diff --git a/.circleci/config.yml b/.circleci/config.yml index 06ceebcc5b4..e49dca6faae 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -83,8 +83,8 @@ jobs: pip install "google-generativeai==0.3.2" pip install "google-cloud-aiplatform==1.43.0" pip install pyarrow - pip install "boto3==1.34.34" - pip install "aioboto3==12.3.0" + pip install "boto3==1.36.0" + pip install "aioboto3==13.4.0" pip install langchain pip install lunary==0.2.5 pip install "azure-identity==1.16.1" @@ -95,7 +95,7 @@ jobs: pip install opentelemetry-api==1.25.0 pip install opentelemetry-sdk==1.25.0 pip install opentelemetry-exporter-otlp==1.25.0 - pip install openai==1.81.0 + pip install openai==1.100.1 pip install prisma==0.11.0 pip install "detect_secrets==1.5.0" pip install "httpx==0.24.1" @@ -208,8 +208,8 @@ jobs: pip install "google-generativeai==0.3.2" pip install "google-cloud-aiplatform==1.43.0" pip install pyarrow - pip install "boto3==1.34.34" - pip install "aioboto3==12.3.0" + pip install "boto3==1.36.0" + pip install "aioboto3==13.4.0" pip install langchain pip install lunary==0.2.5 pip install "azure-identity==1.16.1" @@ -220,7 +220,7 @@ jobs: pip install opentelemetry-api==1.25.0 pip install opentelemetry-sdk==1.25.0 pip install opentelemetry-exporter-otlp==1.25.0 - pip install openai==1.81.0 + pip install openai==1.100.1 pip install prisma==0.11.0 pip install "detect_secrets==1.5.0" pip install "httpx==0.24.1" @@ -315,8 +315,8 @@ jobs: pip install "google-generativeai==0.3.2" pip install "google-cloud-aiplatform==1.43.0" pip install pyarrow - pip install "boto3==1.34.34" - pip install "aioboto3==12.3.0" + pip install "boto3==1.36.0" + pip install "aioboto3==13.4.0" pip install langchain pip install lunary==0.2.5 pip install "azure-identity==1.16.1" @@ -327,7 +327,7 @@ jobs: pip install opentelemetry-api==1.25.0 pip install opentelemetry-sdk==1.25.0 pip install opentelemetry-exporter-otlp==1.25.0 - pip install openai==1.81.0 + pip install openai==1.100.1 pip install prisma==0.11.0 pip install "detect_secrets==1.5.0" pip install "httpx==0.24.1" @@ -439,6 +439,7 @@ jobs: paths: - auth_ui_unit_tests_coverage.xml - auth_ui_unit_tests_coverage + litellm_router_testing: # Runs all tests with the "router" keyword docker: - image: cimg/python:3.11 @@ -469,7 +470,55 @@ jobs: command: | pwd ls - python -m pytest tests/local_testing tests/router_unit_tests --cov=litellm --cov-report=xml -vv -k "router" -x -v --junitxml=test-results/junit.xml --durations=5 + python -m pytest tests/local_testing --cov=litellm --cov-report=xml -vv -k "router" -x -v --junitxml=test-results/junit.xml --durations=5 + no_output_timeout: 120m + - run: + name: Rename the coverage files + command: | + mv coverage.xml litellm_router_coverage.xml + mv .coverage litellm_router_coverage + # Store test results + - store_test_results: + path: test-results + + - persist_to_workspace: + root: . + paths: + - litellm_router_coverage.xml + - litellm_router_coverage + + litellm_router_unit_testing: # Runs all tests with the "router" keyword + docker: + - image: cimg/python:3.11 + auth: + username: ${DOCKERHUB_USERNAME} + password: ${DOCKERHUB_PASSWORD} + working_directory: ~/project + + steps: + - checkout + - setup_google_dns + - run: + name: Install Dependencies + command: | + python -m pip install --upgrade pip + python -m pip install -r requirements.txt + pip install "pytest==7.3.1" + pip install "respx==0.22.0" + pip install "pytest-cov==5.0.0" + pip install "pytest-retry==1.6.3" + pip install "pytest-asyncio==0.21.1" + pip install semantic_router --no-deps + pip install aurelio_sdk --no-deps + pip install "pytest-xdist==3.6.1" + # Run pytest and generate JUnit XML report + - setup_litellm_enterprise_pip + - run: + name: Run tests + command: | + pwd + ls + python -m pytest -vv tests/router_unit_tests --cov=litellm --cov-report=xml -x -s -v --junitxml=test-results/junit.xml --durations=5 no_output_timeout: 120m - run: name: Rename the coverage files @@ -590,8 +639,8 @@ jobs: pip install "google-generativeai==0.3.2" pip install "google-cloud-aiplatform==1.43.0" pip install pyarrow - pip install "boto3==1.34.34" - pip install "aioboto3==12.3.0" + pip install "boto3==1.36.0" + pip install "aioboto3==13.4.0" pip install langchain pip install lunary==0.2.5 pip install "azure-identity==1.16.1" @@ -602,7 +651,7 @@ jobs: pip install opentelemetry-api==1.25.0 pip install opentelemetry-sdk==1.25.0 pip install