+=(--close) + echo "::notice::Manual rollout dispatch with dry_run=false -> heads-up comments WILL be posted." + elif [ "${GITHUB_EVENT_NAME:-}" = "workflow_dispatch" ]; then + echo "::notice::Manual dispatch in dry-run mode -> previewing only, no comments will be posted." + else + echo "::notice::Automatic push trigger -> dry-run preview only. Fire the real rollout sweep with a manual workflow_dispatch (dry_run=false)." + fi + python3 .github/scripts/triage_rollout_heads_up.py "${ARGS[@]}" diff --git a/.github/workflows/zizmor.yml b/.github/workflows/zizmor.yml index 9a1e899fed5..db79fe43038 100644 --- a/.github/workflows/zizmor.yml +++ b/.github/workflows/zizmor.yml @@ -2,9 +2,9 @@ name: GitHub Actions Security Analysis on: push: - branches: [main] + branches: [main, litellm_internal_staging] pull_request: - branches: [main] + branches: [main, litellm_internal_staging] concurrency: group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} @@ -18,9 +18,7 @@ jobs: runs-on: ubuntu-latest timeout-minutes: 5 permissions: - security-events: write contents: read - actions: read steps: - name: Checkout repository uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 @@ -28,4 +26,9 @@ jobs: persist-credentials: false - name: Run zizmor - uses: zizmorcore/zizmor-action@71321a20a9ded102f6e9ce5718a2fcec2c4f70d8 # v0.5.2 + uses: zizmorcore/zizmor-action@5f14fd08f7cf1cb1609c1e344975f152c7ee938d # v0.5.6 + with: + version: "1.24.1" + min-severity: medium + advanced-security: false + annotations: true diff --git a/.gitignore b/.gitignore index 54ae53bb2c9..fda3311fe02 100644 --- a/.gitignore +++ b/.gitignore @@ -74,8 +74,6 @@ tests/local_testing/log.txt .codegpt litellm/proxy/_new_new_secret_config.yaml litellm/proxy/custom_guardrail.py -**/.mypy_cache/ -**/.mypy_cache_any/ litellm/proxy/application.log tests/llm_translation/vertex_test_account.json tests/llm_translation/test_vertex_key.json diff --git a/CLAUDE.md b/CLAUDE.md index 95904ef8abd..2070b6fcdd6 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -36,11 +36,9 @@ Don't hesitate to use values in .env to get needed API keys and other secrets, a Run tests, format your code, and lint your code before each commit -When you fix violations gated by `ruff-strict-budget.json`, `mypy-code-budget.json`, `basedpyright-code-budget.json`, or `any-discipline-budget.json`, run `make lint-budget-update` and commit the lowered baselines so the ceilings ratchet down instead of leaving stale headroom +When you fix violations gated by `ruff-strict-budget.json` or `basedpyright-code-budget.json`, run `make lint-budget-update` and commit the lowered baselines so the ceilings ratchet down instead of leaving stale headroom -If you're trying to create a new function that relies on untyped stuff, instead of adding more Any's and bringing it closer to the max, just validate it in the caller with Pydantic (a model or `TypeAdapter` that returns the typed thing or raises will do) and then pass the now typed variable in - -The Any-discipline gate (`make lint-any`, also a CI job) fails when a changed file under `litellm/` carries more `Any`-typed values than its grandfathered ceiling in `any-discipline-budget.json` (each file's captured count plus 50% headroom). It flags values whose inferred type *contains* `Any`, including the `X | Any` unions mypy/basedpyright accept. Editing a legacy file is fine as long as you don't push its `Any` count past the ceiling; a brand-new file must be `Any`-free. Fix a value by giving it a concrete type (if you're given untyped input, validate with Pydantic). Ideally `# any-ok: ` is never used; treat it as a last resort for a genuine typed/untyped boundary that Pydantic truly can't model +If you're trying to create a new function that relies on untyped stuff, instead of adding more Any's and pushing `reportAny` / `reportExplicitAny` closer to their basedpyright ceilings, just validate it in the caller with Pydantic (a model or `TypeAdapter` that returns the typed thing or raises will do) and then pass the now typed variable in If you get an LIT001 or LIT002 fail, refactor the code to follow functional programming best practices rather than introducing mutable data structures. For example, build values in one shot with comprehensions or generators wrapped in `tuple()` / `frozenset()` instead of seeding an empty `list`/`dict`/`set` and mutating it over time. Ideally `# mutable-ok` is never used; reach for it only as a genuine last resort when an immutable rewrite is truly impossible, and always pair it with a real reason diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 9643a58742c..1080579d0fa 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -154,8 +154,7 @@ Individual linting commands: ```bash make format-check # Check Black formatting make lint-ruff # Run Ruff linting -make lint-mypy # Run MyPy type checking -make lint-any # Gate changed files against their per-file Any budget +make lint-basedpyright # Run basedpyright type checking make check-circular-imports # Check for circular imports make check-import-safety # Check import safety ``` @@ -217,7 +216,7 @@ LiteLLM follows the [Google Python Style Guide](https://google.github.io/stylegu Our automated quality checks include: - **Black** for consistent code formatting - **Ruff** for linting and code quality -- **MyPy** for static type checking +- **basedpyright** for static type checking - **Circular import detection** - **Import safety validation** @@ -231,7 +230,7 @@ If `make lint` fails: 1. **Formatting issues**: Run `make format` to auto-fix 2. **Ruff issues**: Check the output and fix manually -3. **MyPy issues**: Add proper type hints +3. **basedpyright issues**: Add proper type hints 4. **Circular imports**: Refactor import dependencies 5. **Import safety**: Fix any unprotected imports @@ -246,7 +245,7 @@ If `make test-unit` fails: ### 3. Common Development Tips -- **Use type hints**: MyPy requires proper type annotations +- **Use type hints**: basedpyright requires proper type annotations - **Write descriptive commit messages**: Help reviewers understand your changes - **Keep PRs focused**: One feature/fix per PR - **Test edge cases**: Don't just test the happy path diff --git a/Makefile b/Makefile index 0a6d612e8b8..27150aec938 100644 --- a/Makefile +++ b/Makefile @@ -5,8 +5,8 @@ test-unit-integrations test-unit-core-utils test-unit-other test-unit-root \ test-proxy-unit-a test-proxy-unit-b test-integration test-unit-helm \ info lint lint-dev format \ - lint-mypy lint-mypy-budget-update lint-basedpyright lint-basedpyright-budget-update \ - lint-ruff-budget lint-any lint-ruff-budget-update lint-budget-update lint-any-budget-update \ + lint-basedpyright lint-basedpyright-budget-update \ + lint-ruff-budget lint-ruff-budget-update lint-budget-update lint-gate \ install-dev install-proxy-dev install-test-deps install-hooks \ install-helm-unittest check-circular-imports check-import-safety @@ -22,18 +22,15 @@ help: @echo " make install-hooks - Install git hooks (Conventional Commits + Branches)" @echo " make format - Apply Black code formatting" @echo " make format-check - Check Black code formatting (matches CI)" - @echo " make lint - Run all linting (Ruff, MyPy, Black check, circular imports, import safety)" + @echo " make lint - Run all linting (Ruff, basedpyright, Black check, circular imports, import safety)" @echo " make lint-ruff - Run Ruff linting only" - @echo " make lint-mypy - Run MyPy (disallow_untyped_defs), gated by per-rule error counts" - @echo " make lint-mypy-budget-update - Re-capture the MyPy per-rule budget (ratchet)" @echo " make lint-basedpyright - Run basedpyright strict, gated by per-rule error counts" @echo " make lint-basedpyright-budget-update - Re-capture the basedpyright per-rule budget (ratchet)" @echo " make lint-black - Check Black formatting (matches CI)" @echo " make lint-ruff-budget - Gate the codebase total of each strict ruff rule against its ceiling" - @echo " make lint-any - Gate changed files under litellm/ against their per-file Any budget" + @echo " make lint-gate - Strict ruff gate in CI-parity mode (fetches staging, simulates the merge)" @echo " make lint-ruff-budget-update - Re-capture per-rule baselines in ruff-strict-budget.json (ratchet)" - @echo " make lint-budget-update - Re-capture all four ratchet budgets (ruff + mypy + basedpyright + any)" - @echo " make lint-any-budget-update - Re-capture the per-file Any budget across the whole tree (ratchet)" + @echo " make lint-budget-update - Re-capture all ratchet budgets (ruff + basedpyright)" @echo " make check-circular-imports - Check for circular imports" @echo " make check-import-safety - Check import safety" @echo " make test - Run all tests" @@ -127,34 +124,28 @@ lint-ruff-FULL-dev: install-dev if [ -n "$$files" ]; then echo "$$files" | xargs $(UV_RUN) ruff check; \ else echo "No changed .py files to check."; fi -lint-mypy: install-dev - cd litellm && ($(UV_RUN) mypy . || true) | $(UV_RUN) python ../scripts/type_check_gate.py --tool mypy - -lint-mypy-budget-update: install-dev - cd litellm && ($(UV_RUN) mypy . || true) | $(UV_RUN) python ../scripts/type_check_gate.py --tool mypy --update - lint-basedpyright: install-dev - ($(UV_RUN) basedpyright --outputjson || true) | $(UV_RUN) python scripts/type_check_gate.py --tool basedpyright + ($(UV_RUN) basedpyright --outputjson || true) | $(UV_RUN) python scripts/type_check_gate.py lint-basedpyright-budget-update: install-dev - ($(UV_RUN) basedpyright --outputjson || true) | $(UV_RUN) python scripts/type_check_gate.py --tool basedpyright --update + ($(UV_RUN) basedpyright --outputjson || true) | $(UV_RUN) python scripts/type_check_gate.py --update lint-black: format-check lint-ruff-budget: install-dev $(UV_RUN) python scripts/ruff_strict_gate.py +# Strict gate, invoked the same way CI does in test-linting.yml so a local pass +# means the CI check will pass too. +lint-gate: install-dev + git fetch origin litellm_internal_staging + $(UV_RUN) python scripts/ruff_strict_gate.py --base origin/litellm_internal_staging + lint-ruff-budget-update: install-dev $(UV_RUN) python scripts/ruff_strict_gate.py --update -# Ratchet all four budgets in one shot (ruff strict + mypy + basedpyright + any) -lint-budget-update: lint-ruff-budget-update lint-mypy-budget-update lint-basedpyright-budget-update lint-any-budget-update - -lint-any: install-dev - $(UV_RUN) python scripts/check_any_discipline.py --changed - -lint-any-budget-update: install-dev - $(UV_RUN) python scripts/check_any_discipline.py --update +# Ratchet all budgets in one shot (ruff strict + basedpyright) +lint-budget-update: lint-ruff-budget-update lint-basedpyright-budget-update check-circular-imports: install-dev cd litellm && $(UV_RUN) python ../tests/documentation_tests/test_circular_imports.py && cd .. @@ -163,10 +154,10 @@ check-import-safety: install-dev @$(UV_RUN) python -c "from litellm import *; print('[from litellm import *] OK! no issues!');" || (echo '🚨 import failed, this means you introduced unprotected imports! 🚨'; exit 1) # Combined linting (matches test-linting.yml workflow) -lint: format-check lint-ruff lint-mypy lint-basedpyright check-circular-imports check-import-safety lint-ruff-budget lint-any +lint: format-check lint-ruff lint-basedpyright check-circular-imports check-import-safety lint-ruff-budget # Faster linting for local development (only checks changed code) -lint-dev: lint-format-changed lint-mypy lint-any check-circular-imports check-import-safety +lint-dev: lint-format-changed check-circular-imports check-import-safety # Testing targets test: install-test-deps diff --git a/README.md b/README.md index d7dc665dcec..b26ad39eada 100644 --- a/README.md +++ b/README.md @@ -345,6 +345,7 @@ curl -X POST 'http://0.0.0.0:4000/v1/chat/completions' \ | [OVHCloud AI Endpoints (`ovhcloud`)](https://docs.litellm.ai/docs/providers/ovhcloud) | ✅ | ✅ | ✅ | | | | | | | | | [Perplexity AI (`perplexity`)](https://docs.litellm.ai/docs/providers/perplexity) | ✅ | ✅ | ✅ | | | | | | | | | [Petals (`petals`)](https://docs.litellm.ai/docs/providers/petals) | ✅ | ✅ | ✅ | | | | | | | | +| [Pinstripes (`pinstripes`)](https://docs.litellm.ai/docs/providers/pinstripes) | ✅ | ✅ | ✅ | | | | | | | | | [Predibase (`predibase`)](https://docs.litellm.ai/docs/providers/predibase) | ✅ | ✅ | ✅ | | | | | | | | | [Recraft (`recraft`)](https://docs.litellm.ai/docs/providers/recraft) | | | | | ✅ | | | | | | | [Replicate (`replicate`)](https://docs.litellm.ai/docs/providers/replicate) | ✅ | ✅ | ✅ | | | | | | | | diff --git a/any-discipline-budget.json b/any-discipline-budget.json deleted file mode 100644 index f086bbdeaf6..00000000000 --- a/any-discipline-budget.json +++ /dev/null @@ -1,5974 +0,0 @@ -{ - 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"baseline": 29, - "slack": 15 - }, - "litellm/types/responses/main.py": { - "baseline": 49, - "slack": 25 - }, - "litellm/types/router.py": { - "baseline": 194, - "slack": 97 - }, - "litellm/types/search.py": { - "baseline": 21, - "slack": 11 - }, - "litellm/types/services.py": { - "baseline": 13, - "slack": 7 - }, - "litellm/types/tag_management.py": { - "baseline": 5, - "slack": 3 - }, - "litellm/types/tool_management.py": { - "baseline": 27, - "slack": 14 - }, - "litellm/types/utils.py": { - "baseline": 1085, - "slack": 543 - }, - "litellm/types/vector_store_files.py": { - "baseline": 38, - "slack": 19 - }, - "litellm/types/vector_stores.py": { - "baseline": 118, - "slack": 59 - }, - "litellm/types/videos/main.py": { - "baseline": 59, - "slack": 30 - }, - "litellm/types/videos/utils.py": { - "baseline": 5, - "slack": 3 - }, - "litellm/utils.py": { - "baseline": 3367, - "slack": 1684 - }, - "litellm/vector_store_files/main.py": { - "baseline": 244, - "slack": 122 - }, - "litellm/vector_store_files/utils.py": { - "baseline": 17, - "slack": 9 - }, - "litellm/vector_stores/main.py": { - "baseline": 268, - "slack": 134 - }, - "litellm/vector_stores/utils.py": { - "baseline": 19, - "slack": 10 - }, - "litellm/vector_stores/vector_store_registry.py": { - "baseline": 94, - "slack": 47 - }, - "litellm/videos/main.py": { - "baseline": 513, - "slack": 257 - }, - "litellm/videos/utils.py": { - "baseline": 54, - "slack": 27 - } -} diff --git a/backend/routes/allowlist.py b/backend/routes/allowlist.py index d1a576aeb33..2f65f99c292 100644 --- a/backend/routes/allowlist.py +++ b/backend/routes/allowlist.py @@ -120,6 +120,9 @@ BACKEND_PATH_PREFIXES: tuple[str, ...] = ( "/robots.txt", # Health (k8s probes) "/health", + # Plugin system + "/api/plugins", + "/plugin-proxy/", ) BACKEND_EXACT_PATHS: frozenset[str] = frozenset( diff --git a/basedpyright-code-budget.json b/basedpyright-code-budget.json index 73bc5c47703..7ba7656e407 100644 --- a/basedpyright-code-budget.json +++ b/basedpyright-code-budget.json @@ -1,31 +1,31 @@ { "reportAny": { - "baseline": 24954, + "baseline": 24989, "slack": 2500 }, "reportArgumentType": { - "baseline": 1863, + "baseline": 1934, "slack": 180 }, "reportAssignmentType": { "baseline": 220, - "slack": 3 + "slack": 22 }, "reportAttributeAccessIssue": { - "baseline": 335, - "slack": 3 + "baseline": 346, + "slack": 35 }, "reportCallIssue": { - "baseline": 77, + "baseline": 87, "slack": 10 }, "reportConstantRedefinition": { "baseline": 39, - "slack": 3 + "slack": 4 }, "reportDeprecated": { "baseline": 217, - "slack": 10 + "slack": 22 }, "reportDuplicateImport": { "baseline": 28, @@ -41,11 +41,11 @@ }, "reportGeneralTypeIssues": { "baseline": 151, - "slack": 3 + "slack": 15 }, "reportIncompatibleMethodOverride": { "baseline": 52, - "slack": 10 + "slack": 5 }, "reportIncompatibleVariableOverride": { "baseline": 8, @@ -73,7 +73,7 @@ }, "reportMissingParameterType": { "baseline": 3933, - "slack": 10 + "slack": 390 }, "reportMissingTypeArgument": { "baseline": 10612, @@ -97,7 +97,7 @@ }, "reportOptionalMemberAccess": { "baseline": 724, - "slack": 10 + "slack": 72 }, "reportOptionalOperand": { "baseline": 3, @@ -120,8 +120,8 @@ "slack": 3 }, "reportReturnType": { - "baseline": 118, - "slack": 10 + "baseline": 126, + "slack": 13 }, "reportTypedDictNotRequiredAccess": { "baseline": 20, @@ -136,19 +136,19 @@ "slack": 3000 }, "reportUnknownLambdaType": { - "baseline": 76, + "baseline": 75, "slack": 10 }, "reportUnknownMemberType": { - "baseline": 27322, + "baseline": 27037, "slack": 2500 }, "reportUnknownParameterType": { - "baseline": 13636, + "baseline": 13612, "slack": 1000 }, "reportUnknownVariableType": { - "baseline": 21776, + "baseline": 21445, "slack": 2000 }, "reportUnnecessaryCast": { @@ -156,7 +156,7 @@ "slack": 10 }, "reportUnnecessaryComparison": { - "baseline": 680, + "baseline": 683, "slack": 10 }, "reportUnnecessaryContains": { @@ -164,12 +164,12 @@ "slack": 3 }, "reportUnnecessaryIsInstance": { - "baseline": 807, - "slack": 10 + "baseline": 808, + "slack": 80 }, "reportUntypedBaseClass": { "baseline": 110, - "slack": 3 + "slack": 11 }, "reportUntypedFunctionDecorator": { "baseline": 22, @@ -185,10 +185,10 @@ }, "reportUnusedImport": { "baseline": 670, - "slack": 10 + "slack": 50 }, "reportUnusedVariable": { "baseline": 865, - "slack": 10 + "slack": 50 } } diff --git a/docker/build_from_pip/litellm_config.yaml b/docker/build_from_pip/litellm_config.yaml index 51223026170..f54647853ef 100644 --- a/docker/build_from_pip/litellm_config.yaml +++ b/docker/build_from_pip/litellm_config.yaml @@ -3,7 +3,7 @@ model_list: litellm_params: model: openai/fake api_key: fake-key - api_base: https://exampleopenaiendpoint-production.up.railway.app/ + api_base: os.environ/FAKE_OPENAI_API_BASE general_settings: alerting: ["slack"] \ No newline at end of file diff --git a/docs/plugin_architecture.md b/docs/plugin_architecture.md new file mode 100644 index 00000000000..8801761531d --- /dev/null +++ b/docs/plugin_architecture.md @@ -0,0 +1,141 @@ +# LiteLLM Plugin Architecture + +Plugins let external services appear as selectable modes in the litellm UI sidebar alongside the AI Gateway. + +--- + +## Quick start + +### 1. Configure the plugin + +Add a `plugins` block to your litellm `config.yaml`: + +```yaml +general_settings: + master_key: sk-... + plugins: + - name: my-plugin # unique identifier (no spaces) + display_name: My Plugin # shown in the UI dropdown + url: "https://my-plugin.example.com" + plugin_key: "sk-..." # plugin's own auth credential +``` + +`plugin_key` is injected as `Authorization: Bearer ` on every +request proxied through `/plugin-proxy/my-plugin/*`. The caller's litellm +credential is stripped before forwarding so the plugin never receives a live +litellm API key. + +### 2. Implement two endpoints on your service + +| Endpoint | Method | Purpose | +|---|---|---| +| `GET /api/plugin-manifest` | public | Returns plugin metadata for the UI | +| `POST /api/plugin-auth` | public | Decrypts the identity claim for seamless sign-in | + +#### `GET /api/plugin-manifest` + +```json +{ + "name": "my-plugin", + "display_name": "My Plugin", + "version": "1.0.0", + "nav_items": [ + { "key": "home", "label": "Home", "icon": "HomeOutlined", "path": "/" }, + { "key": "reports", "label": "Reports", "icon": "BarChartOutlined", "path": "/reports" } + ], + "capabilities": ["reports", "data"] +} +``` + +#### `POST /api/plugin-auth` + +Receives `{ "session_claim": "" }`. + +The proxy never shares `LITELLM_SALT_KEY` with your plugin. Each plugin is +provisioned with its own dedicated key, derived as +`HMAC-SHA256(LITELLM_SALT_KEY, plugin_name)`. Compute it once on the proxy +host and hand the result to your plugin as a secret (e.g. `PLUGIN_AUTH_KEY`): + +```bash +python -c 'import base64,hmac,hashlib,os; \ +print(base64.urlsafe_b64encode(hmac.new(os.environ["LITELLM_SALT_KEY"].encode(), b"my-plugin", hashlib.sha256).digest()).decode())' +``` + +A compromised plugin holding only this scoped key cannot recover +`LITELLM_SALT_KEY` or decrypt any other litellm secret. + +Decrypt and validate the claim with that key: + +```python +import json, os, time +from cryptography.fernet import Fernet + +_CLAIM_TTL_SECONDS = 30 + +def plugin_auth(session_claim: str) -> dict: + cipher = Fernet(os.environ["PLUGIN_AUTH_KEY"].encode()) + claim = json.loads(cipher.decrypt(session_claim.encode(), ttl=_CLAIM_TTL_SECONDS)) + if claim.get("plugin") != "my-plugin": + raise ValueError("claim audience mismatch") + if int(claim.get("exp", 0)) < int(time.time()): + raise ValueError("claim expired") + return claim +``` + +The claim is `{ "plugin", "user_id", "user_role", "exp" }`; it carries no +litellm bearer token. Establish the plugin's own session from `user_id` / +`user_role` and authenticate API calls back to litellm through the +`/plugin-proxy/my-plugin/*` reverse proxy, which injects `plugin_key` for you. + +--- + +## How iframe auth works + +``` +litellm UI + ├─ GET /api/plugins/auth-token -> { session_claim } + └─ postMessage({ type:"litellm-auth", session_claim }, pluginOrigin) + │ + ▼ +Plugin iframe browser + └─ POST /api/plugin-auth { session_claim } + │ + ▼ +Plugin server + ├─ decrypt(session_claim, PLUGIN_AUTH_KEY) -> { user_id, user_role, exp } + └─ establish plugin session -> stored in sessionStorage +``` + +No litellm bearer token ever leaves the proxy; the claim only conveys the +caller's identity and expires after 30 seconds. A postMessage intercept +yields ciphertext that is useless without the plugin's scoped key. + +--- + +## Proxy routes + +- `GET /api/plugins` — list registered plugins (`name`, `display_name`, `url`). `plugin_key` is **never** returned; it stays server-side. Requires an authenticated caller. +- `GET /api/plugins/auth-token?plugin_name=` — short-lived encrypted identity claim for the named plugin. Requires `LITELLM_SALT_KEY` to be set (503 otherwise) and the plugin to be registered (404 otherwise). +- `ANY /plugin-proxy/{name}/{path}` — authenticated reverse proxy to the plugin backend. Restricted to `proxy_admin`. + +--- + +## Reverse proxy behaviour + +When an admin (or server-to-server caller) hits `/plugin-proxy//`, the proxy authenticates the caller locally, then rewrites the request before forwarding it to the plugin's `url`: + +- **Every litellm credential header is stripped** — `Authorization`, `x-api-key`, `API-Key`, `x-goog-api-key`, `Ocp-Apim-Subscription-Key`, `x-litellm-api-key`, any configured `litellm_key_header_name`, plus `Cookie`. The plugin can never be handed the caller's live litellm key. +- **`plugin_key` is injected** as `Authorization: Bearer ` — the only credential the plugin receives. +- **Caller identity is forwarded** as `x-litellm-user-id` and `x-litellm-user-role` so the plugin can run its own authorization. These are informational, not credentials. +- **Responses are sandboxed** — `Content-Security-Policy: sandbox` and `X-Content-Type-Options: nosniff` are set so plugin-controlled bytes served from the litellm origin cannot execute against the dashboard. + +--- + +## Security checklist + +- [ ] `LITELLM_SALT_KEY` is set on the proxy and never shared with the plugin +- [ ] The plugin holds only its derived `HMAC(LITELLM_SALT_KEY, plugin_name)` key, provisioned as a dedicated secret +- [ ] `plugin_key` is a dedicated credential scoped to the plugin (not your litellm master key) +- [ ] Plugin's `POST /api/plugin-auth` enforces the claim's `plugin` audience and `exp` (30s TTL) +- [ ] Plugin treats `x-litellm-user-id` / `x-litellm-user-role` as identity hints, not as proof of authentication +- [ ] Plugin service URL uses HTTPS in production diff --git a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py index 6830147116d..8486e37384e 100644 --- a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py +++ b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py @@ -1,9 +1,9 @@ # What is this? ## This hook is used to check for LiteLLM managed files in the request body, and replace them with model-specific file id -import asyncio import base64 import json +from types import MappingProxyType from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Union, cast from fastapi import HTTPException @@ -1472,8 +1472,8 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): error_message += f" (showing {MAX_BATCHES_IN_ERROR} most recent): {', '.join(batch_statuses)}. " error_message += ( - f"To delete this file before complete cost tracking, please delete or cancel the referencing batch(es) first. " - f"Alternatively, wait for all batches to complete and for cost to be computed (batch_processed=true)." + "To delete this file before complete cost tracking, please delete or cancel the referencing batch(es) first. " + "Alternatively, wait for all batches to complete and for cost to be computed (batch_processed=true)." ) # Record blocked deletion metric @@ -1550,9 +1550,22 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): if specific_model_file_id_mapping: exception_dict = {} - for model_id, file_id in specific_model_file_id_mapping.items(): + for model_id, provider_file_id in specific_model_file_id_mapping.items(): try: - return await llm_router.afile_content(model=model_id, file_id=file_id, **data) # type: ignore + # Cloud-storage providers (e.g. Bedrock S3) validate file ids + # against the deployment's configured bucket, which they only + # trust from this immutable server-side snapshot, never from + # request params. + credentials = llm_router.get_deployment_credentials_with_provider( + model_id=model_id + ) + if credentials is not None: + data["_litellm_internal_model_credentials"] = cast( + Dict, MappingProxyType(dict(credentials)) + ) + else: + data.pop("_litellm_internal_model_credentials", None) + return await llm_router.afile_content(model=model_id, file_id=provider_file_id, **data) # type: ignore except Exception as e: exception_dict[model_id] = str(e) raise Exception( diff --git a/enterprise/pyproject.toml b/enterprise/pyproject.toml index d0432448433..b032942427c 100644 --- a/enterprise/pyproject.toml +++ b/enterprise/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "litellm-enterprise" -version = "0.1.42" +version = "0.1.43" description = "Package for LiteLLM Enterprise features" readme = "README.md" requires-python = ">=3.9" @@ -26,7 +26,7 @@ required-version = ">=0.10.9" module-root = "" [tool.commitizen] -version = "0.1.42" +version = "0.1.43" version_files = [ "pyproject.toml:^version", "../pyproject.toml:litellm-enterprise==", diff --git a/litellm/__init__.py b/litellm/__init__.py index 0d6a788e368..d21234d2a81 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -213,6 +213,15 @@ standard_logging_payload_excluded_fields: Optional[List[str]] = ( log_raw_request_response: bool = False redact_messages_in_exceptions: Optional[bool] = False redact_user_api_key_info: Optional[bool] = False +# When True (default — preserves historical behavior), the Router appends +# internal config names (model_group, fallback model groups, deployment +# timeouts, fallback failure details) onto exception messages and surfaces +# them to clients via ProxyException.message. Set to False if you do NOT +# want the proxy's internal model_name / fallback wiring visible to clients. +# Deprecation: planned to flip to False (redact by default) in a future +# major release; opt in early with `litellm.expose_router_debug_in_errors +# = False`. +expose_router_debug_in_errors: bool = True filter_invalid_headers: Optional[bool] = False add_user_information_to_llm_headers: Optional[bool] = ( None # adds user_id, team_id, token hash (params from StandardLoggingMetadata) to request headers @@ -235,6 +244,17 @@ modify_params = bool(os.getenv("LITELLM_MODIFY_PARAMS", False)) use_chat_completions_url_for_anthropic_messages: bool = bool( os.getenv("LITELLM_USE_CHAT_COMPLETIONS_URL_FOR_ANTHROPIC_MESSAGES", False) ) # When True, routes OpenAI /v1/messages requests to chat/completions instead of the Responses API +# When True, strip the OpenAI-flavored `usage.total_tokens` field that +# LiteLLM injects into non-streaming /v1/messages responses, bringing the +# wire response into line with the Anthropic spec (matches the streaming +# SSE path, which already omits total_tokens). Default False to preserve +# backward compatibility for clients that read the LiteLLM-shaped +# `usage.total_tokens` today. Planned to flip to True in a future major +# release; opt in early via Python: +# `litellm.strip_anthropic_total_tokens = True` +# Or via `litellm_settings.strip_anthropic_total_tokens: true` in +# config.yaml. +strip_anthropic_total_tokens: bool = False route_all_chat_openai_to_responses: bool = ( os.getenv("LITELLM_ROUTE_ALL_CHAT_OPENAI_TO_RESPONSES", "false").lower() == "true" ) # When True, routes all OpenAI /chat/completions requests through the Responses API bridge @@ -413,7 +433,7 @@ anthropic_beta_headers_url: str = os.getenv( "LITELLM_ANTHROPIC_BETA_HEADERS_URL", "https://raw.githubusercontent.com/BerriAI/litellm/main/litellm/anthropic_beta_headers_config.json", ) -suppress_debug_info = False +suppress_debug_info: bool = False dynamodb_table_name: Optional[str] = None s3_callback_params: Optional[Dict] = None s3_audit_callback_params: Optional[Dict] = None @@ -1381,6 +1401,7 @@ from .skills.main import ( from .containers.main import * from .ocr.main import * from .rag.main import * +from .sandbox.main import * from .search.main import * from .realtime_api.main import ( _arealtime, diff --git a/litellm/caching/caching.py b/litellm/caching/caching.py index 997ad10bc33..cb122e90102 100644 --- a/litellm/caching/caching.py +++ b/litellm/caching/caching.py @@ -100,6 +100,8 @@ class Cache: gcs_path: Optional[str] = None, redis_semantic_cache_embedding_model: str = "text-embedding-ada-002", redis_semantic_cache_index_name: Optional[str] = None, + valkey_semantic_cache_embedding_model: str = "text-embedding-ada-002", + valkey_semantic_cache_index_name: str | None = None, redis_flush_size: Optional[int] = None, redis_startup_nodes: Optional[List] = None, disk_cache_dir: Optional[str] = None, @@ -208,6 +210,21 @@ class Cache: index_name=redis_semantic_cache_index_name, **kwargs, ) + elif type == LiteLLMCacheType.VALKEY_SEMANTIC: + # Imported here, not at module top, so the optional redis dependency + # is only required when this backend is actually selected. + from .valkey_semantic_cache import ValkeySemanticCache + + self.cache = ValkeySemanticCache( + host=host, + port=port, + password=password, + similarity_threshold=similarity_threshold, + embedding_model=valkey_semantic_cache_embedding_model, + index_name=valkey_semantic_cache_index_name, + startup_nodes=redis_startup_nodes, + **kwargs, + ) elif type == LiteLLMCacheType.QDRANT_SEMANTIC: self.cache = QdrantSemanticCache( qdrant_api_base=qdrant_api_base, @@ -267,12 +284,50 @@ class Cache: if ( self.type == LiteLLMCacheType.REDIS or self.type == LiteLLMCacheType.REDIS_SEMANTIC + or self.type == LiteLLMCacheType.VALKEY_SEMANTIC ) and default_in_redis_ttl is not None: self.ttl = default_in_redis_ttl if self.namespace is not None and isinstance(self.cache, RedisCache): self.cache.namespace = self.namespace + # Params whose values carry prompt content. Excluded from semantic-cache + # scope keys so differently worded prompts share a bucket and match via + # vector similarity rather than being split into per-wording buckets. + _SEMANTIC_CACHE_SCOPE_EXCLUDED_PARAMS: frozenset = frozenset( + {"messages", "prompt", "input"} + ) + + # Server-set identity (from proxy auth) used to isolate semantic-cache + # buckets per tenant. Required once the prompt is out of the scope key, so a + # similar prompt from another key/team/org stays in a separate bucket. + _SEMANTIC_CACHE_TENANT_SCOPE_FIELDS: tuple[str, ...] = ( + "user_api_key", + "user_api_key_team_id", + "user_api_key_org_id", + ) + + def _is_semantic_cache(self) -> bool: + return self.type in ( + LiteLLMCacheType.REDIS_SEMANTIC, + LiteLLMCacheType.QDRANT_SEMANTIC, + LiteLLMCacheType.VALKEY_SEMANTIC, + ) + + def _get_semantic_cache_tenant_scope(self, kwargs: dict) -> str: + metadata: dict = kwargs.get("metadata") or {} + litellm_params: dict = kwargs.get("litellm_params") or {} + metadata_in_litellm_params: dict = litellm_params.get("metadata") or {} + + scope = "" + for field in self._SEMANTIC_CACHE_TENANT_SCOPE_FIELDS: + value = metadata.get(field) + if value is None: + value = metadata_in_litellm_params.get(field) + if value is not None: + scope += f"{field}: {value}" + return scope + def get_cache_key(self, **kwargs) -> str: """ Get the cache key for the given arguments. @@ -293,7 +348,15 @@ class Cache: combined_kwargs = ModelParamHelper._get_all_llm_api_params() litellm_param_kwargs = all_litellm_params + is_semantic_cache = self._is_semantic_cache() + scope_excluded_params = ( + self._SEMANTIC_CACHE_SCOPE_EXCLUDED_PARAMS + if is_semantic_cache + else frozenset() + ) for param in kwargs: + if param in scope_excluded_params: + continue if param in combined_kwargs: param_value: Optional[str] = self._get_param_value(param, kwargs) if param_value is not None: @@ -309,6 +372,9 @@ class Cache: param_value = kwargs[param] cache_key += f"{str(param)}: {str(param_value)}" + if is_semantic_cache: + cache_key += self._get_semantic_cache_tenant_scope(kwargs) + hashed_cache_key = Cache._get_hashed_cache_key(cache_key) hashed_cache_key = self._add_namespace_to_cache_key(hashed_cache_key, **kwargs) verbose_logger.debug( diff --git a/litellm/caching/gcs_cache.py b/litellm/caching/gcs_cache.py index 3327e094bc2..0e6a111eb2b 100644 --- a/litellm/caching/gcs_cache.py +++ b/litellm/caching/gcs_cache.py @@ -5,6 +5,7 @@ Supports syncing responses to Google Cloud Storage Buckets using HTTP requests. import json import asyncio from typing import Optional +from urllib.parse import quote from litellm._logging import print_verbose, verbose_logger from litellm.integrations.gcs_bucket.gcs_bucket_base import GCSBucketBase @@ -48,7 +49,7 @@ class GCSCache(BaseCache): headers = self._construct_headers() object_name = self.key_prefix + key bucket_name = self.bucket_name - url = f"https://storage.googleapis.com/upload/storage/v1/b/{bucket_name}/o?uploadType=media&name={object_name}" + url = f"https://storage.googleapis.com/upload/storage/v1/b/{bucket_name}/o?uploadType=media&name={quote(object_name, safe='')}" data = json.dumps(value) self.sync_client.post(url=url, data=data, headers=headers) except Exception as e: @@ -59,7 +60,7 @@ class GCSCache(BaseCache): headers = self._construct_headers() object_name = self.key_prefix + key bucket_name = self.bucket_name - url = f"https://storage.googleapis.com/upload/storage/v1/b/{bucket_name}/o?uploadType=media&name={object_name}" + url = f"https://storage.googleapis.com/upload/storage/v1/b/{bucket_name}/o?uploadType=media&name={quote(object_name, safe='')}" data = json.dumps(value) await self.async_client.post(url=url, data=data, headers=headers) except Exception as e: @@ -72,7 +73,7 @@ class GCSCache(BaseCache): headers = self._construct_headers() object_name = self.key_prefix + key bucket_name = self.bucket_name - url = f"https://storage.googleapis.com/storage/v1/b/{bucket_name}/o/{object_name}?alt=media" + url = f"https://storage.googleapis.com/storage/v1/b/{bucket_name}/o/{quote(object_name, safe='')}?alt=media" response = self.sync_client.get(url=url, headers=headers) if response.status_code == 200: cached_response = json.loads(response.text) @@ -91,7 +92,7 @@ class GCSCache(BaseCache): headers = self._construct_headers() object_name = self.key_prefix + key bucket_name = self.bucket_name - url = f"https://storage.googleapis.com/storage/v1/b/{bucket_name}/o/{object_name}?alt=media" + url = f"https://storage.googleapis.com/storage/v1/b/{bucket_name}/o/{quote(object_name, safe='')}?alt=media" response = await self.async_client.get(url=url, headers=headers) if response.status_code == 200: return json.loads(response.text) diff --git a/litellm/caching/redis_cache.py b/litellm/caching/redis_cache.py index 263e1df2ee7..ba07511448a 100644 --- a/litellm/caching/redis_cache.py +++ b/litellm/caching/redis_cache.py @@ -903,6 +903,43 @@ class RedisCache(BaseCache): ) raise e + @_redis_circuit_breaker_guard + async def async_set_max( + self, + key: str, + value: float, + ttl: int | None = None, + ) -> float | None: + """Atomically set ``key`` to ``value`` only when ``value`` is greater + than the stored value (or the key is unset), refreshing the TTL. + + Monotonic by construction: it never lowers the stored value, so a repair + that writes an authoritative-but-slightly-stale total cannot clobber a + concurrent increment that has already pushed the counter higher. The + GET/compare/SET runs in a single Lua call, so it is also atomic across + racing callers and pods. Returns the resulting value. + """ + _redis_client = self.init_async_client() + _used_ttl = self.get_ttl(ttl=ttl) + key = self.check_and_fix_namespace(key=key) + lua = ( + "local cur = redis.call('GET', KEYS[1]) " + "if cur == false or tonumber(cur) < tonumber(ARGV[1]) then " + "redis.call('SET', KEYS[1], ARGV[1]) " + "if tonumber(ARGV[2]) > 0 then redis.call('EXPIRE', KEYS[1], ARGV[2]) end " + "return ARGV[1] end " + "return cur" + ) + result = cast( + "str | bytes | int | float | None", + await _redis_client.eval(lua, 1, key, str(value), str(int(_used_ttl or 0))), + ) + if result is None: + return None + if isinstance(result, bytes): + result = result.decode() + return float(result) + async def flush_cache_buffer(self): print_verbose( f"flushing to redis....reached size of buffer {len(self.redis_batch_writing_buffer)}" diff --git a/litellm/caching/valkey_semantic_cache.py b/litellm/caching/valkey_semantic_cache.py new file mode 100644 index 00000000000..bf368b74d07 --- /dev/null +++ b/litellm/caching/valkey_semantic_cache.py @@ -0,0 +1,353 @@ +""" +Valkey Semantic Cache implementation for LiteLLM + +Backs semantic caching with Valkey (for example AWS ElastiCache for Valkey) +running the valkey-search module. + +RedisVL cannot drive valkey-search: it gates on a RediSearch module version +that valkey-search does not report, and its SemanticCache index uses a TEXT +field that valkey-search does not implement. This backend therefore talks to +valkey-search directly over redis-py, building a vector index from the field +types valkey-search does support (TAG for cache-key isolation and VECTOR for +the prompt embedding) and running KNN queries for retrieval. Prompt extraction, +embedding generation, and cached-response parsing are reused from +RedisSemanticCache since those are backend agnostic. +""" + +import asyncio +import hashlib +import os +import struct +from dataclasses import dataclass +from typing import Any + +from redis import Redis +from redis.asyncio import Redis as AsyncRedis +from redis.commands.search.field import TagField, VectorField +from redis.commands.search.indexDefinition import IndexDefinition, IndexType +from redis.commands.search.query import Query + +from litellm._logging import print_verbose +from litellm._uuid import uuid + +from .redis_semantic_cache import RedisSemanticCache + + +@dataclass(frozen=True, slots=True) +class _ValkeyCacheHit: + response: str + distance: float + + +class ValkeySemanticCache(RedisSemanticCache): + """Valkey-backed semantic cache for LLM responses.""" + + DEFAULT_VALKEY_INDEX_NAME: str = "litellm_semantic_cache_index" + EMBEDDING_FIELD_NAME: str = "embedding" + PROMPT_FIELD_NAME: str = "prompt" + RESPONSE_FIELD_NAME: str = "response" + DISTANCE_FIELD_NAME: str = "vector_distance" + + def __init__( + self, + host: str | None = None, + port: str | None = None, + password: str | None = None, + redis_url: str | None = None, + similarity_threshold: float | None = None, + embedding_model: str = "text-embedding-ada-002", + index_name: str | None = None, + ssl: bool = False, + startup_nodes: list | None = None, + sync_client: Redis | None = None, + async_client: AsyncRedis | None = None, + **kwargs: Any, + ): + if similarity_threshold is None: + raise ValueError("similarity_threshold must be provided, passed None") + + if startup_nodes: + raise ValueError( + "valkey-semantic does not support cluster-mode-enabled (multi-shard) " + "endpoints. The async cluster client cannot route the FT.