opentelemetry-exporter-otlp==1.25.0 - pip install openai==1.81.0 + pip install openai==1.100.1 pip install prisma==0.11.0 pip install "detect_secrets==1.5.0" pip install "httpx==0.24.1" @@ -816,7 +865,7 @@ jobs: pip install "pytest-asyncio==0.21.1" pip install "respx==0.22.0" pip install "pydantic==2.10.2" - pip install "boto3==1.34.34" + pip install "boto3==1.36.0" # Run pytest and generate JUnit XML report - run: name: Run tests @@ -957,6 +1006,7 @@ jobs: pip install "responses==0.25.7" pip install "pytest-xdist==3.6.1" pip install "semantic_router==0.1.10" + pip install "fastapi-offline==1.7.3" - setup_litellm_enterprise_pip # Run pytest and generate JUnit XML report - run: @@ -1237,7 +1287,7 @@ jobs: pip install aiohttp pip install openai pip install click - pip install "boto3==1.34.34" + pip install "boto3==1.36.0" pip install jinja2 pip install "tokenizers==0.20.0" pip install "uvloop==0.21.0" @@ -1506,8 +1556,8 @@ jobs: pip install "google-generativeai==0.3.2" pip install "google-cloud-aiplatform==1.43.0" pip install pyarrow - pip install "boto3==1.34.34" - pip install "aioboto3==12.3.0" + pip install "boto3==1.36.0" + pip install "aioboto3==13.4.0" pip install langchain pip install "langfuse>=2.0.0" pip install "logfire==0.29.0" @@ -1521,7 +1571,7 @@ jobs: pip install "aiodynamo==23.10.1" pip install "asyncio==3.4.3" pip install "PyGithub==1.59.1" - pip install "openai==1.81.0" + pip install "openai==1.100.1" - run: name: Install dockerize command: | @@ -1662,8 +1712,8 @@ jobs: pip install "google-generativeai==0.3.2" pip install "google-cloud-aiplatform==1.43.0" pip install pyarrow - pip install "boto3==1.34.34" - pip install "aioboto3==12.3.0" + pip install "boto3==1.36.0" + pip install "aioboto3==13.4.0" pip install langchain pip install "langchain_mcp_adapters==0.0.5" pip install "langfuse>=2.0.0" @@ -1678,7 +1728,7 @@ jobs: pip install "aiodynamo==23.10.1" pip install "asyncio==3.4.3" pip install "PyGithub==1.59.1" - pip install "openai==1.81.0" + pip install "openai==1.100.1" # Run pytest and generate JUnit XML report - run: name: Install dockerize @@ -1803,8 +1853,8 @@ jobs: pip install "google-generativeai==0.3.2" pip install "google-cloud-aiplatform==1.43.0" pip install pyarrow - pip install "boto3==1.34.34" - pip install "aioboto3==12.3.0" + pip install "boto3==1.36.0" + pip install "aioboto3==13.4.0" pip install langchain pip install "langfuse>=2.0.0" pip install "logfire==0.29.0" @@ -1818,7 +1868,7 @@ jobs: pip install "aiodynamo==23.10.1" pip install "asyncio==3.4.3" pip install "PyGithub==1.59.1" - pip install "openai==1.81.0" + pip install "openai==1.100.1" - run: name: Install dockerize command: | @@ -2398,14 +2448,14 @@ jobs: pip install "pytest-asyncio==0.21.1" pip install "google-cloud-aiplatform==1.43.0" pip install aiohttp - pip install "openai==1.81.0" + pip install "openai==1.100.1" pip install "assemblyai==0.37.0" python -m pip install --upgrade pip pip install "pydantic==2.10.2" pip install "pytest==7.3.1" pip install "pytest-mock==3.12.0" pip install "pytest-asyncio==0.21.1" - pip install "boto3==1.34.34" + pip install "boto3==1.36.0" pip install mypy pip install pyarrow pip install numpydoc @@ -2789,7 +2839,7 @@ jobs: pip install "pytest-retry==1.6.3" pip install "pytest-asyncio==0.21.1" pip install aiohttp - pip install "openai==1.81.0" + pip install "openai==1.100.1" python -m pip install --upgrade pip pip install "pydantic==2.10.2" pip install "pytest==7.3.1" @@ -2981,6 +3031,12 @@ workflows: only: - main - /litellm_.*/ + - litellm_router_unit_testing: + filters: + branches: + only: + - main + - /litellm_.*/ - check_code_and_doc_quality: filters: branches: @@ -3127,6 +3183,7 @@ workflows: - image_gen_testing - logging_testing - litellm_router_testing + - litellm_router_unit_testing - caching_unit_tests - litellm_proxy_unit_testing - litellm_security_tests @@ -3186,6 +3243,7 @@ workflows: - image_gen_testing - logging_testing - litellm_router_testing + - litellm_router_unit_testing - caching_unit_tests - langfuse_logging_unit_tests - litellm_assistants_api_testing diff --git a/.circleci/requirements.txt b/.circleci/requirements.txt index a1e5cb99416..f41a5291e50 100644 --- a/.circleci/requirements.txt +++ b/.circleci/requirements.txt @@ -1,5 +1,5 @@ # used by CI/CD testing -openai==1.81.0 +openai==1.100.1 python-dotenv tiktoken importlib_metadata diff --git a/.github/scripts/scan_keywords.py b/.github/scripts/scan_keywords.py new file mode 100644 index 00000000000..98d32b61afe --- /dev/null +++ b/.github/scripts/scan_keywords.py @@ -0,0 +1,133 @@ +#!/usr/bin/env python3 +import json +import os +import sys +import urllib.request +import urllib.error + + +def read_event_payload() -> dict: + event_path = os.environ.get("GITHUB_EVENT_PATH") + if not event_path or not os.path.exists(event_path): + return {} + with open(event_path, "r", encoding="utf-8") as f: + return json.load(f) + + +def get_issue_text(event: dict) -> tuple[str, str, int, str, str]: + issue = event.get("issue") or {} + title = (issue.get("title") or "").strip() + body = (issue.get("body") or "").strip() + number = issue.get("number") or 0 + html_url = issue.get("html_url") or "" + author = ((issue.get("user") or {}).get("login") or "").strip() + return title, body, number, html_url, author + + +def detect_keywords(text: str, keywords: list[str]) -> list[str]: + lowered = text.lower() + matches = [] + for keyword in keywords: + k = keyword.strip().lower() + if not k: + continue + if k in lowered: + matches.append(keyword.strip()) + # Deduplicate while preserving order + seen = set() + unique_matches = [] + for m in matches: + if m not in seen: + unique_matches.append(m) + seen.add(m) + return unique_matches + + +def send_webhook(webhook_url: str, payload: dict) -> None: + if not webhook_url: + return + data = json.dumps(payload).encode("utf-8") + req = urllib.request.Request( + webhook_url, + data=data, + headers={"Content-Type": "application/json"}, + method="POST", + ) + try: + with urllib.request.urlopen(req, timeout=10) as resp: + resp.read() + except urllib.error.HTTPError as e: + print(f"Webhook HTTP error: {e.code} {e.reason}", file=sys.stderr) + except urllib.error.URLError as e: + print(f"Webhook URL error: {e.reason}", file=sys.stderr) + except Exception as e: + print(f"Webhook unexpected error: {e}", file=sys.stderr) + + +def _excerpt(text: str, max_len: int = 400) -> str: + if not text: + return "" + + # Keep original formatting + if len(text) <= max_len: + return text + return text[: max_len - 1] + "…" + + + +def main() -> int: + event = read_event_payload() + if not event: + print("::warning::No event payload found; exiting without labeling.") + return 0 + + # Read issue details + title, body, number, html_url, author = get_issue_text(event) + combined_text = f"{title}\n\n{body}".strip() + + # Keywords from env or defaults + keywords_env = os.environ.get("KEYWORDS", "") + default_keywords = ["azure", "openai", "bedrock", "vertexai", "vertex ai", "anthropic"] + keywords = [k.strip() for k in keywords_env.split(",")] if keywords_env else default_keywords + + matches = detect_keywords(combined_text, keywords) + found = bool(matches) + + # Emit outputs + github_output = os.environ.get("GITHUB_OUTPUT") + if github_output: + with open(github_output, "a", encoding="utf-8") as fh: + fh.write(f"found={'true' if found else 'false'}\n") + fh.write(f"matches={','.join(matches)}\n") + + # Optional webhook notification + webhook_url = os.environ.get("PROVIDER_ISSUE_WEBHOOK_URL", "").strip() + if found and webhook_url: + repo_full = (event.get("repository") or {}).get("full_name", "") + title_part = f"*{title}*" if title else "New issue" + author_part = f" by @{author}" if author else "" + body_preview = _excerpt(body) + preview_block = f"\n{body_preview}" if body_preview else "" + payload = { + "text": ( + f"New issue 🚨\n" + f"{title_part}\n\n{preview_block}\n" + f"<{html_url}|View issue>\n" + f"Author: {author}" + ) + } + send_webhook(webhook_url, payload) + + # Print a short log line for Actions UI + if found: + print(f"Detected provider keywords: {', '.join(matches)}") + else: + print("No provider keywords detected.") + + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) + + diff --git a/.github/workflows/issue-keyword-labeler.yml b/.github/workflows/issue-keyword-labeler.yml new file mode 100644 index 00000000000..60c18e3b9af --- /dev/null +++ b/.github/workflows/issue-keyword-labeler.yml @@ -0,0 +1,64 @@ +name: Issue Keyword Labeler + +on: + issues: + types: + - opened + +jobs: + scan-and-label: + runs-on: ubuntu-latest + permissions: + issues: write + contents: read + steps: + - name: Checkout code + uses: actions/checkout@v4 + + - name: Scan for provider keywords + id: scan + env: + PROVIDER_ISSUE_WEBHOOK_URL: ${{ secrets.PROVIDER_ISSUE_WEBHOOK_URL }} + KEYWORDS: azure,openai,bedrock,vertexai,vertex ai,anthropic + run: python3 .github/scripts/scan_keywords.py + + - name: Ensure label exists + if: steps.scan.outputs.found == 'true' + uses: actions/github-script@v7 + with: + github-token: ${{ secrets.GITHUB_TOKEN }} + script: | + const labelName = 'llm translation'; + try { + await github.rest.issues.getLabel({ + owner: context.repo.owner, + repo: context.repo.repo, + name: labelName + }); + } catch (error) { + if (error.status === 404) { + await github.rest.issues.createLabel({ + owner: context.repo.owner, + repo: context.repo.repo, + name: labelName, + color: 'c1ff72', + description: 'Issues related to LLM provider translation/mapping' + }); + } else { + throw error; + } + } + + - name: Add label to the issue + if: steps.scan.outputs.found == 'true' + uses: actions/github-script@v7 + with: + github-token: ${{ secrets.GITHUB_TOKEN }} + script: | + await github.rest.issues.addLabels({ + owner: context.repo.owner, + repo: context.repo.repo, + issue_number: context.issue.number, + labels: ['llm translation'] + }); + diff --git a/.github/workflows/test-linting.yml b/.github/workflows/test-linting.yml index ceeedbe7e13..ffca305a0d0 100644 --- a/.github/workflows/test-linting.yml +++ b/.github/workflows/test-linting.yml @@ -22,11 +22,8 @@ jobs: - name: Install dependencies run: | - pip install openai==1.81.0 poetry install --with dev - pip install openai==1.81.0 - - + poetry run pip install openai==1.100.1 - name: Run Black formatting run: | @@ -40,6 +37,10 @@ jobs: poetry run ruff check . cd .. + - name: Print OpenAI version + run: | + poetry run python -c "import openai; print(f'OpenAI version: {openai.__version__}')" + - name: Run MyPy type checking run: | cd litellm diff --git a/.github/workflows/test-litellm.yml b/.github/workflows/test-litellm.yml index 2f6e81c8ceb..7e67aee8d73 100644 --- a/.github/workflows/test-litellm.yml +++ b/.github/workflows/test-litellm.yml @@ -7,7 +7,7 @@ on: jobs: test: runs-on: ubuntu-latest - timeout-minutes: 20 + timeout-minutes: 25 steps: - uses: actions/checkout@v4 @@ -31,6 +31,7 @@ jobs: poetry run pip install "pytest-retry==1.6.3" poetry run pip install pytest-xdist poetry run pip install "google-genai==1.22.0" + poetry run pip install "fastapi-offline==1.7.3" - name: Setup litellm-enterprise as local package run: | cd enterprise diff --git a/.gitignore b/.gitignore index f8d028ff47b..ed8c88c8990 100644 --- a/.gitignore +++ b/.gitignore @@ -86,6 +86,7 @@ litellm/proxy/db/migrations/0_init/migration.sql litellm/proxy/db/migrations/* litellm/proxy/migrations/*config.yaml litellm/proxy/migrations/* +litellm/proxy/to_delete_loadtest_work/* config.yaml tests/litellm/litellm_core_utils/llm_cost_calc/log.txt tests/test_custom_dir/* @@ -93,4 +94,5 @@ test.py litellm_config.yaml .cursor -.vscode/launch.json \ No newline at end of file +.vscode/launch.json +litellm/proxy/to_delete_loadtest_work/* \ No newline at end of file diff --git a/Dockerfile b/Dockerfile index 9261d55d7fe..addc109e10c 100644 --- a/Dockerfile +++ b/Dockerfile @@ -65,8 +65,8 @@ COPY --from=builder /wheels/ /wheels/ # Install the built wheel using pip; again using a wildcard if it's the only file RUN pip install *.whl /wheels/* --no-index --find-links=/wheels/ && rm -f *.whl && rm -rf /wheels -# Install semantic_router without dependencies -RUN pip install semantic_router --no-deps +# Install semantic_router and aurelio-sdk using script +RUN chmod +x docker/install_auto_router.sh && ./docker/install_auto_router.sh # Generate prisma client RUN prisma generate diff --git a/Makefile b/Makefile index 077641b0f28..edeb27bac3f 100644 --- a/Makefile +++ b/Makefile @@ -34,13 +34,13 @@ install-proxy-dev: # CI-compatible installations (matches GitHub workflows exactly) install-dev-ci: - pip install openai==1.81.0 + pip install openai==1.99.5 poetry install --with dev - pip install openai==1.81.0 + pip install openai==1.99.5 install-proxy-dev-ci: poetry install --with dev,proxy-dev --extras proxy - pip install openai==1.81.0 + pip install openai==1.99.5 install-test-deps: install-proxy-dev poetry run pip install "pytest-retry==1.6.3" diff --git a/README.md b/README.md index 528dd53581c..45f0bbe1395 100644 --- a/README.md +++ b/README.md @@ -47,7 +47,7 @@ Support for more providers. Missing a provider or LLM Platform, raise a [feature # Usage ([**Docs**](https://docs.litellm.ai/docs/)) > [!IMPORTANT] -> LiteLLM v1.0.0 now requires `openai>=1.0.0`. Migration guide [here](https://docs.litellm.ai/docs/migration) +> LiteLLM v1.0.0 now requires `openai>=1.0.0`. Migration guide [here](https://docs.litellm.ai/docs/migration) > LiteLLM v1.40.14+ now requires `pydantic>=2.0.0`. No changes required. @@ -132,7 +132,7 @@ print(response) ## Streaming ([Docs](https://docs.litellm.ai/docs/completion/stream)) -liteLLM supports streaming the model response back, pass `stream=True` to get a streaming iterator in response. +liteLLM supports streaming the model response back, pass `stream=True` to get a streaming iterator in response. Streaming is supported for all models (Bedrock, Huggingface, TogetherAI, Azure, OpenAI, etc.) ```python @@ -234,7 +234,7 @@ $ litellm --model huggingface/bigcode/starcoder > [!IMPORTANT] -> 💡 [Use LiteLLM Proxy with Langchain (Python, JS), OpenAI SDK (Python, JS) Anthropic SDK, Mistral SDK, LlamaIndex, Instructor, Curl](https://docs.litellm.ai/docs/proxy/user_keys) +> 💡 [Use LiteLLM Proxy with Langchain (Python, JS), OpenAI SDK (Python, JS) Anthropic SDK, Mistral SDK, LlamaIndex, Instructor, Curl](https://docs.litellm.ai/docs/proxy/user_keys) ```python import openai # openai v1.0.0+ @@ -266,7 +266,7 @@ echo 'LITELLM_MASTER_KEY="sk-1234"' > .env # Add the litellm salt key - you cannot change this after adding a model # It is used to encrypt / decrypt your LLM API Key credentials -# We recommend - https://1password.com/password-generator/ +# We recommend - https://1password.com/password-generator/ # password generator to get a random hash for litellm salt key echo 'LITELLM_SALT_KEY="sk-1234"' >> .env @@ -340,6 +340,7 @@ curl 'http://0.0.0.0:4000/key/generate' \ | [xinference [Xorbits Inference]](https://docs.litellm.ai/docs/providers/xinference) | | | | | ✅ | | | [FriendliAI](https://docs.litellm.ai/docs/providers/friendliai) | ✅ | ✅ | ✅ | ✅ | | | | [Galadriel](https://docs.litellm.ai/docs/providers/galadriel) | ✅ | ✅ | ✅ | ✅ | | | +| [GradientAI](https://docs.litellm.ai/docs/providers/gradient_ai) | ✅ | ✅ | | | | | | [Novita AI](https://novita.ai/models/llm?utm_source=github_litellm&utm_medium=github_readme&utm_campaign=github_link) | ✅ | ✅ | ✅ | ✅ | | | | [Featherless AI](https://docs.litellm.ai/docs/providers/featherless_ai) | ✅ | ✅ | ✅ | ✅ | | | | [Nebius AI Studio](https://docs.litellm.ai/docs/providers/nebius) | ✅ | ✅ | ✅ | ✅ | ✅ | | @@ -348,7 +349,7 @@ curl 'http://0.0.0.0:4000/key/generate' \ ## Contributing -Interested in contributing? Contributions to LiteLLM Python SDK, Proxy Server, and LLM integrations are both accepted and highly encouraged! +Interested in contributing? Contributions to LiteLLM Python SDK, Proxy Server, and LLM integrations are both accepted and highly encouraged! **Quick start:** `git clone` → `make install-dev` → `make format` → `make lint` → `make test-unit` @@ -359,7 +360,7 @@ For companies that need better security, user management and professional suppor [Talk to founders](https://calendly.com/d/4mp-gd3-k5k/litellm-1-1-onboarding-chat) -This covers: +This covers: - ✅ **Features under the [LiteLLM Commercial License](https://docs.litellm.ai/docs/proxy/enterprise):** - ✅ **Feature Prioritization** - ✅ **Custom Integrations** @@ -373,6 +374,8 @@ We welcome contributions to LiteLLM! Whether you're fixing bugs, adding features ## Quick Start for Contributors +This requires poetry to be installed. + ```bash git clone https://github.com/BerriAI/litellm.git cd litellm @@ -380,6 +383,7 @@ make install-dev # Install development dependencies make format # Format your code make lint # Run all linting checks make test-unit # Run unit tests +make format-check # Check formatting only ``` For detailed contributing guidelines, see [CONTRIBUTING.md](CONTRIBUTING.md). @@ -395,11 +399,6 @@ Our automated checks include: - **Circular import detection** - **Import safety checks** -Run all checks locally: -```bash -make lint # Run all linting (matches CI) -make format-check # Check formatting only -``` All these checks must pass before your PR can be merged. @@ -441,7 +440,7 @@ All these checks must pass before your PR can be merged. 1. (In root) create virtual environment `python -m venv .venv` 2. Activate virtual environment `source .venv/bin/activate` 3. Install dependencies `pip install -e ".