* search " + "commands reliably. Point it at a cluster-mode-disabled endpoint " + "instead (a primary with replicas is fine; only horizontal sharding " + "is unsupported), or pass a single redis_url. On AWS, vector search " + "needs ElastiCache for Valkey 8.2+ on a node-based cluster." + ) + + self.similarity_threshold = similarity_threshold + self.embedding_model = embedding_model + self.index_name = index_name or self.DEFAULT_VALKEY_INDEX_NAME + self.key_prefix = f"{self.index_name}:" + self._index_dim: int | None = None + + resolved_url = None + if sync_client is None or async_client is None: + resolved_url = redis_url or self._build_valkey_url( + host, port, password, ssl + ) + self.sync_client = ( + sync_client if sync_client is not None else Redis.from_url(resolved_url) # type: ignore[arg-type] + ) + self.async_client = ( + async_client + if async_client is not None + else AsyncRedis.from_url(resolved_url) # type: ignore[arg-type] + ) + + print_verbose(f"Valkey semantic-cache initializing index - {self.index_name}") + + @staticmethod + def _build_valkey_url( + host: str | None, port: str | None, password: str | None, ssl: bool = False + ) -> str: + host = host or os.environ.get("VALKEY_HOST") or os.environ.get("REDIS_HOST") + port = port or os.environ.get("VALKEY_PORT") or os.environ.get("REDIS_PORT") + password = ( + password + or os.environ.get("VALKEY_PASSWORD") + or os.environ.get("REDIS_PASSWORD") + ) + + if not host or not port: + raise ValueError( + "Missing required Valkey configuration. Provide host and port " + "(or VALKEY_HOST/VALKEY_PORT), or pass redis_url." + ) + + credentials = f":{password}@" if password else "" + scheme = "rediss" if ssl else "redis" + return f"{scheme}://{credentials}{host}:{port}" + + @classmethod + def _scope_tag(cls, key: str) -> str: + # valkey-search TAG fields tokenize on punctuation and do not honour + # backslash escaping, so an arbitrary cache key cannot be matched + # verbatim. Hashing to hex yields a token that is always exact-match + # safe and still uniquely isolates a caller's scope. + return hashlib.sha256(str(key).encode("utf-8")).hexdigest() + + @staticmethod + def _embedding_to_bytes(embedding: list[float]) -> bytes: + return struct.pack(f"<{len(embedding)}f", *embedding) + + def _index_schema(self, dim: int) -> tuple[TagField, VectorField]: + return ( + TagField(self.CACHE_KEY_FIELD_NAME), + VectorField( + self.EMBEDDING_FIELD_NAME, + "HNSW", + {"TYPE": "FLOAT32", "DIM": dim, "DISTANCE_METRIC": "COSINE"}, + ), + ) + + def _index_definition(self) -> IndexDefinition: + return IndexDefinition(prefix=[self.key_prefix], index_type=IndexType.HASH) + + @staticmethod + def _is_index_exists_error(exc: Exception) -> bool: + return "already exists" in str(exc).lower() + + @staticmethod + def _extract_index_dim(info: dict) -> int | None: + # FT.INFO nests the vector field's "dimensions" one level inside its + # "index" block, so flatten each field descriptor a single level and + # scan for the dimensions marker. + for field in info.get("attributes") or []: + if not isinstance(field, (list, tuple)): + continue + flat = [ + sub + for item in field + for sub in (item if isinstance(item, (list, tuple)) else [item]) + ] + for i, marker in enumerate(flat): + if marker in (b"dimensions", "dimensions") and i + 1 < len(flat): + return int(flat[i + 1]) + return None + + def _assert_dim_matches(self, info: dict, dim: int) -> None: + existing_dim = self._extract_index_dim(info) + if existing_dim is not None and existing_dim != dim: + raise ValueError( + f"Valkey semantic-cache index '{self.index_name}' already exists with " + f"embedding dimension {existing_dim}, but the configured embedding " + f"model produced dimension {dim}. Use a different " + f"valkey_semantic_cache_index_name or drop the existing index." + ) + + def _ensure_index_sync(self, dim: int) -> None: + if self._index_dim == dim: + return + try: + self.sync_client.ft(self.index_name).create_index( + self._index_schema(dim), definition=self._index_definition() + ) + except Exception as exc: + if not self._is_index_exists_error(exc): + raise + self._assert_dim_matches(self.sync_client.ft(self.index_name).info(), dim) + self._index_dim = dim + + async def _ensure_index_async(self, dim: int) -> None: + if self._index_dim == dim: + return + try: + await self.async_client.ft(self.index_name).create_index( + self._index_schema(dim), definition=self._index_definition() + ) + except Exception as exc: + if not self._is_index_exists_error(exc): + raise + info = await self.async_client.ft(self.index_name).info() + self._assert_dim_matches(info, dim) + self._index_dim = dim + + def _doc_key(self, key: str) -> str: + return f"{self.key_prefix}{self._scope_tag(key)}:{uuid.uuid4()}" + + def _doc_mapping( + self, key: str, prompt: str, value_str: str, embedding: list[float] + ) -> dict: + return { + self.CACHE_KEY_FIELD_NAME: self._scope_tag(key), + self.PROMPT_FIELD_NAME: prompt, + self.RESPONSE_FIELD_NAME: value_str, + self.EMBEDDING_FIELD_NAME: self._embedding_to_bytes(embedding), + } + + def _knn_query(self, key: str) -> Query: + scope = self._scope_tag(key) + query_string = ( + f"(@{self.CACHE_KEY_FIELD_NAME}:{{{scope}}})" + f"=>[KNN 1 @{self.EMBEDDING_FIELD_NAME} $vec AS {self.DISTANCE_FIELD_NAME}]" + ) + return ( + Query(query_string) + .return_fields(self.RESPONSE_FIELD_NAME, self.DISTANCE_FIELD_NAME) + .dialect(2) + ) + + @classmethod + def _first_hit(cls, search_result: Any) -> _ValkeyCacheHit | None: + docs = getattr(search_result, "docs", []) + if not docs: + return None + doc = docs[0] + return _ValkeyCacheHit( + response=str(getattr(doc, cls.RESPONSE_FIELD_NAME)), + distance=float(getattr(doc, cls.DISTANCE_FIELD_NAME)), + ) + + def _resolve_hit(self, hit: _ValkeyCacheHit | None, key: str, **kwargs: Any) -> Any: + if hit is None: + kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 + return None + + similarity = 1 - hit.distance + kwargs.setdefault("metadata", {})["semantic-similarity"] = similarity + + if similarity < self.similarity_threshold: + return None + return self._get_cache_logic(cached_response=hit.response) + + def set_cache(self, key: str, value: Any, **kwargs: Any) -> None: + print_verbose(f"Valkey semantic-cache set_cache, kwargs: {kwargs}") + try: + prompt = self._get_prompt_from_kwargs(**kwargs) + if prompt is None: + print_verbose("No prompt provided for semantic caching") + return + + embedding = self._get_embedding(prompt) + self._ensure_index_sync(len(embedding)) + + doc_key = self._doc_key(key) + self.sync_client.hset( + doc_key, mapping=self._doc_mapping(key, prompt, str(value), embedding) + ) + ttl = self._get_ttl(**kwargs) + if ttl is not None: + self.sync_client.expire(doc_key, ttl) + except Exception as e: + print_verbose(f"Error in Valkey semantic-cache set_cache: {str(e)}") + + def get_cache(self, key: str, **kwargs: Any) -> Any: + print_verbose(f"Valkey semantic-cache get_cache, kwargs: {kwargs}") + try: + prompt = self._get_prompt_from_kwargs(**kwargs) + if prompt is None: + kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 + return None + + embedding = self._get_embedding(prompt) + self._ensure_index_sync(len(embedding)) + + search_result = self.sync_client.ft(self.index_name).search( + self._knn_query(key), + query_params={"vec": self._embedding_to_bytes(embedding)}, + ) + return self._resolve_hit(self._first_hit(search_result), key, **kwargs) + except Exception as e: + print_verbose(f"Error in Valkey semantic-cache get_cache: {str(e)}") + kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 + + async def async_set_cache(self, key: str, value: Any, **kwargs: Any) -> None: + print_verbose(f"Async Valkey semantic-cache set_cache, kwargs: {kwargs}") + try: + prompt = self._get_prompt_from_kwargs(**kwargs) + if prompt is None: + print_verbose("No prompt provided for semantic caching") + return + + embedding = await self._get_async_embedding(prompt, **kwargs) + await self._ensure_index_async(len(embedding)) + + doc_key = self._doc_key(key) + await self.async_client.hset( + doc_key, mapping=self._doc_mapping(key, prompt, str(value), embedding) + ) + ttl = self._get_ttl(**kwargs) + if ttl is not None: + await self.async_client.expire(doc_key, ttl) + except Exception as e: + print_verbose(f"Error in async Valkey semantic-cache set_cache: {str(e)}") + + async def async_get_cache(self, key: str, **kwargs: Any) -> Any: + print_verbose(f"Async Valkey semantic-cache get_cache, kwargs: {kwargs}") + try: + prompt = self._get_prompt_from_kwargs(**kwargs) + if prompt is None: + kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 + return None + + embedding = await self._get_async_embedding(prompt, **kwargs) + await self._ensure_index_async(len(embedding)) + + search_result = await self.async_client.ft(self.index_name).search( + self._knn_query(key), + query_params={"vec": self._embedding_to_bytes(embedding)}, + ) + return self._resolve_hit(self._first_hit(search_result), key, **kwargs) + except Exception as e: + print_verbose(f"Error in async Valkey semantic-cache get_cache: {str(e)}") + kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 + + async def async_set_cache_pipeline( + self, cache_list: list[tuple[str, Any]], **kwargs: Any + ) -> None: + try: + await asyncio.gather( + *[ + self.async_set_cache(key, value, **kwargs) + for key, value in cache_list + ] + ) + except Exception as e: + print_verbose( + f"Error in Valkey semantic-cache async_set_cache_pipeline: {str(e)}" + ) + + async def _index_info(self) -> dict: + return await self.async_client.ft(self.index_name).info() diff --git a/litellm/constants.py b/litellm/constants.py index a3ea68c7949..c0e265c0e4a 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -802,6 +802,7 @@ openai_compatible_endpoints: List = [ "https://api.inference.wandb.ai/v1", "https://api.clarifai.com/v2/ext/openai/v1", "https://api.libertai.io/v1", + "https://pinstripes.io/v1", ] @@ -865,6 +866,7 @@ openai_compatible_providers: List = [ "clarifai", "docker_model_runner", "ragflow", + "pinstripes", # Pinstripes - JSON-configured provider ] openai_text_completion_compatible_providers: List = ( [ # providers that support `/v1/completions` diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py index 5c77400651b..27a146df7bf 100644 --- a/litellm/cost_calculator.py +++ b/litellm/cost_calculator.py @@ -94,6 +94,7 @@ from litellm.types.utils import ( LlmProviders, LlmProvidersSet, ModelInfo, + ServiceTier, StandardBuiltInToolsParams, TranscriptionUsageDurationObject, TranscriptionUsageTokensObject, @@ -614,7 +615,9 @@ def cost_per_token( service_tier=service_tier, ) elif custom_llm_provider == "anthropic": - return anthropic_cost_per_token(model=model, usage=usage_block) + return anthropic_cost_per_token( + model=model, usage=usage_block, service_tier=service_tier + ) elif custom_llm_provider == "bedrock": return bedrock_cost_per_token( model=model, usage=usage_block, service_tier=service_tier @@ -885,6 +888,23 @@ def _map_traffic_type_to_service_tier(traffic_type: Optional[str]) -> Optional[s return service_tier +def _normalize_service_tier(service_tier: object) -> str | None: + """ + Reduce a service_tier value to a concrete billable tier string or None. + + "auto" is a routing preference and any non-string value is not a billable + tier, so both defer to standard pricing (or to the tier the provider reports + on the response usage) instead of crashing the downstream cost-key lookup, + which calls service_tier.lower() + """ + if ( + not isinstance(service_tier, str) + or service_tier.lower() == ServiceTier.AUTO.value + ): + return None + return service_tier + + def _get_usage_object( completion_response: Any, ) -> Optional[Usage]: @@ -1224,6 +1244,8 @@ def completion_cost( if service_tier is None and optional_params is not None: service_tier = optional_params.get("service_tier") + service_tier = _normalize_service_tier(service_tier) + # Extract service_tier from completion_response if not provided if service_tier is None and completion_response is not None: if isinstance(completion_response, BaseModel): @@ -1231,6 +1253,8 @@ def completion_cost( elif isinstance(completion_response, dict): service_tier = completion_response.get("service_tier") + service_tier = _normalize_service_tier(service_tier) + # Extract service_tier from usage object if not provided if service_tier is None and cost_per_token_usage_object is not None: if isinstance(cost_per_token_usage_object, BaseModel): @@ -1240,6 +1264,8 @@ def completion_cost( elif isinstance(cost_per_token_usage_object, dict): service_tier = cost_per_token_usage_object.get("service_tier") + service_tier = _normalize_service_tier(service_tier) + selected_model = _select_model_name_for_cost_calc( model=model, completion_response=completion_response, diff --git a/litellm/integrations/code_interpreter_interception/__init__.py b/litellm/integrations/code_interpreter_interception/__init__.py new file mode 100644 index 00000000000..2256356b6f5 --- /dev/null +++ b/litellm/integrations/code_interpreter_interception/__init__.py @@ -0,0 +1,15 @@ +""" +Code Interpreter Interception Module + +Converts the native OpenAI Responses ``code_interpreter`` tool into a function +tool, runs the model-emitted code in a sandbox, and feeds the result back into +the agentic loop. +""" + +from litellm.integrations.code_interpreter_interception.handler import ( + CodeInterpreterInterceptionLogger, +) + +__all__ = [ + "CodeInterpreterInterceptionLogger", +] diff --git a/litellm/integrations/code_interpreter_interception/handler.py b/litellm/integrations/code_interpreter_interception/handler.py new file mode 100644 index 00000000000..da8149eab9b --- /dev/null +++ b/litellm/integrations/code_interpreter_interception/handler.py @@ -0,0 +1,473 @@ +""" +Code Interpreter Interception Handler + +CustomLogger that swaps the native OpenAI Responses ``code_interpreter`` tool for +a function tool, executes the code the model emits inside a sandbox, and feeds the +captured stdout back through the typed agentic loop plan. +""" + +import json +import time +import uuid +from typing import Any, cast + +import litellm +from litellm._logging import verbose_logger +from litellm.integrations.custom_logger import CustomLogger +from litellm.types.integrations.code_interpreter_interception import ( + CodeInterpreterInterceptionConfig, +) +from litellm.types.integrations.custom_logger import ( + AgenticLoopPlan, + AgenticLoopRequestPatch, +) +from litellm.types.utils import CallTypes + +LITELLM_CODE_EXECUTION_TOOL_NAME = "litellm_code_execution" +_INTERCEPTION_ACTIVE_KEY = "_code_interpreter_interception_active" +_SANDBOX_KEY = "_code_interpreter_interception_sandbox_key" +_CACHE_TTL_SECONDS = 15 * 60 + + +def _resolve_sandbox_tool(sandbox_tool_name: str | None) -> dict[str, Any] | None: + try: + from litellm.sandbox.sandbox_tools import resolve_sandbox_tool + except ImportError: + return None + return resolve_sandbox_tool(sandbox_tool_name) + + +class CodeInterpreterInterceptionLogger(CustomLogger): + """ + CustomLogger that implements transparent code-interpreter execution loops. + + Flow: + 1. Replace the native ``code_interpreter`` tool with a function tool in the + pre-call hook so the model emits code as function-call arguments. + 2. Detect ``litellm_code_execution`` function calls in the model response. + 3. Run the emitted code in a sandbox (reused per request via a server-minted + sandbox key) and build a typed rerun plan that appends the + function_call_output. + """ + + def __init__( + self, + enabled: bool = True, + enabled_providers: list[str] | None = None, + sandbox_tool_name: str | None = None, + sandbox_config: Any | None = None, + ): + super().__init__() + self.enabled = enabled + self.enabled_providers = enabled_providers + self.sandbox_tool_name = sandbox_tool_name + self.sandbox_config = sandbox_config + self._container_cache: dict[str, tuple[Any, dict[str, Any] | None, float]] = {} + + @classmethod + def from_config_yaml( + cls, config: CodeInterpreterInterceptionConfig + ) -> "CodeInterpreterInterceptionLogger": + return cls( + enabled=bool(config.get("enabled", True)), + enabled_providers=config.get("enabled_providers"), + sandbox_tool_name=config.get("sandbox_tool_name"), + ) + + @staticmethod + def initialize_from_proxy_config( + litellm_settings: dict[str, Any], + callback_specific_params: dict[str, Any], + ) -> "CodeInterpreterInterceptionLogger": + params: CodeInterpreterInterceptionConfig = {} + if "code_interpreter_interception_params" in litellm_settings: + params = litellm_settings["code_interpreter_interception_params"] + elif "code_interpreter_interception" in callback_specific_params and isinstance( + callback_specific_params["code_interpreter_interception"], dict + ): + params = cast( + CodeInterpreterInterceptionConfig, + callback_specific_params["code_interpreter_interception"], + ) + return CodeInterpreterInterceptionLogger.from_config_yaml(params) + + async def async_pre_call_deployment_hook( + self, kwargs: dict[str, Any], call_type: CallTypes | None + ) -> dict | None: + if not kwargs.get("_agentic_loop_depth"): + kwargs.pop(_INTERCEPTION_ACTIVE_KEY, None) + kwargs.pop(_SANDBOX_KEY, None) + if not self.enabled: + return None + if call_type not in (CallTypes.responses, CallTypes.aresponses): + return None + if ( + self.enabled_providers is not None + and self._resolve_provider(kwargs) not in self.enabled_providers + ): + return None + + tools = kwargs.get("tools") + if not isinstance(tools, list): + return None + if not any( + isinstance(tool, dict) and tool.get("type") == "code_interpreter" + for tool in tools + ): + return None + + kwargs[_INTERCEPTION_ACTIVE_KEY] = True + kwargs[_SANDBOX_KEY] = uuid.uuid4().hex + if kwargs.get("stream"): + kwargs["stream"] = False + kwargs["_code_interpreter_interception_converted_stream"] = True + + function_tool = { + "type": "function", + "name": LITELLM_CODE_EXECUTION_TOOL_NAME, + "description": "Execute python code in a sandbox and return stdout.", + "parameters": { + "type": "object", + "properties": {"code": {"type": "string"}}, + "required": ["code"], + }, + } + kwargs["tools"] = [ + ( + function_tool + if isinstance(tool, dict) and tool.get("type") == "code_interpreter" + else tool + ) + for tool in tools + ] + if self._tool_choice_targets_code_interpreter(kwargs.get("tool_choice")): + kwargs["tool_choice"] = { + "type": "function", + "name": LITELLM_CODE_EXECUTION_TOOL_NAME, + } + return kwargs + + @staticmethod + def _tool_choice_targets_code_interpreter(tool_choice: Any) -> bool: + if not isinstance(tool_choice, dict): + return False + return ( + tool_choice.get("type") == "code_interpreter" + or tool_choice.get("name") == "code_interpreter" + ) + + def _resolve_provider(self, kwargs: dict[str, Any]) -> str | None: + provider = kwargs.get("custom_llm_provider") + if provider: + return provider + model = kwargs.get("model") + if not isinstance(model, str): + return None + try: + return litellm.get_llm_provider(model=model)[1] + except Exception: + return None + + async def async_should_run_agentic_loop( + self, + response: Any, + model: str, + messages: list[dict], + tools: list[dict] | None, + stream: bool, + custom_llm_provider: str, + kwargs: dict, + ) -> tuple[bool, dict]: + if not self.enabled: + return False, {} + if not kwargs.get(_INTERCEPTION_ACTIVE_KEY): + return False, {} + if ( + self.enabled_providers is not None + and custom_llm_provider not in self.enabled_providers + ): + return False, {} + + tool_calls = self._extract_code_execution_tool_calls(response=response) + if not tool_calls: + return False, {} + + return True, {"tool_calls": tool_calls} + + async def async_build_agentic_loop_plan( + self, + tools: dict, + model: str, + messages: list[dict], + response: Any, + anthropic_messages_provider_config: Any, + anthropic_messages_optional_request_params: dict, + logging_obj: Any, + stream: bool, + kwargs: dict, + ) -> AgenticLoopPlan: + await self._prune_expired_cache() + tool_calls = cast(list[dict[str, Any]], tools.get("tool_calls", [])) + sandbox_key = kwargs.get(_SANDBOX_KEY) + container, params = await self._get_or_create_container(cache_key=sandbox_key) + + try: + container_id = getattr(container, "id", None) + input_list = self._normalize_messages(messages) + code_interpreter_calls = [] + for tool_call in tool_calls: + arguments = tool_call.get("arguments", "") + code = self._parse_code(arguments) + stdout = await self._run_tool_call( + container=container, params=params, arguments=arguments + ) + input_list.append( + { + "type": "function_call", + "call_id": tool_call.get("call_id"), + "name": LITELLM_CODE_EXECUTION_TOOL_NAME, + "arguments": arguments, + } + ) + input_list.append( + { + "type": "function_call_output", + "call_id": tool_call.get("call_id"), + "output": stdout, + } + ) + code_interpreter_calls.append( + { + "id": f"ci_{uuid.uuid4().hex}", + "type": "code_interpreter_call", + "status": "completed", + "code": code, + "container_id": container_id, + "outputs": ( + [{"type": "logs", "logs": stdout}] if stdout else [] + ), + } + ) + except Exception: + await self._delete_container_for_cache_key(sandbox_key) + raise + + optional_params = anthropic_messages_optional_request_params + request_patch = AgenticLoopRequestPatch( + model=model, + messages=input_list, + tools=optional_params.get("tools"), + optional_params={k: v for k, v in optional_params.items() if k != "tools"}, + kwargs={k: v for k, v in kwargs.items() if k != "litellm_logging_obj"}, + ) + + return AgenticLoopPlan( + run_agentic_loop=True, + request_patch=request_patch, + metadata={ + "tool_type": "code_interpreter", + "sandbox_key": sandbox_key or "", + "code_interpreter_calls": code_interpreter_calls, + }, + ) + + async def async_agentic_loop_cleanup_hook( + self, plan: AgenticLoopPlan, kwargs: dict + ) -> None: + metadata = plan.metadata or {} if plan else {} + await self._delete_container_for_cache_key(metadata.get("sandbox_key")) + + async def async_post_agentic_loop_response_hook( + self, response: Any, plan: AgenticLoopPlan, kwargs: dict + ) -> Any: + metadata = plan.metadata or {} if plan else {} + await self._delete_container_for_cache_key(metadata.get("sandbox_key")) + + calls = metadata.get("code_interpreter_calls") + if not calls: + return response + + is_dict = isinstance(response, dict) + output = ( + response.get("output") if is_dict else getattr(response, "output", None) + ) + if not isinstance(output, list): + return response + + def _item_type(item: Any) -> Any: + return ( + item.get("type") + if isinstance(item, dict) + else getattr(item, "type", None) + ) + + insert_at = next( + (i for i, item in enumerate(output) if _item_type(item) == "message"), + len(output), + ) + new_output = output[:insert_at] + list(calls) + output[insert_at:] + if is_dict: + response["output"] = new_output + else: + response.output = new_output + return response + + @staticmethod + def _parse_code(arguments: str) -> str: + try: + return json.loads(arguments).get("code", "") if arguments else "" + except (json.JSONDecodeError, TypeError, AttributeError): + return "" + + async def _run_tool_call( + self, container: Any, params: dict[str, Any] | None, arguments: str + ) -> str: + try: + code = json.loads(arguments).get("code", "") if arguments else "" + except (json.JSONDecodeError, TypeError): + return "[invalid tool arguments: could not parse code]" + + result = await self._run_code(container=container, params=params, code=code) + if getattr(result, "error", None): + error = result.error + message = ( + error.get("value") or error.get("name") + if isinstance(error, dict) + else str(error) + ) + return f"[execution error] {message}" + return getattr(result, "stdout", "") or "" + + async def _get_or_create_container( + self, cache_key: str | None + ) -> tuple[Any, dict[str, Any] | None]: + if cache_key: + cached = self._container_cache.get(cache_key) + if cached is not None: + return cached[0], cached[1] + + container, params = await self._create_container() + if cache_key: + self._container_cache[cache_key] = (container, params, time.time()) + return container, params + + async def _create_container(self) -> tuple[Any, dict[str, Any] | None]: + if self.sandbox_config is not None: + return await self.sandbox_config.acreate_sandbox(), None + + params = _resolve_sandbox_tool(self.sandbox_tool_name) + if params is None: + raise ValueError( + "CodeInterpreterInterception: no sandbox available. Provide a " + "sandbox_config or configure a sandbox tool resolvable via " + "sandbox_tool_name." + ) + container = await litellm.acreate_sandbox( + provider=params["sandbox_provider"], + api_key=params.get("api_key"), + api_base=params.get("api_base"), + ) + return container, params + + async def _run_code( + self, container: Any, params: dict[str, Any] | None, code: str + ) -> Any: + if self.sandbox_config is not None: + return await self.sandbox_config.arun_code(container=container, code=code) + if params is None: + raise ValueError( + "CodeInterpreterInterception: no sandbox available to run code." + ) + return await litellm.arun_code( + provider=params["sandbox_provider"], + container=container, + code=code, + api_key=params.get("api_key"), + ) + + async def _delete_container( + self, container: Any, params: dict[str, Any] | None + ) -> None: + try: + if self.sandbox_config is not None: + await self.sandbox_config.adelete_sandbox(container=container) + return + if params is None: + return + await litellm.adelete_sandbox( + provider=params["sandbox_provider"], + container=container, + api_key=params.get("api_key"), + api_base=params.get("api_base"), + ) + except Exception: + verbose_logger.exception( + "CodeInterpreterInterception: failed to delete sandbox container" + ) + + async def _delete_container_for_cache_key(self, cache_key: str | None) -> None: + if not cache_key: + return + cached = self._container_cache.pop(cache_key, None) + if cached is None: + return + await self._delete_container(container=cached[0], params=cached[1]) + + def _normalize_messages(self, messages: Any) -> list[dict[str, Any]]: + if isinstance(messages, str): + return [{"role": "user", "content": messages}] + if isinstance(messages, list): + return list(messages) + return [] + + def _extract_code_execution_tool_calls(self, response: Any) -> list[dict[str, Any]]: + if isinstance(response, dict): + output = response.get("output", []) + else: + output = getattr(response, "output", []) or [] + if not isinstance(output, list): + return [] + + return [ + { + "call_id": ( + item.get("call_id") + if isinstance(item, dict) + else getattr(item, "call_id", None) + ), + "name": LITELLM_CODE_EXECUTION_TOOL_NAME, + "arguments": ( + item.get("arguments") + if isinstance(item, dict) + else getattr(item, "arguments", "") + ), + } + for item in output + if self._is_code_execution_call(item) + ] + + def _is_code_execution_call(self, item: Any) -> bool: + if isinstance(item, dict): + return ( + item.get("type") == "function_call" + and item.get("name") == LITELLM_CODE_EXECUTION_TOOL_NAME + ) + return ( + getattr(item, "type", None) == "function_call" + and getattr(item, "name", None) == LITELLM_CODE_EXECUTION_TOOL_NAME + ) + + async def _prune_expired_cache(self) -> None: + now = time.time() + expired = [ + (cache_key, container, params) + for cache_key, ( + container, + params, + created_at, + ) in self._container_cache.items() + if now - created_at > _CACHE_TTL_SECONDS + ] + for cache_key, container, params in expired: + self._container_cache.pop(cache_key, None) + await self._delete_container(container=container, params=params) diff --git a/litellm/integrations/custom_logger.py b/litellm/integrations/custom_logger.py index 481cf7fce8e..94fb97dff53 100644 --- a/litellm/integrations/custom_logger.py +++ b/litellm/integrations/custom_logger.py @@ -718,6 +718,24 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac """ return response + async def async_agentic_loop_cleanup_hook( + self, + plan: AgenticLoopPlan, + kwargs: dict, + ) -> None: + """ + Release resources held for an agentic-loop iteration. + + Runs in a ``finally`` around the follow-up provider call, so it fires + whether the rerun returns normally, hits a loop safety abort, or raises + an upstream error. Implementations must be idempotent because the + post-response hook may already have released the same resource on the + success path. Use ``plan.metadata`` to locate what to clean up. + + Default does nothing. + """ + return None + async def async_should_run_chat_completion_agentic_loop( self, response: Any, diff --git a/litellm/integrations/otel/logger.py b/litellm/integrations/otel/logger.py index 1869e9ca388..79931c0796c 100644 --- a/litellm/integrations/otel/logger.py +++ b/litellm/integrations/otel/logger.py @@ -3,7 +3,7 @@ from collections import OrderedDict from contextlib import contextmanager from datetime import datetime -from typing import TYPE_CHECKING, Any, Iterator, Mapping, cast +from typing import TYPE_CHECKING, Any, Callable, Iterator, Mapping, Sequence, cast from opentelemetry.context import attach, get_current from opentelemetry.sdk.trace import TracerProvider @@ -546,6 +546,58 @@ class OpenTelemetryV2(CustomLogger): return span +def select_global_otel_v2_logger( + in_memory_loggers: Sequence[object], + registered: "OpenTelemetryV2 | None" = None, +) -> "OpenTelemetryV2": + """The single ``OpenTelemetryV2`` whose provider should become the OTel global. + + The callback factory designates one logger as canonical the moment it builds + the first one (``_init_otel_logger_on_litellm_proxy`` sets + ``proxy_server.open_telemetry_logger``), and every other v2 entry point — + guardrail, identity seeding, phase spans — already routes through that same + ``registered`` owner. Reuse it here too so the global provider has one source + of truth instead of a second, independently-derived guess; this is the logger + a preset (arize, langfuse, …) folds the ``OTEL_*`` base exporter and its own + exporter into, so the FastAPI server span and the gen-ai spans share one + provider and one trace. + + Fall back to ``in_memory_loggers`` for the SDK path, where no proxy global is + set (selecting from there, not ``service_callback``, which a preset logger does + not always reach), and build a generic logger from ``OTEL_*`` only when none was + configured at all. Each fallback still avoids the second generic logger that + orphaned the gen-ai spans onto a different backend than the server span. + """ + if registered is not None: + return registered + existing = next( + (cb for cb in in_memory_loggers if isinstance(cb, OpenTelemetryV2)), None + ) + return existing if existing is not None else OpenTelemetryV2() + + +def publish_global_otel_v2_provider( + in_memory_loggers: Sequence[object], + set_global_provider: Callable[[TracerProvider], None], + registered: "OpenTelemetryV2 | None" = None, +) -> "OpenTelemetryV2": + """Select the single v2 logger and publish its provider as the OTel global. + + The proxy calls this once at startup, after callbacks are initialized, so the + preset logger already exists; it passes ``registered`` (the canonical owner the + factory designated as ``proxy_server.open_telemetry_logger``) so the global + provider reuses the same logger the rest of the v2 code emits through (see + :func:`select_global_otel_v2_logger`). Both ``registered`` and + ``set_global_provider`` (the proxy passes + ``opentelemetry.trace.set_tracer_provider``) are injected so the publish step is + unit-testable without reading or mutating real global OTel state. Returns the + logger whose provider was published. + """ + logger = select_global_otel_v2_logger(in_memory_loggers, registered=registered) + set_global_provider(logger._tracer_provider) + return logger + + def _registered_v2_logger() -> "OpenTelemetryV2 | None": try: from litellm.proxy import proxy_server diff --git a/litellm/integrations/otel/model/config.py b/litellm/integrations/otel/model/config.py index 4f7c3277ebb..991b156ae64 100644 --- a/litellm/integrations/otel/model/config.py +++ b/litellm/integrations/otel/model/config.py @@ -1,5 +1,7 @@ """Typed configuration for the OpenTelemetry instrumentation.""" +from enum import Enum +from functools import lru_cache from typing import Any, List from pydantic import AliasChoices, BaseModel, Field, field_validator, model_validator @@ -23,13 +25,35 @@ class CaptureMessageContent(str): SPAN_AND_EVENT = "span_and_event" +class ExporterOwner(str, Enum): + """The preset that contributed an exporter. Values match the callback names + in ``presets.PRESET_BY_CALLBACK`` so per-request dynamic-credential routing + can match an exporter's owner against the credential source's callback name. + A ``str`` enum so the value compares equal to the bare callback-name string.""" + + # Arize AX (the hosted platform) and Arize Phoenix (the open-source / Phoenix + # Cloud tracer) are distinct backends with separate config and auth, so they + # are separate owners. The member value stays the public callback name. + ARIZE_AX = "arize" + ARIZE_PHOENIX = "arize_phoenix" + LANGFUSE_OTEL = "langfuse_otel" + WEAVE_OTEL = "weave_otel" + LEVO = "levo" + AGENTOPS = "agentops" + + class _OTelV2Flag(BaseSettings): model_config = SettingsConfigDict(extra="ignore") enabled: bool = Field(default=False, validation_alias=AliasChoices(OTEL_V2_ENV)) +@lru_cache(maxsize=1) def is_otel_v2_enabled() -> bool: + # Resolved once at startup and cached: constructing the pydantic-settings + # model re-scans the environment and cost ~28us, which on the proxy hot path + # (auth, logging-callback setup) compounded into a measurable throughput + # regression. Tests that toggle the env must call ``is_otel_v2_enabled.cache_clear()``. return _OTelV2Flag().enabled @@ -49,6 +73,15 @@ class ExporterSpec(BaseModel): ) endpoint: str | None = None headers: str | None = None + owner: ExporterOwner | None = Field( + default=None, + description=( + "The preset that contributed this exporter. Per-request dynamic OTLP " + "credentials are applied only to the exporter whose owner matches the " + "credential source, so one tenant's vendor key never lands on a " + "different backend's exporter." + ), + ) options: dict[str, str] | None = Field( default=None, description=( diff --git a/litellm/integrations/otel/plumbing/routing.py b/litellm/integrations/otel/plumbing/routing.py index 4d0943a263a..1f2f1b202d9 100644 --- a/litellm/integrations/otel/plumbing/routing.py +++ b/litellm/integrations/otel/plumbing/routing.py @@ -88,13 +88,23 @@ class TenantTracerCache: return get_tracer(provider, self._tracer_name) def _config_with_headers(self, headers: Mapping[str, str]) -> OpenTelemetryV2Config: - """Clone the config, replacing OTLP exporter headers with ``headers``.""" + """Clone the config, stamping ``headers`` onto the credential's own exporter. + + ``headers`` are the per-request credentials of ``self._callback_name`` (the + integration that built this cache), so they apply only to the exporter that + integration contributed (``spec.owner``). A request that carries one + tenant's Arize key must never rewrite the headers of a co-configured + Langfuse or self-hosted collector exporter, which would leak that key to a + different backend. + """ header_str = ",".join(f"{key}={value}" for key, value in headers.items()) + header_update: dict[str, str] = {"headers": header_str} exporters = [ ( - spec - if spec.kind.lower() in _NON_OTLP_KINDS - else spec.model_copy(update={"headers": header_str}) + spec.model_copy(update=header_update) + if spec.owner == self._callback_name + and spec.kind.lower() not in _NON_OTLP_KINDS + else spec ) for spec in self._config.exporters ] diff --git a/litellm/integrations/otel/presets/agentops.py b/litellm/integrations/otel/presets/agentops.py index 5a12818fd99..7b0783935ac 100644 --- a/litellm/integrations/otel/presets/agentops.py +++ b/litellm/integrations/otel/presets/agentops.py @@ -16,7 +16,11 @@ from pydantic import Field from pydantic_settings import BaseSettings, SettingsConfigDict from litellm._logging import verbose_logger -from litellm.integrations.otel.model.config import ExporterSpec, OpenTelemetryV2Config +from litellm.integrations.otel.model.config import ( + ExporterOwner, + ExporterSpec, + OpenTelemetryV2Config, +) from litellm.integrations.otel.plumbing.providers import register_exporter_factory _AGENTOPS_ENDPOINT = "https://otlp.agentops.cloud/v1/traces" @@ -59,6 +63,7 @@ def agentops_preset( options=( {"api_key": settings.api_key} if settings.api_key else None ), + owner=ExporterOwner.AGENTOPS, ), ], "resource_attributes": { diff --git a/litellm/integrations/otel/presets/arize.py b/litellm/integrations/otel/presets/arize.py index 4df15125f5a..b6af88c6b34 100644 --- a/litellm/integrations/otel/presets/arize.py +++ b/litellm/integrations/otel/presets/arize.py @@ -4,7 +4,11 @@ from pydantic import Field from pydantic_settings import BaseSettings, SettingsConfigDict from litellm.integrations.arize.arize import ArizeLogger as _V1ArizeLogger -from litellm.integrations.otel.model.config import ExporterSpec, OpenTelemetryV2Config +from litellm.integrations.otel.model.config import ( + ExporterOwner, + ExporterSpec, + OpenTelemetryV2Config, +) from litellm.integrations.otel.presets.utils import ensure_mappers from litellm.types.utils import StandardCallbackDynamicParams @@ -34,6 +38,7 @@ def arize_preset( kind=arize_cfg.protocol or "otlp_grpc", endpoint=arize_cfg.endpoint or "https://otlp.arize.com/v1", headers=headers, + owner=ExporterOwner.ARIZE_AX, ), ], "mapper_names": ensure_mappers(base.mapper_names, "openinference"), diff --git a/litellm/integrations/otel/presets/langfuse.py b/litellm/integrations/otel/presets/langfuse.py index 011545384b9..5631da6429f 100644 --- a/litellm/integrations/otel/presets/langfuse.py +++ b/litellm/integrations/otel/presets/langfuse.py @@ -3,7 +3,11 @@ from litellm.integrations.langfuse.langfuse_otel import ( LangfuseOtelLogger as _V1Langfuse, ) -from litellm.integrations.otel.model.config import ExporterSpec, OpenTelemetryV2Config +from litellm.integrations.otel.model.config import ( + ExporterOwner, + ExporterSpec, + OpenTelemetryV2Config, +) from litellm.integrations.otel.presets.utils import ensure_mappers from litellm.types.utils import StandardCallbackDynamicParams @@ -23,6 +27,7 @@ def langfuse_preset( kind=kind, endpoint=cfg.endpoint, headers=cfg.headers, + owner=ExporterOwner.LANGFUSE_OTEL, ), ], "mapper_names": ensure_mappers(base.mapper_names, "langfuse"), diff --git a/litellm/integrations/otel/presets/levo.py b/litellm/integrations/otel/presets/levo.py index 4c4cba982a4..74a95b100cb 100644 --- a/litellm/integrations/otel/presets/levo.py +++ b/litellm/integrations/otel/presets/levo.py @@ -1,7 +1,11 @@ """Levo preset — OTLP/HTTP to a Levo collector with org+workspace headers.""" from litellm.integrations.levo.levo import LevoLogger as _V1Levo -from litellm.integrations.otel.model.config import ExporterSpec, OpenTelemetryV2Config +from litellm.integrations.otel.model.config import ( + ExporterOwner, + ExporterSpec, + OpenTelemetryV2Config, +) def levo_preset( @@ -18,6 +22,7 @@ def levo_preset( kind="otlp_http", endpoint=cfg.endpoint, headers=cfg.otlp_auth_headers, + owner=ExporterOwner.LEVO, ), ], } diff --git a/litellm/integrations/otel/presets/phoenix.py b/litellm/integrations/otel/presets/phoenix.py index 4c2b165ffca..5485b599321 100644 --- a/litellm/integrations/otel/presets/phoenix.py +++ b/litellm/integrations/otel/presets/phoenix.py @@ -6,7 +6,11 @@ from pydantic_settings import BaseSettings, SettingsConfigDict from litellm.integrations.arize.arize_phoenix import ( ArizePhoenixLogger as _V1Phoenix, ) -from litellm.integrations.otel.model.config import ExporterSpec, OpenTelemetryV2Config +from litellm.integrations.otel.model.config import ( + ExporterOwner, + ExporterSpec, + OpenTelemetryV2Config, +) from litellm.integrations.otel.presets.utils import ensure_mappers @@ -37,6 +41,7 @@ def phoenix_preset( kind=cfg.protocol if hasattr(cfg, "protocol") else "otlp_http", endpoint=cfg.endpoint, headers=headers, + owner=ExporterOwner.ARIZE_PHOENIX, ), ], "mapper_names": ensure_mappers(base.mapper_names, "openinference"), diff --git a/litellm/integrations/otel/presets/weave.py b/litellm/integrations/otel/presets/weave.py index 9fc03c84a6d..d22f7641289 100644 --- a/litellm/integrations/otel/presets/weave.py +++ b/litellm/integrations/otel/presets/weave.py @@ -1,6 +1,10 @@ """Weave (W&B) preset.""" -from litellm.integrations.otel.model.config import ExporterSpec, OpenTelemetryV2Config +from litellm.integrations.otel.model.config import ( + ExporterOwner, + ExporterSpec, + OpenTelemetryV2Config, +) from litellm.integrations.otel.presets.utils import ensure_mappers from litellm.integrations.weave.weave_otel import ( _get_weave_authorization_header, @@ -23,6 +27,7 @@ def weave_preset( kind=weave_cfg.protocol or "otlp_http", endpoint=weave_cfg.endpoint, headers=weave_cfg.otlp_auth_headers, + owner=ExporterOwner.WEAVE_OTEL, ), ], # Weave consumes OpenInference + a small Weave-specific overlay. diff --git a/litellm/litellm_core_utils/cloud_storage_security.py b/litellm/litellm_core_utils/cloud_storage_security.py index daa3dc60320..a75d1178d5a 100644 --- a/litellm/litellm_core_utils/cloud_storage_security.py +++ b/litellm/litellm_core_utils/cloud_storage_security.py @@ -15,8 +15,23 @@ BEDROCK_MANAGED_S3_PREFIXES = ( BEDROCK_MANAGED_S3_UPLOAD_PREFIX, BEDROCK_MANAGED_S3_OUTPUT_PREFIX, ) +MANAGED_CLOUD_STORAGE_SCHEMES = ("s3://", "gs://") _MAPPING_PROXY_TYPE: type = type(MappingProxyType({})) + +def is_managed_cloud_storage_uri(file_id: str) -> bool: + """ + True if file_id is a raw cloud-storage object URI (e.g. ``s3://bucket/key``). + + These are internal provider artifacts. On the multi-tenant proxy they must be + retrieved through their managed unified file id so owner/team access is enforced; + a raw URI supplied by a caller bypasses that check. + """ + return isinstance(file_id, str) and file_id.startswith( + MANAGED_CLOUD_STORAGE_SCHEMES + ) + + _SAFE_OBJECT_COMPONENT_PATTERN = re.compile(r"[^A-Za-z0-9._-]+") diff --git a/litellm/litellm_core_utils/exception_mapping_utils.py b/litellm/litellm_core_utils/exception_mapping_utils.py index 6087e55b136..9b2a9af4126 100644 --- a/litellm/litellm_core_utils/exception_mapping_utils.py +++ b/litellm/litellm_core_utils/exception_mapping_utils.py @@ -1,7 +1,7 @@ import json import re import traceback -from typing import Any, Optional +from typing import Any, Optional, Protocol, cast import httpx @@ -170,6 +170,16 @@ def get_error_message(error_obj) -> Optional[str]: ####### EXCEPTION MAPPING ################ +def _get_body_error_code(error_str: str) -> int | None: + """Return error.code from a JSON error body, or None if not parseable.""" + try: + body = json.loads(error_str) + code = body.get("error", {}).get("code") + return int(code) if code is not None else None + except Exception: + return None + + def _get_response_headers(original_exception: Exception) -> Optional[httpx.Headers]: """ Extract and return the response headers from an exception, if present. @@ -234,6 +244,2011 @@ def extract_and_raise_litellm_exception( ) +class _ProviderHTTPException(Protocol): + status_code: int + message: str + response: httpx.Response + request: httpx.Request + body: object + code: str + llm_provider: str + + +def _map_openai_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + # custom_llm_provider is openai, make it OpenAI + message = get_error_message(error_obj=original_exception) + if message is None: + if hasattr(original_exception, "message"): + message = original_exception.message + else: + message = str(original_exception) + + if message is not None and isinstance( + message, str + ): # done to prevent user-confusion. Relevant issue - https://github.com/BerriAI/litellm/issues/1414 + message = message.replace("OPENAI", custom_llm_provider.upper()) + message = message.replace( + "openai.OpenAIError", + "{}.{}Error".format(custom_llm_provider, custom_llm_provider), + ) + if custom_llm_provider == "openai": + exception_provider = "OpenAI" + "Exception" + else: + exception_provider = ( + custom_llm_provider[0].upper() + custom_llm_provider[1:] + "Exception" + ) + + if ExceptionCheckers.is_error_str_rate_limit(error_str): + raise RateLimitError( + message=f"RateLimitError: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + ) + elif ExceptionCheckers.is_error_str_context_window_exceeded(error_str): + raise ContextWindowExceededError( + message=f"ContextWindowExceededError: {exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif "invalid_request_error" in error_str and "model_not_found" in error_str: + raise NotFoundError( + message=f"{exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif "A timeout occurred" in error_str: + raise Timeout( + message=f"{exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif ( + ( + "invalid_request_error" in error_str + and "content_policy_violation" in error_str + ) + or ("Invalid prompt" in error_str and "violating our usage policy" in error_str) + or ( + "request was rejected as a result of the safety system" in error_str.lower() + ) + ): + raise ContentPolicyViolationError( + message=f"ContentPolicyViolationError: {exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif ( + "invalid_encrypted_content" in error_str or "could not be verified" in error_str + ): + helpful_message = ( + f"{exception_provider} - {message}\n\n" + " This error occurs when load balancing Responses API across deployments with different API keys.\n" + " Encrypted content is tied to the organization that created it and cannot be decrypted by other organizations.