[all]"` -4. Start proxy backend `uvicorn litellm.proxy.proxy_server:app --host localhost --port 4000 --reload` +4. Start proxy backend `python3 /path/to/litellm/proxy_cli.py` ### Frontend 1. Navigate to `ui/litellm-dashboard` diff --git a/cookbook/liteLLM_Baseten.ipynb b/cookbook/liteLLM_Baseten.ipynb index e03bb3254a5..0a5bc5f1df7 100644 --- a/cookbook/liteLLM_Baseten.ipynb +++ b/cookbook/liteLLM_Baseten.ipynb @@ -6,19 +6,21 @@ "id": "gZx-wHJapG5w" }, "source": [ - "# Use liteLLM to call Falcon, Wizard, MPT 7B using OpenAI chatGPT Input/output\n", + "# LiteLLM with Baseten Model APIs\n", "\n", - "* Falcon 7B: https://app.baseten.co/explore/falcon_7b\n", - "* Wizard LM: https://app.baseten.co/explore/wizardlm\n", - "* MPT 7B Base: https://app.baseten.co/explore/mpt_7b_instruct\n", + "This notebook demonstrates how to use LiteLLM with Baseten's Model APIs instead of dedicated deployments.\n", "\n", - "\n", - "## Call all baseten llm models using OpenAI chatGPT Input/Output using liteLLM\n", - "Example call\n", + "## Example Usage\n", "```python\n", - "model = \"q841o8w\" # baseten model version ID\n", - "response = completion(model=model, messages=messages, custom_llm_provider=\"baseten\")\n", - "```" + "response = completion(\n", + " model=\"baseten/openai/gpt-oss-120b\",\n", + " messages=[{\"role\": \"user\", \"content\": \"Hello!\"}],\n", + " max_tokens=1000,\n", + " temperature=0.7\n", + ")\n", + "```\n", + "\n", + "## Setup" ] }, { @@ -29,20 +31,25 @@ }, "outputs": [], "source": [ - "!pip install litellm==0.1.399\n", - "!pip install baseten urllib3" + "%pip install litellm" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "id": "VEukLhDzo4vw" }, "outputs": [], "source": [ "import os\n", - "from litellm import completion" + "from litellm import completion\n", + "\n", + "# Set your Baseten API key\n", + "os.environ['BASETEN_API_KEY'] = \"\" #@param {type:\"string\"}\n", + "\n", + "# Test message\n", + "messages = [{\"role\": \"user\", \"content\": \"What is AGI?\"}]" ] }, { @@ -51,19 +58,31 @@ "id": "4STYM2OHFNlc" }, "source": [ - "## Setup" + "## Example 1: Basic Completion\n", + "\n", + "Simple completion with the GPT-OSS 120B model" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": { "id": "DorpLxw1FHbC" }, "outputs": [], "source": [ - "os.environ['BASETEN_API_KEY'] = \"\" #@param\n", - "messages = [{ \"content\": \"what does Baseten do? \",\"role\": \"user\"}]" + "print(\"=== Basic Completion ===\")\n", + "response = completion(\n", + " model=\"baseten/openai/gpt-oss-120b\",\n", + " messages=messages,\n", + " max_tokens=1000,\n", + " temperature=0.7,\n", + " top_p=0.9,\n", + " presence_penalty=0.1,\n", + " frequency_penalty=0.1,\n", + ")\n", + "print(f\"Response: {response.choices[0].message.content}\")\n", + "print(f\"Usage: {response.usage}\")" ] }, { @@ -72,13 +91,14 @@ "id": "syF3dTdKFSQQ" }, "source": [ - "## Calling Falcon 7B: https://app.baseten.co/explore/falcon_7b\n", - "### Pass Your Baseten model `Version ID` as `model`" + "## Example 2: Streaming Completion\n", + "\n", + "Streaming completion with usage statistics" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -86,137 +106,26 @@ "id": "rPgSoMlsojz0", "outputId": "81d6dc7b-1681-4ae4-e4c8-5684eb1bd050" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32mINFO\u001b[0m API key set.\n", - "INFO:baseten:API key set.\n" - ] - }, - { - "data": { - "text/plain": [ - "{'choices': [{'finish_reason': 'stop',\n", - " 'index': 0,\n", - " 'message': {'role': 'assistant',\n", - " 'content': \"what does Baseten do? \\nI'm sorry, I cannot provide a specific answer as\"}}],\n", - " 'created': 1692135883.699066,\n", - " 'model': 'qvv0xeq'}" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "model = \"qvv0xeq\"\n", - "response = completion(model=model, messages=messages, custom_llm_provider=\"baseten\")\n", - "response" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "7n21UroEGCGa" - }, - "source": [ - "## Calling Wizard LM https://app.baseten.co/explore/wizardlm\n", - "### Pass Your Baseten model `Version ID` as `model`" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "uLVWFH899lAF", - "outputId": "61c2bc74-673b-413e-bb40-179cf408523d" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32mINFO\u001b[0m API key set.\n", - "INFO:baseten:API key set.\n" - ] - }, - { - "data": { - "text/plain": [ - "{'choices': [{'finish_reason': 'stop',\n", - " 'index': 0,\n", - " 'message': {'role': 'assistant',\n", - " 'content': 'As an AI language model, I do not have personal beliefs or practices, but based on the information available online, Baseten is a popular name for a traditional Ethiopian dish made with injera, a spongy flatbread, and wat, a spicy stew made with meat or vegetables. It is typically served for breakfast or dinner and is a staple in Ethiopian cuisine. The name Baseten is also used to refer to a traditional Ethiopian coffee ceremony, where coffee is brewed and served in a special ceremony with music and food.'