\n\n" + " Solution: Enable 'encrypted_content_affinity' to route follow-up requests to the correct deployment:\n\n" + " router_settings:\n" + " enable_pre_call_checks: true\n" + " optional_pre_call_checks:\n" + " - encrypted_content_affinity\n\n" + " Learn more: https://docs.litellm.ai/docs/response_api#encrypted-content-affinity-multi-region-load-balancing" + ) + raise BadRequestError( + message=helpful_message, + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + body=getattr(original_exception, "body", None), + ) + elif ( + "invalid_request_error" in error_str + and "Incorrect API key provided" not in error_str + ): + raise BadRequestError( + message=f"{exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + body=getattr(original_exception, "body", None), + ) + elif ( + "Web server is returning an unknown error" in error_str + or "The server had an error processing your request." in error_str + ): + raise litellm.InternalServerError( + message=f"{exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + ) + elif "Request too large" in error_str: + raise RateLimitError( + message=f"RateLimitError: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif ( + "The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_API_KEY environment variable" + in error_str + ): + raise AuthenticationError( + message=f"AuthenticationError: {exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif "Mistral API raised a streaming error" in error_str: + _request = httpx.Request(method="POST", url="https://api.openai.com/v1") + raise APIError( + status_code=500, + message=f"{exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + request=_request, + litellm_debug_info=extra_information, + ) + elif hasattr(original_exception, "status_code"): + if original_exception.status_code == 400: + raise BadRequestError( + message=f"{exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 401: + raise AuthenticationError( + message=f"AuthenticationError: {exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 404: + raise NotFoundError( + message=f"NotFoundError: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"Timeout Error: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 422: + raise BadRequestError( + message=f"{exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + body=getattr(original_exception, "body", None), + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"RateLimitError: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 500: + raise InternalServerError( + message=f"InternalServerError: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 502: + raise BadGatewayError( + message=f"BadGatewayError: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 503: + raise ServiceUnavailableError( + message=f"ServiceUnavailableError: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 504: # gateway timeout error + raise Timeout( + message=f"Timeout Error: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + exception_status_code=original_exception.status_code, + ) + else: + raise APIError( + status_code=original_exception.status_code, + message=f"APIError: {exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + request=getattr(original_exception, "request", None), + litellm_debug_info=extra_information, + ) + else: + # if no status code then it is an APIConnectionError: https://github.com/openai/openai-python#handling-errors + # exception_mapping_worked = True + raise APIConnectionError( + message=f"APIConnectionError: {exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + litellm_debug_info=extra_information, + request=httpx.Request(method="POST", url="https://api.openai.com/v1/"), + ) + + +def _map_anthropic_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if ( + "prompt is too long" in error_str + or "prompt: length" in error_str + or ExceptionCheckers.is_error_str_context_window_exceeded(error_str) + ): + raise ContextWindowExceededError( + message="AnthropicError - {}".format(error_str), + model=model, + llm_provider="anthropic", + ) + elif "overloaded_error" in error_str or "Overloaded" in error_str: + raise InternalServerError( + message="AnthropicError - {}".format(error_str), + model=model, + llm_provider="anthropic", + ) + if "Invalid API Key" in error_str: + raise AuthenticationError( + message="AnthropicError - {}".format(error_str), + model=model, + llm_provider="anthropic", + ) + if "content filtering policy" in error_str: + raise ContentPolicyViolationError( + message="AnthropicError - {}".format(error_str), + model=model, + llm_provider="anthropic", + ) + if "Client error '400 Bad Request'" in error_str: + raise BadRequestError( + message="AnthropicError - {}".format(error_str), + model=model, + llm_provider="anthropic", + ) + if hasattr(original_exception, "status_code"): + verbose_logger.debug(f"status_code: {original_exception.status_code}") + if original_exception.status_code == 401: + raise AuthenticationError( + message=f"AnthropicException - {error_str}", + llm_provider="anthropic", + model=model, + ) + elif ( + original_exception.status_code == 400 + or original_exception.status_code == 413 + ): + raise BadRequestError( + message=f"AnthropicException - {error_str}", + model=model, + llm_provider="anthropic", + ) + elif original_exception.status_code == 404: + raise NotFoundError( + message=f"AnthropicException - {error_str}", + model=model, + llm_provider="anthropic", + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"AnthropicException - {error_str}", + model=model, + llm_provider="anthropic", + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"AnthropicException - {error_str}", + llm_provider="anthropic", + model=model, + ) + elif ( + original_exception.status_code == 500 + or original_exception.status_code == 529 + ): + raise litellm.InternalServerError( + message=f"AnthropicException - {error_str}. Handle with `litellm.InternalServerError`.", + llm_provider="anthropic", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 502: + raise BadGatewayError( + message=f"AnthropicException BadGatewayError - {error_str}", + llm_provider="anthropic", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 503: + raise litellm.ServiceUnavailableError( + message=f"AnthropicException - {error_str}. Handle with `litellm.ServiceUnavailableError`.", + llm_provider="anthropic", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 504: # gateway timeout error + raise Timeout( + message=f"AnthropicException Timeout - {error_str}", + model=model, + llm_provider="anthropic", + exception_status_code=original_exception.status_code, + ) + + +def _map_replicate_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if "Incorrect authentication token" in error_str: + raise AuthenticationError( + message=f"ReplicateException - {error_str}", + llm_provider="replicate", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "input is too long" in error_str: + raise ContextWindowExceededError( + message=f"ReplicateException - {error_str}", + model=model, + llm_provider="replicate", + response=getattr(original_exception, "response", None), + ) + elif exception_type == "ModelError": + raise BadRequestError( + message=f"ReplicateException - {error_str}", + model=model, + llm_provider="replicate", + response=getattr(original_exception, "response", None), + ) + elif "Request was throttled" in error_str: + raise RateLimitError( + message=f"ReplicateException - {error_str}", + llm_provider="replicate", + model=model, + response=getattr(original_exception, "response", None), + ) + elif hasattr(original_exception, "status_code"): + if original_exception.status_code == 401: + raise AuthenticationError( + message=f"ReplicateException - {original_exception.message}", + llm_provider="replicate", + model=model, + response=getattr(original_exception, "response", None), + ) + elif ( + original_exception.status_code == 400 + or original_exception.status_code == 413 + ): + raise BadRequestError( + message=f"ReplicateException - {original_exception.message}", + model=model, + llm_provider="replicate", + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 422: + raise UnprocessableEntityError( + message=f"ReplicateException - {original_exception.message}", + model=model, + llm_provider="replicate", + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"ReplicateException - {original_exception.message}", + model=model, + llm_provider="replicate", + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"ReplicateException - {original_exception.message}", + llm_provider="replicate", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 500: + raise ServiceUnavailableError( + message=f"ReplicateException - {original_exception.message}", + llm_provider="replicate", + model=model, + response=getattr(original_exception, "response", None), + ) + raise APIError( + status_code=500, + message=f"ReplicateException - {str(original_exception)}", + llm_provider="replicate", + model=model, + request=httpx.Request( + method="POST", + url="https://api.replicate.com/v1/deployments", + ), + ) + + +def _map_openai_like_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if "authorization denied for" in error_str: + + # Predibase returns the raw API Key in the response - this block ensures it's not returned in the exception + if ( + error_str is not None + and isinstance(error_str, str) + and "bearer" in error_str.lower() + ): + # only keep the first 10 chars after the occurnence of "bearer" + _bearer_token_start_index = error_str.lower().find("bearer") + error_str = error_str[: _bearer_token_start_index + 14] + error_str += "XXXXXXX" + '"' + + raise AuthenticationError( + message=f"{custom_llm_provider.capitalize()}Exception: Authentication Error - {error_str}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif ExceptionCheckers.is_error_str_context_window_exceeded(error_str): + raise ContextWindowExceededError( + message=f"{custom_llm_provider.capitalize()}Exception: Context Window Error - {error_str}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif "token_quota_reached" in error_str: + raise RateLimitError( + message=f"{custom_llm_provider.capitalize()}Exception: Rate Limit Errror - {error_str}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + ) + elif ( + "The server received an invalid response from an upstream server." in error_str + ): + raise litellm.InternalServerError( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + llm_provider=custom_llm_provider, + model=model, + ) + elif "model_no_support_for_function" in error_str: + raise BadRequestError( + message=f"{custom_llm_provider.capitalize()}Exception - Use 'watsonx_text' route instead. IBM WatsonX does not support `/text/chat` endpoint. - {error_str}", + llm_provider=custom_llm_provider, + model=model, + ) + elif hasattr(original_exception, "status_code"): + if original_exception.status_code == 500: + raise litellm.InternalServerError( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + llm_provider=custom_llm_provider, + model=model, + ) + elif ( + original_exception.status_code == 401 + or original_exception.status_code == 403 + ): + raise AuthenticationError( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + llm_provider=custom_llm_provider, + model=model, + ) + elif original_exception.status_code == 400: + raise BadRequestError( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + llm_provider=custom_llm_provider, + model=model, + ) + elif original_exception.status_code == 404: + raise NotFoundError( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + llm_provider=custom_llm_provider, + model=model, + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif ( + original_exception.status_code == 422 + or original_exception.status_code == 424 + ): + raise BadRequestError( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 503: + raise ServiceUnavailableError( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 504: # gateway timeout error + raise Timeout( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + exception_status_code=original_exception.status_code, + ) + + +def _map_bedrock_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if ( + "too many tokens" in error_str + or "expected maxLength:" in error_str + or "Input is too long" in error_str + or "prompt is too long" in error_str + or "prompt: length: 1.." in error_str + or "Too many input tokens" in error_str + ): + raise ContextWindowExceededError( + message=f"BedrockException: Context Window Error - {error_str}", + model=model, + llm_provider="bedrock", + ) + elif ( + "Conversation blocks and tool result blocks cannot be provided in the same turn." + in error_str + ): + raise BadRequestError( + message=f"BedrockException - {error_str}\n. Enable 'litellm.modify_params=True' (for PROXY do: `litellm_settings::modify_params: True`) to insert a dummy assistant message and fix this error.", + model=model, + llm_provider="bedrock", + response=getattr(original_exception, "response", None), + ) + elif "Malformed input request" in error_str: + raise BadRequestError( + message=f"BedrockException - {error_str}", + model=model, + llm_provider="bedrock", + response=getattr(original_exception, "response", None), + ) + elif "A conversation must start with a user message." in error_str: + raise BadRequestError( + message=f"BedrockException - {error_str}\n. Pass in default user message via `completion(..,user_continue_message=)` or enable `litellm.modify_params=True`.\nFor Proxy: do via `litellm_settings::modify_params: True` or user_continue_message under `litellm_params`", + model=model, + llm_provider="bedrock", + response=getattr(original_exception, "response", None), + ) + elif ( + "Unable to locate credentials" in error_str + or "The security token included in the request is invalid" in error_str + ): + raise AuthenticationError( + message=f"BedrockException Invalid Authentication - {error_str}", + model=model, + llm_provider="bedrock", + response=getattr(original_exception, "response", None), + ) + elif "AccessDeniedException" in error_str: + raise PermissionDeniedError( + message=f"BedrockException PermissionDeniedError - {error_str}", + model=model, + llm_provider="bedrock", + response=getattr(original_exception, "response", None), + ) + elif "throttlingException" in error_str or "ThrottlingException" in error_str: + raise RateLimitError( + message=f"BedrockException: Rate Limit Error - {error_str}", + model=model, + llm_provider="bedrock", + response=getattr(original_exception, "response", None), + ) + elif "Connect timeout on endpoint URL" in error_str or "timed out" in error_str: + raise Timeout( + message=f"BedrockException: Timeout Error - {error_str}", + model=model, + llm_provider="bedrock", + ) + elif "Could not process image" in error_str: + raise litellm.InternalServerError( + message=f"BedrockException - {error_str}", + model=model, + llm_provider="bedrock", + ) + elif hasattr(original_exception, "status_code"): + if original_exception.status_code == 500: + raise ServiceUnavailableError( + message=f"BedrockException - {original_exception.message}", + llm_provider="bedrock", + model=model, + response=httpx.Response( + status_code=500, + request=httpx.Request( + method="POST", url="https://api.openai.com/v1/" + ), + ), + ) + elif original_exception.status_code == 401: + raise AuthenticationError( + message=f"BedrockException - {original_exception.message}", + llm_provider="bedrock", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 400: + raise BadRequestError( + message=f"BedrockException - {original_exception.message}", + llm_provider="bedrock", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 404: + raise NotFoundError( + message=f"BedrockException - {original_exception.message}", + llm_provider="bedrock", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"BedrockException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 422: + raise BadRequestError( + message=f"BedrockException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"BedrockException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 503: + raise ServiceUnavailableError( + message=f"BedrockException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 504: # gateway timeout error + raise Timeout( + message=f"BedrockException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + exception_status_code=original_exception.status_code, + ) + + +def _map_sagemaker_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if "Unable to locate credentials" in error_str: + raise BadRequestError( + message=f"litellm.BadRequestError: SagemakerException - {error_str}", + model=model, + llm_provider="sagemaker", + response=getattr(original_exception, "response", None), + ) + elif "Input validation error: `best_of` must be > 0 and <= 2" in error_str: + raise BadRequestError( + message="SagemakerException - the value of 'n' must be > 0 and <= 2 for sagemaker endpoints", + model=model, + llm_provider="sagemaker", + response=getattr(original_exception, "response", None), + ) + elif ( + "`inputs` tokens + `max_new_tokens` must be <=" in error_str + or "instance type with more CPU capacity or memory" in error_str + ): + raise ContextWindowExceededError( + message=f"SagemakerException - {error_str}", + model=model, + llm_provider="sagemaker", + response=getattr(original_exception, "response", None), + ) + elif hasattr(original_exception, "status_code"): + if original_exception.status_code == 500: + raise ServiceUnavailableError( + message=f"SagemakerException - {original_exception.message}", + llm_provider=custom_llm_provider, + model=model, + response=httpx.Response( + status_code=500, + request=httpx.Request( + method="POST", url="https://api.openai.com/v1/" + ), + ), + ) + elif original_exception.status_code == 401: + raise AuthenticationError( + message=f"SagemakerException - {original_exception.message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 400: + raise BadRequestError( + message=f"SagemakerException - {original_exception.message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 404: + raise NotFoundError( + message=f"SagemakerException - {original_exception.message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"SagemakerException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif ( + original_exception.status_code == 422 + or original_exception.status_code == 424 + ): + raise BadRequestError( + message=f"SagemakerException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"SagemakerException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 503: + raise ServiceUnavailableError( + message=f"SagemakerException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 504: # gateway timeout error + raise Timeout( + message=f"SagemakerException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + exception_status_code=original_exception.status_code, + ) + + +def _map_vertex_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if ( + "Vertex AI API has not been used in project" in error_str + or "Unable to find your project" in error_str + ): + raise BadRequestError( + message=f"litellm.BadRequestError: {custom_llm_provider}Exception - {error_str}", + model=model, + llm_provider=custom_llm_provider, + response=httpx.Response( + status_code=400, + request=httpx.Request( + method="POST", + url=" https://cloud.google.com/vertex-ai/", + ), + ), + litellm_debug_info=extra_information, + ) + if "400 Request payload size exceeds" in error_str: + raise ContextWindowExceededError( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", + model=model, + llm_provider=custom_llm_provider, + ) + elif ExceptionCheckers.is_error_str_context_window_exceeded(error_str): + raise ContextWindowExceededError( + message=f"ContextWindowExceededError: {custom_llm_provider.capitalize()}Exception - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif "None Unknown Error." in error_str or "Content has no parts." in error_str: + raise litellm.InternalServerError( + message=f"litellm.InternalServerError: {custom_llm_provider}Exception - {error_str}", + model=model, + llm_provider=custom_llm_provider, + response=httpx.Response( + status_code=500, + content=str(original_exception), + request=httpx.Request(method="completion", url="https://github.com/BerriAI/litellm"), # type: ignore + ), + litellm_debug_info=extra_information, + ) + elif "API key not valid." in error_str: + raise AuthenticationError( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif "403" in error_str: + raise BadRequestError( + message=f"{custom_llm_provider.capitalize()}Exception BadRequestError - {error_str}", + model=model, + llm_provider=custom_llm_provider, + response=httpx.Response( + status_code=403, + request=httpx.Request( + method="POST", + url=" https://cloud.google.com/vertex-ai/", + ), + ), + litellm_debug_info=extra_information, + ) + elif ( + "The response was blocked." in error_str + or "Output blocked by content filtering policy" + in error_str # anthropic on vertex ai + ): + raise ContentPolicyViolationError( + message=f"{custom_llm_provider.capitalize()}Exception ContentPolicyViolationError - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + response=httpx.Response( + status_code=400, + request=httpx.Request( + method="POST", + url=" https://cloud.google.com/vertex-ai/", + ), + ), + ) + elif ( + "429 Quota exceeded" in error_str + or "Quota exceeded for" in error_str + or "Resource exhausted" in error_str + or "IndexError: list index out of range" in error_str + or "429 Unable to submit request because the service is temporarily out of capacity." + in error_str + ): + raise RateLimitError( + message=f"litellm.RateLimitError: {custom_llm_provider}Exception - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + response=httpx.Response( + status_code=429, + request=httpx.Request( + method="POST", + url=" https://cloud.google.com/vertex-ai/", + ), + ), + ) + elif ( + isinstance(getattr(original_exception, "status_code", None), int) + and 500 <= original_exception.status_code < 600 + and _get_body_error_code(error_str) == 429 + ): + # upstream gateway wraps a 429 inside a 5xx envelope + # e.g. HTTP 500/503 with {"error":{"code":429,...}}. + # Scoped to 5xx so HTTP 400/401 with body code:429 + # still maps to BadRequestError / AuthenticationError. + raise RateLimitError( + message=f"litellm.RateLimitError: {custom_llm_provider}Exception - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + response=httpx.Response( + status_code=429, + request=httpx.Request( + method="POST", + url=" https://cloud.google.com/vertex-ai/", + ), + ), + ) + elif ( + "500 Internal Server Error" in error_str + or "The model is overloaded." in error_str + ): + raise litellm.InternalServerError( + message=f"litellm.InternalServerError: {custom_llm_provider}Exception - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 400: + raise BadRequestError( + message=f"{custom_llm_provider.capitalize()}Exception BadRequestError - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + response=httpx.Response( + status_code=400, + request=httpx.Request( + method="POST", + url="https://cloud.google.com/vertex-ai/", + ), + ), + ) + if original_exception.status_code == 401: + raise AuthenticationError( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", + llm_provider=custom_llm_provider, + model=model, + ) + if original_exception.status_code == 403: + raise PermissionDeniedError( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", + llm_provider=custom_llm_provider, + model=model, + response=httpx.Response( + status_code=403, + request=httpx.Request( + method="POST", + url="https://cloud.google.com/vertex-ai/", + ), + ), + ) + if original_exception.status_code == 404: + raise NotFoundError( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", + llm_provider=custom_llm_provider, + model=model, + ) + if original_exception.status_code == 408: + raise Timeout( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", + llm_provider=custom_llm_provider, + model=model, + ) + + if original_exception.status_code == 429: + raise RateLimitError( + message=f"litellm.RateLimitError: {custom_llm_provider.capitalize()}Exception - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + response=httpx.Response( + status_code=429, + request=httpx.Request( + method="POST", + url=" https://cloud.google.com/vertex-ai/", + ), + ), + ) + if original_exception.status_code == 500: + raise litellm.InternalServerError( + message=f"{custom_llm_provider.capitalize()}Exception InternalServerError - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + response=httpx.Response( + status_code=500, + content=str(original_exception), + request=httpx.Request(method="completion", url="https://github.com/BerriAI/litellm"), # type: ignore + ), + ) + if original_exception.status_code == 502: + raise APIConnectionError( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", + llm_provider=custom_llm_provider, + model=model, + ) + if original_exception.status_code == 503: + raise ServiceUnavailableError( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", + llm_provider=custom_llm_provider, + model=model, + ) + + +def _map_cloudflare_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if "Authentication error" in error_str: + raise AuthenticationError( + message=f"Cloudflare Exception - {original_exception.message}", + llm_provider="cloudflare", + model=model, + response=getattr(original_exception, "response", None), + ) + if "must have required property" in error_str: + raise BadRequestError( + message=f"Cloudflare Exception - {original_exception.message}", + llm_provider="cloudflare", + model=model, + response=getattr(original_exception, "response", None), + ) + + +def _map_cohere_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if "invalid api token" in error_str or "No API key provided." in error_str: + raise AuthenticationError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "invalid type: parameter" in error_str: + raise BadRequestError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "too many tokens" in error_str: + raise ContextWindowExceededError( + message=f"CohereException - {original_exception.message}", + model=model, + llm_provider="cohere", + response=getattr(original_exception, "response", None), + ) + elif "internal server error" in error_str.lower(): + raise InternalServerError( + message=f"CohereException - {error_str}", + model=model, + llm_provider="cohere", + response=getattr(original_exception, "response", None), + ) + elif hasattr(original_exception, "status_code"): + if ( + original_exception.status_code == 400 + or original_exception.status_code == 498 + ): + raise BadRequestError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + ) + elif original_exception.status_code == 500: + raise InternalServerError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=getattr(original_exception, "response", None), + ) + elif ( + "CohereConnectionError" in exception_type + ): # cohere seems to fire these errors when we load test it (1k+ messages / min) + raise RateLimitError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "invalid type:" in error_str: + raise BadRequestError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "Unexpected server error" in error_str: + raise InternalServerError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=getattr(original_exception, "response", None), + ) + else: + if hasattr(original_exception, "status_code"): + raise APIError( + status_code=original_exception.status_code, + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + request=getattr(original_exception, "request", None), + ) + raise cast(Exception, original_exception) + + +def _map_huggingface_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if "length limit exceeded" in error_str: + raise ContextWindowExceededError( + message=error_str, + model=model, + llm_provider="huggingface", + response=getattr(original_exception, "response", None), + ) + elif "A valid user token is required" in error_str: + raise BadRequestError( + message=error_str, + llm_provider="huggingface", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "Rate limit reached" in error_str: + raise RateLimitError( + message=error_str, + llm_provider="huggingface", + model=model, + response=getattr(original_exception, "response", None), + ) + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 401: + raise AuthenticationError( + message=f"HuggingfaceException - {original_exception.message}", + llm_provider="huggingface", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 400: + raise BadRequestError( + message=f"HuggingfaceException - {original_exception.message}", + model=model, + llm_provider="huggingface", + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"HuggingfaceException - {original_exception.message}", + model=model, + llm_provider="huggingface", + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"HuggingfaceException - {original_exception.message}", + llm_provider="huggingface", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 503: + raise ServiceUnavailableError( + message=f"HuggingfaceException - {original_exception.message}", + llm_provider="huggingface", + model=model, + response=getattr(original_exception, "response", None), + ) + else: + raise APIError( + status_code=original_exception.status_code, + message=f"HuggingfaceException - {original_exception.message}", + llm_provider="huggingface", + model=model, + request=getattr(original_exception, "request", None), + ) + + +def _map_ai21_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if hasattr(original_exception, "message"): + if "Prompt has too many tokens" in original_exception.message: + raise ContextWindowExceededError( + message=f"AI21Exception - {original_exception.message}", + model=model, + llm_provider="ai21", + response=getattr(original_exception, "response", None), + ) + if "Bad or missing API token." in original_exception.message: + raise BadRequestError( + message=f"AI21Exception - {original_exception.message}", + model=model, + llm_provider="ai21", + response=getattr(original_exception, "response", None), + ) + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 401: + raise AuthenticationError( + message=f"AI21Exception - {original_exception.message}", + llm_provider="ai21", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"AI21Exception - {original_exception.message}", + model=model, + llm_provider="ai21", + ) + if original_exception.status_code == 422: + raise BadRequestError( + message=f"AI21Exception - {original_exception.message}", + model=model, + llm_provider="ai21", + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"AI21Exception - {original_exception.message}", + llm_provider="ai21", + model=model, + response=getattr(original_exception, "response", None), + ) + else: + raise APIError( + status_code=original_exception.status_code, + message=f"AI21Exception - {original_exception.message}", + llm_provider="ai21", + model=model, + request=getattr(original_exception, "request", None), + ) + + +def _map_nlp_cloud_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if "detail" in error_str: + if "Input text length should not exceed" in error_str: + raise ContextWindowExceededError( + message=f"NLPCloudException - {error_str}", + model=model, + llm_provider="nlp_cloud", + response=getattr(original_exception, "response", None), + ) + elif "value is not a valid" in error_str: + raise BadRequestError( + message=f"NLPCloudException - {error_str}", + model=model, + llm_provider="nlp_cloud", + response=getattr(original_exception, "response", None), + ) + else: + raise APIError( + status_code=500, + message=f"NLPCloudException - {error_str}", + model=model, + llm_provider="nlp_cloud", + request=getattr(original_exception, "request", None), + ) + if hasattr( + original_exception, "status_code" + ): # https://docs.nlpcloud.com/?shell#errors + if ( + original_exception.status_code == 400 + or original_exception.status_code == 406 + or original_exception.status_code == 413 + or original_exception.status_code == 422 + ): + raise BadRequestError( + message=f"NLPCloudException - {original_exception.message}", + llm_provider="nlp_cloud", + model=model, + response=getattr(original_exception, "response", None), + ) + elif ( + original_exception.status_code == 401 + or original_exception.status_code == 403 + ): + raise AuthenticationError( + message=f"NLPCloudException - {original_exception.message}", + llm_provider="nlp_cloud", + model=model, + response=getattr(original_exception, "response", None), + ) + elif ( + original_exception.status_code == 522 + or original_exception.status_code == 524 + ): + raise Timeout( + message=f"NLPCloudException - {original_exception.message}", + model=model, + llm_provider="nlp_cloud", + ) + elif ( + original_exception.status_code == 429 + or original_exception.status_code == 402 + ): + raise RateLimitError( + message=f"NLPCloudException - {original_exception.message}", + llm_provider="nlp_cloud", + model=model, + response=getattr(original_exception, "response", None), + ) + elif ( + original_exception.status_code == 500 + or original_exception.status_code == 503 + ): + raise APIError( + status_code=original_exception.status_code, + message=f"NLPCloudException - {original_exception.message}", + llm_provider="nlp_cloud", + model=model, + request=getattr(original_exception, "request", None), + ) + elif ( + original_exception.status_code == 504 + or original_exception.status_code == 520 + ): + raise ServiceUnavailableError( + message=f"NLPCloudException - {original_exception.message}", + model=model, + llm_provider="nlp_cloud", + response=getattr(original_exception, "response", None), + ) + else: + raise APIError( + status_code=original_exception.status_code, + message=f"NLPCloudException - {original_exception.message}", + llm_provider="nlp_cloud", + model=model, + request=getattr(original_exception, "request", None), + ) + + +def _map_together_ai_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + try: + error_response = json.loads(error_str) + except Exception: + error_response = {"error": error_str} + if ( + "error" in error_response + and "`inputs` tokens + `max_new_tokens` must be <=" in error_response["error"] + ): + raise ContextWindowExceededError( + message=f"TogetherAIException - {error_response['error']}", + model=model, + llm_provider="together_ai", + response=getattr(original_exception, "response", None), + ) + elif "error" in error_response and "invalid private key" in error_response["error"]: + raise AuthenticationError( + message=f"TogetherAIException - {error_response['error']}", + llm_provider="together_ai", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "error" in error_response and "INVALID_ARGUMENT" in error_response["error"]: + raise BadRequestError( + message=f"TogetherAIException - {error_response['error']}", + model=model, + llm_provider="together_ai", + response=getattr(original_exception, "response", None), + ) + elif "A timeout occurred" in error_str: + raise Timeout( + message=f"TogetherAIException - {error_str}", + model=model, + llm_provider="together_ai", + ) + elif ( + "error" in error_response + and "API key doesn't match expected format." in error_response["error"] + ): + raise BadRequestError( + message=f"TogetherAIException - {error_response['error']}", + model=model, + llm_provider="together_ai", + response=getattr(original_exception, "response", None), + ) + elif ( + "error_type" in error_response and error_response["error_type"] == "validation" + ): + raise BadRequestError( + message=f"TogetherAIException - {error_response['error']}", + model=model, + llm_provider="together_ai", + response=getattr(original_exception, "response", None), + ) + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 408: + raise Timeout( + message=f"TogetherAIException - {original_exception.message}", + model=model, + llm_provider="together_ai", + ) + elif original_exception.status_code == 422: + raise BadRequestError( + message=f"TogetherAIException - {error_response['error']}", + model=model, + llm_provider="together_ai", + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"TogetherAIException - {original_exception.message}", + llm_provider="together_ai", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 524: + raise Timeout( + message=f"TogetherAIException - {original_exception.message}", + llm_provider="together_ai", + model=model, + ) + else: + raise APIError( + status_code=original_exception.status_code, + message=f"TogetherAIException - {original_exception.message}", + llm_provider="together_ai", + model=model, + request=getattr(original_exception, "request", None), + ) + + +def _map_aleph_alpha_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if "This is longer than the model's maximum context length" in error_str: + raise ContextWindowExceededError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "InvalidToken" in error_str or "No token provided" in error_str: + raise BadRequestError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model, + response=getattr(original_exception, "response", None), + ) + elif hasattr(original_exception, "status_code"): + verbose_logger.debug(f"status code: {original_exception.status_code}") + if original_exception.status_code == 401: + raise AuthenticationError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model, + ) + elif original_exception.status_code == 400: + raise BadRequestError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 500: + raise ServiceUnavailableError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model, + response=getattr(original_exception, "response", None), + ) + raise cast(Exception, original_exception) + raise cast(Exception, original_exception) + + +def _map_ollama_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if isinstance(original_exception, dict): + error_str = original_exception.get("error", "") + else: + error_str = str(original_exception) + if "no such file or directory" in error_str: + raise BadRequestError( + message=f"OllamaException: Invalid Model/Model not loaded - {original_exception}", + model=model, + llm_provider="ollama", + response=getattr(original_exception, "response", None), + ) + elif "Failed to establish a new connection" in error_str: + raise ServiceUnavailableError( + message=f"OllamaException: {original_exception}", + llm_provider="ollama", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "Invalid response object from API" in error_str: + raise BadRequestError( + message=f"OllamaException: {original_exception}", + llm_provider="ollama", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "Read timed out" in error_str: + raise Timeout( + message=f"OllamaException: {original_exception}", + llm_provider="ollama", + model=model, + ) + + +def _map_vllm_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 0: + raise APIConnectionError( + message=f"VLLMException - {original_exception.message}", + llm_provider="vllm", + model=model, + request=getattr(original_exception, "request", None), + ) + + +def _map_azure_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + message = get_error_message(error_obj=original_exception) + if message is None: + if hasattr(original_exception, "message"): + message = original_exception.message + else: + message = str(original_exception) + + # Azure OpenAI (especially Images) often nests error details under + # body["error"]. Detect content policy violations using the structured + # payload in addition to string matching. + azure_error_code: Optional[str] = None + try: + body_dict = getattr(original_exception, "body", None) or {} + if isinstance(body_dict, dict): + if isinstance(body_dict.get("error"), dict): + azure_error_code = body_dict["error"].get("code") # type: ignore[index] + # Also check inner_error for + # ResponsibleAIPolicyViolation which indicates a + # content policy violation even when the top-level + # code is generic (e.g. "invalid_request_error"). + if azure_error_code != "content_policy_violation": + _inner = body_dict["error"].get( + "inner_error" + ) or body_dict[ # type: ignore[index] + "error" + ].get( + "innererror" + ) # type: ignore[index] + if ( + isinstance(_inner, dict) + and _inner.get("code") == "ResponsibleAIPolicyViolation" + ): + azure_error_code = "content_policy_violation" + else: + azure_error_code = body_dict.get("code") + except Exception: + azure_error_code = None + + if "Internal server error" in error_str: + raise litellm.InternalServerError( + message=f"AzureException Internal server error - {message}", + llm_provider="azure", + model=model, + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + ) + elif "This model's maximum context length is" in error_str: + raise ContextWindowExceededError( + message=f"AzureException ContextWindowExceededError - {message}", + llm_provider="azure", + model=model, + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + ) + elif "DeploymentNotFound" in error_str: + raise NotFoundError( + message=f"AzureException NotFoundError - {message}", + llm_provider="azure", + model=model, + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + ) + elif ( + azure_error_code == "content_policy_violation" + or ExceptionCheckers.is_azure_content_policy_violation_error(error_str) + ): + from litellm.llms.azure.exception_mapping import ( + AzureOpenAIExceptionMapping, + ) + + raise AzureOpenAIExceptionMapping.create_content_policy_violation_error( + message=message, + model=model, + extra_information=extra_information, + original_exception=original_exception, + ) + elif ( + azure_error_code == "invalid_encrypted_content" + or "could not be verified" in error_str + ): + helpful_message = ( + f"AzureException - {message}\n\n" + "This error occurs when load balancing Responses API across deployments with different API keys.\n" + " Encrypted content is tied to the organization that created it and cannot be decrypted by other organizations.\n\n" + " Solution: Enable 'encrypted_content_affinity' to route follow-up requests to the correct deployment:\n\n" + " router_settings:\n" + " enable_pre_call_checks: true\n" + " optional_pre_call_checks:\n" + " - encrypted_content_affinity\n\n" + " Learn more: https://docs.litellm.ai/docs/response_api#encrypted-content-affinity-multi-region-load-balancing" + ) + raise BadRequestError( + message=helpful_message, + llm_provider="azure", + model=model, + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + body=getattr(original_exception, "body", None), + ) + elif "invalid_request_error" in error_str: + raise BadRequestError( + message=f"AzureException BadRequestError - {message}", + llm_provider="azure", + model=model, + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + body=getattr(original_exception, "body", None), + ) + elif ( + "The api_key client option must be set either by passing api_key to the client or by setting" + in error_str + ): + raise AuthenticationError( + message=f"{exception_provider} AuthenticationError - {message}", + llm_provider=custom_llm_provider, + model=model, + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + ) + elif "Connection error" in error_str: + raise APIConnectionError( + message=f"{exception_provider} APIConnectionError - {message}", + llm_provider=custom_llm_provider, + model=model, + litellm_debug_info=extra_information, + ) + elif hasattr(original_exception, "status_code"): + if original_exception.status_code == 400: + raise BadRequestError( + message=f"AzureException - {message}", + llm_provider="azure", + model=model, + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + body=getattr(original_exception, "body", None), + ) + elif original_exception.status_code == 401: + raise AuthenticationError( + message=f"AzureException AuthenticationError - {message}", + llm_provider="azure", + model=model, + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"AzureException Timeout - {message}", + model=model, + litellm_debug_info=extra_information, + llm_provider="azure", + ) + elif original_exception.status_code == 422: + raise BadRequestError( + message=f"AzureException BadRequestError - {message}", + model=model, + llm_provider="azure", + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"AzureException RateLimitError - {message}", + model=model, + llm_provider="azure", + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 502: + raise BadGatewayError( + message=f"AzureException BadGatewayError - {message}", + model=model, + llm_provider="azure", + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 503: + raise ServiceUnavailableError( + message=f"AzureException ServiceUnavailableError - {message}", + model=model, + llm_provider="azure", + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 504: # gateway timeout error + raise Timeout( + message=f"AzureException Timeout - {message}", + model=model, + litellm_debug_info=extra_information, + llm_provider="azure", + exception_status_code=original_exception.status_code, + ) + else: + raise APIError( + status_code=original_exception.status_code, + message=f"AzureException APIError - {message}", + llm_provider="azure", + litellm_debug_info=extra_information, + model=model, + request=httpx.Request(method="POST", url="https://openai.com/"), + ) + else: + # if no status code then it is an APIConnectionError: https://github.com/openai/openai-python#handling-errors + raise APIConnectionError( + message=f"{exception_provider} APIConnectionError - {message}\n{_redact_string(traceback.format_exc())}", + llm_provider="azure", + model=model, + litellm_debug_info=extra_information, + request=httpx.Request(method="POST", url="https://openai.com/"), + ) + + +def _map_openrouter_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 400: + raise BadRequestError( + message=f"{exception_provider} - {error_str}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 401: + raise AuthenticationError( + message=f"AuthenticationError: {exception_provider} - {error_str}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 404: + raise NotFoundError( + message=f"NotFoundError: {exception_provider} - {error_str}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"Timeout Error: {exception_provider} - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 422: + raise BadRequestError( + message=f"BadRequestError: {exception_provider} - {error_str}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"RateLimitError: {exception_provider} - {error_str}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 503: + raise ServiceUnavailableError( + message=f"ServiceUnavailableError: {exception_provider} - {error_str}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 504: # gateway timeout error + raise Timeout( + message=f"Timeout Error: {exception_provider} - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + exception_status_code=original_exception.status_code, + ) + else: + raise APIError( + status_code=original_exception.status_code, + message=f"APIError: {exception_provider} - {error_str}", + llm_provider=custom_llm_provider, + model=model, + request=getattr(original_exception, "request", None), + litellm_debug_info=extra_information, + ) + else: + # if no status code then it is an APIConnectionError: https://github.com/openai/openai-python#handling-errors + raise APIConnectionError( + message=f"APIConnectionError: {exception_provider} - {error_str}", + llm_provider=custom_llm_provider, + model=model, + litellm_debug_info=extra_information, + request=httpx.Request(method="POST", url="https://api.openai.com/v1/"), + ) + + def exception_type( # type: ignore model, original_exception, @@ -249,6 +2264,9 @@ def exception_type( # type: ignore return original_exception exception_mapping_worked = False exception_provider = custom_llm_provider + mappable_exception: _ProviderHTTPException = cast( + "_ProviderHTTPException", original_exception + ) if litellm.suppress_debug_info is False: print() # noqa: T201 print( # noqa: T201 @@ -372,2055 +2390,199 @@ def exception_type( # type: ignore or custom_llm_provider in litellm.openai_compatible_providers or custom_llm_provider == "mistral" ): - # custom_llm_provider is openai, make it OpenAI - message = get_error_message(error_obj=original_exception) - if message is None: - if hasattr(original_exception, "message"): - message = original_exception.message - else: - message = str(original_exception) - - if message is not None and isinstance( - message, str - ): # done to prevent user-confusion. Relevant issue - https://github.com/BerriAI/litellm/issues/1414 - message = message.replace("OPENAI", custom_llm_provider.upper()) - message = message.replace( - "openai.OpenAIError", - "{}.{}Error".format(custom_llm_provider, custom_llm_provider), - ) - if custom_llm_provider == "openai": - exception_provider = "OpenAI" + "Exception" - else: - exception_provider = ( - custom_llm_provider[0].upper() - + custom_llm_provider[1:] - + "Exception" - ) - - if ExceptionCheckers.is_error_str_rate_limit(error_str): - exception_mapping_worked = True - raise RateLimitError( - message=f"RateLimitError: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - ) - elif ExceptionCheckers.is_error_str_context_window_exceeded(error_str): - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"ContextWindowExceededError: {exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif ( - "invalid_request_error" in error_str - and "model_not_found" in error_str - ): - exception_mapping_worked = True - raise NotFoundError( - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif "A timeout occurred" in error_str: - exception_mapping_worked = True - raise Timeout( - message=f"{exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif ( - ( - "invalid_request_error" in error_str - and "content_policy_violation" in error_str - ) - or ( - "Invalid prompt" in error_str - and "violating our usage policy" in error_str - ) - or ( - "request was rejected as a result of the safety system" - in error_str.lower() - ) - ): - exception_mapping_worked = True - raise ContentPolicyViolationError( - message=f"ContentPolicyViolationError: {exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif ( - "invalid_encrypted_content" in error_str - or "could not be verified" in error_str - ): - exception_mapping_worked = True - helpful_message = ( - f"{exception_provider} - {message}\n\n" - " This error occurs when load balancing Responses API across deployments with different API keys.\n" - " Encrypted content is tied to the organization that created it and cannot be decrypted by other organizations.\n\n" - " Solution: Enable 'encrypted_content_affinity' to route follow-up requests to the correct deployment:\n\n" - " router_settings:\n" - " enable_pre_call_checks: true\n" - " optional_pre_call_checks:\n" - " - encrypted_content_affinity\n\n" - " Learn more: https://docs.litellm.ai/docs/response_api#encrypted-content-affinity-multi-region-load-balancing" - ) - raise BadRequestError( - message=helpful_message, - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - body=getattr(original_exception, "body", None), - ) - elif ( - "invalid_request_error" in error_str - and "Incorrect API key provided" not in error_str - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - body=getattr(original_exception, "body", None), - ) - elif ( - "Web server is returning an unknown error" in error_str - or "The server had an error processing your request." in error_str - ): - exception_mapping_worked = True - raise litellm.InternalServerError( - message=f"{exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - ) - elif "Request too large" in error_str: - exception_mapping_worked = True - raise RateLimitError( - message=f"RateLimitError: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif ( - "The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_API_KEY environment variable" - in error_str - ): - exception_mapping_worked = True - raise AuthenticationError( - message=f"AuthenticationError: {exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif "Mistral API raised a streaming error" in error_str: - exception_mapping_worked = True - _request = httpx.Request( - method="POST", url="https://api.openai.com/v1" - ) - raise APIError( - status_code=500, - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - request=_request, - litellm_debug_info=extra_information, - ) - elif hasattr(original_exception, "status_code"): - exception_mapping_worked = True - if original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"AuthenticationError: {exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 404: - exception_mapping_worked = True - raise NotFoundError( - message=f"NotFoundError: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"Timeout Error: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 422: - exception_mapping_worked = True - raise BadRequestError( - message=f"{exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - body=getattr(original_exception, "body", None), - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"RateLimitError: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 500: - exception_mapping_worked = True - raise InternalServerError( - message=f"InternalServerError: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 502: - exception_mapping_worked = True - raise BadGatewayError( - message=f"BadGatewayError: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 503: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"ServiceUnavailableError: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 504: # gateway timeout error - exception_mapping_worked = True - raise Timeout( - message=f"Timeout Error: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - exception_status_code=original_exception.status_code, - ) - else: - exception_mapping_worked = True - raise APIError( - status_code=original_exception.status_code, - message=f"APIError: {exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - request=getattr(original_exception, "request", None), - litellm_debug_info=extra_information, - ) - else: - # if no status code then it is an APIConnectionError: https://github.com/openai/openai-python#handling-errors - # exception_mapping_worked = True - raise APIConnectionError( - message=f"APIConnectionError: {exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - litellm_debug_info=extra_information, - request=httpx.Request( - method="POST", url="https://api.openai.com/v1/" - ), - ) + _map_openai_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif ( custom_llm_provider == "anthropic" or custom_llm_provider == "anthropic_text" ): # one of the anthropics - if ( - "prompt is too long" in error_str - or "prompt: length" in error_str - or ExceptionCheckers.is_error_str_context_window_exceeded(error_str) - ): - exception_mapping_worked = True - raise ContextWindowExceededError( - message="AnthropicError - {}".format(error_str), - model=model, - llm_provider="anthropic", - ) - elif "overloaded_error" in error_str or "Overloaded" in error_str: - exception_mapping_worked = True - raise InternalServerError( - message="AnthropicError - {}".format(error_str), - model=model, - llm_provider="anthropic", - ) - if "Invalid API Key" in error_str: - exception_mapping_worked = True - raise AuthenticationError( - message="AnthropicError - {}".format(error_str), - model=model, - llm_provider="anthropic", - ) - if "content filtering policy" in error_str: - exception_mapping_worked = True - raise ContentPolicyViolationError( - message="AnthropicError - {}".format(error_str), - model=model, - llm_provider="anthropic", - ) - if "Client error '400 Bad Request'" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message="AnthropicError - {}".format(error_str), - model=model, - llm_provider="anthropic", - ) - if hasattr(original_exception, "status_code"): - verbose_logger.debug( - f"status_code: {original_exception.status_code}" - ) - if original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"AnthropicException - {error_str}", - llm_provider="anthropic", - model=model, - ) - elif ( - original_exception.status_code == 400 - or original_exception.status_code == 413 - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"AnthropicException - {error_str}", - model=model, - llm_provider="anthropic", - ) - elif original_exception.status_code == 404: - exception_mapping_worked = True - raise NotFoundError( - message=f"AnthropicException - {error_str}", - model=model, - llm_provider="anthropic", - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"AnthropicException - {error_str}", - model=model, - llm_provider="anthropic", - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"AnthropicException - {error_str}", - llm_provider="anthropic", - model=model, - ) - elif ( - original_exception.status_code == 500 - or original_exception.status_code == 529 - ): - exception_mapping_worked = True - raise litellm.InternalServerError( - message=f"AnthropicException - {error_str}. Handle with `litellm.InternalServerError`.", - llm_provider="anthropic", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 502: - exception_mapping_worked = True - raise BadGatewayError( - message=f"AnthropicException BadGatewayError - {error_str}", - llm_provider="anthropic", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 503: - exception_mapping_worked = True - raise litellm.ServiceUnavailableError( - message=f"AnthropicException - {error_str}. Handle with `litellm.ServiceUnavailableError`.", - llm_provider="anthropic", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 504: # gateway timeout error - exception_mapping_worked = True - raise Timeout( - message=f"AnthropicException Timeout - {error_str}", - model=model, - llm_provider="anthropic", - exception_status_code=original_exception.status_code, - ) - elif custom_llm_provider == "replicate": - if "Incorrect authentication token" in error_str: - exception_mapping_worked = True - raise AuthenticationError( - message=f"ReplicateException - {error_str}", - llm_provider="replicate", - model=model, - response=getattr(original_exception, "response", None), - ) - elif "input is too long" in error_str: - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"ReplicateException - {error_str}", - model=model, - llm_provider="replicate", - response=getattr(original_exception, "response", None), - ) - elif exception_type == "ModelError": - exception_mapping_worked = True - raise BadRequestError( - message=f"ReplicateException - {error_str}", - model=model, - llm_provider="replicate", - response=getattr(original_exception, "response", None), - ) - elif "Request was throttled" in error_str: - exception_mapping_worked = True - raise RateLimitError( - message=f"ReplicateException - {error_str}", - llm_provider="replicate", - model=model, - response=getattr(original_exception, "response", None), - ) - elif hasattr(original_exception, "status_code"): - if original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"ReplicateException - {original_exception.message}", - llm_provider="replicate", - model=model, - response=getattr(original_exception, "response", None), - ) - elif ( - original_exception.status_code == 400 - or original_exception.status_code == 413 - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"ReplicateException - {original_exception.message}", - model=model, - llm_provider="replicate", - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 422: - exception_mapping_worked = True - raise UnprocessableEntityError( - message=f"ReplicateException - {original_exception.message}", - model=model, - llm_provider="replicate", - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"ReplicateException - {original_exception.message}", - model=model, - llm_provider="replicate", - ) - elif original_exception.status_code == 422: - exception_mapping_worked = True - raise UnprocessableEntityError( - message=f"ReplicateException - {original_exception.message}", - llm_provider="replicate", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"ReplicateException - {original_exception.message}", - llm_provider="replicate", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 500: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"ReplicateException - {original_exception.message}", - llm_provider="replicate", - model=model, - response=getattr(original_exception, "response", None), - ) - exception_mapping_worked = True - raise APIError( - status_code=500, - message=f"ReplicateException - {str(original_exception)}", - llm_provider="replicate", + _map_anthropic_exception( model=model, - request=httpx.Request( - method="POST", - url="https://api.replicate.com/v1/deployments", - ), + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) + elif custom_llm_provider == "replicate": + _map_replicate_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, ) elif custom_llm_provider in litellm._openai_like_providers: - if "authorization denied for" in error_str: - exception_mapping_worked = True - - # Predibase returns the raw API Key in the response - this block ensures it's not returned in the exception - if ( - error_str is not None - and isinstance(error_str, str) - and "bearer" in error_str.lower() - ): - # only keep the first 10 chars after the occurnence of "bearer" - _bearer_token_start_index = error_str.lower().find("bearer") - error_str = error_str[: _bearer_token_start_index + 14] - error_str += "XXXXXXX" + '"' - - raise AuthenticationError( - message=f"{custom_llm_provider.capitalize()}Exception: Authentication Error - {error_str}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif ExceptionCheckers.is_error_str_context_window_exceeded(error_str): - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"{custom_llm_provider.capitalize()}Exception: Context Window Error - {error_str}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif "token_quota_reached" in error_str: - exception_mapping_worked = True - raise RateLimitError( - message=f"{custom_llm_provider.capitalize()}Exception: Rate Limit Errror - {error_str}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - ) - elif ( - "The server received an invalid response from an upstream server." - in error_str - ): - exception_mapping_worked = True - raise litellm.InternalServerError( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - llm_provider=custom_llm_provider, - model=model, - ) - elif "model_no_support_for_function" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"{custom_llm_provider.capitalize()}Exception - Use 'watsonx_text' route instead. IBM WatsonX does not support `/text/chat` endpoint. - {error_str}", - llm_provider=custom_llm_provider, - model=model, - ) - elif hasattr(original_exception, "status_code"): - if original_exception.status_code == 500: - exception_mapping_worked = True - raise litellm.InternalServerError( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - llm_provider=custom_llm_provider, - model=model, - ) - elif ( - original_exception.status_code == 401 - or original_exception.status_code == 403 - ): - exception_mapping_worked = True - raise AuthenticationError( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - llm_provider=custom_llm_provider, - model=model, - ) - elif original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - llm_provider=custom_llm_provider, - model=model, - ) - elif original_exception.status_code == 404: - exception_mapping_worked = True - raise NotFoundError( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - llm_provider=custom_llm_provider, - model=model, - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif ( - original_exception.status_code == 422 - or original_exception.status_code == 424 - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 503: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 504: # gateway timeout error - exception_mapping_worked = True - raise Timeout( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - exception_status_code=original_exception.status_code, - ) + _map_openai_like_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif custom_llm_provider == "bedrock": - if ( - "too many tokens" in error_str - or "expected maxLength:" in error_str - or "Input is too long" in error_str - or "prompt is too long" in error_str - or "prompt: length: 1.." in error_str - or "Too many input tokens" in error_str - ): - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"BedrockException: Context Window Error - {error_str}", - model=model, - llm_provider="bedrock", - ) - elif ( - "Conversation blocks and tool result blocks cannot be provided in the same turn." - in error_str - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"BedrockException - {error_str}\n. Enable 'litellm.modify_params=True' (for PROXY do: `litellm_settings::modify_params: True`) to insert a dummy assistant message and fix this error.", - model=model, - llm_provider="bedrock", - response=getattr(original_exception, "response", None), - ) - elif "Malformed input request" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"BedrockException - {error_str}", - model=model, - llm_provider="bedrock", - response=getattr(original_exception, "response", None), - ) - elif "A conversation must start with a user message." in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"BedrockException - {error_str}\n. Pass in default user message via `completion(..,user_continue_message=)` or enable `litellm.modify_params=True`.\nFor Proxy: do via `litellm_settings::modify_params: True` or user_continue_message under `litellm_params`", - model=model, - llm_provider="bedrock", - response=getattr(original_exception, "response", None), - ) - elif ( - "Unable to locate credentials" in error_str - or "The security token included in the request is invalid" - in error_str - ): - exception_mapping_worked = True - raise AuthenticationError( - message=f"BedrockException Invalid Authentication - {error_str}", - model=model, - llm_provider="bedrock", - response=getattr(original_exception, "response", None), - ) - elif "AccessDeniedException" in error_str: - exception_mapping_worked = True - raise PermissionDeniedError( - message=f"BedrockException PermissionDeniedError - {error_str}", - model=model, - llm_provider="bedrock", - response=getattr(original_exception, "response", None), - ) - elif ( - "throttlingException" in error_str - or "ThrottlingException" in error_str - ): - exception_mapping_worked = True - raise RateLimitError( - message=f"BedrockException: Rate Limit Error - {error_str}", - model=model, - llm_provider="bedrock", - response=getattr(original_exception, "response", None), - ) - elif ( - "Connect timeout on endpoint URL" in error_str - or "timed out" in error_str - ): - exception_mapping_worked = True - raise Timeout( - message=f"BedrockException: Timeout Error - {error_str}", - model=model, - llm_provider="bedrock", - ) - elif "Could not process image" in error_str: - exception_mapping_worked = True - raise litellm.InternalServerError( - message=f"BedrockException - {error_str}", - model=model, - llm_provider="bedrock", - ) - elif hasattr(original_exception, "status_code"): - if original_exception.status_code == 500: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"BedrockException - {original_exception.message}", - llm_provider="bedrock", - model=model, - response=httpx.Response( - status_code=500, - request=httpx.Request( - method="POST", url="https://api.openai.com/v1/" - ), - ), - ) - elif original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"BedrockException - {original_exception.message}", - llm_provider="bedrock", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"BedrockException - {original_exception.message}", - llm_provider="bedrock", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 404: - exception_mapping_worked = True - raise NotFoundError( - message=f"BedrockException - {original_exception.message}", - llm_provider="bedrock", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"BedrockException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 422: - exception_mapping_worked = True - raise BadRequestError( - message=f"BedrockException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"BedrockException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 503: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"BedrockException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 504: # gateway timeout error - exception_mapping_worked = True - raise Timeout( - message=f"BedrockException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - exception_status_code=original_exception.status_code, - ) + _map_bedrock_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif ( custom_llm_provider == "sagemaker" or custom_llm_provider == "sagemaker_chat" ): - if "Unable to locate credentials" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"litellm.BadRequestError: SagemakerException - {error_str}", - model=model, - llm_provider="sagemaker", - response=getattr(original_exception, "response", None), - ) - elif ( - "Input validation error: `best_of` must be > 0 and <= 2" - in error_str - ): - exception_mapping_worked = True - raise BadRequestError( - message="SagemakerException - the value of 'n' must be > 0 and <= 2 for sagemaker endpoints", - model=model, - llm_provider="sagemaker", - response=getattr(original_exception, "response", None), - ) - elif ( - "`inputs` tokens + `max_new_tokens` must be <=" in error_str - or "instance type with more CPU capacity or memory" in error_str - ): - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"SagemakerException - {error_str}", - model=model, - llm_provider="sagemaker", - response=getattr(original_exception, "response", None), - ) - elif hasattr(original_exception, "status_code"): - if original_exception.status_code == 500: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"SagemakerException - {original_exception.message}", - llm_provider=custom_llm_provider, - model=model, - response=httpx.Response( - status_code=500, - request=httpx.Request( - method="POST", url="https://api.openai.com/v1/" - ), - ), - ) - elif original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"SagemakerException - {original_exception.message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"SagemakerException - {original_exception.message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 404: - exception_mapping_worked = True - raise NotFoundError( - message=f"SagemakerException - {original_exception.message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"SagemakerException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif ( - original_exception.status_code == 422 - or original_exception.status_code == 424 - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"SagemakerException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"SagemakerException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 503: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"SagemakerException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 504: # gateway timeout error - exception_mapping_worked = True - raise Timeout( - message=f"SagemakerException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - exception_status_code=original_exception.status_code, - ) + _map_sagemaker_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif ( custom_llm_provider == LlmProviders.VERTEX_AI or custom_llm_provider == LlmProviders.VERTEX_AI_BETA or custom_llm_provider == LlmProviders.GEMINI ): - if ( - "Vertex AI API has not been used in project" in error_str - or "Unable to find your project" in error_str - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"litellm.BadRequestError: {custom_llm_provider}Exception - {error_str}", - model=model, - llm_provider=custom_llm_provider, - response=httpx.Response( - status_code=400, - request=httpx.Request( - method="POST", - url=" https://cloud.google.com/vertex-ai/", - ), - ), - litellm_debug_info=extra_information, - ) - if "400 Request payload size exceeds" in error_str: - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", - model=model, - llm_provider=custom_llm_provider, - ) - elif ExceptionCheckers.is_error_str_context_window_exceeded(error_str): - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"ContextWindowExceededError: {custom_llm_provider.capitalize()}Exception - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif ( - "None Unknown Error." in error_str - or "Content has no parts." in error_str - ): - exception_mapping_worked = True - raise litellm.InternalServerError( - message=f"litellm.InternalServerError: {custom_llm_provider}Exception - {error_str}", - model=model, - llm_provider=custom_llm_provider, - response=httpx.Response( - status_code=500, - content=str(original_exception), - request=httpx.Request(method="completion", url="https://github.com/BerriAI/litellm"), # type: ignore - ), - litellm_debug_info=extra_information, - ) - elif "API key not valid." in error_str: - exception_mapping_worked = True - raise AuthenticationError( - message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif "403" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"{custom_llm_provider.capitalize()}Exception BadRequestError - {error_str}", - model=model, - llm_provider=custom_llm_provider, - response=httpx.Response( - status_code=403, - request=httpx.Request( - method="POST", - url=" https://cloud.google.com/vertex-ai/", - ), - ), - litellm_debug_info=extra_information, - ) - elif ( - "The response was blocked." in error_str - or "Output blocked by content filtering policy" - in error_str # anthropic on vertex ai - ): - exception_mapping_worked = True - raise ContentPolicyViolationError( - message=f"{custom_llm_provider.capitalize()}Exception ContentPolicyViolationError - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - response=httpx.Response( - status_code=400, - request=httpx.Request( - method="POST", - url=" https://cloud.google.com/vertex-ai/", - ), - ), - ) - elif ( - "429 Quota exceeded" in error_str - or "Quota exceeded for" in error_str - or "Resource exhausted" in error_str - or "IndexError: list index out of range" in error_str - or "429 Unable to submit request because the service is temporarily out of capacity." - in error_str - ): - exception_mapping_worked = True - raise RateLimitError( - message=f"litellm.RateLimitError: {custom_llm_provider}Exception - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - response=httpx.Response( - status_code=429, - request=httpx.Request( - method="POST", - url=" https://cloud.google.com/vertex-ai/", - ), - ), - ) - elif ( - "500 Internal Server Error" in error_str - or "The model is overloaded." in error_str - ): - exception_mapping_worked = True - raise litellm.InternalServerError( - message=f"litellm.InternalServerError: {custom_llm_provider}Exception - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - if hasattr(original_exception, "status_code"): - if original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"{custom_llm_provider.capitalize()}Exception BadRequestError - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - response=httpx.Response( - status_code=400, - request=httpx.Request( - method="POST", - url="https://cloud.google.com/vertex-ai/", - ), - ), - ) - if original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", - llm_provider=custom_llm_provider, - model=model, - ) - if original_exception.status_code == 403: - exception_mapping_worked = True - raise PermissionDeniedError( - message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", - llm_provider=custom_llm_provider, - model=model, - response=httpx.Response( - status_code=403, - request=httpx.Request( - method="POST", - url="https://cloud.google.com/vertex-ai/", - ), - ), - ) - if original_exception.status_code == 404: - exception_mapping_worked = True - raise NotFoundError( - message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", - llm_provider=custom_llm_provider, - model=model, - ) - if original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", - llm_provider=custom_llm_provider, - model=model, - ) - - if original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"litellm.RateLimitError: {custom_llm_provider.capitalize()}Exception - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - response=httpx.Response( - status_code=429, - request=httpx.Request( - method="POST", - url=" https://cloud.google.com/vertex-ai/", - ), - ), - ) - if original_exception.status_code == 500: - exception_mapping_worked = True - raise litellm.InternalServerError( - message=f"{custom_llm_provider.capitalize()}Exception InternalServerError - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - response=httpx.Response( - status_code=500, - content=str(original_exception), - request=httpx.Request(method="completion", url="https://github.com/BerriAI/litellm"), # type: ignore - ), - ) - if original_exception.status_code == 502: - exception_mapping_worked = True - raise APIConnectionError( - message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", - llm_provider=custom_llm_provider, - model=model, - ) - if original_exception.status_code == 503: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", - llm_provider=custom_llm_provider, - model=model, - ) + _map_vertex_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif custom_llm_provider == "cloudflare": - if "Authentication error" in error_str: - exception_mapping_worked = True - raise AuthenticationError( - message=f"Cloudflare Exception - {original_exception.message}", - llm_provider="cloudflare", - model=model, - response=getattr(original_exception, "response", None), - ) - if "must have required property" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"Cloudflare Exception - {original_exception.message}", - llm_provider="cloudflare", - model=model, - response=getattr(original_exception, "response", None), - ) + _map_cloudflare_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif ( custom_llm_provider == "cohere" or custom_llm_provider == "cohere_chat" ): # Cohere - if ( - "invalid api token" in error_str - or "No API key provided." in error_str - ): - exception_mapping_worked = True - raise AuthenticationError( - message=f"CohereException - {original_exception.message}", - llm_provider="cohere", - model=model, - response=getattr(original_exception, "response", None), - ) - elif "invalid type: parameter" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"CohereException - {original_exception.message}", - llm_provider="cohere", - model=model, - response=getattr(original_exception, "response", None), - ) - elif "too many tokens" in error_str: - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"CohereException - {original_exception.message}", - model=model, - llm_provider="cohere", - response=getattr(original_exception, "response", None), - ) - elif "internal server error" in error_str.lower(): - exception_mapping_worked = True - raise InternalServerError( - message=f"CohereException - {error_str}", - model=model, - llm_provider="cohere", - response=getattr(original_exception, "response", None), - ) - elif hasattr(original_exception, "status_code"): - if ( - original_exception.status_code == 400 - or original_exception.status_code == 498 - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"CohereException - {original_exception.message}", - llm_provider="cohere", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"CohereException - {original_exception.message}", - llm_provider="cohere", - model=model, - ) - elif original_exception.status_code == 500: - exception_mapping_worked = True - raise InternalServerError( - message=f"CohereException - {original_exception.message}", - llm_provider="cohere", - model=model, - response=getattr(original_exception, "response", None), - ) - elif ( - "CohereConnectionError" in exception_type - ): # cohere seems to fire these errors when we load test it (1k+ messages / min) - exception_mapping_worked = True - raise RateLimitError( - message=f"CohereException - {original_exception.message}", - llm_provider="cohere", - model=model, - response=getattr(original_exception, "response", None), - ) - elif "invalid type:" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"CohereException - {original_exception.message}", - llm_provider="cohere", - model=model, - response=getattr(original_exception, "response", None), - ) - elif "Unexpected server error" in error_str: - exception_mapping_worked = True - raise InternalServerError( - message=f"CohereException - {original_exception.message}", - llm_provider="cohere", - model=model, - response=getattr(original_exception, "response", None), - ) - else: - if hasattr(original_exception, "status_code"): - exception_mapping_worked = True - raise APIError( - status_code=original_exception.status_code, - message=f"CohereException - {original_exception.message}", - llm_provider="cohere", - model=model, - request=getattr(original_exception, "request", None), - ) - raise original_exception + _map_cohere_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif custom_llm_provider == "huggingface": - if "length limit exceeded" in error_str: - exception_mapping_worked = True - raise ContextWindowExceededError( - message=error_str, - model=model, - llm_provider="huggingface", - response=getattr(original_exception, "response", None), - ) - elif "A valid user token is required" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=error_str, - llm_provider="huggingface", - model=model, - response=getattr(original_exception, "response", None), - ) - elif "Rate limit reached" in error_str: - exception_mapping_worked = True - raise RateLimitError( - message=error_str, - llm_provider="huggingface", - model=model, - response=getattr(original_exception, "response", None), - ) - if hasattr(original_exception, "status_code"): - if original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"HuggingfaceException - {original_exception.message}", - llm_provider="huggingface", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"HuggingfaceException - {original_exception.message}", - model=model, - llm_provider="huggingface", - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"HuggingfaceException - {original_exception.message}", - model=model, - llm_provider="huggingface", - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"HuggingfaceException - {original_exception.message}", - llm_provider="huggingface", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 503: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"HuggingfaceException - {original_exception.message}", - llm_provider="huggingface", - model=model, - response=getattr(original_exception, "response", None), - ) - else: - exception_mapping_worked = True - raise APIError( - status_code=original_exception.status_code, - message=f"HuggingfaceException - {original_exception.message}", - llm_provider="huggingface", - model=model, - request=getattr(original_exception, "request", None), - ) + _map_huggingface_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif custom_llm_provider == "ai21": - if hasattr(original_exception, "message"): - if "Prompt has too many tokens" in original_exception.message: - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"AI21Exception - {original_exception.message}", - model=model, - llm_provider="ai21", - response=getattr(original_exception, "response", None), - ) - if "Bad or missing API token." in original_exception.message: - exception_mapping_worked = True - raise BadRequestError( - message=f"AI21Exception - {original_exception.message}", - model=model, - llm_provider="ai21", - response=getattr(original_exception, "response", None), - ) - if hasattr(original_exception, "status_code"): - if original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"AI21Exception - {original_exception.message}", - llm_provider="ai21", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"AI21Exception - {original_exception.message}", - model=model, - llm_provider="ai21", - ) - if original_exception.status_code == 422: - exception_mapping_worked = True - raise BadRequestError( - message=f"AI21Exception - {original_exception.message}", - model=model, - llm_provider="ai21", - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"AI21Exception - {original_exception.message}", - llm_provider="ai21", - model=model, - response=getattr(original_exception, "response", None), - ) - else: - exception_mapping_worked = True - raise APIError( - status_code=original_exception.status_code, - message=f"AI21Exception - {original_exception.message}", - llm_provider="ai21", - model=model, - request=getattr(original_exception, "request", None), - ) + _map_ai21_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif custom_llm_provider == "nlp_cloud": - if "detail" in error_str: - if "Input text length should not exceed" in error_str: - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"NLPCloudException - {error_str}", - model=model, - llm_provider="nlp_cloud", - response=getattr(original_exception, "response", None), - ) - elif "value is not a valid" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"NLPCloudException - {error_str}", - model=model, - llm_provider="nlp_cloud", - response=getattr(original_exception, "response", None), - ) - else: - exception_mapping_worked = True - raise APIError( - status_code=500, - message=f"NLPCloudException - {error_str}", - model=model, - llm_provider="nlp_cloud", - request=getattr(original_exception, "request", None), - ) - if hasattr( - original_exception, "status_code" - ): # https://docs.nlpcloud.com/?shell#errors - if ( - original_exception.status_code == 400 - or original_exception.status_code == 406 - or original_exception.status_code == 413 - or original_exception.status_code == 422 - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"NLPCloudException - {original_exception.message}", - llm_provider="nlp_cloud", - model=model, - response=getattr(original_exception, "response", None), - ) - elif ( - original_exception.status_code == 401 - or original_exception.status_code == 403 - ): - exception_mapping_worked = True - raise AuthenticationError( - message=f"NLPCloudException - {original_exception.message}", - llm_provider="nlp_cloud", - model=model, - response=getattr(original_exception, "response", None), - ) - elif ( - original_exception.status_code == 522 - or original_exception.status_code == 524 - ): - exception_mapping_worked = True - raise Timeout( - message=f"NLPCloudException - {original_exception.message}", - model=model, - llm_provider="nlp_cloud", - ) - elif ( - original_exception.status_code == 429 - or original_exception.status_code == 402 - ): - exception_mapping_worked = True - raise RateLimitError( - message=f"NLPCloudException - {original_exception.message}", - llm_provider="nlp_cloud", - model=model, - response=getattr(original_exception, "response", None), - ) - elif ( - original_exception.status_code == 500 - or original_exception.status_code == 503 - ): - exception_mapping_worked = True - raise APIError( - status_code=original_exception.status_code, - message=f"NLPCloudException - {original_exception.message}", - llm_provider="nlp_cloud", - model=model, - request=getattr(original_exception, "request", None), - ) - elif ( - original_exception.status_code == 504 - or original_exception.status_code == 520 - ): - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"NLPCloudException - {original_exception.message}", - model=model, - llm_provider="nlp_cloud", - response=getattr(original_exception, "response", None), - ) - else: - exception_mapping_worked = True - raise APIError( - status_code=original_exception.status_code, - message=f"NLPCloudException - {original_exception.message}", - llm_provider="nlp_cloud", - model=model, - request=getattr(original_exception, "request", None), - ) + _map_nlp_cloud_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif custom_llm_provider == "together_ai": - try: - error_response = json.loads(error_str) - except Exception: - error_response = {"error": error_str} - if ( - "error" in error_response - and "`inputs` tokens + `max_new_tokens` must be <=" - in error_response["error"] - ): - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"TogetherAIException - {error_response['error']}", - model=model, - llm_provider="together_ai", - response=getattr(original_exception, "response", None), - ) - elif ( - "error" in error_response - and "invalid private key" in error_response["error"] - ): - exception_mapping_worked = True - raise AuthenticationError( - message=f"TogetherAIException - {error_response['error']}", - llm_provider="together_ai", - model=model, - response=getattr(original_exception, "response", None), - ) - elif ( - "error" in error_response - and "INVALID_ARGUMENT" in error_response["error"] - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"TogetherAIException - {error_response['error']}", - model=model, - llm_provider="together_ai", - response=getattr(original_exception, "response", None), - ) - elif "A timeout occurred" in error_str: - exception_mapping_worked = True - raise Timeout( - message=f"TogetherAIException - {error_str}", - model=model, - llm_provider="together_ai", - ) - elif ( - "error" in error_response - and "API key doesn't match expected format." - in error_response["error"] - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"TogetherAIException - {error_response['error']}", - model=model, - llm_provider="together_ai", - response=getattr(original_exception, "response", None), - ) - elif ( - "error_type" in error_response - and error_response["error_type"] == "validation" - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"TogetherAIException - {error_response['error']}", - model=model, - llm_provider="together_ai", - response=getattr(original_exception, "response", None), - ) - if hasattr(original_exception, "status_code"): - if original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"TogetherAIException - {original_exception.message}", - model=model, - llm_provider="together_ai", - ) - elif original_exception.status_code == 422: - exception_mapping_worked = True - raise BadRequestError( - message=f"TogetherAIException - {error_response['error']}", - model=model, - llm_provider="together_ai", - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"TogetherAIException - {original_exception.message}", - llm_provider="together_ai", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 524: - exception_mapping_worked = True - raise Timeout( - message=f"TogetherAIException - {original_exception.message}", - llm_provider="together_ai", - model=model, - ) - else: - exception_mapping_worked = True - raise APIError( - status_code=original_exception.status_code, - message=f"TogetherAIException - {original_exception.message}", - llm_provider="together_ai", - model=model, - request=getattr(original_exception, "request", None), - ) + _map_together_ai_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif custom_llm_provider == "aleph_alpha": - if ( - "This is longer than the model's maximum context length" - in error_str - ): - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"AlephAlphaException - {original_exception.message}", - llm_provider="aleph_alpha", - model=model, - response=getattr(original_exception, "response", None), - ) - elif "InvalidToken" in error_str or "No token provided" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"AlephAlphaException - {original_exception.message}", - llm_provider="aleph_alpha", - model=model, - response=getattr(original_exception, "response", None), - ) - elif hasattr(original_exception, "status_code"): - verbose_logger.debug( - f"status code: {original_exception.status_code}" - ) - if original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"AlephAlphaException - {original_exception.message}", - llm_provider="aleph_alpha", - model=model, - ) - elif original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"AlephAlphaException - {original_exception.message}", - llm_provider="aleph_alpha", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"AlephAlphaException - {original_exception.message}", - llm_provider="aleph_alpha", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 500: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"AlephAlphaException - {original_exception.message}", - llm_provider="aleph_alpha", - model=model, - response=getattr(original_exception, "response", None), - ) - raise original_exception - raise original_exception + _map_aleph_alpha_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif ( custom_llm_provider == "ollama" or custom_llm_provider == "ollama_chat" ): - if isinstance(original_exception, dict): - error_str = original_exception.get("error", "") - else: - error_str = str(original_exception) - if "no such file or directory" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"OllamaException: Invalid Model/Model not loaded - {original_exception}", - model=model, - llm_provider="ollama", - response=getattr(original_exception, "response", None), - ) - elif "Failed to establish a new connection" in error_str: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"OllamaException: {original_exception}", - llm_provider="ollama", - model=model, - response=getattr(original_exception, "response", None), - ) - elif "Invalid response object from API" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"OllamaException: {original_exception}", - llm_provider="ollama", - model=model, - response=getattr(original_exception, "response", None), - ) - elif "Read timed out" in error_str: - exception_mapping_worked = True - raise Timeout( - message=f"OllamaException: {original_exception}", - llm_provider="ollama", - model=model, - ) + _map_ollama_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif custom_llm_provider == "vllm": - if hasattr(original_exception, "status_code"): - if original_exception.status_code == 0: - exception_mapping_worked = True - raise APIConnectionError( - message=f"VLLMException - {original_exception.message}", - llm_provider="vllm", - model=model, - request=getattr(original_exception, "request", None), - ) + _map_vllm_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif custom_llm_provider == "azure" or custom_llm_provider == "azure_text": - message = get_error_message(error_obj=original_exception) - if message is None: - if hasattr(original_exception, "message"): - message = original_exception.message - else: - message = str(original_exception) - - # Azure OpenAI (especially Images) often nests error details under - # body["error"]. Detect content policy violations using the structured - # payload in addition to string matching. - azure_error_code: Optional[str] = None - try: - body_dict = getattr(original_exception, "body", None) or {} - if isinstance(body_dict, dict): - if isinstance(body_dict.get("error"), dict): - azure_error_code = body_dict["error"].get("code") # type: ignore[index] - # Also check inner_error for - # ResponsibleAIPolicyViolation which indicates a - # content policy violation even when the top-level - # code is generic (e.g. "invalid_request_error"). - if azure_error_code != "content_policy_violation": - _inner = body_dict["error"].get( - "inner_error" - ) or body_dict[ # type: ignore[index] - "error" - ].get( - "innererror" - ) # type: ignore[index] - if ( - isinstance(_inner, dict) - and _inner.get("code") - == "ResponsibleAIPolicyViolation" - ): - azure_error_code = "content_policy_violation" - else: - azure_error_code = body_dict.get("code") - except Exception: - azure_error_code = None - - if "Internal server error" in error_str: - exception_mapping_worked = True - raise litellm.InternalServerError( - message=f"AzureException Internal server error - {message}", - llm_provider="azure", - model=model, - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - ) - elif "This model's maximum context length is" in error_str: - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"AzureException ContextWindowExceededError - {message}", - llm_provider="azure", - model=model, - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - ) - elif "DeploymentNotFound" in error_str: - exception_mapping_worked = True - raise NotFoundError( - message=f"AzureException NotFoundError - {message}", - llm_provider="azure", - model=model, - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - ) - elif ( - azure_error_code == "content_policy_violation" - or ExceptionCheckers.is_azure_content_policy_violation_error( - error_str - ) - ): - exception_mapping_worked = True - from litellm.llms.azure.exception_mapping import ( - AzureOpenAIExceptionMapping, - ) - - raise AzureOpenAIExceptionMapping.create_content_policy_violation_error( - message=message, - model=model, - extra_information=extra_information, - original_exception=original_exception, - ) - elif ( - azure_error_code == "invalid_encrypted_content" - or "could not be verified" in error_str - ): - exception_mapping_worked = True - helpful_message = ( - f"AzureException - {message}\n\n" - "This error occurs when load balancing Responses API across deployments with different API keys.