}}],\n", - " 'created': 1692135900.2806294,\n", - " 'model': 'q841o8w'}" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model = \"q841o8w\"\n", - "response = completion(model=model, messages=messages, custom_llm_provider=\"baseten\")\n", - "response" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6-TFwmPAGPXq" - }, - "source": [ - "## Calling mosaicml/mpt-7b https://app.baseten.co/explore/mpt_7b_instruct\n", - "### Pass Your Baseten model `Version ID` as `model`" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "gbeYZOrUE_Bp", - "outputId": "838d86ea-2143-4cb3-bc80-2acc2346c37a" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32mINFO\u001b[0m API key set.\n", - "INFO:baseten:API key set.\n" - ] - }, - { - "data": { - "text/plain": [ - "{'choices': [{'finish_reason': 'stop',\n", - " 'index': 0,\n", - " 'message': {'role': 'assistant',\n", - " 'content': \"\\n===================\\n\\nIt's a tool to build a local version of a game on your own machine to host\\non your website.\\n\\nIt's used to make game demos and show them on Twitter, Tumblr, and Facebook.\\n\\n\\n\\n## What's built\\n\\n- A directory of all your game directories, named with a version name and build number, with images linked to.\\n- Includes HTML to include in another site.\\n- Includes images for your icons and\"}}],\n", - " 'created': 1692135914.7472186,\n", - " 'model': '31dxrj3'}" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model = \"31dxrj3\"\n", - "response = completion(model=model, messages=messages, custom_llm_provider=\"baseten\")\n", - "response" + "print(\"=== Streaming Completion ===\")\n", + "response = completion(\n", + " model=\"baseten/openai/gpt-oss-120b\",\n", + " messages=[{\"role\": \"user\", \"content\": \"Write a short poem about AI\"}],\n", + " stream=True,\n", + " max_tokens=500,\n", + " temperature=0.8,\n", + " stream_options={\n", + " \"include_usage\": True,\n", + " \"continuous_usage_stats\": True\n", + " },\n", + ")\n", + "\n", + "print(\"Streaming response:\")\n", + "for chunk in response:\n", + " if chunk.choices and chunk.choices[0].delta.content:\n", + " print(chunk.choices[0].delta.content, end=\"\", flush=True)\n", + "print(\"\\n\")" ] } ], @@ -234,4 +143,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/deploy/charts/litellm-helm/Chart.yaml b/deploy/charts/litellm-helm/Chart.yaml index bd63ca6bfca..b6ac264a228 100644 --- a/deploy/charts/litellm-helm/Chart.yaml +++ b/deploy/charts/litellm-helm/Chart.yaml @@ -18,7 +18,7 @@ type: application # This is the chart version. This version number should be incremented each time you make changes # to the chart and its templates, including the app version. # Versions are expected to follow Semantic Versioning (https://semver.org/) -version: 0.4.4 +version: 0.4.5 # This is the version number of the application being deployed. This version number should be # incremented each time you make changes to the application. Versions are not expected to diff --git a/deploy/charts/litellm-helm/README.md b/deploy/charts/litellm-helm/README.md index cef2b8d162d..73b722b64c6 100644 --- a/deploy/charts/litellm-helm/README.md +++ b/deploy/charts/litellm-helm/README.md @@ -24,7 +24,7 @@ If `db.useStackgresOperator` is used (not yet implemented): | `replicaCount` | The number of LiteLLM Proxy pods to be deployed | `1` | | `masterkeySecretName` | The name of the Kubernetes Secret that contains the Master API Key for LiteLLM. If not specified, use the generated secret name. | N/A | | `masterkeySecretKey` | The key within the Kubernetes Secret that contains the Master API Key for LiteLLM. If not specified, use `masterkey` as the key. | N/A | -| `masterkey` | The Master API Key for LiteLLM. If not specified, a random key is generated. | N/A | +| `masterkey` | The Master API Key for LiteLLM. If not specified, a random key in the `sk-...