\n" - " Encrypted content is tied to the organization that created it and cannot be decrypted by other organizations.\n\n" - " Solution: Enable 'encrypted_content_affinity' to route follow-up requests to the correct deployment:\n\n" - " router_settings:\n" - " enable_pre_call_checks: true\n" - " optional_pre_call_checks:\n" - " - encrypted_content_affinity\n\n" - " Learn more: https://docs.litellm.ai/docs/response_api#encrypted-content-affinity-multi-region-load-balancing" - ) - raise BadRequestError( - message=helpful_message, - llm_provider="azure", - model=model, - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - body=getattr(original_exception, "body", None), - ) - elif "invalid_request_error" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"AzureException BadRequestError - {message}", - llm_provider="azure", - model=model, - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - body=getattr(original_exception, "body", None), - ) - elif ( - "The api_key client option must be set either by passing api_key to the client or by setting" - in error_str - ): - exception_mapping_worked = True - raise AuthenticationError( - message=f"{exception_provider} AuthenticationError - {message}", - llm_provider=custom_llm_provider, - model=model, - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - ) - elif "Connection error" in error_str: - exception_mapping_worked = True - raise APIConnectionError( - message=f"{exception_provider} APIConnectionError - {message}", - llm_provider=custom_llm_provider, - model=model, - litellm_debug_info=extra_information, - ) - elif hasattr(original_exception, "status_code"): - exception_mapping_worked = True - if original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"AzureException - {message}", - llm_provider="azure", - model=model, - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - body=getattr(original_exception, "body", None), - ) - elif original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"AzureException AuthenticationError - {message}", - llm_provider="azure", - model=model, - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"AzureException Timeout - {message}", - model=model, - litellm_debug_info=extra_information, - llm_provider="azure", - ) - elif original_exception.status_code == 422: - exception_mapping_worked = True - raise BadRequestError( - message=f"AzureException BadRequestError - {message}", - model=model, - llm_provider="azure", - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"AzureException RateLimitError - {message}", - model=model, - llm_provider="azure", - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 502: - exception_mapping_worked = True - raise BadGatewayError( - message=f"AzureException BadGatewayError - {message}", - model=model, - llm_provider="azure", - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 503: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"AzureException ServiceUnavailableError - {message}", - model=model, - llm_provider="azure", - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 504: # gateway timeout error - exception_mapping_worked = True - raise Timeout( - message=f"AzureException Timeout - {message}", - model=model, - litellm_debug_info=extra_information, - llm_provider="azure", - exception_status_code=original_exception.status_code, - ) - else: - exception_mapping_worked = True - raise APIError( - status_code=original_exception.status_code, - message=f"AzureException APIError - {message}", - llm_provider="azure", - litellm_debug_info=extra_information, - model=model, - request=httpx.Request( - method="POST", url="https://openai.com/" - ), - ) - else: - # if no status code then it is an APIConnectionError: https://github.com/openai/openai-python#handling-errors - raise APIConnectionError( - message=f"{exception_provider} APIConnectionError - {message}\n{_redact_string(traceback.format_exc())}", - llm_provider="azure", - model=model, - litellm_debug_info=extra_information, - request=httpx.Request(method="POST", url="https://openai.com/"), - ) + _map_azure_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) if custom_llm_provider == "openrouter": - if hasattr(original_exception, "status_code"): - exception_mapping_worked = True - if original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"{exception_provider} - {error_str}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"AuthenticationError: {exception_provider} - {error_str}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 404: - exception_mapping_worked = True - raise NotFoundError( - message=f"NotFoundError: {exception_provider} - {error_str}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"Timeout Error: {exception_provider} - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 422: - exception_mapping_worked = True - raise BadRequestError( - message=f"BadRequestError: {exception_provider} - {error_str}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"RateLimitError: {exception_provider} - {error_str}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 503: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"ServiceUnavailableError: {exception_provider} - {error_str}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 504: # gateway timeout error - exception_mapping_worked = True - raise Timeout( - message=f"Timeout Error: {exception_provider} - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - exception_status_code=original_exception.status_code, - ) - else: - exception_mapping_worked = True - raise APIError( - status_code=original_exception.status_code, - message=f"APIError: {exception_provider} - {error_str}", - llm_provider=custom_llm_provider, - model=model, - request=getattr(original_exception, "request", None), - litellm_debug_info=extra_information, - ) - else: - # if no status code then it is an APIConnectionError: https://github.com/openai/openai-python#handling-errors - raise APIConnectionError( - message=f"APIConnectionError: {exception_provider} - {error_str}", - llm_provider=custom_llm_provider, - model=model, - litellm_debug_info=extra_information, - request=httpx.Request( - method="POST", url="https://api.openai.com/v1/" - ), - ) + _map_openrouter_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) if ( "BadRequestError.__init__() missing 1 required positional argument: 'param'" in str(original_exception) diff --git a/litellm/litellm_core_utils/get_llm_provider_logic.py b/litellm/litellm_core_utils/get_llm_provider_logic.py index 4941d52d7d6..bb8b1a82996 100644 --- a/litellm/litellm_core_utils/get_llm_provider_logic.py +++ b/litellm/litellm_core_utils/get_llm_provider_logic.py @@ -388,6 +388,9 @@ def get_llm_provider( elif endpoint == "https://api.inference.wandb.ai/v1": custom_llm_provider = "wandb" dynamic_api_key = get_secret_str("WANDB_API_KEY") + elif endpoint == "https://pinstripes.io/v1": + custom_llm_provider = "pinstripes" + dynamic_api_key = get_secret_str("PINSTRIPES_API_KEY") if api_base is not None and not isinstance(api_base, str): raise Exception( @@ -641,7 +644,7 @@ def _get_openai_compatible_provider_info( api_base, dynamic_api_key, ) = litellm.BedrockMantleChatConfig()._get_openai_compatible_provider_info( - api_base, api_key, litellm_params=litellm_params + api_base, api_key, litellm_params=litellm_params, model=model ) elif custom_llm_provider == "nvidia_nim": # nvidia_nim is openai compatible, we just need to set this to custom_openai and have the api_base be https://api.endpoints.anyscale.com/v1 diff --git a/litellm/litellm_core_utils/health_check_helpers.py b/litellm/litellm_core_utils/health_check_helpers.py index 9e972f1910b..5a29ea73a74 100644 --- a/litellm/litellm_core_utils/health_check_helpers.py +++ b/litellm/litellm_core_utils/health_check_helpers.py @@ -44,8 +44,8 @@ class HealthCheckHelpers: model_params["litellm_logging_obj"] = litellm_logging_obj model_params["fallbacks"] = fallback_models model_params["max_tokens"] = model_params.get( - "max_tokens", 10 - ) # gpt-5-nano throws errors for max_tokens=1 + "max_tokens", 16 + ) # GPT-5 models require max_output_tokens >= 16 await acompletion(**model_params) return {} diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 7ece944fd0e..d750a509054 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -2975,7 +2975,12 @@ class Logging(LiteLLMLoggingBaseClass): ) self.model_call_details["end_time"] = end_time self.model_call_details.setdefault("original_response", None) - self.model_call_details["response_cost"] = 0 + # A stream interrupted mid-flight still billed the provider for the + # chunks already delivered; the router stashes that recovered usage as + # ``combined_usage_object`` and pre-computes its cost, so preserve it + # here instead of zeroing the spend on an otherwise-failed request. + if self.model_call_details.get("combined_usage_object") is None: + self.model_call_details["response_cost"] = 0 if hasattr(exception, "headers") and isinstance(exception.headers, dict): self.model_call_details.setdefault("litellm_params", {}) @@ -5416,19 +5421,20 @@ class StandardLoggingPayloadSetup: tb_lines[:MAXIMUM_TRACEBACK_LINES_TO_LOG] ) # Limit to first 100 lines + # Prefer the `.message` attribute (set by ProxyException and every + # litellm.exceptions.* class) over str(exc); ProxyException does not + # call super().__init__() nor define __str__, so str() on it returns + # an empty string, which used to silently strip the human-readable + # message from spend_logs.metadata.error_information. + # Use isinstance, not truthiness: an explicit empty string on + # `.message` is a deliberate value and must not be replaced by + # `str(exc)`. explicit_message = getattr(original_exception, "message", None) - error_message = ( - explicit_message - if isinstance(explicit_message, str) and explicit_message - else str(original_exception) - ) + if isinstance(explicit_message, str): + error_message = explicit_message + else: + error_message = str(original_exception) if original_exception else "" - # Duck-typed read so bare-Exception subclasses like - # `litellm.BudgetExceededError` can participate without joining the - # RateLimitError hierarchy (which would break `except BudgetExceededError`). - # Validated against the enum value sets so a third-party exception that - # happens to declare a `.category` or `.rate_limit_type` string attribute - # can't leak garbage into the payload or Prometheus label cardinality. rate_limit_category = validate_rate_limit_category( getattr(original_exception, "category", None) ) @@ -5441,7 +5447,7 @@ class StandardLoggingPayloadSetup: error_class=error_class, llm_provider=_llm_provider_in_exception, traceback=traceback_info, - error_message=error_message if original_exception else "", + error_message=error_message, error_rate_limit_category=rate_limit_category, error_rate_limit_type=rate_limit_type, ) @@ -5455,21 +5461,19 @@ class StandardLoggingPayloadSetup: error_information = StandardLoggingPayloadSetup.get_error_information( original_exception=original_exception, ) - if not metadata.get("client_disconnected"): # any-ok: untyped metadata + if not metadata.get("client_disconnected"): return error_information, error_str - client_disconnect_error = metadata.get( # any-ok: untyped metadata - "error_information" - ) - if isinstance(client_disconnect_error, dict): # any-ok: untyped metadata + client_disconnect_error = metadata.get("error_information") + if isinstance(client_disconnect_error, dict): error_information = cast( StandardLoggingPayloadErrorInformation, - client_disconnect_error, # any-ok: untyped metadata + client_disconnect_error, ) else: error_information = cast( StandardLoggingPayloadErrorInformation, - { # any-ok: untyped metadata + { "error_code": "499", "error_message": "Client disconnected the request", "error_class": "ClientDisconnected", @@ -5808,7 +5812,7 @@ def get_standard_logging_object_payload( error_information, error_str = ( StandardLoggingPayloadSetup.get_error_information_for_logging_payload( - metadata=metadata, # any-ok: untyped metadata + metadata=metadata, original_exception=original_exception, error_str=error_str, ) diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py index 6c6b8611da6..7a7fde3087e 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/utils.py +++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py @@ -191,6 +191,11 @@ def _get_service_tier_cost_key(base_key: str, service_tier: Optional[str]) -> st return base_key +def _parse_above_token_threshold(key: str) -> float: + threshold_str = key.split("_above_")[1].split("_tokens")[0] + return float(threshold_str.replace("k", "")) * (1000 if "k" in threshold_str else 1) + + def _get_token_base_cost( model_info: ModelInfo, usage: Usage, service_tier: Optional[str] = None ) -> Tuple[float, float, float, float, float]: @@ -256,15 +261,13 @@ def _get_token_base_cost( # Only sort the threshold keys (typically 1-2 keys instead of 66+) threshold: Optional[float] = None - for key in sorted(threshold_keys, reverse=True): + for key in sorted(threshold_keys, key=_parse_above_token_threshold, reverse=True): value = model_info.get(key) if value is not None: try: # Handle both formats: _above_128k_tokens and _above_128_tokens threshold_str = key.split("_above_")[1].split("_tokens")[0] - threshold = float(threshold_str.replace("k", "")) * ( - 1000 if "k" in threshold_str else 1 - ) + threshold = _parse_above_token_threshold(key) if usage.prompt_tokens > threshold: # Prefer a service_tier-specific above-threshold key when available, # e.g. input_cost_per_token_priority_above_200k_tokens for Gemini diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py index 888a9658396..e278483d689 100644 --- a/litellm/litellm_core_utils/streaming_handler.py +++ b/litellm/litellm_core_utils/streaming_handler.py @@ -6,6 +6,7 @@ import logging import threading import time import traceback +from dataclasses import dataclass from typing import ( Any, AsyncIterator, @@ -97,6 +98,19 @@ def print_verbose(print_statement): pass +@dataclass(frozen=True, slots=True) +class _ProviderChunkParsed: + response_obj: dict[str, Any] + + +@dataclass(frozen=True, slots=True) +class _ProviderChunkEarlyReturn: + value: Any + + +_ProviderChunkResult = Union[_ProviderChunkParsed, _ProviderChunkEarlyReturn] + + class CustomStreamWrapper: def __init__( self, @@ -1145,381 +1159,392 @@ class CustomStreamWrapper: del model_response.choices[0].delta.reasoning_content return + def _dispatch_provider_chunk( + self, + chunk: Any, + model_response: ModelResponseStream, + completion_obj: dict[str, Any], + ) -> _ProviderChunkResult: + response_obj: dict[str, Any] = {} + if ( + isinstance(chunk, ModelResponseStream) + and self.custom_llm_provider is not None + and self.custom_llm_provider in litellm._custom_providers + ): + _has_content = bool( + chunk.choices + and chunk.choices[0].delta is not None + and ( + chunk.choices[0].delta.content or chunk.choices[0].delta.tool_calls + ) + ) + if self.received_finish_reason is not None: + if not _has_content: + raise StopIteration + if chunk.choices and chunk.choices[0].finish_reason: + self.received_finish_reason = chunk.choices[0].finish_reason + if not _has_content: + return _ProviderChunkEarlyReturn(None) + # Strip finish_reason from the content chunk so it appears + # only on the trailing empty-delta chunk (OpenAI spec). + # finish_reason_handler() will emit the proper terminal chunk. + chunk.choices[0].finish_reason = None # type: ignore[assignment] + return _ProviderChunkEarlyReturn(chunk) + + if ( + isinstance(chunk, dict) + and generic_chunk_has_all_required_fields( + chunk=chunk + ) # check if chunk is a generic streaming chunk + ) or ( + self.custom_llm_provider + and self.custom_llm_provider in litellm._custom_providers + ): + if self.received_finish_reason is not None: + _chunk_has_content = isinstance(chunk, dict) and ( + bool(chunk.get("text", "")) + or chunk.get("tool_use") is not None + # Usage-only final chunks are valid and needed to surface + # finish_reason/usage to downstream translators. + or chunk.get("usage") is not None + ) + if not _chunk_has_content and ( + not isinstance(chunk, dict) + or "provider_specific_fields" not in chunk + ): + raise StopIteration + anthropic_response_obj: GChunk = cast(GChunk, chunk) + completion_obj["content"] = anthropic_response_obj["text"] + if anthropic_response_obj["is_finished"]: + self.received_finish_reason = anthropic_response_obj["finish_reason"] + + if anthropic_response_obj["finish_reason"]: + self.intermittent_finish_reason = anthropic_response_obj[ + "finish_reason" + ] + + if anthropic_response_obj["usage"] is not None: + setattr( + model_response, + "usage", + litellm.Usage(**anthropic_response_obj["usage"]), + ) + + if ( + "tool_use" in anthropic_response_obj + and anthropic_response_obj["tool_use"] is not None + ): + completion_obj["tool_calls"] = [anthropic_response_obj["tool_use"]] + + if ( + "provider_specific_fields" in anthropic_response_obj + and anthropic_response_obj["provider_specific_fields"] is not None + ): + for key, value in anthropic_response_obj[ + "provider_specific_fields" + ].items(): + setattr(model_response, key, value) + + response_obj = cast(dict[str, Any], anthropic_response_obj) + elif self.model == "replicate" or self.custom_llm_provider == "replicate": + response_obj = self.handle_replicate_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + elif self.custom_llm_provider and self.custom_llm_provider == "predibase": + response_obj = self.handle_predibase_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + elif ( + self.custom_llm_provider and self.custom_llm_provider == "baseten" + ): # baseten doesn't provide streaming + completion_obj["content"] = self.handle_baseten_chunk(chunk) + elif ( + self.custom_llm_provider and self.custom_llm_provider == "ai21" + ): # ai21 doesn't provide streaming + response_obj = self.handle_ai21_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + elif self.custom_llm_provider and self.custom_llm_provider == "maritalk": + response_obj = self.handle_maritalk_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + elif self.custom_llm_provider and self.custom_llm_provider == "vllm": + completion_obj["content"] = chunk[0].outputs[0].text + elif ( + self.custom_llm_provider and self.custom_llm_provider == "aleph_alpha" + ): # aleph alpha doesn't provide streaming + response_obj = self.handle_aleph_alpha_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + elif self.custom_llm_provider == "nlp_cloud": + try: + response_obj = self.handle_nlp_cloud_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + except Exception as e: + if self.received_finish_reason: + raise e + else: + if self.sent_first_chunk is False: + raise Exception("An unknown error occurred with the stream") + self.received_finish_reason = "stop" + elif self.custom_llm_provider == "vertex_ai" and not isinstance( + chunk, ModelResponseStream + ): + chunk = cast(Any, chunk) + import proto # type: ignore + + if hasattr(chunk, "candidates") is True: + try: + try: + completion_obj["content"] = chunk.text # type: ignore + except Exception as e: + original_exception = e + if "Part has no text." in str(e): + ## check for function calling + function_call = ( + chunk.candidates[0].content.parts[0].function_call # type: ignore + ) + + args_dict = {} + + # Check if it's a RepeatedComposite instance + for key, val in function_call.args.items(): + if isinstance( + val, + proto.marshal.collections.repeated.RepeatedComposite, # type: ignore + ): + # If so, convert to list + args_dict[key] = [v for v in val] + else: + args_dict[key] = val + + try: + args_str = json.dumps(args_dict) + except Exception as e: + raise e + _delta_obj = litellm.utils.Delta( + content=None, + tool_calls=[ + { + "id": f"call_{str(uuid.uuid4())}", + "function": { + "arguments": args_str, + "name": function_call.name, + }, + "type": "function", + } + ], + ) + _streaming_response = StreamingChoices(delta=_delta_obj) + _model_response = ModelResponseStream() + _model_response.choices = [_streaming_response] + response_obj = {"original_chunk": _model_response} + else: + raise original_exception + if ( + hasattr(chunk.candidates[0], "finish_reason") # type: ignore + and chunk.candidates[0].finish_reason.name # type: ignore + != "FINISH_REASON_UNSPECIFIED" + ): # every non-final chunk in vertex ai has this + self.received_finish_reason = map_finish_reason( # type: ignore + chunk.candidates[0].finish_reason.name + ) + except Exception: + if chunk.candidates[0].finish_reason.name == "SAFETY": # type: ignore + raise Exception( + f"The response was blocked by VertexAI. {str(chunk)}" + ) + else: + completion_obj["content"] = str(chunk) + elif self.custom_llm_provider == "petals": + if self.completion_stream is None or len(self.completion_stream) == 0: + if self.received_finish_reason is not None: + raise StopIteration + else: + self.received_finish_reason = "stop" + chunk_size = 30 + stream = cast(Any, self.completion_stream) + new_chunk = stream[:chunk_size] + completion_obj["content"] = new_chunk + self.completion_stream = stream[chunk_size:] + elif self.custom_llm_provider == "palm": + # fake streaming + response_obj = {} + if self.completion_stream is None or len(self.completion_stream) == 0: + if self.received_finish_reason is not None: + raise StopIteration + else: + self.received_finish_reason = "stop" + chunk_size = 30 + stream = cast(Any, self.completion_stream) + new_chunk = stream[:chunk_size] + completion_obj["content"] = new_chunk + self.completion_stream = stream[chunk_size:] + elif self.custom_llm_provider == "triton": + response_obj = self.handle_triton_stream(chunk) + completion_obj["content"] = response_obj["text"] + print_verbose(f"completion obj content: {completion_obj['content']}") + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + elif self.custom_llm_provider == "text-completion-openai": + response_obj = self.handle_openai_text_completion_chunk(chunk) + completion_obj["content"] = response_obj["text"] + print_verbose(f"completion obj content: {completion_obj['content']}") + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + if response_obj["usage"] is not None: + setattr( + model_response, + "usage", + litellm.Usage( + prompt_tokens=response_obj["usage"].prompt_tokens, + completion_tokens=response_obj["usage"].completion_tokens, + total_tokens=response_obj["usage"].total_tokens, + ), + ) + elif self.custom_llm_provider == "text-completion-codestral": + if not isinstance(chunk, str): + raise ValueError(f"chunk is not a string: {chunk}") + response_obj = cast( + dict[str, Any], + litellm.CodestralTextCompletionConfig()._chunk_parser(chunk), + ) + completion_obj["content"] = response_obj["text"] + print_verbose(f"completion obj content: {completion_obj['content']}") + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + if "usage" in response_obj is not None: + setattr( + model_response, + "usage", + litellm.Usage( + prompt_tokens=response_obj["usage"].prompt_tokens, + completion_tokens=response_obj["usage"].completion_tokens, + total_tokens=response_obj["usage"].total_tokens, + ), + ) + elif self.custom_llm_provider == "azure_text": + response_obj = self.handle_azure_text_completion_chunk(chunk) + completion_obj["content"] = response_obj["text"] + print_verbose(f"completion obj content: {completion_obj['content']}") + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + elif self.custom_llm_provider == "cached_response": + chunk = cast(ModelResponseStream, chunk) + response_obj = { + "text": chunk.choices[0].delta.content, + "is_finished": True, + "finish_reason": chunk.choices[0].finish_reason, + "original_chunk": chunk, + "tool_calls": ( + chunk.choices[0].delta.tool_calls + if hasattr(chunk.choices[0].delta, "tool_calls") + else None + ), + } + + completion_obj["content"] = response_obj["text"] + if response_obj["tool_calls"] is not None: + completion_obj["tool_calls"] = response_obj["tool_calls"] + print_verbose(f"completion obj content: {completion_obj['content']}") + if hasattr(chunk, "id"): + model_response.id = chunk.id + self.response_id = chunk.id + if hasattr(chunk, "system_fingerprint"): + self.system_fingerprint = chunk.system_fingerprint + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + else: # openai / azure chat model + if self.custom_llm_provider in [ + LlmProviders.AZURE.value, + LlmProviders.AZURE_AI.value, + ]: + if isinstance(chunk, BaseModel) and hasattr(chunk, "model"): + # for azure, we need to pass the model from the original chunk + self.model = getattr(chunk, "model", self.model) + response_obj = self.handle_openai_chat_completion_chunk(chunk) + if response_obj is None: + return _ProviderChunkEarlyReturn(None) + completion_obj["content"] = response_obj["text"] + self.intermittent_finish_reason = response_obj.get("finish_reason", None) + if response_obj["is_finished"]: + if response_obj["finish_reason"] == "error": + raise Exception( + "{} raised a streaming error - finish_reason: error, no content string given. Received Chunk={}".format( + self.custom_llm_provider, response_obj + ) + ) + self.received_finish_reason = response_obj["finish_reason"] + if response_obj.get("original_chunk", None) is not None: + if hasattr(response_obj["original_chunk"], "id"): + model_response = self.set_model_id( + response_obj["original_chunk"].id, model_response + ) + if hasattr(response_obj["original_chunk"], "system_fingerprint"): + model_response.system_fingerprint = response_obj[ + "original_chunk" + ].system_fingerprint + self.system_fingerprint = response_obj[ + "original_chunk" + ].system_fingerprint + if response_obj["logprobs"] is not None: + model_response.choices[0].logprobs = response_obj["logprobs"] + + if response_obj["usage"] is not None: + if isinstance(response_obj["usage"], dict): + setattr( + model_response, + "usage", + litellm.Usage( + prompt_tokens=response_obj["usage"].get( + "prompt_tokens", None + ) + or None, + completion_tokens=response_obj["usage"].get( + "completion_tokens", None + ) + or None, + total_tokens=response_obj["usage"].get("total_tokens", None) + or None, + ), + ) + elif isinstance(response_obj["usage"], Usage): + setattr( + model_response, + "usage", + response_obj["usage"], + ) + elif isinstance(response_obj["usage"], BaseModel): + setattr( + model_response, + "usage", + litellm.Usage(**response_obj["usage"].model_dump()), + ) + return _ProviderChunkParsed(response_obj) + def chunk_creator(self, chunk: Any): # type: ignore if hasattr(chunk, "id"): self.response_id = chunk.id model_response = self.model_response_creator() - response_obj: Dict[str, Any] = {} + response_obj: dict[str, Any] = {} try: # return this for all models - completion_obj: Dict[str, Any] = {"content": ""} - from litellm.types.utils import GenericStreamingChunk as GChunk - - if ( - isinstance(chunk, ModelResponseStream) - and self.custom_llm_provider is not None - and self.custom_llm_provider in litellm._custom_providers - ): - _has_content = bool( - chunk.choices - and chunk.choices[0].delta is not None - and ( - chunk.choices[0].delta.content - or chunk.choices[0].delta.tool_calls - ) - ) - if self.received_finish_reason is not None: - if not _has_content: - raise StopIteration - if chunk.choices and chunk.choices[0].finish_reason: - self.received_finish_reason = chunk.choices[0].finish_reason - if not _has_content: - return None - # Strip finish_reason from the content chunk so it appears - # only on the trailing empty-delta chunk (OpenAI spec). - # finish_reason_handler() will emit the proper terminal chunk. - chunk.choices[0].finish_reason = None # type: ignore[assignment] - return chunk - - if ( - isinstance(chunk, dict) - and generic_chunk_has_all_required_fields( - chunk=chunk - ) # check if chunk is a generic streaming chunk - ) or ( - self.custom_llm_provider - and self.custom_llm_provider in litellm._custom_providers - ): - if self.received_finish_reason is not None: - _chunk_has_content = isinstance(chunk, dict) and ( - bool(chunk.get("text", "")) - or chunk.get("tool_use") is not None - # Usage-only final chunks are valid and needed to surface - # finish_reason/usage to downstream translators. - or chunk.get("usage") is not None - ) - if not _chunk_has_content and ( - not isinstance(chunk, dict) - or "provider_specific_fields" not in chunk - ): - raise StopIteration - anthropic_response_obj: GChunk = cast(GChunk, chunk) - completion_obj["content"] = anthropic_response_obj["text"] - if anthropic_response_obj["is_finished"]: - self.received_finish_reason = anthropic_response_obj[ - "finish_reason" - ] - - if anthropic_response_obj["finish_reason"]: - self.intermittent_finish_reason = anthropic_response_obj[ - "finish_reason" - ] - - if anthropic_response_obj["usage"] is not None: - setattr( - model_response, - "usage", - litellm.Usage(**anthropic_response_obj["usage"]), - ) - - if ( - "tool_use" in anthropic_response_obj - and anthropic_response_obj["tool_use"] is not None - ): - completion_obj["tool_calls"] = [anthropic_response_obj["tool_use"]] - - if ( - "provider_specific_fields" in anthropic_response_obj - and anthropic_response_obj["provider_specific_fields"] is not None - ): - for key, value in anthropic_response_obj[ - "provider_specific_fields" - ].items(): - setattr(model_response, key, value) - - response_obj = cast(Dict[str, Any], anthropic_response_obj) - elif self.model == "replicate" or self.custom_llm_provider == "replicate": - response_obj = self.handle_replicate_chunk(chunk) - completion_obj["content"] = response_obj["text"] - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - elif self.custom_llm_provider and self.custom_llm_provider == "predibase": - response_obj = self.handle_predibase_chunk(chunk) - completion_obj["content"] = response_obj["text"] - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - elif ( - self.custom_llm_provider and self.custom_llm_provider == "baseten" - ): # baseten doesn't provide streaming - completion_obj["content"] = self.handle_baseten_chunk(chunk) - elif ( - self.custom_llm_provider and self.custom_llm_provider == "ai21" - ): # ai21 doesn't provide streaming - response_obj = self.handle_ai21_chunk(chunk) - completion_obj["content"] = response_obj["text"] - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - elif self.custom_llm_provider and self.custom_llm_provider == "maritalk": - response_obj = self.handle_maritalk_chunk(chunk) - completion_obj["content"] = response_obj["text"] - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - elif self.custom_llm_provider and self.custom_llm_provider == "vllm": - completion_obj["content"] = chunk[0].outputs[0].text - elif ( - self.custom_llm_provider and self.custom_llm_provider == "aleph_alpha" - ): # aleph alpha doesn't provide streaming - response_obj = self.handle_aleph_alpha_chunk(chunk) - completion_obj["content"] = response_obj["text"] - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - elif self.custom_llm_provider == "nlp_cloud": - try: - response_obj = self.handle_nlp_cloud_chunk(chunk) - completion_obj["content"] = response_obj["text"] - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - except Exception as e: - if self.received_finish_reason: - raise e - else: - if self.sent_first_chunk is False: - raise Exception("An unknown error occurred with the stream") - self.received_finish_reason = "stop" - elif self.custom_llm_provider == "vertex_ai" and not isinstance( - chunk, ModelResponseStream - ): - import proto # type: ignore - - if hasattr(chunk, "candidates") is True: - try: - try: - completion_obj["content"] = chunk.text # type: ignore - except Exception as e: - original_exception = e - if "Part has no text." in str(e): - ## check for function calling - function_call = ( - chunk.candidates[0].content.parts[0].function_call # type: ignore - ) - - args_dict = {} - - # Check if it's a RepeatedComposite instance - for key, val in function_call.args.items(): - if isinstance( - val, - proto.marshal.collections.repeated.RepeatedComposite, # type: ignore - ): - # If so, convert to list - args_dict[key] = [v for v in val] - else: - args_dict[key] = val - - try: - args_str = json.dumps(args_dict) - except Exception as e: - raise e - _delta_obj = litellm.utils.Delta( - content=None, - tool_calls=[ - { - "id": f"call_{str(uuid.uuid4())}", - "function": { - "arguments": args_str, - "name": function_call.name, - }, - "type": "function", - } - ], - ) - _streaming_response = StreamingChoices(delta=_delta_obj) - _model_response = ModelResponseStream() - _model_response.choices = [_streaming_response] - response_obj = {"original_chunk": _model_response} - else: - raise original_exception - if ( - hasattr(chunk.candidates[0], "finish_reason") # type: ignore - and chunk.candidates[0].finish_reason.name # type: ignore - != "FINISH_REASON_UNSPECIFIED" - ): # every non-final chunk in vertex ai has this - self.received_finish_reason = map_finish_reason( # type: ignore - chunk.candidates[0].finish_reason.name - ) - except Exception: - if chunk.candidates[0].finish_reason.name == "SAFETY": # type: ignore - raise Exception( - f"The response was blocked by VertexAI. {str(chunk)}" - ) - else: - completion_obj["content"] = str(chunk) - elif self.custom_llm_provider == "petals": - if self.completion_stream is None or len(self.completion_stream) == 0: - if self.received_finish_reason is not None: - raise StopIteration - else: - self.received_finish_reason = "stop" - chunk_size = 30 - new_chunk = self.completion_stream[:chunk_size] # type: ignore[index] - completion_obj["content"] = new_chunk - self.completion_stream = self.completion_stream[chunk_size:] # type: ignore[index] - elif self.custom_llm_provider == "palm": - # fake streaming - response_obj = {} - if self.completion_stream is None or len(self.completion_stream) == 0: - if self.received_finish_reason is not None: - raise StopIteration - else: - self.received_finish_reason = "stop" - chunk_size = 30 - new_chunk = self.completion_stream[:chunk_size] # type: ignore[index] - completion_obj["content"] = new_chunk - self.completion_stream = self.completion_stream[chunk_size:] # type: ignore[index] - elif self.custom_llm_provider == "triton": - response_obj = self.handle_triton_stream(chunk) - completion_obj["content"] = response_obj["text"] - print_verbose(f"completion obj content: {completion_obj['content']}") - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - elif self.custom_llm_provider == "text-completion-openai": - response_obj = self.handle_openai_text_completion_chunk(chunk) - completion_obj["content"] = response_obj["text"] - print_verbose(f"completion obj content: {completion_obj['content']}") - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - if response_obj["usage"] is not None: - setattr( - model_response, - "usage", - litellm.Usage( - prompt_tokens=response_obj["usage"].prompt_tokens, - completion_tokens=response_obj["usage"].completion_tokens, - total_tokens=response_obj["usage"].total_tokens, - ), - ) - elif self.custom_llm_provider == "text-completion-codestral": - if not isinstance(chunk, str): - raise ValueError(f"chunk is not a string: {chunk}") - response_obj = cast( - Dict[str, Any], - litellm.CodestralTextCompletionConfig()._chunk_parser(chunk), - ) - completion_obj["content"] = response_obj["text"] - print_verbose(f"completion obj content: {completion_obj['content']}") - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - if "usage" in response_obj is not None: - setattr( - model_response, - "usage", - litellm.Usage( - prompt_tokens=response_obj["usage"].prompt_tokens, - completion_tokens=response_obj["usage"].completion_tokens, - total_tokens=response_obj["usage"].total_tokens, - ), - ) - elif self.custom_llm_provider == "azure_text": - response_obj = self.handle_azure_text_completion_chunk(chunk) - completion_obj["content"] = response_obj["text"] - print_verbose(f"completion obj content: {completion_obj['content']}") - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - elif self.custom_llm_provider == "cached_response": - chunk = cast(ModelResponseStream, chunk) - response_obj = { - "text": chunk.choices[0].delta.content, - "is_finished": True, - "finish_reason": chunk.choices[0].finish_reason, - "original_chunk": chunk, - "tool_calls": ( - chunk.choices[0].delta.tool_calls - if hasattr(chunk.choices[0].delta, "tool_calls") - else None - ), - } - - completion_obj["content"] = response_obj["text"] - if response_obj["tool_calls"] is not None: - completion_obj["tool_calls"] = response_obj["tool_calls"] - print_verbose(f"completion obj content: {completion_obj['content']}") - if hasattr(chunk, "id"): - model_response.id = chunk.id - self.response_id = chunk.id - if hasattr(chunk, "system_fingerprint"): - self.system_fingerprint = chunk.system_fingerprint - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - else: # openai / azure chat model - if self.custom_llm_provider in [ - LlmProviders.AZURE.value, - LlmProviders.AZURE_AI.value, - ]: - if isinstance(chunk, BaseModel) and hasattr(chunk, "model"): - # for azure, we need to pass the model from the original chunk - self.model = getattr(chunk, "model", self.model) - response_obj = self.handle_openai_chat_completion_chunk(chunk) - if response_obj is None: - return - completion_obj["content"] = response_obj["text"] - self.intermittent_finish_reason = response_obj.get( - "finish_reason", None - ) - if response_obj["is_finished"]: - if response_obj["finish_reason"] == "error": - raise Exception( - "{} raised a streaming error - finish_reason: error, no content string given. Received Chunk={}".format( - self.custom_llm_provider, response_obj - ) - ) - self.received_finish_reason = response_obj["finish_reason"] - if response_obj.get("original_chunk", None) is not None: - if hasattr(response_obj["original_chunk"], "id"): - model_response = self.set_model_id( - response_obj["original_chunk"].id, model_response - ) - if hasattr(response_obj["original_chunk"], "system_fingerprint"): - model_response.system_fingerprint = response_obj[ - "original_chunk" - ].system_fingerprint - self.system_fingerprint = response_obj[ - "original_chunk" - ].system_fingerprint - if response_obj["logprobs"] is not None: - model_response.choices[0].logprobs = response_obj["logprobs"] - - if response_obj["usage"] is not None: - if isinstance(response_obj["usage"], dict): - setattr( - model_response, - "usage", - litellm.Usage( - prompt_tokens=response_obj["usage"].get( - "prompt_tokens", None - ) - or None, - completion_tokens=response_obj["usage"].get( - "completion_tokens", None - ) - or None, - total_tokens=response_obj["usage"].get( - "total_tokens", None - ) - or None, - ), - ) - elif isinstance(response_obj["usage"], Usage): - setattr( - model_response, - "usage", - response_obj["usage"], - ) - elif isinstance(response_obj["usage"], BaseModel): - setattr( - model_response, - "usage", - litellm.Usage(**response_obj["usage"].model_dump()), - ) + completion_obj: dict[str, Any] = {"content": ""} + dispatch_result = self._dispatch_provider_chunk( + chunk=chunk, + model_response=model_response, + completion_obj=completion_obj, + ) + if isinstance(dispatch_result, _ProviderChunkEarlyReturn): + return dispatch_result.value + response_obj = dispatch_result.response_obj model_response.model = self.model ## FUNCTION CALL PARSING @@ -1980,11 +2005,29 @@ class CustomStreamWrapper: except StopIteration: if self.sent_last_chunk is True: - complete_streaming_response = litellm.stream_chunk_builder( - chunks=self.chunks, - messages=self.messages, - logging_obj=self.logging_obj, - ) + try: + complete_streaming_response = litellm.stream_chunk_builder( + chunks=self.chunks, + messages=self.messages, + logging_obj=self.logging_obj, + ) + except Exception as e: + # stream_chunk_builder can re-raise (as APIError) on large agentic + # streams. The raise originates inside this except-StopIteration block, + # so the sibling `except Exception` below does not catch it; it would + # escape __next__ and drop the request from SpendLogs. Recover + # best-effort usage from the raw chunks so cost is still tracked + verbose_logger.warning( + "stream_chunk_builder raised at end-of-stream (%s); logging " + "best-effort usage from chunks.", + str(e), + ) + try: + complete_streaming_response = self.model_response_creator( + chunk={"usage": calculate_total_usage(chunks=self.chunks)} + ) + except Exception: + complete_streaming_response = None response = self.model_response_creator() if complete_streaming_response is not None: @@ -2209,11 +2252,27 @@ class CustomStreamWrapper: except (StopAsyncIteration, StopIteration): if self.sent_last_chunk is True: # log the final chunk with accurate streaming values - complete_streaming_response = litellm.stream_chunk_builder( - chunks=self.chunks, - messages=self.messages, - logging_obj=self.logging_obj, - ) + try: + complete_streaming_response = litellm.stream_chunk_builder( + chunks=self.chunks, + messages=self.messages, + logging_obj=self.logging_obj, + ) + except Exception as e: + # see sync __next__: a raise from stream_chunk_builder inside this + # except handler escapes __anext__ and drops the request from SpendLogs. + # Recover best-effort usage from the raw chunks so cost is still tracked + verbose_logger.warning( + "stream_chunk_builder raised at end-of-stream (%s); logging " + "best-effort usage from chunks.", + str(e), + ) + try: + complete_streaming_response = self.model_response_creator( + chunk={"usage": calculate_total_usage(chunks=self.chunks)} + ) + except Exception: + complete_streaming_response = None response = self.model_response_creator() if complete_streaming_response is not None: @@ -2290,6 +2349,7 @@ class CustomStreamWrapper: litellm.request_timeout ) if self.logging_obj is not None: + self._record_partial_usage_for_failure() ## LOGGING threading.Thread( target=self.logging_obj.failure_handler, @@ -2303,6 +2363,7 @@ class CustomStreamWrapper: except Exception as e: traceback_exception = traceback.format_exc() if self.logging_obj is not None: + self._record_partial_usage_for_failure() ## LOGGING threading.Thread( target=self.logging_obj.failure_handler, @@ -2314,6 +2375,33 @@ class CustomStreamWrapper: ) self._handle_stream_fallback_error(e) + def _record_partial_usage_for_failure(self) -> None: + """ + A stream that breaks mid-flight still billed the provider for the chunks + already delivered. Recover that partial usage from the chunks seen so + far and stash it, with its cost, on the logging object so the failure + handler records the real partial spend instead of zero. A request that + later recovers via a router fallback overwrites this with the combined + success log on the same request id, so this never double counts. + """ + if self.logging_obj is None or not self.chunks: + return + try: + partial_response = litellm.stream_chunk_builder(chunks=self.chunks) + usage = cast(Optional[Usage], getattr(partial_response, "usage", None)) + if usage is None: + return + self.logging_obj.model_call_details["combined_usage_object"] = usage + self.logging_obj.model_call_details["response_cost"] = ( + self.logging_obj._response_cost_calculator(result=partial_response) + or 0.0 + ) + except Exception as recover_error: + verbose_logger.debug( + "could not recover partial usage for interrupted stream: %s", + recover_error, + ) + def _handle_stream_fallback_error(self, e: Exception) -> "NoReturn": """ Common error handling for both __next__ and __anext__. diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py index cf97c946f1c..c24c990f356 100644 --- a/litellm/llms/anthropic/chat/transformation.py +++ b/litellm/llms/anthropic/chat/transformation.py @@ -2213,6 +2213,10 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): inference_geo: Optional[str] = None if "inference_geo" in _usage and _usage["inference_geo"] is not None: inference_geo = _usage["inference_geo"] + service_tier = cast( + str | None, + _usage.get("service_tier"), + ) iterations: Optional[List[Any]] = _usage.get("iterations") if iterations: @@ -2324,6 +2328,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): ), inference_geo=inference_geo, speed=speed, + service_tier=service_tier, ) return usage diff --git a/litellm/llms/anthropic/cost_calculation.py b/litellm/llms/anthropic/cost_calculation.py index 6a031498dae..44081ea9e79 100644 --- a/litellm/llms/anthropic/cost_calculation.py +++ b/litellm/llms/anthropic/cost_calculation.py @@ -18,7 +18,9 @@ if TYPE_CHECKING: import litellm -def _compute_cache_only_cost(model_info: "ModelInfo", usage: "Usage") -> float: +def _compute_cache_only_cost( + model_info: "ModelInfo", usage: "Usage", service_tier: str | None = None +) -> float: """ Return only the cache-related portion of the prompt cost (cache read + cache write). @@ -36,7 +38,9 @@ def _compute_cache_only_cost(model_info: "ModelInfo", usage: "Usage") -> float: cache_creation_cost, cache_creation_cost_above_1hr, cache_read_cost, - ) = _get_token_base_cost(model_info=model_info, usage=usage) + ) = _get_token_base_cost( + model_info=model_info, usage=usage, service_tier=service_tier + ) cache_cost = float(prompt_tokens_details["cache_hit_tokens"]) * cache_read_cost @@ -56,19 +60,26 @@ def _compute_cache_only_cost(model_info: "ModelInfo", usage: "Usage") -> float: return cache_cost -def cost_per_token(model: str, usage: "Usage") -> Tuple[float, float]: +def cost_per_token( + model: str, usage: "Usage", service_tier: str | None = None +) -> Tuple[float, float]: """ Calculates the cost per token for a given model, prompt tokens, and completion tokens. Input: - model: str, the model name without provider prefix - usage: LiteLLM Usage block, containing anthropic caching information + - service_tier: the service tier the request was served at (e.g. "priority"), + read from the Anthropic response usage and used to select tier-specific pricing Returns: Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd """ prompt_cost, completion_cost = generic_cost_per_token( - model=model, usage=usage, custom_llm_provider="anthropic" + model=model, + usage=usage, + custom_llm_provider="anthropic", + service_tier=service_tier, ) # Apply provider_specific_entry multipliers for geo/speed routing @@ -89,7 +100,9 @@ def cost_per_token(model: str, usage: "Usage") -> Tuple[float, float]: multiplier *= provider_specific_entry.get("fast", 1.0) if multiplier != 1.0: - cache_cost = _compute_cache_only_cost(model_info=model_info, usage=usage) + cache_cost = _compute_cache_only_cost( + model_info=model_info, usage=usage, service_tier=service_tier + ) prompt_cost = (prompt_cost - cache_cost) * multiplier + cache_cost completion_cost *= multiplier except Exception: diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py index bf425637b56..75a8acdfcc3 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py @@ -859,7 +859,17 @@ class LiteLLMAnthropicMessagesAdapter: """ new_tools: List[ChatCompletionToolParam] = [] tool_name_mapping: Dict[str, str] = {} - mapped_tool_params = ["name", "input_schema", "description", "cache_control"] + # "type" is the Anthropic tool type (e.g. "custom"); it must not be + # merged into the OpenAI function `parameters` schema below, or it + # overwrites the real parameters.type ("object") and the provider + # rejects the request. See #30557. + mapped_tool_params = [ + "name", + "input_schema", + "description", + "cache_control", + "type", + ] for idx, tool in enumerate(tools): # Check if this is an Anthropic-native tool that should be kept as-is diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/interceptors/advisor.py b/litellm/llms/anthropic/experimental_pass_through/messages/interceptors/advisor.py index c7c110ff3e3..8714939f025 100644 --- a/litellm/llms/anthropic/experimental_pass_through/messages/interceptors/advisor.py +++ b/litellm/llms/anthropic/experimental_pass_through/messages/interceptors/advisor.py @@ -84,8 +84,14 @@ class AdvisorOrchestrationHandler(MessagesInterceptor): ) # Optional routing overrides for the advisor sub-call (e.g. proxy routing). # If not set in the tool definition, litellm resolves from env vars. - advisor_api_key: Optional[str] = advisor_tool.get("api_key") - advisor_api_base: Optional[str] = advisor_tool.get("api_base") + # The advisor tool is caller-controlled; only honor a client-supplied + # api_base/api_key when the proxy has enabled clientside credentials, + # otherwise let litellm resolve from server config. + advisor_api_key: Optional[str] = None + advisor_api_base: Optional[str] = None + if _allow_client_side_advisor_credentials(): + advisor_api_key = advisor_tool.get("api_key") + advisor_api_base = advisor_tool.get("api_base") # Build the synthetic tool definition the provider will receive. synthetic_advisor_tool = _make_synthetic_advisor_tool() @@ -181,6 +187,20 @@ class AdvisorOrchestrationHandler(MessagesInterceptor): # --------------------------------------------------------------------------- +def _allow_client_side_advisor_credentials() -> bool: + """Whether a caller-supplied advisor api_base/api_key may be honored. + + Gated on the proxy's ``allow_client_side_credentials`` opt-in. When the + interceptor runs outside the proxy (SDK use), there is no admin boundary + to protect, so client-supplied routing is allowed. + """ + try: + from litellm.proxy.proxy_server import general_settings + except (ImportError, ModuleNotFoundError): + return True + return general_settings.get("allow_client_side_credentials") is True + + def _make_synthetic_advisor_tool() -> Dict: """Build a regular tool definition the executor provider can understand.""" return { diff --git a/litellm/llms/base_llm/sandbox/__init__.py b/litellm/llms/base_llm/sandbox/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/litellm/llms/base_llm/sandbox/transformation.py b/litellm/llms/base_llm/sandbox/transformation.py new file mode 100644 index 00000000000..6ad945f47a3 --- /dev/null +++ b/litellm/llms/base_llm/sandbox/transformation.py @@ -0,0 +1,79 @@ +""" +Base Sandbox transformation configuration. + +A sandbox provider runs an executable string inside an isolated container and +returns whatever the sandbox produced. The lifecycle is create container -> +run code -> delete container; `code_interpreter_tool` combines all three. +""" + +from typing import Any, Union + +from pydantic import Field, PrivateAttr + +from litellm.types.llms.base import LiteLLMPydanticObjectBase + + +class ContainerHandle(LiteLLMPydanticObjectBase): + """A live sandbox container. Carries everything needed to reach it again.""" + + id: str + provider: str + domain: str | None = None + + model_config = {"extra": "allow"} + + _hidden_params: dict = PrivateAttr(default_factory=dict) + + +class CodeExecutionResult(LiteLLMPydanticObjectBase): + """Passthrough of the sandbox's own execution output.""" + + stdout: str = "" + stderr: str = "" + results: list[dict[str, Any]] = Field(default_factory=list) + error: dict[str, Any] | None = None + execution_count: int | None = None + object: str = "code_execution" + + model_config = {"extra": "allow"} + + _hidden_params: dict = PrivateAttr(default_factory=dict) + + +class BaseSandboxConfig: + """Provider-agnostic sandbox operations.""" + + def validate_environment(self, api_key: str | None = None, **kwargs) -> str: + raise NotImplementedError( + "validate_environment must be implemented by provider" + ) + + async def acreate_sandbox( + self, + *, + template: str | None = None, + timeout: int | None = None, + allow_internet_access: bool = True, + api_key: str | None = None, + **kwargs, + ) -> ContainerHandle: + raise NotImplementedError("acreate_sandbox must be implemented by provider") + + async def arun_code( + self, + *, + container: Union[ContainerHandle, str], + code: str, + api_key: str | None = None, + **kwargs, + ) -> CodeExecutionResult: + raise NotImplementedError("arun_code must be implemented by provider") + + async def adelete_sandbox( + self, + *, + container: Union[ContainerHandle, str], + api_key: str | None = None, + **kwargs, + ) -> bool: + raise NotImplementedError("adelete_sandbox must be implemented by provider") diff --git a/litellm/llms/bedrock/base_aws_llm.py b/litellm/llms/bedrock/base_aws_llm.py index 2c9ea187912..c31462a735b 100644 --- a/litellm/llms/bedrock/base_aws_llm.py +++ b/litellm/llms/bedrock/base_aws_llm.py @@ -10,7 +10,6 @@ from typing import ( Callable, ClassVar, Dict, - List, Literal, Optional, Tuple, @@ -210,32 +209,11 @@ class BaseAWSLLM: """ Return a boto3.Credentials object """ - ## CHECK IS 'os.environ/' passed in - params_to_check: List[Optional[str]] = [ - aws_access_key_id, - aws_secret_access_key, - aws_session_token, - aws_region_name, - aws_session_name, - aws_profile_name, - aws_role_name, - aws_web_identity_token, - aws_sts_endpoint, - aws_external_id, - ] - - # Iterate over parameters and update if needed - for i, param in enumerate(params_to_check): - if param and param.startswith("os.environ/"): - _v = get_secret(param) - if _v is not None and isinstance(_v, str): - params_to_check[i] = _v - elif param is None: # check if uppercase value in env - key = self.aws_authentication_params[i] - if key.upper() in os.environ: - params_to_check[i] = os.getenv(key.upper()) - - # Assign updated values back to parameters + # Only config-sourced credentials are expanded against the environment. + # os.environ/ references in the model config are resolved at load time, + # so any reference still present at this point is caller-supplied input and is + # left as-is rather than expanded into a process environment variable. Each + # unset param falls back to its matching fixed AWS_* ambient env var. ( aws_access_key_id, aws_secret_access_key, @@ -247,7 +225,21 @@ class BaseAWSLLM: aws_web_identity_token, aws_sts_endpoint, aws_external_id, - ) = params_to_check + ) = tuple( + value if value is not None else os.getenv(env_var) + for value, env_var in ( + (aws_access_key_id, "AWS_ACCESS_KEY_ID"), + (aws_secret_access_key, "AWS_SECRET_ACCESS_KEY"), + (aws_session_token, "AWS_SESSION_TOKEN"), + (aws_region_name, "AWS_REGION_NAME"), + (aws_session_name, "AWS_SESSION_NAME"), + (aws_profile_name, "AWS_PROFILE_NAME"), + (aws_role_name, "AWS_ROLE_NAME"), + (aws_web_identity_token, "AWS_WEB_IDENTITY_TOKEN"), + (aws_sts_endpoint, "AWS_STS_ENDPOINT"), + (aws_external_id, "AWS_EXTERNAL_ID"), + ) + ) verbose_logger.debug( "in get credentials\n" @@ -845,6 +837,20 @@ class BaseAWSLLM: f"IN Web Identity Token: {aws_web_identity_token} | Role Name: {aws_role_name} | Session Name: {aws_session_name}" ) + # get_secret() expands environment-variable references (an os.environ/ + # prefix, or a bare name matching an environment variable). Config-sourced + # references are expanded at load time, so such a reference reaching here is + # caller-supplied input; reject it rather than expanding a process-environment + # value for use as the token. + if ( + aws_web_identity_token.startswith("os.environ/") + or aws_web_identity_token in os.environ + ): + raise AwsAuthError( + message="Invalid web identity token reference.", + status_code=400, + ) + oidc_token = get_secret(aws_web_identity_token) if oidc_token is None: diff --git a/litellm/llms/bedrock/chat/agentcore/transformation.py b/litellm/llms/bedrock/chat/agentcore/transformation.py index 44ba1ce3c86..3cd3a249c33 100644 --- a/litellm/llms/bedrock/chat/agentcore/transformation.py +++ b/litellm/llms/bedrock/chat/agentcore/transformation.py @@ -218,8 +218,20 @@ class AmazonAgentCoreConfig(BaseConfig, BaseAWSLLM): - Qualifier goes as query parameter - Only the payload goes in the request body + Payload shape: + - ``prompt`` is always present and contains the text-only flatten of the + last message's content (existing behavior). + - ``content`` is added ONLY when the ``forward_multimodal_content`` litellm + param is truthy AND the last message's ``content`` is a list containing a + non-text block (e.g. ``image_url``, ``file``, ``input_audio``). The list is + forwarded verbatim so the agent's ``@app.entrypoint`` handler can parse the + OpenAI-shaped multimodal blocks. This is opt-in because an AgentCore agent + must be explicitly written to read ``payload["content"]``; by default the + payload stays byte-identical to the legacy ``{"prompt": "..."}`` shape. + Returns: - dict: Payload dict containing the prompt + dict: Payload dict containing the prompt and (optionally) the OpenAI + content list. """ verbose_logger.debug( f"AgentCore transform_request - optional_params keys: {list(optional_params.keys())}" @@ -231,6 +243,20 @@ class AmazonAgentCoreConfig(BaseConfig, BaseAWSLLM): # Create the payload - this is what goes in the body (raw JSON) payload: dict = {"prompt": prompt} + # Opt-in: when forward_multimodal_content is set, forward the OpenAI content + # list verbatim under "content" so an attachment-aware agent can read the raw + # blocks (image_url, file, etc.). Default off keeps the payload byte-identical + # to the legacy {"prompt": "..."} shape for agents that only read the prompt. + if self._should_forward_multimodal_content(optional_params, litellm_params): + last_content = messages[-1].get("content") + if isinstance(last_content, list) and any( + isinstance(block, dict) and block.get("type") not in (None, "text") + for block in last_content + ): + # Copy so the payload never aliases messages[-1]["content"]; shallow, + # not deep, to avoid cloning large base64 media on the request path. + payload["content"] = list(last_content) + # Get or generate session ID - this goes in the header runtime_session_id = self._get_runtime_session_id(optional_params) headers["X-Amzn-Bedrock-AgentCore-Runtime-Session-Id"] = runtime_session_id @@ -246,6 +272,29 @@ class AmazonAgentCoreConfig(BaseConfig, BaseAWSLLM): verbose_logger.debug(f"PAYLOAD: {payload}") return payload + @staticmethod + def _should_forward_multimodal_content( + optional_params: dict, litellm_params: dict + ) -> bool: + """Whether to forward raw OpenAI content blocks under ``payload["content"]``. + + Opt-in via the ``forward_multimodal_content`` litellm param (default ``False``) + because AgentCore agents must be explicitly written to read the field. The + value may arrive as a bool or a config/env string ("true", "1", ...). Checks + ``optional_params`` first (where other AgentCore params land), then + ``litellm_params``. + """ + for source in (optional_params, litellm_params): + if not isinstance(source, dict): + continue + value = source.get("forward_multimodal_content") + if value is None: + continue + if isinstance(value, str): + return value.strip().lower() in ("1", "true", "yes", "on") + return bool(value) + return False + def _extract_sse_json(self, line: str) -> Optional[Dict]: """Extract and parse JSON from an SSE data line.""" if not line.startswith("data:"): diff --git a/litellm/llms/bedrock/files/handler.py b/litellm/llms/bedrock/files/handler.py index ecf157e12ee..b6aae2159c1 100644 --- a/litellm/llms/bedrock/files/handler.py +++ b/litellm/llms/bedrock/files/handler.py @@ -1,8 +1,6 @@ import asyncio -import base64 -import os -from types import MappingProxyType -from typing import Any, Coroutine, Mapping, Optional, Tuple, Union, cast +from collections.abc import Mapping +from typing import Any, Coroutine, Optional, Tuple, Union import httpx @@ -17,7 +15,6 @@ from litellm.types.llms.openai import ( FileContentRequest, HttpxBinaryResponseContent, ) -from litellm.types.utils import SpecialEnums from ..base_aws_llm import BaseAWSLLM @@ -37,40 +34,9 @@ class BedrockFilesHandler(BaseAWSLLM): ) def _extract_s3_uri_from_file_id(self, file_id: str) -> str: - """ - Extract S3 URI from encoded file ID. + from .transformation import extract_s3_uri_from_file_id - The file ID can be in two formats: - 1. Base64-encoded unified file ID containing: llm_output_file_id,s3://bucket/path - 2. Direct S3 URI: s3://bucket/litellm-managed-prefix/path - - Args: - file_id: Encoded file ID or direct S3 URI - - Returns: - S3 URI (e.g., "s3://bucket-name/path/to/file") - """ - # First, try to decode if it's a base64-encoded unified file ID - try: - # Add padding if needed - padded = file_id + "=" * (-len(file_id) % 4) - decoded = base64.urlsafe_b64decode(padded).decode() - - # Check if it's a unified file ID format - if decoded.startswith(SpecialEnums.LITELM_MANAGED_FILE_ID_PREFIX.value): - # Extract llm_output_file_id from the decoded string - if "llm_output_file_id," in decoded: - s3_uri = decoded.split("llm_output_file_id,")[1].split(";")[0] - return s3_uri - except Exception: - pass - - # If not base64 encoded or doesn't contain llm_output_file_id, accept only - # explicit S3 URIs. Bucket and key validation happens before any S3 call. - if file_id.startswith("s3://"): - return file_id - - raise ValueError("file_id must be a managed LiteLLM S3 file id") + return extract_s3_uri_from_file_id(file_id) def _parse_s3_uri( self, @@ -95,26 +61,12 @@ class BedrockFilesHandler(BaseAWSLLM): allow_legacy_cloud_file_ids=allow_legacy_cloud_file_ids, ) - def _get_configured_s3_bucket_name(self, litellm_params: dict) -> str: - trusted_model_credentials = litellm_params.get( - "_litellm_internal_model_credentials" - ) - bucket_name = None - if isinstance(trusted_model_credentials, type(MappingProxyType({}))): - trusted_model_credentials_mapping = cast( - Mapping[str, Any], trusted_model_credentials - ) - candidate_bucket_name = trusted_model_credentials_mapping.get( - "s3_bucket_name" - ) - if isinstance(candidate_bucket_name, str): - bucket_name = candidate_bucket_name - bucket_name = bucket_name or os.getenv("AWS_S3_BUCKET_NAME") - if not bucket_name: - raise ValueError( - "S3 bucket_name is required. Set 's3_bucket_name' in proxy config or AWS_S3_BUCKET_NAME for Bedrock file content retrieval." - ) - return bucket_name + def _get_configured_s3_bucket_name( + self, litellm_params: Mapping[str, object] + ) -> str: + from .transformation import get_configured_s3_bucket_name + + return get_configured_s3_bucket_name(litellm_params) async def afile_content( self, diff --git a/litellm/llms/bedrock/files/transformation.py b/litellm/llms/bedrock/files/transformation.py index cec2e934af8..6cfaa88275d 100644 --- a/litellm/llms/bedrock/files/transformation.py +++ b/litellm/llms/bedrock/files/transformation.py @@ -1,23 +1,37 @@ +import base64 import json import os import time -from typing import Any, Dict, List, Optional, Tuple, Union +from collections.abc import Mapping, MutableMapping +from types import MappingProxyType +from typing import ( + Any, + Dict, + List, + Optional, + Tuple, + Union, +) from urllib.parse import unquote import httpx from httpx import Headers, Response from openai.types.file_deleted import FileDeleted +from pydantic import BaseModel, ConfigDict from litellm._logging import verbose_logger from litellm._uuid import uuid from litellm.files.utils import FilesAPIUtils from litellm.litellm_core_utils.cloud_storage_security import ( BEDROCK_MANAGED_S3_BATCH_PREFIX, + BEDROCK_MANAGED_S3_PREFIXES, BEDROCK_MANAGED_S3_UPLOAD_PREFIX, build_managed_cloud_object_name, encode_s3_object_key_for_url, sanitize_cloud_object_component, + should_allow_legacy_cloud_file_ids, split_configured_cloud_bucket_name, + validate_managed_cloud_file_id, ) from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data from litellm.llms.base_llm.chat.transformation import BaseLLMException @@ -28,18 +42,98 @@ from litellm.llms.base_llm.files.transformation import ( from litellm.types.llms.openai import ( AllMessageValues, CreateFileRequest, + FileContentRequest, FileTypes, HttpxBinaryResponseContent, OpenAICreateFileRequestOptionalParams, OpenAIFileObject, PathLike, ) -from litellm.types.utils import ExtractedFileData, LlmProviders +from litellm.types.utils import ExtractedFileData, LlmProviders, SpecialEnums from litellm.utils import get_llm_provider from ..base_aws_llm import BaseAWSLLM from ..common_utils import BedrockError +# litellm_params key used to hand the SigV4-signed GET headers from +# `transform_file_content_request` to `validate_environment` (the only hook +# the shared file-content HTTP handler exposes for setting request headers). +# Same pattern as the `upload_url` handoff in `transform_create_file_request`. +S3_SIGNED_GET_HEADERS_PARAM = "_s3_signed_get_headers" + + +class _BedrockS3RequestParams(BaseModel): + """Typed view of the credential/region params the S3 GetObject path reads.""" + + model_config = ConfigDict(extra="ignore") + + aws_access_key_id: str | None = None + aws_secret_access_key: str | None = None + aws_session_token: str | None = None + aws_region_name: str | None = None + aws_session_name: str | None = None + aws_profile_name: str | None = None + aws_role_name: str | None = None + aws_web_identity_token: str | None = None + aws_sts_endpoint: str | None = None + s3_region_name: str | None = None + s3_endpoint_url: str | None = None + + +class _TrustedS3ModelCredentials(BaseModel): + """The S3 bucket the server trusts file ids against, from the deployment snapshot.""" + + model_config = ConfigDict(extra="ignore") + + s3_bucket_name: str | None = None + + +def extract_s3_uri_from_file_id(file_id: str) -> str: + """ + Resolve a Bedrock file id to its S3 URI. + + Accepts either a base64-encoded LiteLLM unified file id (whose decoded + form carries `llm_output_file_id,s3://...`) or a direct `s3://` URI. + """ + try: + padded = file_id + "=" * (-len(file_id) % 4) + decoded = base64.urlsafe_b64decode(padded).decode() + + if decoded.startswith(SpecialEnums.LITELM_MANAGED_FILE_ID_PREFIX.value): + if "llm_output_file_id," in decoded: + return decoded.split("llm_output_file_id,")[1].split(";")[0] + except Exception: + pass + + if file_id.startswith("s3://"): + return file_id + + raise ValueError("file_id must be a managed LiteLLM S3 file id") + + +def get_configured_s3_bucket_name(litellm_params: Mapping[str, object]) -> str: + """ + Resolve the server-configured S3 bucket for Bedrock file operations. + + Only trusts the immutable server-side credential snapshot or the + environment; never a request-supplied param, since the bucket is what + `validate_managed_cloud_file_id` checks file ids against. + """ + trusted_model_credentials = litellm_params.get( + "_litellm_internal_model_credentials" + ) + bucket_name: str | None = None + if isinstance(trusted_model_credentials, MappingProxyType): + snapshot: dict[str, object] = {} + snapshot.update(trusted_model_credentials) # any-ok: untyped snapshot + bucket_name = _TrustedS3ModelCredentials.model_validate(snapshot).s3_bucket_name + bucket_name = bucket_name or os.getenv("AWS_S3_BUCKET_NAME") + if not bucket_name: + raise ValueError( + "S3 bucket_name is required. Set 's3_bucket_name' in proxy config or AWS_S3_BUCKET_NAME for Bedrock file content retrieval." + ) + return bucket_name + class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): """ @@ -63,16 +157,21 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): def validate_environment( self, - headers: dict, + headers: MutableMapping[str, object], model: str, messages: List[AllMessageValues], optional_params: dict, - litellm_params: dict, - api_key: Optional[str] = None, - api_base: Optional[str] = None, + litellm_params: MutableMapping[str, object], + api_key: str | None = None, + api_base: str | None = None, ) -> dict: - # No additional headers needed for S3 uploads - AWS credentials handled by BaseAWSLLM - return headers + result: dict[str, object] = {} + result.update(headers) + signed_headers = litellm_params.pop(S3_SIGNED_GET_HEADERS_PARAM, None) + if isinstance(signed_headers, Mapping): + result.update(signed_headers) # any-ok: untyped handoff headers + # otherwise no extra headers - AWS credentials are handled by BaseAWSLLM + return result def _get_content_from_openai_file(self, openai_file_content: FileTypes) -> str: """ @@ -927,23 +1026,114 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): def transform_file_content_request( self, - file_content_request, - optional_params: dict, - litellm_params: dict, - ) -> tuple[str, dict]: - raise NotImplementedError( - "BedrockFilesConfig does not support file content retrieval" + file_content_request: FileContentRequest, + optional_params: Mapping[str, object], + litellm_params: MutableMapping[str, object], + ) -> tuple[str, dict[str, str]]: + """ + Build a SigV4-signed S3 GetObject request for a Bedrock batch file. + + Bedrock batch file ids are `s3://bucket/key` URIs (or unified ids + that decode to one); the bucket and key are validated against the + server-configured bucket before any request is signed. + """ + file_id = file_content_request.get("file_id") + if not file_id: + raise ValueError("file_id is required for Bedrock file content retrieval") + + s3_uri = extract_s3_uri_from_file_id(file_id) + bucket_name, object_key = validate_managed_cloud_file_id( + file_id=s3_uri, + scheme="s3://", + configured_bucket_name=get_configured_s3_bucket_name(litellm_params), + allowed_object_prefixes=BEDROCK_MANAGED_S3_PREFIXES, + allow_legacy_cloud_file_ids=should_allow_legacy_cloud_file_ids( + litellm_params + ), ) + # The shared file-content handler passes optional_params={}, so AWS + # credentials/region arrive via litellm_params here (unlike the upload + # path). s3_region_name wins over aws_region_name, same priority as + # get_complete_file_url above. + merged_params: dict[str, object] = {} + merged_params.update(litellm_params) + merged_params.update(optional_params) + request_params = _BedrockS3RequestParams.model_validate(merged_params) + + region_preference = ( + request_params.s3_region_name or request_params.aws_region_name + ) + region_params: dict[str, str | None] = {"aws_region_name": region_preference} + aws_region_name = self._get_aws_region_name( + optional_params=region_params, model="" + ) + + s3_endpoint_url = ( + request_params.s3_endpoint_url + or f"https://s3.{aws_region_name}.amazonaws.com" + ).rstrip("/") + url = f"{s3_endpoint_url}/{bucket_name}/{encode_s3_object_key_for_url(object_key)}" + + litellm_params[S3_SIGNED_GET_HEADERS_PARAM] = self._sign_s3_get_request( + api_base=url, + aws_region_name=aws_region_name, + request_params=request_params, + ) + return url, {} + + def _sign_s3_get_request( + self, + api_base: str, + aws_region_name: str, + request_params: _BedrockS3RequestParams, + ) -> dict[str, str]: + """ + SigV4-sign an S3 GetObject request, mirroring `_sign_s3_request` (PUT). + """ + try: + import hashlib + + from botocore.auth import SigV4Auth + from botocore.awsrequest import AWSRequest + except ImportError: + raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.") + + credentials = self.get_credentials( # any-ok: boto3 Credentials is untyped + aws_access_key_id=request_params.aws_access_key_id, + aws_secret_access_key=request_params.aws_secret_access_key, + aws_session_token=request_params.aws_session_token, + aws_region_name=aws_region_name, + aws_session_name=request_params.aws_session_name, + aws_profile_name=request_params.aws_profile_name, + aws_role_name=request_params.aws_role_name, + aws_web_identity_token=request_params.aws_web_identity_token, + aws_sts_endpoint=request_params.aws_sts_endpoint, + ) + + empty_body_hash = hashlib.sha256(b"").hexdigest() + aws_request = AWSRequest( # any-ok: botocore AWSRequest is untyped + method="GET", + url=api_base, + headers={"x-amz-content-sha256": empty_body_hash}, + ) + auth = SigV4Auth(credentials, "s3", aws_region_name) # any-ok: botocore untyped + auth.add_auth(aws_request) # any-ok: botocore request mutation is untyped + return dict(aws_request.headers) # any-ok: botocore headers are untyped + def transform_file_content_response( self, raw_response: httpx.Response, logging_obj: LiteLLMLoggingObj, litellm_params: dict, ) -> HttpxBinaryResponseContent: - raise NotImplementedError( - "BedrockFilesConfig does not support file content retrieval" - ) + if raw_response.status_code >= 400: + raise BedrockError( + status_code=raw_response.status_code, + message=raw_response.text, + headers=raw_response.headers, + ) + return HttpxBinaryResponseContent(response=raw_response) class BedrockJsonlFilesTransformation: diff --git a/litellm/llms/bedrock_mantle/chat/transformation.py b/litellm/llms/bedrock_mantle/chat/transformation.py index 18f051f8524..f688cea10f1 100644 --- a/litellm/llms/bedrock_mantle/chat/transformation.py +++ b/litellm/llms/bedrock_mantle/chat/transformation.py @@ -4,8 +4,10 @@ Amazon Bedrock Mantle - OpenAI-compatible inference engine in Amazon Bedrock. API docs: https://docs.aws.amazon.com/bedrock/latest/userguide/bedrock-mantle.html Base URL: https://bedrock-mantle.{region}.api.aws/v1 -Auth: AWS Bedrock API key as Bearer token (set via BEDROCK_MANTLE_API_KEY env var) - or region-aware key via BEDROCK_MANTLE_{REGION}_API_KEY. +Auth: Bearer token (litellm_params.api_key, BEDROCK_MANTLE_API_KEY, or the + standard AWS_BEARER_TOKEN_BEDROCK) when present; otherwise AWS SigV4 + (service "bedrock") over the standard credential chain. See + BedrockMantleAuthMixin in common_utils. """ from typing import Iterator, AsyncIterator, Any, List, Optional, Tuple, Union @@ -13,20 +15,27 @@ from typing import Iterator, AsyncIterator, Any, List, Optional, Tuple, Union import litellm from litellm._logging import verbose_logger from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM +from litellm.llms.bedrock_mantle.common_utils import ( + BEDROCK_MANTLE_DEFAULT_REGION, + BedrockMantleAuthMixin, +) from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import AllMessageValues from litellm.types.router import GenericLiteLLMParams +from ..common_utils import mantle_base_segment from ...openai_like.chat.transformation import OpenAILikeChatConfig -BEDROCK_MANTLE_DEFAULT_REGION = "us-east-1" - -class BedrockMantleChatConfig(OpenAILikeChatConfig): +class BedrockMantleChatConfig(BedrockMantleAuthMixin, OpenAILikeChatConfig): """ Transformation config for Amazon Bedrock Mantle OpenAI-compatible API. """ + def __init__(self, aws_signer: BaseAWSLLM | None = None): + super().__init__() + self._aws_signer = aws_signer or BaseAWSLLM() + @property def custom_llm_provider(self) -> Optional[str]: return "bedrock_mantle" @@ -40,6 +49,7 @@ class BedrockMantleChatConfig(OpenAILikeChatConfig): api_base: Optional[str], api_key: Optional[str], litellm_params: Optional[GenericLiteLLMParams] = None, + model: str | None = None, ) -> Tuple[Optional[str], Optional[str]]: region = ( (litellm_params.aws_region_name if litellm_params else None) @@ -49,12 +59,15 @@ class BedrockMantleChatConfig(OpenAILikeChatConfig): or BEDROCK_MANTLE_DEFAULT_REGION ) BaseAWSLLM._validate_aws_region_name(region) + # The base path segment is data-driven per model (use_openai_responses_path + # flag): gemma-4-* and gpt-5.x are served on /openai/v1, everything else on + # /v1. An explicit api_base still wins over the derived default. api_base = ( api_base or get_secret_str("BEDROCK_MANTLE_API_BASE") - or f"https://bedrock-mantle.{region}.api.aws/v1" + or f"https://bedrock-mantle.{region}.api.aws/{mantle_base_segment(model, litellm.model_cost)}" ) - dynamic_api_key = api_key or get_secret_str("BEDROCK_MANTLE_API_KEY") + dynamic_api_key = self._resolve_bearer_token(api_key) return api_base, dynamic_api_key def validate_environment( diff --git a/litellm/llms/bedrock_mantle/common_utils.py b/litellm/llms/bedrock_mantle/common_utils.py new file mode 100644 index 00000000000..d517ab940ce --- /dev/null +++ b/litellm/llms/bedrock_mantle/common_utils.py @@ -0,0 +1,147 @@ +"""Shared auth, region resolution, and routing helpers for the Amazon Bedrock Mantle provider. + +Mantle authenticates with a Bearer token when one is available +(litellm_params.api_key, BEDROCK_MANTLE_API_KEY, or the standard +AWS_BEARER_TOKEN_BEDROCK); otherwise it falls back to AWS SigV4 (service +"bedrock") over the standard credential chain (IAM role / access key / profile / +web identity). The Chat Completions and Responses backends share this behaviour +through BedrockMantleAuthMixin so the two paths can never drift apart. + +The two routing helpers (mantle_supports_responses, mantle_base_segment) are +pure functions of (model, model_cost) so they can be unit-tested without patching +global state. +""" + +import re +from typing import Tuple + +from botocore.exceptions import ( + CredentialRetrievalError, + NoCredentialsError, + PartialCredentialsError, + ProfileNotFound, +) + +from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM +from litellm.secret_managers.main import get_secret_str + +BEDROCK_MANTLE_DEFAULT_REGION = "us-east-1" + +# Standard Mantle host: https://bedrock-mantle..api.aws (group 1 = region). +MANTLE_HOST_RE = re.compile( + r"^https?://bedrock-mantle\.([^/.]+)\.api\.aws", re.IGNORECASE +) + + +class BedrockMantleAuthMixin: + _aws_signer: BaseAWSLLM + + @staticmethod + def _resolve_bearer_token(api_key: str | None) -> str | None: + return ( + api_key + or get_secret_str("BEDROCK_MANTLE_API_KEY") + or get_secret_str("AWS_BEARER_TOKEN_BEDROCK") + ) + + @staticmethod + def _resolve_region(params: dict) -> str: + region = params.get("aws_region_name") + if region: + BaseAWSLLM._validate_aws_region_name(region) + return region + base = params.get("api_base") or get_secret_str("BEDROCK_MANTLE_API_BASE") + if base: + match = MANTLE_HOST_RE.match(base.rstrip("/")) + if match: + return match.group(1) + return ( + get_secret_str("BEDROCK_MANTLE_REGION") + or get_secret_str("AWS_REGION_NAME") + or get_secret_str("AWS_REGION") + or BEDROCK_MANTLE_DEFAULT_REGION + ) + + def sign_request( + self, + headers: dict, + optional_params: dict, + request_data: dict, + api_base: str, + api_key: str | None = None, + model: str | None = None, + stream: bool | None = None, + fake_stream: bool | None = None, + ) -> Tuple[dict, bytes | None]: + bearer = self._resolve_bearer_token(api_key) + if not bearer: + # Pin the credential-scope region to the region of the actual signing URL + # so the SigV4 scope and URL host can never disagree, even when a stale + # api_base and aws_region_name point at different regions. + host_match = MANTLE_HOST_RE.match(api_base.rstrip("/")) + optional_params = { + **optional_params, + "aws_region_name": ( + host_match.group(1) + if host_match + else self._resolve_region({**optional_params, "api_base": api_base}) + ), + } + headers = {k: v for k, v in headers.items() if k.lower() != "authorization"} + try: + return self._aws_signer._sign_request( + service_name="bedrock", + headers=headers, + optional_params=optional_params, + request_data=request_data, + api_base=api_base, + api_key=bearer, + model=model, + stream=stream, + fake_stream=fake_stream, + ) + except ( + NoCredentialsError, + PartialCredentialsError, + ProfileNotFound, + CredentialRetrievalError, + ) as e: + raise ValueError( + "Bedrock Mantle auth failed: no Bearer token and no usable AWS " + "credentials. Set BEDROCK_MANTLE_API_KEY (or AWS_BEARER_TOKEN_BEDROCK) " + "or pass api_key for Bearer auth, or provide AWS credentials " + "(IAM role / access key / profile / web identity) for SigV4." + ) from e + + +def mantle_supports_responses(model: str | None, model_cost: dict) -> bool: + """Whether a Bedrock Mantle model can serve the native Responses API. + + Purely data-driven from the model's price-map capability signal -- either + /v1/responses in supported_endpoints, or mode=responses -- both overridable + via register_model and proxy model_info, so onboarding a model is a JSON + change, never a code change. There is deliberately NO model-name match here: + capability is per-model, not per-family (openai.gpt-oss-120b supports + Responses while openai.gpt-oss-safeguard-120b does not, despite sharing the + gpt-oss substring), so a substring gate would be wrong. A model absent from + model_cost simply has no signal and returns False (chat-completions emulation). + """ + entry = model_cost.get(f"bedrock_mantle/{model}", {}) + if "/v1/responses" in (entry.get("supported_endpoints") or []): + return True + return entry.get("mode") == "responses" + + +def mantle_base_segment(model: str | None, model_cost: dict) -> str: + """Return the base path segment for a Bedrock Mantle model's OpenAI surface. + + Data-driven from the model's price-map use_openai_responses_path flag + (overridable via register_model / proxy model_info). Per the AWS model cards, + gpt-5.x and the google gemma-4-* family carry that flag and are served on the + /openai/v1 base (.../openai/v1/responses and .../openai/v1/chat/completions); + every other model including gpt-oss uses the standard /v1 base. The segment is + the base for the model's whole OpenAI-compatible surface, so both the chat and + responses configs derive from it -- there is no separate model-name rule. + """ + entry = model_cost.get(f"bedrock_mantle/{model}", {}) + return "openai/v1" if entry.get("use_openai_responses_path") is True else "v1" diff --git a/litellm/llms/bedrock_mantle/responses/transformation.py b/litellm/llms/bedrock_mantle/responses/transformation.py index b409666a967..2e30f85fd0e 100644 --- a/litellm/llms/bedrock_mantle/responses/transformation.py +++ b/litellm/llms/bedrock_mantle/responses/transformation.py @@ -15,26 +15,20 @@ role / access key / profile / web identity), signed via the shared BaseAWSLLM._sign_request after the request body is finalized. """ -import re -from typing import Any, Dict, List, Optional, Tuple - -from botocore.exceptions import ( - CredentialRetrievalError, - NoCredentialsError, - PartialCredentialsError, - ProfileNotFound, -) +from typing import Any, Dict, List, Optional from litellm._logging import verbose_logger from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM +from litellm.llms.bedrock_mantle.common_utils import ( + MANTLE_HOST_RE, + BedrockMantleAuthMixin, +) from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import ResponsesAPIOptionalRequestParams from litellm.types.router import GenericLiteLLMParams from litellm.types.utils import LlmProviders -BEDROCK_MANTLE_DEFAULT_REGION = "us-east-1" - # Checked longest/most-specific first so a full endpoint URL collapses to host # in one pass and the appended path never doubles. _BASE_SUFFIXES_TO_STRIP = ( @@ -45,18 +39,13 @@ _BASE_SUFFIXES_TO_STRIP = ( "/v1", ) -# Standard Mantle host: https://bedrock-mantle..api.aws (group 1 = region). -_MANTLE_HOST_RE = re.compile( - r"^https?://bedrock-mantle\.([^/.]+)\.api\.aws", re.IGNORECASE -) - # Per Bedrock Mantle Responses API validation errors. _BEDROCK_MANTLE_SUPPORTED_RESPONSE_TOOL_TYPES = frozenset( {"function", "mcp", "custom", "namespace", "tool_search"} ) -class BedrockMantleResponsesAPIConfig(OpenAIResponsesAPIConfig): +class BedrockMantleResponsesAPIConfig(BedrockMantleAuthMixin, OpenAIResponsesAPIConfig): def __init__( self, aws_signer: Optional[BaseAWSLLM] = None, @@ -70,24 +59,6 @@ class BedrockMantleResponsesAPIConfig(OpenAIResponsesAPIConfig): def custom_llm_provider(self) -> LlmProviders: return LlmProviders.BEDROCK_MANTLE - @staticmethod - def _resolve_region(params: dict) -> str: - region = params.get("aws_region_name") - if region: - BaseAWSLLM._validate_aws_region_name(region) - return region - base = params.get("api_base") or get_secret_str("BEDROCK_MANTLE_API_BASE") - if base: - match = _MANTLE_HOST_RE.match(base.rstrip("/")) - if match: - return match.group(1) - return ( - get_secret_str("BEDROCK_MANTLE_REGION") - or get_secret_str("AWS_REGION_NAME") - or get_secret_str("AWS_REGION") - or BEDROCK_MANTLE_DEFAULT_REGION - ) - def get_complete_url( self, api_base: Optional[str], @@ -107,7 +78,7 @@ class BedrockMantleResponsesAPIConfig(OpenAIResponsesAPIConfig): # For the standard Mantle host (including the default-region base that # responses/main.py auto-injects into litellm_params.api_base), pin to the # single resolved region so aws_region_name wins; preserve custom proxy hosts. - if _MANTLE_HOST_RE.match(base): + if MANTLE_HOST_RE.match(base): base = f"https://bedrock-mantle.{region}.api.aws" path = "/openai/v1/responses" if self.use_openai_path else "/v1/responses" return f"{base}{path}" @@ -116,13 +87,9 @@ class BedrockMantleResponsesAPIConfig(OpenAIResponsesAPIConfig): self, headers: dict, model: str, litellm_params: Optional[GenericLiteLLMParams] ) -> dict: litellm_params = litellm_params or GenericLiteLLMParams() - api_key = ( - litellm_params.api_key - or get_secret_str("BEDROCK_MANTLE_API_KEY") - or get_secret_str("AWS_BEARER_TOKEN_BEDROCK") - ) - if api_key: - headers["Authorization"] = f"Bearer {api_key}" + bearer = self._resolve_bearer_token(litellm_params.api_key) + if bearer: + headers["Authorization"] = f"Bearer {bearer}" if litellm_params.aws_bedrock_project_id: headers["OpenAI-Project"] = litellm_params.aws_bedrock_project_id return headers @@ -182,58 +149,3 @@ class BedrockMantleResponsesAPIConfig(OpenAIResponsesAPIConfig): params.pop("tools", None) return params - - def sign_request( - self, - headers: dict, - optional_params: dict, - request_data: dict, - api_base: str, - api_key: Optional[str] = None, - model: Optional[str] = None, - stream: Optional[bool] = None, - fake_stream: Optional[bool] = None, - ) -> Tuple[dict, Optional[bytes]]: - bearer = ( - api_key - or get_secret_str("BEDROCK_MANTLE_API_KEY") - or get_secret_str("AWS_BEARER_TOKEN_BEDROCK") - ) - if not bearer: - # SigV4 path. Pin the credential-scope region to the region of the actual - # signing URL (api_base, already region-resolved by get_complete_url) so the - # SigV4 scope and the URL host can never disagree. Resolve from api_base first, - # then fall back to the regular precedence. Also drop any caller Authorization - # so _sign_request's restore-original-Authorization step cannot override the - # SigV4 header. - optional_params = { - **optional_params, - "aws_region_name": self._resolve_region( - {**optional_params, "api_base": api_base} - ), - } - headers = {k: v for k, v in headers.items() if k.lower() != "authorization"} - try: - return self._aws_signer._sign_request( - service_name="bedrock", - headers=headers, - optional_params=optional_params, - request_data=request_data, - api_base=api_base, - api_key=bearer, - model=model, - stream=stream, - fake_stream=fake_stream, - ) - except ( - NoCredentialsError, - PartialCredentialsError, - ProfileNotFound, - CredentialRetrievalError, - ) as e: - raise ValueError( - "Bedrock Mantle auth failed: no Bearer token and no usable AWS " - "credentials. Set BEDROCK_MANTLE_API_KEY (or AWS_BEARER_TOKEN_BEDROCK) " - "or pass api_key for Bearer auth, or provide AWS credentials " - "(IAM role / access key / profile / web identity) for SigV4." - ) from e diff --git a/litellm/llms/custom_httpx/aiohttp_transport.py b/litellm/llms/custom_httpx/aiohttp_transport.py index 62f707b3622..b97a59a93a6 100644 --- a/litellm/llms/custom_httpx/aiohttp_transport.py +++ b/litellm/llms/custom_httpx/aiohttp_transport.py @@ -116,6 +116,16 @@ class AiohttpResponseStream(httpx.AsyncByteStream): # For other exceptions, use the normal mapping with map_aiohttp_exceptions(): raise + finally: + # Release the aiohttp connection when iteration ends for any + # reason (read timeout, cancellation from a client disconnect, + # GeneratorExit). Without this, abnormally terminated streams + # permanently hold a slot in the TCPConnector pool; once the + # pool is exhausted every request to that host times out (408) + # until the proxy is restarted, even after the backend recovers. + # On a fully-read response the connection was already released + # at EOF and close() is a no-op. + self._aiohttp_response.close() async def aclose(self) -> None: with map_aiohttp_exceptions(): diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index 8ac5b47c6e7..790bd0519d7 100644 --- a/litellm/llms/custom_httpx/llm_http_handler.py +++ b/litellm/llms/custom_httpx/llm_http_handler.py @@ -2407,11 +2407,17 @@ class BaseLLMHTTPHandler: provider_config=responses_api_provider_config, ) - return responses_api_provider_config.transform_response_api_response( - model=model, - raw_response=response, - logging_obj=logging_obj, + initial_response = ( + responses_api_provider_config.transform_response_api_response( + model=model, + raw_response=response, + logging_obj=logging_obj, + ) ) + # Responses agentic interception (e.g. code interpreter) runs the follow-up + # loop via the async hook, so it is async-only for now; the sync path returns + # the initial response unchanged. + return initial_response async def async_response_api_handler( self, @@ -2570,12 +2576,44 @@ class BaseLLMHTTPHandler: provider_config=responses_api_provider_config, ) - return responses_api_provider_config.transform_response_api_response( - model=model, - raw_response=response, - logging_obj=logging_obj, + initial_response = ( + responses_api_provider_config.transform_response_api_response( + model=model, + raw_response=response, + logging_obj=logging_obj, + ) ) + final_response = await self._call_agentic_completion_hooks( + response=initial_response, + model=model, + messages=( + input + if isinstance(input, list) + else [{"role": "user", "content": input}] + ), + anthropic_messages_provider_config=responses_api_provider_config, + anthropic_messages_optional_request_params=response_api_optional_request_params, + logging_obj=logging_obj, + stream=False, + custom_llm_provider=custom_llm_provider, + kwargs=dict(litellm_params), + api_surface="responses", + ) + + result = final_response if final_response is not None else initial_response + if litellm_params.get( + "_code_interpreter_interception_converted_stream" + ) and not litellm_params.get("_agentic_loop_depth"): + return self._wrap_responses_response_as_fake_stream( + result=result, + model=model, + responses_api_provider_config=responses_api_provider_config, + logging_obj=logging_obj, + custom_llm_provider=custom_llm_provider, + ) + return result + async def async_delete_response_api_handler( self, response_id: str, @@ -4875,6 +4913,132 @@ class BaseLLMHTTPHandler: return response + async def _execute_responses_agentic_plan( + self, + plan: AgenticLoopPlan, + model: str, + response_api_optional_request_params: dict, + logging_obj: "LiteLLMLoggingObj", + kwargs: dict, + depth: int, + max_loops: int, + fingerprints: list[str], + fingerprint: str, + callback: Any | None = None, + ) -> Any: + patch = plan.request_patch or AgenticLoopRequestPatch() + if patch.messages is None: + raise ValueError("Agentic loop plan missing patched responses input") + + optional_params = dict(response_api_optional_request_params) + optional_params.update(patch.optional_params) + if patch.tools is not None: + optional_params["tools"] = patch.tools + optional_params = { + k: v + for k, v in optional_params.items() + if k != "stream" and k != "_code_interpreter_interception_converted_stream" + } + + internal_keys = {"litellm_logging_obj"} + kwargs_for_followup = { + k: v + for k, v in kwargs.items() + if not k.startswith("_websearch_interception") + and not k.startswith("_compression_interception") + and k != "_code_interpreter_interception_converted_stream" + and k not in internal_keys + and k not in optional_params + } + kwargs_for_followup.update(patch.kwargs) + kwargs_for_followup["_agentic_loop_depth"] = depth + 1 + kwargs_for_followup["max_agentic_loops"] = max_loops + kwargs_for_followup["_agentic_loop_fingerprints"] = fingerprints + [fingerprint] + + try: + response = await litellm.aresponses( + model=patch.model or model, + input=patch.messages, + **optional_params, + **kwargs_for_followup, + ) + + if callback is not None: + try: + response = await callback.async_post_agentic_loop_response_hook( + response=response, plan=plan, kwargs=kwargs + ) + except Exception as e: + _call_id = getattr(logging_obj, "litellm_call_id", "unknown") + verbose_logger.exception( + "LiteLLM.AgenticHookError: Exception in " + "async_post_agentic_loop_response_hook [call_id=%s model=%s]: %s", + _call_id, + model, + str(e), + ) + + return response + finally: + if callback is not None: + await self._run_agentic_loop_cleanup( + callback=callback, + plan=plan, + kwargs=kwargs, + logging_obj=logging_obj, + model=model, + ) + + @staticmethod + async def _run_agentic_loop_cleanup( + callback: Any, + plan: AgenticLoopPlan, + kwargs: dict, + logging_obj: "LiteLLMLoggingObj", + model: str, + ) -> None: + try: + await callback.async_agentic_loop_cleanup_hook(plan=plan, kwargs=kwargs) + except Exception as e: + _call_id = getattr(logging_obj, "litellm_call_id", "unknown") + verbose_logger.exception( + "LiteLLM.AgenticHookError: Exception in " + "async_agentic_loop_cleanup_hook [call_id=%s model=%s]: %s", + _call_id, + model, + str(e), + ) + + def _wrap_responses_response_as_fake_stream( + self, + result: Any, + model: str, + responses_api_provider_config: Any, + logging_obj: "LiteLLMLoggingObj", + custom_llm_provider: str, + ) -> Any: + """ + Wrap a completed responses result as a synthetic stream. + + Used when an interceptor forced stream=False to run the agentic loop on + the non-streaming path, but the caller originally asked for streaming. + """ + import httpx + + from litellm.responses.streaming_iterator import ( + MockResponsesAPIStreamingIterator, + ) + + payload = result.model_dump() if hasattr(result, "model_dump") else result + raw_response = httpx.Response(status_code=200, json=payload) + return MockResponsesAPIStreamingIterator( + response=raw_response, + model=model, + responses_api_provider_config=responses_api_provider_config, + logging_obj=logging_obj, + custom_llm_provider=custom_llm_provider, + ) + async def _execute_chat_completion_agentic_plan( self, plan: AgenticLoopPlan, @@ -4940,6 +5104,7 @@ class BaseLLMHTTPHandler: stream: bool, custom_llm_provider: str, kwargs: Dict, + api_surface: str = "anthropic_messages", ) -> Optional[Any]: """ Call agentic completion hooks for all custom loggers (Anthropic Messages API). @@ -5046,6 +5211,20 @@ class BaseLLMHTTPHandler: if not plan.run_agentic_loop: continue + if api_surface == "responses": + return await self._execute_responses_agentic_plan( + plan=plan, + model=model, + response_api_optional_request_params=anthropic_messages_optional_request_params, + logging_obj=logging_obj, + kwargs=kwargs_with_provider, + depth=depth, + max_loops=max_loops, + fingerprints=fingerprints, + fingerprint=fingerprint, + callback=callback, + ) + return await self._execute_anthropic_agentic_plan( plan=plan, model=model, @@ -5083,7 +5262,7 @@ class BaseLLMHTTPHandler: else False ) - if websearch_converted_stream: + if api_surface == "anthropic_messages" and websearch_converted_stream: from typing import cast from litellm._logging import verbose_logger diff --git a/litellm/llms/e2b/__init__.py b/litellm/llms/e2b/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/litellm/llms/e2b/sandbox/__init__.py b/litellm/llms/e2b/sandbox/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/litellm/llms/e2b/sandbox/transformation.py b/litellm/llms/e2b/sandbox/transformation.py new file mode 100644 index 00000000000..c279fab22ab --- /dev/null +++ b/litellm/llms/e2b/sandbox/transformation.py @@ -0,0 +1,224 @@ +""" +e2b sandbox provider. + +Talks to e2b's REST API directly over httpx (no e2b SDK dependency): + - create: POST {api_base}/sandboxes + - run: POST https://{JUPYTER_PORT}-{sandboxID}.{domain}/execute (NDJSON stream) + - delete: DELETE {api_base}/sandboxes/{sandboxID} +""" + +import json +from typing import Union, cast + +import httpx + +from litellm.llms.base_llm.sandbox.transformation import ( + BaseSandboxConfig, + CodeExecutionResult, + ContainerHandle, +) +from litellm.llms.custom_httpx.http_handler import ( + AsyncHTTPHandler, + get_async_httpx_client, +) +from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.custom_http import httpxSpecialProvider + +E2B_API_BASE = "https://api.e2b.app" +E2B_DEFAULT_TEMPLATE = "code-interpreter-v1" +E2B_DEFAULT_DOMAIN = "e2b.app" +JUPYTER_PORT = 49999 +DEFAULT_SANDBOX_TIMEOUT = 300 +MAX_OUTPUT_BYTES = 10 * 1024 * 1024 + + +class E2BSandboxConfig(BaseSandboxConfig): + def _http(self, client: AsyncHTTPHandler | None) -> AsyncHTTPHandler: + if client is not None: + return client + return get_async_httpx_client(llm_provider=httpxSpecialProvider.Sandbox) + + def validate_environment(self, api_key: str | None = None, **kwargs) -> str: + key = api_key or get_secret_str("E2B_API_KEY") + if not key: + raise ValueError("E2B API key not set. Set E2B_API_KEY or pass api_key=...") + return key + + async def acreate_sandbox( + self, + *, + template: str | None = None, + timeout: int | None = None, + allow_internet_access: bool = True, + api_key: str | None = None, + api_base: str | None = None, + metadata: dict | None = None, + client: AsyncHTTPHandler | None = None, + **kwargs, + ) -> ContainerHandle: + key = self.validate_environment(api_key=api_key) + base = api_base or E2B_API_BASE + body = { + "templateID": template or E2B_DEFAULT_TEMPLATE, + "timeout": timeout if timeout is not None else DEFAULT_SANDBOX_TIMEOUT, + "secure": True, + "allow_internet_access": allow_internet_access, + } + if metadata: + body["metadata"] = metadata + + response = cast( + httpx.Response, + await self._http(client).post( + url=f"{base}/sandboxes", + headers={"X-API-Key": key, "Content-Type": "application/json"}, + json=body, + ), + ) + data = response.json() + + handle = ContainerHandle( + id=data["sandboxID"], + provider="e2b", + domain=data.get("domain") or E2B_DEFAULT_DOMAIN, + ) + handle._hidden_params = { + "envd_access_token": data.get("envdAccessToken"), + "traffic_access_token": data.get("trafficAccessToken"), + "api_key": key, + "api_base": base, + } + return handle + + async def arun_code( + self, + *, + container: Union[ContainerHandle, str], + code: str, + api_key: str | None = None, + env_vars: dict | None = None, + client: AsyncHTTPHandler | None = None, + **kwargs, + ) -> CodeExecutionResult: + handle = self._as_handle(container) + + token = handle._hidden_params.get("envd_access_token") + if not token: + raise ValueError( + "Cannot run code from a sandbox id alone. e2b secure sandboxes " + "require the access token returned by acreate_sandbox; pass the " + "ContainerHandle it returned instead of a bare sandbox id." + ) + + headers = {"Content-Type": "application/json", "X-Access-Token": token} + traffic_token = handle._hidden_params.get("traffic_access_token") + if traffic_token: + headers["E2B-Traffic-Access-Token"] = traffic_token + + url = f"https://{JUPYTER_PORT}-{handle.id}.{handle.domain}/execute" + response = cast( + httpx.Response, + await self._http(client).post( + url=url, + headers=headers, + json={"code": code, "context_id": None, "env_vars": env_vars}, + stream=True, + ), + ) + lines = await self._read_capped_lines(response) + return self._parse_lines(lines) + + async def adelete_sandbox( + self, + *, + container: Union[ContainerHandle, str], + api_key: str | None = None, + api_base: str | None = None, + client: AsyncHTTPHandler | None = None, + **kwargs, + ) -> bool: + handle = self._as_handle(container) + key = ( + api_key + or handle._hidden_params.get("api_key") + or self.validate_environment() + ) + base = api_base or handle._hidden_params.get("api_base") or E2B_API_BASE + try: + response = cast( + httpx.Response, + await self._http(client).delete( + url=f"{base}/sandboxes/{handle.id}", + headers={"X-API-Key": key}, + ), + ) + except httpx.HTTPStatusError as e: + if e.response.status_code == 404: + return False + raise + return 200 <= response.status_code < 300 + + @staticmethod + def _as_handle(container: Union[ContainerHandle, str]) -> ContainerHandle: + if isinstance(container, ContainerHandle): + return container + handle = ContainerHandle( + id=str(container), provider="e2b", domain=E2B_DEFAULT_DOMAIN + ) + handle._hidden_params = {} + return handle + + @staticmethod + async def _read_capped_lines(response: httpx.Response) -> list[str]: + lines: list[str] = [] + total = 0 + async for line in response.aiter_lines(): + total += len(line.encode("utf-8")) + if total > MAX_OUTPUT_BYTES: + raise ValueError( + f"Sandbox output exceeded {MAX_OUTPUT_BYTES} bytes; aborting to " + "avoid unbounded memory use." + ) + lines.append(line) + return lines + + @staticmethod + def _parse_lines(lines: list[str]) -> CodeExecutionResult: + def _try_parse(stripped: str): + try: + return json.loads(stripped) + except json.JSONDecodeError: + return None + + messages = tuple( + parsed + for stripped in (line.strip() for line in lines) + if stripped + for parsed in (_try_parse(stripped),) + if parsed is not None + ) + + def of_type(message_type: str): + return (m for m in messages if m.get("type") == message_type) + + error = next( + ( + {key: m.get(key) for key in ("name", "value", "traceback")} + for m in of_type("error") + ), + None, + ) + execution_count = next( + (m.get("execution_count") for m in of_type("number_of_executions")), + None, + ) + + return CodeExecutionResult( + stdout="".join(m.get("text", "") for m in of_type("stdout")), + stderr="".join(m.get("text", "") for m in of_type("stderr")), + results=[ + {k: v for k, v in m.items() if k != "type"} for m in of_type("result") + ], + error=error, + execution_count=execution_count, + ) diff --git a/litellm/llms/fireworks_ai/chat/transformation.py b/litellm/llms/fireworks_ai/chat/transformation.py index cca3b3da37a..7e4395959b9 100644 --- a/litellm/llms/fireworks_ai/chat/transformation.py +++ b/litellm/llms/fireworks_ai/chat/transformation.py @@ -1,5 +1,15 @@ import json -from typing import Any, List, Literal, Optional, Tuple, Union, cast +from typing import ( + Any, + AsyncIterator, + Iterator, + List, + Literal, + Optional, + Tuple, + Union, + cast, +) import httpx @@ -15,7 +25,6 @@ from litellm.litellm_core_utils.llm_response_utils.get_headers import ( from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import ( AllMessageValues, - ChatCompletionImageObject, ChatCompletionToolParam, OpenAIChatCompletionToolParam, ) @@ -25,6 +34,7 @@ from litellm.types.utils import ( Function, Message, ModelResponse, + ModelResponseStream, ProviderSpecificModelInfo, ) from litellm.utils import ( @@ -34,10 +44,34 @@ from litellm.utils import ( supports_tool_choice, ) -from ...openai.chat.gpt_transformation import OpenAIGPTConfig +from ...openai.chat.gpt_transformation import ( + OpenAIChatCompletionStreamingHandler, + OpenAIGPTConfig, +) from ..common_utils import FireworksAIException +def _extract_fireworks_hidden_params(payload: dict) -> dict: + """ + Collect Fireworks-specific response fields (perf_metrics, prompt_token_ids, + per-choice raw_output and token_ids) from a non-streaming completion payload + or a single streaming chunk, so the same data lands in ``_hidden_params`` on + both response paths. + """ + choices = [c for c in (payload.get("choices") or []) if isinstance(c, dict)] + top_level = { + f"fireworks_{field}": payload[field] + for field in ("perf_metrics", "prompt_token_ids") + if field in payload + } + per_choice = { + f"fireworks_{dest}": [c[field] for c in choices if field in c] + for field, dest in (("raw_output", "raw_outputs"), ("token_ids", "token_ids")) + if any(field in c for c in choices) + } + return {**top_level, **per_choice} + + class FireworksAIConfig(OpenAIGPTConfig): """ Reference: https://docs.fireworks.ai/api-reference/post-chatcompletions @@ -60,8 +94,7 @@ class FireworksAIConfig(OpenAIGPTConfig): logprobs: Optional[int] = None reasoning_effort: Optional[str] = None - # Non OpenAI parameters - Fireworks AI only params - prompt_truncate_length: Optional[int] = None + prompt_truncate_len: Optional[int] = None context_length_exceeded_behavior: Optional[Literal["error", "truncate"]] = None def __init__( @@ -80,7 +113,7 @@ class FireworksAIConfig(OpenAIGPTConfig): user: Optional[str] = None, logprobs: Optional[int] = None, reasoning_effort: Optional[str] = None, - prompt_truncate_length: Optional[int] = None, + prompt_truncate_len: Optional[int] = None, context_length_exceeded_behavior: Optional[Literal["error", "truncate"]] = None, ) -> None: locals_ = locals().copy() @@ -108,8 +141,30 @@ class FireworksAIConfig(OpenAIGPTConfig): "response_format", "user", "logprobs", - "prompt_truncate_length", + "prompt_truncate_len", "context_length_exceeded_behavior", + "seed", + "top_logprobs", + "min_p", + "typical_p", + "repetition_penalty", + "mirostat_target", + "mirostat_lr", + "logit_bias", + "echo", + "echo_last", + "ignore_eos", + "prompt_cache_key", + "prompt_cache_isolation_key", + "raw_output", + "perf_metrics_in_response", + "return_token_ids", + "safe_tokenization", + "service_tier", + "speculation", + "prediction", + "stream_options", + "sampling_mask", ] # Only add tools for models that support function calling @@ -133,9 +188,11 @@ class FireworksAIConfig(OpenAIGPTConfig): if supports_tool_choice(model=model, custom_llm_provider="fireworks_ai"): supported_params.append("tool_choice") - # Only add reasoning_effort for models that support it + # Only add reasoning params for models that support it if supports_reasoning(model=model, custom_llm_provider="fireworks_ai"): supported_params.append("reasoning_effort") + supported_params.append("reasoning_history") + supported_params.append("thinking") return supported_params @@ -151,6 +208,18 @@ class FireworksAIConfig(OpenAIGPTConfig): param == "tools" and value is not None for param, value in non_default_params.items() ) + if ( + non_default_params.get("thinking") is not None + and non_default_params.get("reasoning_effort") is not None + ): + raise litellm.BadRequestError( + message=( + "Fireworks AI chat completions does not support specifying both " + "`thinking` and `reasoning_effort` in the same request." + ), + model=model, + llm_provider="fireworks_ai", + ) for param, value in non_default_params.items(): if param == "tool_choice": @@ -174,40 +243,19 @@ class FireworksAIConfig(OpenAIGPTConfig): optional_params["response_format"] = value elif param == "max_completion_tokens": optional_params["max_tokens"] = value + elif param == "reasoning_effort": + if value is True: + optional_params["reasoning_effort"] = "medium" + elif value is False: + optional_params["reasoning_effort"] = "none" + else: + optional_params["reasoning_effort"] = value elif param in supported_openai_params: if value is not None: optional_params[param] = value return optional_params - def _add_transform_inline_image_block( - self, - content: ChatCompletionImageObject, - model: str, - disable_add_transform_inline_image_block: Optional[bool], - ) -> ChatCompletionImageObject: - """ - Add transform_inline to the image_url (allows non-vision models to parse documents/images/etc.) - - ignore if model is a vision model - - ignore if user has disabled this feature - """ - if ( - "vision" in model or disable_add_transform_inline_image_block - ): # allow user to toggle this feature. - return content - if isinstance(content["image_url"], str): - # Skip base64 data URLs — appending #transform=inline corrupts the - # base64 payload and causes an "Incorrect padding" decode error on - # the Fireworks side. Data URLs are already inlined by definition. - # Lower-case before checking: URI schemes are case-insensitive (RFC 3986). - if not content["image_url"].lower().startswith("data:"): - content["image_url"] = f"{content['image_url']}#transform=inline" - elif isinstance(content["image_url"], dict): - url = content["image_url"]["url"] - if not url.lower().startswith("data:"): - content["image_url"]["url"] = f"{url}#transform=inline" - return content - def _transform_tools( self, tools: List[OpenAIChatCompletionToolParam] ) -> List[OpenAIChatCompletionToolParam]: @@ -225,36 +273,46 @@ class FireworksAIConfig(OpenAIGPTConfig): self, messages: List[AllMessageValues], model: str, litellm_params: dict ) -> List[AllMessageValues]: """ - Add 'transform=inline' to the url of the image_url + Strip fields not permitted by FireworksAI from messages. """ from litellm.litellm_core_utils.prompt_templates.common_utils import ( filter_value_from_dict, - migrate_file_to_image_url, ) - disable_add_transform_inline_image_block = cast( - Optional[bool], - litellm_params.get("disable_add_transform_inline_image_block") - or litellm.disable_add_transform_inline_image_block, + supports_vision_value = self._get_model_cost_capability_exact( + model=model, capability="supports_vision" ) - ## For any 'file' message type with pdf content, move to 'image_url' message type - for message in messages: - if message["role"] == "user": - _message_content = message.get("content") - if _message_content is not None and isinstance(_message_content, list): - for idx, content in enumerate(_message_content): - if content["type"] == "file": - _message_content[idx] = migrate_file_to_image_url(content) for message in messages: if message["role"] == "user": _message_content = message.get("content") if _message_content is not None and isinstance(_message_content, list): for content in _message_content: - if content["type"] == "image_url": - content = self._add_transform_inline_image_block( - content=content, + if not isinstance(content, dict): + continue + if content.get("type") == "file": + raise litellm.BadRequestError( + message=( + "Fireworks AI chat completions does not support " + "file content blocks. For PDFs, convert pages to " + "images and send image_url blocks to a Fireworks " + "vision model, or extract text before calling a " + "text-only model." + ), model=model, - disable_add_transform_inline_image_block=disable_add_transform_inline_image_block, + llm_provider="fireworks_ai", + ) + if ( + content.get("type") == "image_url" + and supports_vision_value is False + ): + raise litellm.BadRequestError( + message=( + f"Fireworks AI model {model} does not support " + "image inputs. Use a Fireworks vision model or " + "remove image_url content blocks." + ), + model=model, + llm_provider="fireworks_ai", ) filter_value_from_dict(cast(dict, message), "cache_control") # Remove fields not permitted by FireworksAI (additionalProperties: false @@ -317,43 +375,55 @@ class FireworksAIConfig(OpenAIGPTConfig): return True return ("-" + key_short + "-") in short_name - def _get_model_cost_capability(self, model: str, capability: str) -> Optional[bool]: + @staticmethod + def _short_model_name(model: str) -> str: short_name = model if short_name.startswith("fireworks_ai/"): short_name = short_name[len("fireworks_ai/") :] if short_name.startswith("accounts/fireworks/models/"): short_name = short_name[len("accounts/fireworks/models/") :] + return short_name - candidate_keys = [ + def _get_model_cost_capability_exact( + self, model: str, capability: str + ) -> Optional[bool]: + short_name = self._short_model_name(model) + candidate_keys = ( model, f"fireworks_ai/{short_name}", f"fireworks_ai/accounts/fireworks/models/{short_name}", - ] - + ) for candidate_key in candidate_keys: model_info = litellm.model_cost.get(candidate_key) if model_info is not None and model_info.get(capability) is not None: return cast(Optional[bool], model_info.get(capability)) + return None - # Fallback: preserve historical substring matching for model name - # variants (e.g. fine-tuned or regionally-suffixed versions of a - # known model). Pick the *longest* matching entry so a more specific - # known model (e.g. "qwen3-8b-instruct") wins over a less specific - # one (e.g. "qwen3-8b") when the query model is more specific still. - # Use hyphen-aligned matching to avoid false positives where a short - # known model name is an unrelated substring of a longer one. - best_match_short: Optional[str] = None - best_match_value: Optional[bool] = None - for key_short, model_info in self._get_fireworks_index(): - if model_info.get(capability) is None: - continue - if not self._matches_on_hyphen_boundary(short_name, key_short): - continue - if best_match_short is None or len(key_short) > len(best_match_short): - best_match_short = key_short - best_match_value = cast(Optional[bool], model_info.get(capability)) + def _get_model_cost_capability(self, model: str, capability: str) -> Optional[bool]: + exact = self._get_model_cost_capability_exact( + model=model, capability=capability + ) + if exact is not None: + return exact - return best_match_value + # Fallback: substring matching for model name variants (e.g. fine-tuned + # or regionally-suffixed versions of a known model). Pick the *longest* + # matching entry so a more specific known model (e.g. "qwen3-8b-instruct") + # wins over a less specific one (e.g. "qwen3-8b"). Hyphen-aligned matching + # avoids false positives where a short known name is an unrelated + # substring of a longer one. This stays a soft signal: capability-gated + # hard rejections use the exact lookup so a fuzzy match never blocks a + # custom deployment. + short_name = self._short_model_name(model) + matches = [ + (key_short, cast(Optional[bool], model_info.get(capability))) + for key_short, model_info in self._get_fireworks_index() + if model_info.get(capability) is not None + and self._matches_on_hyphen_boundary(short_name, key_short) + ] + if not matches: + return None + return max(matches, key=lambda match: len(match[0]))[1] def get_provider_info(self, model: str) -> ProviderSpecificModelInfo: supports_function_calling_value = self._get_model_cost_capability( @@ -362,12 +432,16 @@ class FireworksAIConfig(OpenAIGPTConfig): supports_reasoning_value = self._get_model_cost_capability( model=model, capability="supports_reasoning" ) + supports_vision_value = self._get_model_cost_capability( + model=model, capability="supports_vision" + ) + supports_pdf_input_value = self._get_model_cost_capability( + model=model, capability="supports_pdf_input" + ) provider_specific_model_info: ProviderSpecificModelInfo = { "supports_function_calling": True, "supports_prompt_caching": True, # https://docs.fireworks.ai/guides/prompt-caching - "supports_pdf_input": True, # via document inlining - "supports_vision": True, # via document inlining } if supports_function_calling_value is not None: @@ -381,6 +455,14 @@ class FireworksAIConfig(OpenAIGPTConfig): supports_reasoning_value ) + if supports_vision_value is not None: + provider_specific_model_info["supports_vision"] = supports_vision_value + + if supports_pdf_input_value is not None: + provider_specific_model_info["supports_pdf_input"] = ( + supports_pdf_input_value + ) + return provider_specific_model_info def transform_request( @@ -392,13 +474,25 @@ class FireworksAIConfig(OpenAIGPTConfig): headers: dict, ) -> dict: if not model.startswith("accounts/") and "#" not in model: - model = f"accounts/fireworks/models/{model}" + if model.endswith("-fast"): + model = f"accounts/fireworks/routers/{model}" + else: + model = f"accounts/fireworks/models/{model}" messages = self._transform_messages_helper( messages=messages, model=model, litellm_params=litellm_params ) if "tools" in optional_params and optional_params["tools"] is not None: tools = self._transform_tools(tools=optional_params["tools"]) optional_params["tools"] = tools + if optional_params.get("stream"): + stream_options = optional_params.get("stream_options") + if stream_options is None: + optional_params["stream_options"] = {"include_usage": True} + elif stream_options.get("include_usage") is not False: + optional_params["stream_options"] = { + **stream_options, + "include_usage": True, + } return super().transform_request( model=model, messages=messages, @@ -491,10 +585,25 @@ class FireworksAIConfig(OpenAIGPTConfig): ) ) - response._hidden_params = {"additional_headers": additional_headers} + response._hidden_params = { + "additional_headers": additional_headers, + **_extract_fireworks_hidden_params(completion_response), + } return response + def get_model_response_iterator( + self, + streaming_response: Union[Iterator[str], AsyncIterator[str], ModelResponse], + sync_stream: bool, + json_mode: Optional[bool] = False, + ) -> Any: + return FireworksAIChatCompletionStreamingHandler( + streaming_response=streaming_response, + sync_stream=sync_stream, + json_mode=json_mode, + ) + def _get_openai_compatible_provider_info( self, api_base: Optional[str], api_key: Optional[str] ) -> Tuple[Optional[str], Optional[str]]: @@ -551,3 +660,15 @@ class FireworksAIConfig(OpenAIGPTConfig): or get_secret_str("FIREWORKSAI_API_KEY") or get_secret_str("FIREWORKS_AI_TOKEN") ) + + +class FireworksAIChatCompletionStreamingHandler(OpenAIChatCompletionStreamingHandler): + def chunk_parser(self, chunk: dict) -> ModelResponseStream: + parsed = super().chunk_parser(chunk) + fireworks_fields = _extract_fireworks_hidden_params(chunk) + if fireworks_fields: + parsed.provider_specific_fields = { + **(getattr(parsed, "provider_specific_fields", None) or {}), + **fireworks_fields, + } + return parsed diff --git a/litellm/llms/hosted_vllm/chat/transformation.py b/litellm/llms/hosted_vllm/chat/transformation.py index 40906e83a9d..7c42e6a9a00 100644 --- a/litellm/llms/hosted_vllm/chat/transformation.py +++ b/litellm/llms/hosted_vllm/chat/transformation.py @@ -205,53 +205,40 @@ class HostedVLLMChatConfig(OpenAIGPTConfig): tool_calls: list[ChatCompletionAssistantToolCall] = [] content_blocks: list[object] = [] has_structured_content = False - for c in existing_content: # any-ok: untyped content - if ( - isinstance(c, dict) # any-ok: untyped content - and c.get("type") == "text" # any-ok: untyped content - ): - text_parts.append( # any-ok: untyped content - c.get("text", "") # any-ok: untyped content - ) - content_blocks.append(c) # any-ok: untyped content - elif ( - isinstance(c, dict) # any-ok: untyped content - and c.get("type") == "tool_use" # any-ok: untyped content - ): - tool_input = c.get("input", {}) # any-ok: untyped content + for c in existing_content: + if isinstance(c, dict) and c.get("type") == "text": + text_parts.append(c.get("text", "")) + content_blocks.append(c) + elif isinstance(c, dict) and c.get("type") == "tool_use": + tool_input = c.get("input", {}) tool_calls.append( ChatCompletionAssistantToolCall( - id=c.get("id"), # any-ok: untyped content + id=c.get("id"), type="function", function=ChatCompletionToolCallFunctionChunk( - name=c.get("name"), # any-ok: untyped content + name=c.get("name"), arguments=( tool_input if isinstance( - tool_input, # any-ok: untyped content - str, # any-ok: untyped content - ) - else json.dumps( - tool_input # any-ok: untyped content + tool_input, + str, ) + else json.dumps(tool_input) ), ), ) ) else: - content_blocks.append(c) # any-ok: untyped content + content_blocks.append(c) has_structured_content = True if tool_calls: existing_tool_calls = message.get("tool_calls") if isinstance(existing_tool_calls, list): existing_tool_call_ids = { - tool_call.get("id") # any-ok: untyped content + tool_call.get("id") for tool_call in existing_tool_calls - if isinstance( - tool_call, dict - ) # any-ok: untyped content - and tool_call.get("id") - is not None # any-ok: untyped content + if isinstance(tool_call, dict) + and tool_call.get("id") is not None } new_tool_calls = [ tool_call @@ -264,7 +251,7 @@ class HostedVLLMChatConfig(OpenAIGPTConfig): ) else: message["tool_calls"] = tool_calls - content_str = "\n".join(text_parts) # any-ok: untyped content + content_str = "\n".join(text_parts) new_content = ( content_blocks if has_structured_content else content_str ) diff --git a/litellm/llms/openai/chat/gpt_transformation.py b/litellm/llms/openai/chat/gpt_transformation.py index 5464b5bb7ee..b8b750b8c12 100644 --- a/litellm/llms/openai/chat/gpt_transformation.py +++ b/litellm/llms/openai/chat/gpt_transformation.py @@ -18,6 +18,9 @@ from typing import ( overload, ) +import os +from urllib.parse import urlparse + import httpx import litellm @@ -426,6 +429,32 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): ) return messages, tools + def _should_preserve_cache_control_for_endpoint( + self, + custom_llm_provider: str | None, + api_base: str | None, + ) -> bool: + """ + The generic `openai` provider also reaches OpenAI-compatible endpoints + (a LiteLLM proxy, vLLM, an Anthropic-compatible gateway) via a custom + api_base. Those can understand cache_control, so it must survive there. + Real OpenAI cannot, so it is still stripped for an openai.com host. + """ + if custom_llm_provider != "openai": + return False + resolved_api_base = ( + api_base + or litellm.api_base + or os.getenv("OPENAI_BASE_URL") + or os.getenv("OPENAI_API_BASE") + ) + if not resolved_api_base: + return False + hostname = urlparse(resolved_api_base).hostname + if hostname is None: + return False + return hostname != "openai.com" and not hostname.endswith(".openai.com") + def transform_request( self, model: str, @@ -441,11 +470,14 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): dict: The transformed request. Sent as the body of the API call. """ messages = self._transform_messages(messages=messages, model=model) - messages, tools = self.remove_cache_control_flag_from_messages_and_tools( - model=model, messages=messages, tools=optional_params.get("tools", []) - ) - if tools is not None and len(tools) > 0: - optional_params["tools"] = tools + if not self._should_preserve_cache_control_for_endpoint( + litellm_params.get("custom_llm_provider"), litellm_params.get("api_base") + ): + messages, tools = self.remove_cache_control_flag_from_messages_and_tools( + model=model, messages=messages, tools=optional_params.get("tools", []) + ) + if tools is not None and len(tools) > 0: + optional_params["tools"] = tools optional_params.pop("max_retries", None) @@ -466,16 +498,19 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): transformed_messages = await self._transform_messages( messages=messages, model=model, is_async=True ) - ( - transformed_messages, - tools, - ) = self.remove_cache_control_flag_from_messages_and_tools( - model=model, - messages=transformed_messages, - tools=optional_params.get("tools", []), - ) - if tools is not None and len(tools) > 0: - optional_params["tools"] = tools + if not self._should_preserve_cache_control_for_endpoint( + litellm_params.get("custom_llm_provider"), litellm_params.get("api_base") + ): + ( + transformed_messages, + tools, + ) = self.remove_cache_control_flag_from_messages_and_tools( + model=model, + messages=transformed_messages, + tools=optional_params.get("tools", []), + ) + if tools is not None and len(tools) > 0: + optional_params["tools"] = tools if self.__class__._is_base_class: return { "model": model, diff --git a/litellm/llms/openai/transcriptions/whisper_transformation.py b/litellm/llms/openai/transcriptions/whisper_transformation.py index fa507e1bc26..2c01156fe05 100644 --- a/litellm/llms/openai/transcriptions/whisper_transformation.py +++ b/litellm/llms/openai/transcriptions/whisper_transformation.py @@ -1,3 +1,4 @@ +import json from typing import List, Optional, Union from httpx import Headers, Response @@ -107,9 +108,7 @@ class OpenAIWhisperAudioTranscriptionConfig(BaseAudioTranscriptionConfig): """ data = {"model": model, "file": audio_file, **optional_params} - if "response_format" not in data or ( - data["response_format"] == "text" or data["response_format"] == "json" - ): + if "response_format" not in data: data["response_format"] = ( "verbose_json" # ensures 'duration' is received - used for cost calculation ) @@ -133,10 +132,11 @@ class OpenAIWhisperAudioTranscriptionConfig(BaseAudioTranscriptionConfig): ) -> TranscriptionResponse: try: raw_response_json = raw_response.json() - except Exception as e: - raise ValueError( - f"Error transforming response to json: {str(e)}\nResponse: {raw_response.text}" - ) + except json.JSONDecodeError: + content_type = raw_response.headers.get("content-type", "").lower() + if "application/json" in content_type: + raise + return TranscriptionResponse(text=raw_response.text) if any( key in raw_response_json diff --git a/litellm/llms/openai_like/providers.json b/litellm/llms/openai_like/providers.json index 0dda047d1ca..24943563937 100644 --- a/litellm/llms/openai_like/providers.json +++ b/litellm/llms/openai_like/providers.json @@ -159,5 +159,14 @@ "max_completion_tokens": "max_tokens" }, "supported_endpoints": ["/v1/chat/completions", "/v1/responses"] + }, + "pinstripes": { + "base_url": "https://pinstripes.io/v1", + "api_key_env": "PINSTRIPES_API_KEY", + "api_base_env": "PINSTRIPES_API_BASE", + "param_mappings": { + "max_completion_tokens": "max_tokens" + }, + "supported_endpoints": ["/v1/chat/completions", "/v1/responses", "/v1/embeddings"] } } diff --git a/litellm/llms/perplexity/cost_calculator.py b/litellm/llms/perplexity/cost_calculator.py index 0f9c3cad841..bf055f91aa0 100644 --- a/litellm/llms/perplexity/cost_calculator.py +++ b/litellm/llms/perplexity/cost_calculator.py @@ -58,11 +58,8 @@ def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]: ## CALCULATE OUTPUT COST output_cost_per_token = _safe_float_cast(model_info.get("output_cost_per_token")) - completion_cost: float = (usage.completion_tokens or 0) * output_cost_per_token - ## ADD REASONING TOKENS COST (if present) reasoning_tokens = getattr(usage, "reasoning_tokens", 0) or 0 - # Also check completion_tokens_details if reasoning_tokens is not directly available if ( reasoning_tokens == 0 and hasattr(usage, "completion_tokens_details") @@ -73,9 +70,19 @@ def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]: ) reasoning_cost_value = model_info.get("output_cost_per_reasoning_token") + + # `completion_tokens` includes `reasoning_tokens` per the OpenAI/Perplexity usage + # convention (codified for the central path in PR #18607). When a reasoning rate is + # configured we subtract before the output-rate multiplication so the reasoning + # tokens are not billed twice. if reasoning_tokens > 0 and reasoning_cost_value is not None: - reasoning_cost_per_token = _safe_float_cast(reasoning_cost_value) - completion_cost += reasoning_tokens * reasoning_cost_per_token + non_reasoning_completion_tokens = max( + 0, (usage.completion_tokens or 0) - reasoning_tokens + ) + completion_cost: float = non_reasoning_completion_tokens * output_cost_per_token + completion_cost += reasoning_tokens * _safe_float_cast(reasoning_cost_value) + else: + completion_cost = (usage.completion_tokens or 0) * output_cost_per_token ## ADD SEARCH QUERIES COST (if present) num_search_queries = 0 diff --git a/litellm/llms/tinyfish/search/__init__.py b/litellm/llms/tinyfish/search/__init__.py new file mode 100644 index 00000000000..9777e735aac --- /dev/null +++ b/litellm/llms/tinyfish/search/__init__.py @@ -0,0 +1,3 @@ +from litellm.llms.tinyfish.search.transformation import TinyfishSearchConfig + +__all__ = ["TinyfishSearchConfig"] diff --git a/litellm/llms/tinyfish/search/transformation.py b/litellm/llms/tinyfish/search/transformation.py new file mode 100644 index 00000000000..c4949380e3a --- /dev/null +++ b/litellm/llms/tinyfish/search/transformation.py @@ -0,0 +1,164 @@ +""" +TinyFish Search API. +Endpoint: GET https://api.search.tinyfish.ai +Docs: https://docs.tinyfish.ai/search-api +""" + +from __future__ import annotations + +from typing import Literal, TypedDict +from urllib.parse import urlencode + +import httpx +from pydantic import BaseModel, TypeAdapter, ValidationError + +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.base_llm.search.transformation import ( + BaseSearchConfig, + SearchResponse, + SearchResult, +) +from litellm.secret_managers.main import get_secret_str + + +class _TinyfishSearchRequestRequired(TypedDict): + query: str + + +class TinyfishSearchRequest(_TinyfishSearchRequestRequired, total=False): + location: str + language: str + page: int + include_thumbnail: bool + max_results: int + + +class _TinyfishResultItem(BaseModel, frozen=True): + title: str = "" + url: str = "" + snippet: str = "" + + +class _TinyfishApiResponse(BaseModel, frozen=True): + results: tuple[_TinyfishResultItem, ...] = () + + +_UrlEncodableParams = TypeAdapter(dict[str, str | int | bool]) +_StrList = TypeAdapter(list[str]) +_StrFrozenSet = TypeAdapter(frozenset[str]) + +_TINYFISH_PARAMS_KEY = "_tinyfish_params" + + +class TinyfishSearchConfig(BaseSearchConfig): + TINYFISH_API_BASE = "https://api.search.tinyfish.ai" + + @staticmethod + def ui_friendly_name() -> str: + return "TinyFish" + + def get_http_method(self) -> Literal["GET", "POST"]: + return "GET" + + def validate_environment( + self, + headers: dict[str, str], + api_key: str | None = None, + api_base: str | None = None, + **kwargs: object, + ) -> dict[str, str]: + resolved_key = api_key or get_secret_str("TINYFISH_API_KEY") + if not resolved_key: + raise ValueError( + "TINYFISH_API_KEY is not set. Set `TINYFISH_API_KEY` environment variable." + ) + return {**headers, "X-API-Key": resolved_key, "Accept": "application/json"} + + def get_complete_url( + self, + api_base: str | None, + optional_params: dict[str, object], + data: dict[str, object] | list[dict[str, object]] | None = None, + **kwargs: object, + ) -> str: + resolved_base = ( + api_base or get_secret_str("TINYFISH_API_BASE") or self.TINYFISH_API_BASE + ) + if isinstance(data, dict) and _TINYFISH_PARAMS_KEY in data: + validated_params = _UrlEncodableParams.validate_python( + data[_TINYFISH_PARAMS_KEY] + ) + return f"{resolved_base}?{urlencode(validated_params, doseq=True)}" + return resolved_base + + def transform_search_request( + self, + query: str | list[str], + optional_params: dict[str, object], + **kwargs: object, + ) -> dict[str, object]: + resolved_query = " ".join(query) if isinstance(query, list) else query + + request_data: TinyfishSearchRequest = {"query": resolved_query} + + country = optional_params.get("country") + if isinstance(country, str): + request_data["location"] = country + + raw_max = optional_params.get("max_results") + if isinstance(raw_max, (int, float, str)): + request_data["max_results"] = max(1, min(int(raw_max), 20)) + + try: + domains = _StrList.validate_python( + optional_params.get("search_domain_filter") + ) + except (ValidationError, TypeError): + domains = [] + if domains: + request_data["query"] = _append_domain_filters( + request_data["query"], domains + ) + + result_data: dict[str, object] = dict(request_data) + + raw_supported: object = ( + self.get_supported_perplexity_optional_params() # any-ok: base class returns bare set + ) + supported_perplexity = _StrFrozenSet.validate_python(raw_supported) + for param, value in optional_params.items(): + if param not in supported_perplexity and param not in result_data: + result_data[param] = value + + return {_TINYFISH_PARAMS_KEY: result_data} + + def transform_search_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj | None, + **kwargs: object, + ) -> SearchResponse: + raw_json: object = raw_response.json() # any-ok: httpx Response.json() -> Any + parsed = _TinyfishApiResponse.model_validate(raw_json) + + max_results_str: str = "20" + if raw_response.request: + raw_param: object = ( + raw_response.request.url.params.get( # any-ok: httpx QueryParams.get() -> Any + "max_results", "20" + ) + ) + max_results_str = str(raw_param) + max_results: int = min(int(max_results_str), 20) + + results = [ + SearchResult(title=item.title, url=item.url, snippet=item.snippet) + for item in parsed.results[:max_results] + ] + + return SearchResponse(results=results, object="search") + + +def _append_domain_filters(query: str, domains: list[str]) -> str: + domain_clauses = " OR ".join(f"site:{d}" for d in domains) + return f"({query}) ({domain_clauses})" diff --git a/litellm/llms/vertex_ai/common_utils.py b/litellm/llms/vertex_ai/common_utils.py index 85c23d8603c..5028c0cf5c8 100644 --- a/litellm/llms/vertex_ai/common_utils.py +++ b/litellm/llms/vertex_ai/common_utils.py @@ -271,7 +271,7 @@ def supports_response_json_schema(model: str) -> bool: # Gemini 2.0+ and 2.5+ models support responseJsonSchema # Pattern matches: gemini-2.0-*, gemini-2.5-*, gemini-3-*, etc. - gemini_2_plus_pattern = re.compile(r"gemini-([2-9]|[1-9]\d+)\.") + gemini_2_plus_pattern = re.compile(r"gemini-(?:[2-9]|[1-9]\d+)(?:\.|\-)") return bool(gemini_2_plus_pattern.search(model_lower)) diff --git a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py index a2634ffaa40..c171538b9c0 100644 --- a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py +++ b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py @@ -2844,7 +2844,7 @@ async def make_call( sync_stream=False, logging_obj=logging_obj, response_headers=response.headers, - response=response, # any-ok: untyped stream + response=response, ) # LOGGING logging_obj.post_call( @@ -2888,7 +2888,7 @@ def make_sync_call( sync_stream=True, logging_obj=logging_obj, response_headers=response.headers, - response=response, # any-ok: untyped stream + response=response, ) # LOGGING @@ -3661,16 +3661,14 @@ class ModelResponseIterator: raise RuntimeError(f"Error parsing chunk: {e},\nReceived chunk: {chunk}") async def aclose(self) -> None: - iterator = getattr( # any-ok: untyped stream + iterator = getattr( self, "async_response_iterator", - self.streaming_response, # any-ok: untyped stream + self.streaming_response, ) - if iterator is not None and hasattr( # any-ok: untyped stream - iterator, "aclose" # any-ok: untyped stream - ): + if iterator is not None and hasattr(iterator, "aclose"): try: - await iterator.aclose() # any-ok: untyped stream + await iterator.aclose() except Exception as e: # noqa: BLE001 verbose_logger.debug( "ModelResponseIterator.aclose: error closing iterator: %s", e @@ -3684,14 +3682,10 @@ class ModelResponseIterator: ) def close(self) -> None: - iterator = getattr( # any-ok: untyped stream - self, "response_iterator", self.streaming_response # any-ok: untyped stream - ) - if iterator is not None and hasattr( # any-ok: untyped stream - iterator, "close" # any-ok: untyped stream - ): + iterator = getattr(self, "response_iterator", self.streaming_response) + if iterator is not None and hasattr(iterator, "close"): try: - iterator.close() # any-ok: untyped stream + iterator.close() except Exception as e: # noqa: BLE001 verbose_logger.debug( "ModelResponseIterator.close: error closing iterator: %s", e diff --git a/litellm/llms/watsonx/embed/transformation.py b/litellm/llms/watsonx/embed/transformation.py index 930212e3ef3..ae873d63fe9 100644 --- a/litellm/llms/watsonx/embed/transformation.py +++ b/litellm/llms/watsonx/embed/transformation.py @@ -43,8 +43,19 @@ class IBMWatsonXEmbeddingConfig(IBMWatsonXMixin, BaseEmbeddingConfig): api_params=watsonx_api_params, ) + if isinstance(input, str): + inputs: list[str] = [input] + elif isinstance(input, list): + if len(input) > 0 and isinstance(input[0], (list, int)): + raise ValueError( + "WatsonX embeddings require a string or list of strings" + ) + inputs = input + else: + inputs = [input] + return { - "inputs": input, + "inputs": inputs, "parameters": optional_params, **watsonx_auth_payload, } diff --git a/litellm/main.py b/litellm/main.py index 80176cc8b16..63c5798e70a 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -7434,22 +7434,7 @@ def speech( async def ahealth_check( model_params: dict, - mode: Optional[ - Literal[ - "chat", - "completion", - "embedding", - "audio_speech", - "audio_transcription", - "image_generation", - "video_generation", - "batch", - "rerank", - "realtime", - "responses", - "ocr", - ] - ] = "chat", + mode: str | None = "chat", prompt: Optional[str] = None, input: Optional[List] = None, ): diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 0ee4a33c4ca..5c962cf8440 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -10912,13 +10912,13 @@ "supports_tool_choice": true }, "command-r7b-12-2024": { - "input_cost_per_token": 1.5e-07, + "input_cost_per_token": 3.75e-08, "litellm_provider": "cohere_chat", "max_input_tokens": 128000, "max_output_tokens": 4096, "max_tokens": 4096, "mode": "chat", - "output_cost_per_token": 3.75e-08, + "output_cost_per_token": 1.5e-07, "source": "https://docs.cohere.com/v2/docs/command-r7b", "supports_function_calling": true, "supports_tool_choice": true @@ -14612,6 +14612,38 @@ "supports_response_schema": true, "supports_tool_choice": true }, + "fireworks_ai/accounts/fireworks/models/deepseek-v4-flash": { + "cache_read_input_token_cost": 2.8e-08, + "input_cost_per_token": 1.4e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 1048576, + "max_output_tokens": 384000, + "max_tokens": 384000, + "mode": "chat", + "output_cost_per_token": 2.8e-07, + "source": "https://docs.fireworks.ai/serverless/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": false + }, + "fireworks_ai/accounts/fireworks/models/deepseek-v4-pro": { + "cache_read_input_token_cost": 1.45e-07, + "input_cost_per_token": 1.74e-06, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 1048576, + "max_output_tokens": 384000, + "max_tokens": 384000, + "mode": "chat", + "output_cost_per_token": 3.48e-06, + "source": "https://docs.fireworks.ai/serverless/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": false + }, "fireworks_ai/accounts/fireworks/models/firefunction-v2": { "input_cost_per_token": 9e-07, "litellm_provider": "fireworks_ai", @@ -14687,43 +14719,64 @@ "input_cost_per_token": 1.4e-06, "litellm_provider": "fireworks_ai", "max_input_tokens": 202800, - "max_output_tokens": 202800, - "max_tokens": 202800, + "max_output_tokens": 131072, + "max_tokens": 131072, "mode": "chat", "output_cost_per_token": 4.4e-06, - "source": "https://fireworks.ai/models/fireworks/glm-5p1", + "source": "https://docs.fireworks.ai/serverless/pricing", "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "supports_vision": false + }, + "fireworks_ai/accounts/fireworks/models/glm-5p2": { + "cache_read_input_token_cost": 2.6e-07, + "input_cost_per_token": 1.4e-06, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 1048576, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 4.4e-06, + "source": "https://docs.fireworks.ai/serverless/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": false }, "fireworks_ai/accounts/fireworks/models/gpt-oss-120b": { + "cache_read_input_token_cost": 1.5e-08, "input_cost_per_token": 1.5e-07, "litellm_provider": "fireworks_ai", "max_input_tokens": 131072, - "max_output_tokens": 131072, - "max_tokens": 131072, + "max_output_tokens": 32768, + "max_tokens": 32768, "mode": "chat", "output_cost_per_token": 6e-07, - "source": "https://fireworks.ai/pricing", + "source": "https://docs.fireworks.ai/serverless/pricing", "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "supports_vision": false }, "fireworks_ai/accounts/fireworks/models/gpt-oss-20b": { - "input_cost_per_token": 5e-08, + "cache_read_input_token_cost": 3.5e-08, + "input_cost_per_token": 7e-08, "litellm_provider": "fireworks_ai", "max_input_tokens": 131072, - "max_output_tokens": 131072, - "max_tokens": 131072, + "max_output_tokens": 32768, + "max_tokens": 32768, "mode": "chat", - "output_cost_per_token": 2e-07, - "source": "https://fireworks.ai/pricing", + "output_cost_per_token": 3e-07, + "source": "https://docs.fireworks.ai/serverless/pricing", "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "supports_vision": false }, "fireworks_ai/accounts/fireworks/models/kimi-k2-instruct": { "input_cost_per_token": 6e-07, @@ -14779,6 +14832,38 @@ "supports_response_schema": true, "supports_tool_choice": true }, + "fireworks_ai/accounts/fireworks/models/kimi-k2p6": { + "cache_read_input_token_cost": 1.6e-07, + "input_cost_per_token": 9.5e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 4e-06, + "source": "https://docs.fireworks.ai/serverless/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "fireworks_ai/accounts/fireworks/models/kimi-k2p7-code": { + "cache_read_input_token_cost": 1.9e-07, + "input_cost_per_token": 9.5e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 4e-06, + "source": "https://docs.fireworks.ai/serverless/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, "fireworks_ai/accounts/fireworks/models/llama-v3p1-405b-instruct": { "input_cost_per_token": 3e-06, "litellm_provider": "fireworks_ai", @@ -14896,6 +14981,38 @@ "supports_response_schema": true, "supports_tool_choice": true }, + "fireworks_ai/accounts/fireworks/models/minimax-m2p7": { + "cache_read_input_token_cost": 6e-08, + "input_cost_per_token": 3e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 196608, + "max_output_tokens": 196608, + "max_tokens": 196608, + "mode": "chat", + "output_cost_per_token": 1.2e-06, + "source": "https://docs.fireworks.ai/serverless/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": false + }, + "fireworks_ai/accounts/fireworks/models/minimax-m3": { + "cache_read_input_token_cost": 6e-08, + "input_cost_per_token": 3e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 512000, + "max_output_tokens": 512000, + "max_tokens": 512000, + "mode": "chat", + "output_cost_per_token": 1.2e-06, + "source": "https://docs.fireworks.ai/serverless/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, "fireworks_ai/accounts/fireworks/models/mixtral-8x22b-instruct-hf": { "input_cost_per_token": 1.2e-06, "litellm_provider": "fireworks_ai", @@ -14948,6 +15065,38 @@ "supports_response_schema": true, "supports_tool_choice": false }, + "fireworks_ai/deepseek-v4-flash": { + "cache_read_input_token_cost": 2.8e-08, + "input_cost_per_token": 1.4e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 1048576, + "max_output_tokens": 384000, + "max_tokens": 384000, + "mode": "chat", + "output_cost_per_token": 2.8e-07, + "source": "https://docs.fireworks.ai/serverless/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": false + }, + "fireworks_ai/deepseek-v4-pro": { + "cache_read_input_token_cost": 1.45e-07, + "input_cost_per_token": 1.74e-06, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 1048576, + "max_output_tokens": 384000, + "max_tokens": 384000, + "mode": "chat", + "output_cost_per_token": 3.48e-06, + "source": "https://docs.fireworks.ai/serverless/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": false + }, "fireworks_ai/glm-4p7": { "cache_read_input_token_cost": 3e-07, "input_cost_per_token": 6e-07, @@ -14968,15 +15117,80 @@ "input_cost_per_token": 1.4e-06, "litellm_provider": "fireworks_ai", "max_input_tokens": 202800, - "max_output_tokens": 202800, - "max_tokens": 202800, + "max_output_tokens": 131072, + "max_tokens": 131072, "mode": "chat", "output_cost_per_token": 4.4e-06, - "source": "https://fireworks.ai/models/fireworks/glm-5p1", + "source": "https://docs.fireworks.ai/serverless/pricing", "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "supports_vision": false + }, + "fireworks_ai/glm-5p1-fast": { + "cache_read_input_token_cost": 5.2e-07, + "input_cost_per_token": 2.8e-06, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 202800, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 8.8e-06, + "source": "https://docs.fireworks.ai/serverless/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": false + }, + "fireworks_ai/glm-5p2": { + "cache_read_input_token_cost": 2.6e-07, + "input_cost_per_token": 1.4e-06, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 1048576, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 4.4e-06, + "source": "https://docs.fireworks.ai/serverless/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": false + }, + "fireworks_ai/gpt-oss-120b": { + "cache_read_input_token_cost": 1.5e-08, + "input_cost_per_token": 1.5e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 131072, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 6e-07, + "source": "https://docs.fireworks.ai/serverless/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": false + }, + "fireworks_ai/gpt-oss-20b": { + "cache_read_input_token_cost": 3.5e-08, + "input_cost_per_token": 7e-08, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 131072, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 3e-07, + "source": "https://docs.fireworks.ai/serverless/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": false }, "fireworks_ai/kimi-k2p5": { "cache_read_input_token_cost": 1e-07, @@ -14992,6 +15206,70 @@ "supports_response_schema": true, "supports_tool_choice": true }, + "fireworks_ai/kimi-k2p6": { + "cache_read_input_token_cost": 1.6e-07, + "input_cost_per_token": 9.5e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 4e-06, + "source": "https://docs.fireworks.ai/serverless/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "fireworks_ai/kimi-k2p6-fast": { + "cache_read_input_token_cost": 3e-07, + "input_cost_per_token": 2e-06, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 8e-06, + "source": "https://docs.fireworks.ai/serverless/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "fireworks_ai/kimi-k2p7-code": { + "cache_read_input_token_cost": 1.9e-07, + "input_cost_per_token": 9.5e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 4e-06, + "source": "https://docs.fireworks.ai/serverless/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "fireworks_ai/kimi-k2p7-code-fast": { + "cache_read_input_token_cost": 3.8e-07, + "input_cost_per_token": 1.9e-06, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 8e-06, + "source": "https://docs.fireworks.ai/serverless/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, "fireworks_ai/minimax-m2p1": { "cache_read_input_token_cost": 3e-08, "input_cost_per_token": 3e-07, @@ -15006,6 +15284,54 @@ "supports_response_schema": true, "supports_tool_choice": true }, + "fireworks_ai/minimax-m2p7": { + "cache_read_input_token_cost": 6e-08, + "input_cost_per_token": 3e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 196608, + "max_output_tokens": 196608, + "max_tokens": 196608, + "mode": "chat", + "output_cost_per_token": 1.2e-06, + "source": "https://docs.fireworks.ai/serverless/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": false + }, + "fireworks_ai/minimax-m3": { + "cache_read_input_token_cost": 6e-08, + "input_cost_per_token": 3e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 512000, + "max_output_tokens": 512000, + "max_tokens": 512000, + "mode": "chat", + "output_cost_per_token": 1.2e-06, + "source": "https://docs.fireworks.ai/serverless/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "fireworks_ai/qwen3p7-plus": { + "cache_read_input_token_cost": 8e-08, + "input_cost_per_token": 4e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 262144, + "max_output_tokens": 65536, + "max_tokens": 65536, + "mode": "chat", + "output_cost_per_token": 1.6e-06, + "source": "https://docs.fireworks.ai/serverless/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, "fireworks_ai/nomic-ai/nomic-embed-text-v1": { "input_cost_per_token": 8e-09, "litellm_provider": "fireworks_ai-embedding-models", @@ -39467,6 +39793,22 @@ "litellm_provider": "fireworks_ai", "mode": "chat" }, + "fireworks_ai/accounts/fireworks/models/qwen3p7-plus": { + "cache_read_input_token_cost": 8e-08, + "input_cost_per_token": 4e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 262144, + "max_output_tokens": 65536, + "max_tokens": 65536, + "mode": "chat", + "output_cost_per_token": 1.6e-06, + "source": "https://docs.fireworks.ai/serverless/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, "fireworks_ai/accounts/fireworks/models/qwq-32b": { "max_tokens": 131072, "max_input_tokens": 131072, @@ -39629,6 +39971,54 @@ "litellm_provider": "fireworks_ai", "mode": "chat" }, + "fireworks_ai/accounts/fireworks/routers/glm-5p1-fast": { + "cache_read_input_token_cost": 5.2e-07, + "input_cost_per_token": 2.8e-06, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 202800, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 8.8e-06, + "source": "https://docs.fireworks.ai/serverless/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": false + }, + "fireworks_ai/accounts/fireworks/routers/kimi-k2p6-fast": { + "cache_read_input_token_cost": 3e-07, + "input_cost_per_token": 2e-06, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 8e-06, + "source": "https://docs.fireworks.ai/serverless/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "fireworks_ai/accounts/fireworks/routers/kimi-k2p7-code-fast": { + "cache_read_input_token_cost": 3.8e-07, + "input_cost_per_token": 1.9e-06, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 8e-06, + "source": "https://docs.fireworks.ai/serverless/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, "novita/deepseek/deepseek-v3.2": { "litellm_provider": "novita", "mode": "chat", @@ -41993,6 +42383,7 @@ "max_output_tokens": 32768, "max_tokens": 32768, "mode": "chat", + "supported_endpoints": ["/v1/chat/completions", "/v1/responses"], "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_reasoning": true, @@ -42007,6 +42398,7 @@ "max_output_tokens": 32768, "max_tokens": 32768, "mode": "chat", + "supported_endpoints": ["/v1/chat/completions", "/v1/responses"], "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_reasoning": true, @@ -42021,6 +42413,7 @@ "max_output_tokens": 65536, "max_tokens": 65536, "mode": "chat", + "supported_endpoints": ["/v1/chat/completions"], "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": true, @@ -42034,6 +42427,7 @@ "max_output_tokens": 65536, "max_tokens": 65536, "mode": "chat", + "supported_endpoints": ["/v1/chat/completions"], "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": true, @@ -42087,6 +42481,8 @@ "max_output_tokens": 256000, "max_tokens": 256000, "mode": "chat", + "use_openai_responses_path": true, + "supported_endpoints": ["/v1/chat/completions", "/v1/responses"], "supports_function_calling": true, "supports_parallel_function_calling": false, "supports_reasoning": true, @@ -42101,6 +42497,8 @@ "max_output_tokens": 256000, "max_tokens": 256000, "mode": "chat", + "use_openai_responses_path": true, + "supported_endpoints": ["/v1/chat/completions", "/v1/responses"], "supports_function_calling": true, "supports_parallel_function_calling": false, "supports_reasoning": true, @@ -42115,6 +42513,8 @@ "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", + "use_openai_responses_path": true, + "supported_endpoints": ["/v1/chat/completions", "/v1/responses"], "supports_function_calling": true, "supports_parallel_function_calling": false, "supports_reasoning": true, @@ -42413,6 +42813,19 @@ ], "supports_audio_input": true }, + "soniox/stt-async-v5": { + "litellm_provider": "soniox", + "max_output_tokens": 8000, + "max_tokens": 8000, + "input_cost_per_second": 0.0, + "output_cost_per_second": 0.0000277778, + "mode": "audio_transcription", + "source": "https://soniox.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ], + "supports_audio_input": true + }, "tensormesh/Qwen/Qwen3.5-397B-A17B-FP8": { "litellm_provider": "tensormesh", "mode": "chat", diff --git a/litellm/mypy.ini b/litellm/mypy.ini deleted file mode 100644 index b65e11bab42..00000000000 --- a/litellm/mypy.ini +++ /dev/null @@ -1,22 +0,0 @@ -[mypy] -warn_return_any = True -ignore_missing_imports = True -disallow_untyped_defs = True -mypy_path = litellm/stubs -namespace_packages = True -disable_error_code = - annotation-unchecked, - import-untyped - -[mypy-litellm.*] -ignore_missing_imports = False - -[mypy-google.*] -ignore_missing_imports = True - -[mypy-cryptography.hazmat.bindings._rust.x509] -ignore_errors = True - -[mypy-fastuuid.*] -ignore_missing_imports = True -ignore_errors = True \ No newline at end of file diff --git a/litellm/proxy/_experimental/mcp_server/AGENTS.md b/litellm/proxy/_experimental/mcp_server/AGENTS.md new file mode 100644 index 00000000000..8eebc3ea3b3 --- /dev/null +++ b/litellm/proxy/_experimental/mcp_server/AGENTS.md @@ -0,0 +1,95 @@ +# Experimental MCP Server Change Guidelines + +Read @../../../../CLAUDE.md and @CLAUDE.md before changing this package. + +This directory owns the proxy-hosted MCP server implementation. Keep changes +inside the module that owns the behavior, and only reach outside this package +when the public type contract, database schema, dashboard, or cross-proxy route +wiring must change with it. + +## File Structure + +Respect the current package boundaries: + +```text +litellm/proxy/_experimental/mcp_server/ + AGENTS.md + CLAUDE.md + server.py # ASGI/MCP route handling, sessions, tool calls [PR7: 7-arm only — move BYOK/OAuth pre-fetch into resolver] + mcp_server_manager.py # upstream server registry, clients, tool routing [PR7: _create_mcp_client swaps resolve_mcp_auth -> resolve_credentials] + auth/ + user_api_key_auth_mcp.py # LiteLLM admission auth and MCP request headers + token_exchange.py # OAuth token exchange handling [unchanged; V1TokenExchangeAdapter delegates here] + litellm_auth_handler.py # authenticated-user adapter for MCP sessions + outbound_credentials/ # NEW — typed upstream-credential resolution (resolve_credentials + arms) + __init__.py # public surface: resolve_credentials, the configs, CredError + result.py # Ok | Error union (pure stdlib) + types.py # AuthConfig union, CredError, Subject, ServerSpec + httpx_auth.py # NoOpAuth, StaticHeaderAuth (every mode -> one httpx.Auth) + resolver.py # resolve_credentials(): exhaustive per-mode match + assert_never + seams.py # injected Protocols (one per cache-touching mode) + v1_adapters.py # v1-backed seam bodies; delegate to auth/oauth2/db owners + adapter.py # to_subject / to_server_spec / raise_public (v1 <-> v2 boundary) + discoverable_endpoints.py # MCP OAuth metadata, authorize, token, callback + byok_oauth_endpoints.py # BYOK OAuth UI/API flow + oauth_utils.py # redirect URI and proxy base URL validation + oauth2_token_cache.py # OAuth2 and per-user token resolution/cache [PR7: resolve_mcp_auth removed; cache class stays, V1OAuth2CacheAdapter delegates to async_get_token] + db.py # MCP server, credential, env var, submission DB access [unchanged; V1ByokStore delegates to _get_byok_credential / get_user_credential] + toolset_db.py # MCP toolset DB access + rest_endpoints.py # proxy REST facade for listing/calling MCP tools [PR7: 7-arm only — pass identity + inbound token down instead of mcp_auth_header] + openapi_to_mcp_generator.py# OpenAPI spec to MCP tool generation + sampling_handler.py # MCP sampling to LiteLLM completion flow + elicitation_handler.py # MCP elicitation relay flow + semantic_tool_filter.py # semantic filtering of available MCP tools + guardrail_translation/ + handler.py # MCP guardrail result translation + sse_transport.py # SSE transport implementation + mcp_context.py # contextvars for MCP request/session metadata + mcp_debug.py # debug helpers + tool_registry.py # in-memory MCP tool registry helpers + cost_calculator.py # MCP tool cost calculation + ui_session_utils.py # dashboard session auth context helpers + utils.py # shared primitives used by several modules +``` + +Do not add broad catch-all modules. Prefer the existing owner above, and add a +new file only for a distinct capability that would otherwise make an existing +module materially harder to understand. + +## Implementation Rules + +- Preserve the boundary between LiteLLM admission auth and upstream MCP auth. + Admission belongs in `auth/user_api_key_auth_mcp.py`; upstream token exchange, + delegated auth, per-user OAuth, BYOK, and raw header forwarding belong in the + dedicated OAuth/header modules. +- Treat `none`, bearer/API key, OAuth, OAuth token exchange, delegated upstream + auth, SSE, streamable HTTP, and stdio as separate flows. Do not collapse them + behind a single generic branch unless tests prove every mode still behaves + correctly. +- Be especially careful with `available_on_public_internet: false` combined with + `delegate_auth_to_upstream: true`. The local `CLAUDE.md` explains the anonymous + upstream PKCE path that must remain intentional. +- Keep database-backed fields in sync across migrations, typed models under + `litellm/types/mcp.py` or `litellm/types/mcp_server/`, config loading, this + package, and dashboard state when the field is user-visible. +- Use the official MCP SDK types and established LiteLLM Pydantic models where + they exist. Avoid untyped protocol dictionaries at package boundaries. +- Keep security-sensitive logic easy to audit. Header forwarding, IP filtering, + public internet checks, token storage, env var interpolation, and credential + encryption need focused tests for both allowed and rejected paths. +- Avoid adding comments to new code unless they explain non-obvious security or + protocol behavior. Prefer clear names and small functions. + +## Tests + +Mirror this package under `tests/test_litellm/proxy/_experimental/mcp_server/`. +For regressions, extend the existing mapped test file instead of creating a new +one. Use subdirectories that match the implementation path, such as +`auth/test_token_exchange.py` for `auth/token_exchange.py` and +`guardrail_translation/test_mcp_guardrail_handler.py` for +`guardrail_translation/handler.py`. + +Use `tests/mcp_tests/` only when extending an existing broader MCP integration +scenario that already lives there. Route, auth, tool listing, tool execution, +OAuth, sampling, elicitation, DB, and dashboard-session changes should have +focused coverage in the mirrored `tests/test_litellm/...` path first. diff --git a/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py b/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py index e47fc84b533..90108de25c3 100644 --- a/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py +++ b/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py @@ -12,6 +12,7 @@ from litellm.proxy._types import ( LiteLLM_TeamTable, ProxyException, SpecialHeaders, + SpecialMCPServerNames, UserAPIKeyAuth, ) from litellm.proxy.auth.ip_address_utils import IPAddressUtils @@ -642,6 +643,15 @@ class MCPRequestHandler: user_api_key_auth ) ) + + # The key explicitly opted out of every MCP server. This overrides + # team inheritance and additive grants (mirrors no-default-models). + if ( + SpecialMCPServerNames.no_mcp_servers.value + in allowed_mcp_servers_for_key + ): + return [] + allowed_mcp_servers_for_team = ( await MCPRequestHandler._get_allowed_mcp_servers_for_team( user_api_key_auth @@ -1058,6 +1068,13 @@ class MCPRequestHandler: if key_object_permission is None: return [] + # Sentinel opt-out: surface it unexpanded so the caller can short-circuit + # to zero servers instead of inheriting the team. + if SpecialMCPServerNames.no_mcp_servers.value in ( + key_object_permission.mcp_servers or [] + ): + return [SpecialMCPServerNames.no_mcp_servers.value] + # Permission entries may be server_ids OR names/aliases — expand to ids. direct_mcp_servers = global_mcp_server_manager.expand_permission_list( key_object_permission.mcp_servers or [] diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index afec884cd96..5e704b889ae 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -80,6 +80,7 @@ from litellm.proxy._types import ( MCPEnvVar, MCPTransport, MCPTransportType, + SpecialMCPServerNames, UserAPIKeyAuth, ) from litellm.proxy.auth.ip_address_utils import IPAddressUtils @@ -1349,6 +1350,17 @@ class MCPServerManager: allow_all_server_ids = self.get_allow_all_keys_server_ids() try: + # The key explicitly opted out of every MCP server. Return zero before + # layering on allow_all_keys servers so the opt-out is absolute. + key_object_permission = ( + user_api_key_auth.object_permission if user_api_key_auth else None + ) + if key_object_permission is not None and ( + SpecialMCPServerNames.no_mcp_servers.value + in (key_object_permission.mcp_servers or []) + ): + return [] + # Check if object_permission.mcp_servers is explicitly set has_explicit_object_permission = False if user_api_key_auth and user_api_key_auth.object_permission: diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py index 08e42e918e9..678137100a5 100644 --- a/litellm/proxy/_experimental/mcp_server/server.py +++ b/litellm/proxy/_experimental/mcp_server/server.py @@ -63,7 +63,7 @@ from litellm.llms.custom_httpx.http_handler import ( get_async_httpx_client, httpxSpecialProvider, ) -from litellm.proxy._types import UserAPIKeyAuth +from litellm.proxy._types import SpecialMCPServerNames, UserAPIKeyAuth from litellm.proxy.auth.ip_address_utils import IPAddressUtils from litellm.proxy.litellm_pre_call_utils import ( LiteLLMProxyRequestSetup, @@ -3352,6 +3352,19 @@ if MCP_AVAILABLE: from litellm.proxy._types import LiteLLM_ObjectPermissionTable from litellm.proxy.management_endpoints.common_utils import _user_has_admin_view + # A key scoped to no MCP servers opts out of every MCP path. Enforce it + # here too, since toolset scoping replaces mcp_servers and would otherwise + # drop the sentinel. Checked before the admin branch, mirroring + # get_allowed_mcp_servers. + original_op = user_api_key_auth.object_permission + if original_op is not None and SpecialMCPServerNames.no_mcp_servers.value in ( + original_op.mcp_servers or [] + ): + raise HTTPException( + status_code=403, + detail="API key is scoped to no MCP servers; toolset access is denied.", + ) + # Access control: non-admin keys must have this toolset in their grant list. # Use _user_has_admin_view so that PROXY_ADMIN_VIEW_ONLY is also treated as admin. is_admin = _user_has_admin_view(user_api_key_auth) diff --git a/litellm/proxy/_experimental/mcp_server/utils.py b/litellm/proxy/_experimental/mcp_server/utils.py index 97cfa74ea45..b0141d3207c 100644 --- a/litellm/proxy/_experimental/mcp_server/utils.py +++ b/litellm/proxy/_experimental/mcp_server/utils.py @@ -23,9 +23,20 @@ import os from urllib.parse import quote # Constants -LITELLM_MCP_SERVER_NAME = "litellm-mcp-server" +# +# NOTE: The environment-backed values below are read once, when this module is +# first imported, and cached for the lifetime of the process. Changing the +# corresponding environment variables after import has no effect unless the +# module is reloaded (e.g. ``importlib.reload``). Tests that override these +# variables must reload this module — see +# ``tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_identity_env.py``. +LITELLM_MCP_SERVER_NAME = os.environ.get( + "LITELLM_MCP_SERVER_NAME", "litellm-mcp-server" +) LITELLM_MCP_SERVER_VERSION = "1.0.0" -LITELLM_MCP_SERVER_DESCRIPTION = "MCP Server for LiteLLM" +LITELLM_MCP_SERVER_DESCRIPTION = os.environ.get( + "LITELLM_MCP_SERVER_DESCRIPTION", "MCP Server for LiteLLM" +) MCP_TOOL_PREFIX_SEPARATOR = os.environ.get("MCP_TOOL_PREFIX_SEPARATOR", "-") MCP_TOOL_PREFIX_FORMAT = "{server_name}{separator}{tool_name}" diff --git a/litellm/proxy/_experimental/out/404.html b/litellm/proxy/_experimental/out/404.html index 45de348c4d5..6d246c81652 100644 --- a/litellm/proxy/_experimental/out/404.html +++ b/litellm/proxy/_experimental/out/404.html @@ -1 +1 @@ -404: This page could not be found.LiteLLM Dashboard

404

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\ No newline at end of file +404: This page could not be found.LiteLLM Dashboard

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404

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\ No newline at end of file +404: This page could not be found.LiteLLM Dashboard

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y=["top","topLeft","topRight","bottom","bottomLeft","bottomRight"],b={topLeft:"left",topRight:"right",bottomLeft:"left",bottomRight:"right",top:"left",bottom:"left"},w=e=>{let{iconCls:t,componentCls:r,boxShadow:n,fontSizeLG:o,notificationMarginBottom:a,borderRadiusLG:i,colorSuccess:l,colorInfo:s,colorWarning:c,colorError:u,colorTextHeading:d,notificationBg:f,notificationPadding:m,notificationMarginEdge:h,notificationProgressBg:v,notificationProgressHeight:y,fontSize:b,lineHeight:w,width:C,notificationIconSize:x,colorText:S,colorSuccessBg:$,colorErrorBg:E,colorInfoBg:k,colorWarningBg:O}=e,j=`${r}-notice`;return{position:"relative",marginBottom:a,marginInlineStart:"auto",background:f,borderRadius:i,boxShadow:n,[j]:{padding:m,width:C,maxWidth:`calc(100vw - 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Object.assign({[`${t}-stack`]:{[`& > ${t}-notice-wrapper`]:Object.assign({transition:`transform ${e.motionDurationSlow}, backdrop-filter 0s`,willChange:"transform, opacity",position:"absolute"},(e=>{let t={};for(let r=1;r ${e.componentCls}-notice`]:{opacity:0,transition:`opacity ${e.motionDurationMid}`}};return Object.assign({[`&:not(:nth-last-child(-n+${e.notificationStackLayer}))`]:{opacity:0,overflow:"hidden",color:"transparent",pointerEvents:"none"}},t)})(e))},[`${t}-stack:not(${t}-stack-expanded)`]:{[`& > ${t}-notice-wrapper`]:Object.assign({},(e=>{let t={};for(let r=1;r ${t}-notice-wrapper`]:{"&:not(:nth-last-child(-n + 1))":{opacity:1,overflow:"unset",color:"inherit",pointerEvents:"auto",[`& > 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h=(0,m.genStyleHooks)("Message",e=>(e=>{let{componentCls:t,iconCls:r,boxShadow:n,colorText:o,colorSuccess:a,colorError:i,colorWarning:l,colorInfo:s,fontSizeLG:c,motionEaseInOutCirc:u,motionDurationSlow:f,marginXS:m,paddingXS:g,borderRadiusLG:h,zIndexPopup:v,contentPadding:y,contentBg:b}=e,w=`${t}-notice`,C=new d.Keyframes("MessageMoveIn",{"0%":{padding:0,transform:"translateY(-100%)",opacity:0},"100%":{padding:g,transform:"translateY(0)",opacity:1}}),x=new d.Keyframes("MessageMoveOut",{"0%":{maxHeight:e.height,padding:g,opacity:1},"100%":{maxHeight:0,padding:0,opacity:0}}),S={padding:g,textAlign:"center",[`${t}-custom-content`]:{display:"flex",alignItems:"center"},[`${t}-custom-content > ${r}`]:{marginInlineEnd:m,fontSize:c},[`${w}-content`]:{display:"inline-block",padding:y,background:b,borderRadius:h,boxShadow:n,pointerEvents:"all"},[`${t}-success > ${r}`]:{color:a},[`${t}-error > ${r}`]:{color:i},[`${t}-warning > ${r}`]:{color:l},[`${t}-info > ${r}, + ${t}-loading > 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`]:{animationPlayState:"running"},[`${t}-move-up-leave`]:{animationName:x,animationDuration:f,animationPlayState:"paused",animationTimingFunction:u},[`${t}-move-up-leave${t}-move-up-leave-active`]:{animationPlayState:"running"},"&-rtl":{direction:"rtl",span:{direction:"rtl"}}})},{[t]:{[`${w}-wrapper`]:Object.assign({},S)}},{[`${t}-notice-pure-panel`]:Object.assign(Object.assign({},S),{padding:0,textAlign:"start"})}]})((0,g.mergeToken)(e,{height:150})),e=>({zIndexPopup:e.zIndexPopupBase+f.CONTAINER_MAX_OFFSET+10,contentBg:e.colorBgElevated,contentPadding:`${(e.controlHeightLG-e.fontSize*e.lineHeight)/2}px ${e.paddingSM}px`}));e.s(["default",0,h],208224);var v=function(e,t){var r={};for(var n in e)Object.prototype.hasOwnProperty.call(e,n)&&0>t.indexOf(n)&&(r[n]=e[n]);if(null!=e&&"function"==typeof Object.getOwnPropertySymbols)for(var o=0,n=Object.getOwnPropertySymbols(e);ot.indexOf(n[o])&&Object.prototype.propertyIsEnumerable.call(e,n[o])&&(r[n[o]]=e[n[o]]);return r};let y={info:t.createElement(a.default,null),success:t.createElement(r.default,null),error:t.createElement(n.default,null),warning:t.createElement(o.default,null),loading:t.createElement(i.default,null)},b=({prefixCls:e,type:r,icon:n,children:o})=>t.createElement("div",{className:(0,l.default)(`${e}-custom-content`,`${e}-${r}`)},n||y[r],t.createElement("span",null,o));e.s(["PureContent",0,b,"default",0,e=>{let{prefixCls:r,className:n,type:o,icon:a,content:i}=e,d=v(e,["prefixCls","className","type","icon","content"]),{getPrefixCls:f}=t.useContext(c.ConfigContext),p=r||f("message"),m=(0,u.default)(p),[g,y,w]=h(p,m);return g(t.createElement(s.Notice,Object.assign({},d,{prefixCls:p,className:(0,l.default)(n,y,`${p}-notice-pure-panel`,w,m),eventKey:"pure",duration:null,content:t.createElement(b,{prefixCls:p,type:o,icon:a},i)})))}],983320)},727749,698173,190702,e=>{"use strict";var t=e.i(271645);e.i(247167);var r=e.i(738275),n=e.i(609587),o=e.i(242064),a=e.i(783164),i=e.i(645384),l=e.i(343794);e.i(792131);var s=e.i(194732),c=e.i(513139),u=e.i(747656),d=e.i(321883),f=e.i(104458),p=e.i(628918),m=function(e,t){var r={};for(var n in e)Object.prototype.hasOwnProperty.call(e,n)&&0>t.indexOf(n)&&(r[n]=e[n]);if(null!=e&&"function"==typeof Object.getOwnPropertySymbols)for(var o=0,n=Object.getOwnPropertySymbols(e);ot.indexOf(n[o])&&Object.prototype.propertyIsEnumerable.call(e,n[o])&&(r[n[o]]=e[n[o]]);return r};let g=({children:e,prefixCls:r})=>{let n=(0,d.default)(r),[o,a,i]=(0,p.default)(r,n);return o(t.default.createElement(s.NotificationProvider,{classNames:{list:(0,l.default)(a,i,n)}},e))},h=(e,{prefixCls:r,key:n})=>t.default.createElement(g,{prefixCls:r,key:n},e),v=t.default.forwardRef((e,r)=>{let{top:n,bottom:a,prefixCls:s,getContainer:u,maxCount:d,rtl:p,onAllRemoved:m,stack:g,duration:v,pauseOnHover:y=!0,showProgress:b}=e,{getPrefixCls:w,getPopupContainer:C,notification:x,direction:S}=(0,t.useContext)(o.ConfigContext),[,$]=(0,f.useToken)(),E=s||w("notification"),[k,O]=(0,c.useNotification)({prefixCls:E,style:e=>(function(e,t,r){let n;switch(e){case"top":n={left:"50%",transform:"translateX(-50%)",right:"auto",top:t,bottom:"auto"};break;case"topLeft":n={left:0,top:t,bottom:"auto"};break;case"topRight":n={right:0,top:t,bottom:"auto"};break;case"bottom":n={left:"50%",transform:"translateX(-50%)",right:"auto",top:"auto",bottom:r};break;case"bottomLeft":n={left:0,top:"auto",bottom:r};break;default:n={right:0,top:"auto",bottom:r}}return n})(e,null!=n?n:24,null!=a?a:24),className:()=>(0,l.default)({[`${E}-rtl`]:null!=p?p:"rtl"===S}),motion:()=>({motionName:`${E}-fade`}),closable:!0,closeIcon:(0,i.getCloseIcon)(E),duration:null!=v?v:4.5,getContainer:()=>(null==u?void 0:u())||(null==C?void 0:C())||document.body,maxCount:d,pauseOnHover:y,showProgress:b,onAllRemoved:m,renderNotifications:h,stack:!1!==g&&{threshold:"object"==typeof g?null==g?void 0:g.threshold:void 0,offset:8,gap:$.margin}});return t.default.useImperativeHandle(r,()=>Object.assign(Object.assign({},k),{prefixCls:E,notification:x})),O});function y(e){let r=t.default.useRef(null);return(0,u.devUseWarning)("Notification"),[t.default.useMemo(()=>{let n=n=>{var 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t?(0,l.jsx)(et,{prebuiltPatterns:t.prebuilt_patterns||[],categories:t.pattern_categories||[],selectedPatterns:$,blockedWords:M,onPatternAdd:e=>E([...$,e]),onPatternRemove:e=>E($.filter(t=>t.id!==e)),onPatternActionChange:(e,t)=>{E($.map(a=>a.id===e?{...a,action:t}:a))},onBlockedWordAdd:e=>R([...M,e]),onBlockedWordRemove:e=>R(M.filter(t=>t.id!==e)),onBlockedWordUpdate:(e,t,a)=>{R(M.map(l=>l.id===e?{...l,[t]:a}:l))},contentCategories:t.content_categories||[],selectedContentCategories:G,onContentCategoryAdd:e=>z([...G,e]),onContentCategoryRemove:e=>z(G.filter(t=>t.id!==e)),onContentCategoryUpdate:(e,t,a)=>{z(G.map(l=>l.id===e?{...l,[t]:a}:l))},pendingCategorySelection:D,onPendingCategorySelectionChange:K,accessToken:a,showStep:e,competitorIntentEnabled:q,competitorIntentConfig:J,onCompetitorIntentChange:(e,t)=>{H(e),U(t)}}):null},eA=ed(f)?[{title:"Basic Info",optional:!1},{title:"Topics",optional:!1},{title:"Patterns",optional:!1},{title:"Keywords",optional:!1},{title:"Endpoint Settings 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openai + +client = openai.OpenAI( + api_key="${x||"YOUR_LITELLM_API_KEY"}", + base_url="${v}" +)`;switch(h){case n.CHAT:{let e=Object.keys($).length>0,i="";if(e){let e=JSON.stringify({metadata:$},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`, + extra_body=${e}`}let o=C.length>0?C:[{role:"user",content:S}];t=` +import base64 + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Example with text only +response = client.chat.completions.create( + model="${k}", + messages=${JSON.stringify(o,null,4)}${i} +) + +print(response) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.chat.completions.create( +# model="${k}", +# messages=[ +# { +# "role": "user", +# "content": [ +# { +# "type": "text", +# "text": "${j}" +# }, +# { +# "type": "image_url", +# "image_url": { +# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file} +# } +# } +# ] +# } +# ]${i} +# ) +# print(response_with_file) +`;break}case n.RESPONSES:{let e=Object.keys($).length>0,i="";if(e){let e=JSON.stringify({metadata:$},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`, + extra_body=${e}`}let o=C.length>0?C:[{role:"user",content:S}];t=` +import base64 + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Example with text only +response = client.responses.create( + model="${k}", + input=${JSON.stringify(o,null,4)}${i} +) + +print(response.output_text) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.responses.create( +# model="${k}", +# input=[ +# { +# "role": "user", +# "content": [ +# {"type": "input_text", "text": "${j}"}, +# { +# "type": "input_image", +# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file} +# }, +# ], +# } +# ]${i} +# ) +# print(response_with_file.output_text) +`;break}case n.IMAGE:t="azure"===_?` +# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI. +# This snippet uses 'client.images.generate' and will create a new image based on your prompt. +# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context. +import os +import requests +import json +import time +from PIL import Image + +result = client.images.generate( + model="${k}", + prompt="${r}", + n=1 +) + +json_response = json.loads(result.model_dump_json()) + +# Set the directory for the stored image +image_dir = os.path.join(os.curdir, 'images') + +# If the directory doesn't exist, create it +if not os.path.isdir(image_dir): + os.mkdir(image_dir) + +# Initialize the image path +image_filename = f"generated_image_{int(time.time())}.png" +image_path = os.path.join(image_dir, image_filename) + +try: + # Retrieve the generated image + if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"): + image_url = json_response["data"][0]["url"] + generated_image = requests.get(image_url).content + with open(image_path, "wb") as image_file: + image_file.write(generated_image) + + print(f"Image saved to {image_path}") + # Display the image + image = Image.open(image_path) + image.show() + else: + print("Could not find image URL in response.") + print("Full response:", json_response) +except Exception as e: + print(f"An error occurred: {e}") + print("Full response:", json_response) +`:` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${j}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${k}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`;break;case n.IMAGE_EDITS:t="azure"===_?` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# The prompt entered by the user +prompt = "${j}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${k}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`:` +import base64 +import os +import time + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${j}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${k}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. 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openai + +client = openai.OpenAI( + api_key="${b||"YOUR_LITELLM_API_KEY"}", + base_url="${v}" +)`;switch(_){case r.CHAT:{let e=Object.keys(k).length>0,i="";if(e){let e=JSON.stringify({metadata:k},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`, + extra_body=${e}`}let a=E.length>0?E:[{role:"user",content:w}];t=` +import base64 + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Example with text only +response = client.chat.completions.create( + model="${I}", + messages=${JSON.stringify(a,null,4)}${i} +) + +print(response) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.chat.completions.create( +# model="${I}", +# messages=[ +# { +# "role": "user", +# "content": [ +# { +# "type": "text", +# "text": "${j}" +# }, +# { +# "type": "image_url", +# "image_url": { +# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file} +# } +# } +# ] +# } +# ]${i} +# ) +# print(response_with_file) +`;break}case r.RESPONSES:{let e=Object.keys(k).length>0,i="";if(e){let e=JSON.stringify({metadata:k},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`, + extra_body=${e}`}let a=E.length>0?E:[{role:"user",content:w}];t=` +import base64 + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Example with text only +response = client.responses.create( + model="${I}", + input=${JSON.stringify(a,null,4)}${i} +) + +print(response.output_text) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.responses.create( +# model="${I}", +# input=[ +# { +# "role": "user", +# "content": [ +# {"type": "input_text", "text": "${j}"}, +# { +# "type": "input_image", +# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file} +# }, +# ], +# } +# ]${i} +# ) +# print(response_with_file.output_text) +`;break}case r.IMAGE:t="azure"===y?` +# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI. +# This snippet uses 'client.images.generate' and will create a new image based on your prompt. +# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context. +import os +import requests +import json +import time +from PIL import Image + +result = client.images.generate( + model="${I}", + prompt="${n}", + n=1 +) + +json_response = json.loads(result.model_dump_json()) + +# Set the directory for the stored image +image_dir = os.path.join(os.curdir, 'images') + +# If the directory doesn't exist, create it +if not os.path.isdir(image_dir): + os.mkdir(image_dir) + +# Initialize the image path +image_filename = f"generated_image_{int(time.time())}.png" +image_path = os.path.join(image_dir, image_filename) + +try: + # Retrieve the generated image + if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"): + image_url = json_response["data"][0]["url"] + generated_image = requests.get(image_url).content + with open(image_path, "wb") as image_file: + image_file.write(generated_image) + + print(f"Image saved to {image_path}") + # Display the image + image = Image.open(image_path) + image.show() + else: + print("Could not find image URL in response.") + print("Full response:", json_response) +except Exception as e: + print(f"An error occurred: {e}") + print("Full response:", json_response) +`:` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${j}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${I}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`;break;case r.IMAGE_EDITS:t="azure"===y?` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# The prompt entered by the user +prompt = "${j}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${I}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`:` +import base64 +import os +import time + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${j}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${I}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`;break;case r.EMBEDDINGS:t=` +response = client.embeddings.create( + input="${n||"Your string here"}", + model="${I}", + encoding_format="base64" # or "float" +) + +print(response.data[0].embedding) +`;break;case r.TRANSCRIPTION:t=` +# Open the audio file +audio_file = open("path/to/your/audio/file.mp3", "rb") + +# Make the transcription request +response = client.audio.transcriptions.create( + model="${I}", + file=audio_file${n?`, + prompt="${n.replace(/\\/g,"\\\\").replace(/"/g,'\\"')}"`:""} +) + +print(response.text) +`;break;case r.SPEECH:t=` +# Make the text-to-speech request +response = client.audio.speech.create( + model="${I}", + input="${n||"Your text to convert to speech here"}", + voice="${f}" # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer +) + +# Save the audio to a file +output_filename = "output_speech.mp3" +response.stream_to_file(output_filename) +print(f"Audio saved to {output_filename}") + +# Optional: Customize response format and speed +# response = client.audio.speech.create( +# model="${I}", +# input="${n||"Your text to convert to speech here"}", +# voice="alloy", +# response_format="mp3", # Options: mp3, opus, aac, flac, wav, pcm +# speed=1.0 # Range: 0.25 to 4.0 +# ) +# response.stream_to_file("output_speech.mp3") +`;break;default:t="\n# Code generation for this endpoint is not implemented yet."}return`${C} +${t}`}],339019)}]); \ No newline at end of file diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/101fb167bf3e83b1.js b/litellm/proxy/_experimental/out/_next/static/chunks/101fb167bf3e83b1.js new file mode 100644 index 00000000000..a820ce54256 --- /dev/null +++ b/litellm/proxy/_experimental/out/_next/static/chunks/101fb167bf3e83b1.js @@ -0,0 +1 @@ +(globalThis.TURBOPACK||(globalThis.TURBOPACK=[])).push(["object"==typeof 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