` format is generated. | N/A | | `environmentSecrets` | An optional array of Secret object names. The keys and values in these secrets will be presented to the LiteLLM proxy pod as environment variables. See below for an example Secret object. | `[]` | | `environmentConfigMaps` | An optional array of ConfigMap object names. The keys and values in these configmaps will be presented to the LiteLLM proxy pod as environment variables. See below for an example Secret object. | `[]` | | `image.repository` | LiteLLM Proxy image repository | `ghcr.io/berriai/litellm` | @@ -135,7 +135,7 @@ service, the **Proxy Endpoint** should be set to `http://-litellm:4000` The **Proxy Key** is the value specified for `masterkey` or, if a `masterkey` was not provided to the helm command line, the `masterkey` is a randomly -generated string stored in the `-litellm-masterkey` Kubernetes Secret. +generated string in the `sk-...` format stored in the `-litellm-masterkey` Kubernetes Secret. ```bash kubectl -n litellm get secret -litellm-masterkey -o jsonpath="{.data.masterkey}" diff --git a/deploy/charts/litellm-helm/templates/deployment.yaml b/deploy/charts/litellm-helm/templates/deployment.yaml index 71412b8052b..b30b8829325 100644 --- a/deploy/charts/litellm-helm/templates/deployment.yaml +++ b/deploy/charts/litellm-helm/templates/deployment.yaml @@ -71,7 +71,14 @@ spec: name: {{ .Values.db.secret.name }} key: {{ .Values.db.secret.passwordKey }} - name: DATABASE_HOST + {{- if .Values.db.secret.endpointKey }} + valueFrom: + secretKeyRef: + name: {{ .Values.db.secret.name }} + key: {{ .Values.db.secret.endpointKey }} + {{- else }} value: {{ .Values.db.endpoint }} + {{- end }} - name: DATABASE_NAME value: {{ .Values.db.database }} - name: DATABASE_URL diff --git a/deploy/charts/litellm-helm/templates/migrations-job.yaml b/deploy/charts/litellm-helm/templates/migrations-job.yaml index cf10be0a76b..4c8925564af 100644 --- a/deploy/charts/litellm-helm/templates/migrations-job.yaml +++ b/deploy/charts/litellm-helm/templates/migrations-job.yaml @@ -49,7 +49,14 @@ spec: name: {{ .Values.db.secret.name }} key: {{ .Values.db.secret.passwordKey }} - name: DATABASE_HOST + {{- if .Values.db.secret.endpointKey }} + valueFrom: + secretKeyRef: + name: {{ .Values.db.secret.name }} + key: {{ .Values.db.secret.endpointKey }} + {{- else }} value: {{ .Values.db.endpoint }} + {{- end }} - name: DATABASE_NAME value: {{ .Values.db.database }} - name: DATABASE_URL @@ -73,6 +80,10 @@ spec: volumeMounts: {{- toYaml . | nindent 12 }} {{- end }} + {{- with .Values.migrationJob.resources }} + resources: + {{- toYaml . | nindent 12 }} + {{- end }} {{- with .Values.migrationJob.extraContainers }} {{- toYaml . | nindent 8 }} {{- end }} diff --git a/deploy/charts/litellm-helm/templates/secret-masterkey.yaml b/deploy/charts/litellm-helm/templates/secret-masterkey.yaml index 5632957dc05..7c8560cc2cc 100644 --- a/deploy/charts/litellm-helm/templates/secret-masterkey.yaml +++ b/deploy/charts/litellm-helm/templates/secret-masterkey.yaml @@ -1,5 +1,5 @@ {{- if not .Values.masterkeySecretName }} -{{ $masterkey := (.Values.masterkey | default (randAlphaNum 17)) }} +{{ $masterkey := (.Values.masterkey | default (printf "sk-%s" (randAlphaNum 18))) }} apiVersion: v1 kind: Secret metadata: diff --git a/deploy/charts/litellm-helm/tests/masterkey-secret_tests.yaml b/deploy/charts/litellm-helm/tests/masterkey-secret_tests.yaml index eb1d3c3967f..bbbade9d802 100644 --- a/deploy/charts/litellm-helm/tests/masterkey-secret_tests.yaml +++ b/deploy/charts/litellm-helm/tests/masterkey-secret_tests.yaml @@ -2,13 +2,19 @@ suite: test masterkey secret templates: - secret-masterkey.yaml tests: - - it: should create a secret if masterkeySecretName is not set + - it: should create a secret if masterkeySecretName is not set. should start with sk-xxxx (base64 encoded as c2st*) template: secret-masterkey.yaml set: masterkeySecretName: "" asserts: - isKind: of: Secret + - matchRegex: + path: data.masterkey + pattern: ^c2st + # Note: The masterkey is generated as "sk-<18-random-chars>" in plain text, + # but stored as base64 encoded in Kubernetes secret (requirement). + # "sk-" base64 encodes to "c2st", so we check for "^c2st" pattern. - it: should not create a secret if masterkeySecretName is set template: secret-masterkey.yaml set: diff --git a/deploy/charts/litellm-helm/values.yaml b/deploy/charts/litellm-helm/values.yaml index f99204cbb4b..0bd95003c10 100644 --- a/deploy/charts/litellm-helm/values.yaml +++ b/deploy/charts/litellm-helm/values.yaml @@ -161,6 +161,8 @@ db: name: postgres usernameKey: username passwordKey: password + # Optional: when set, DATABASE_HOST will be sourced from this secret key instead of db.endpoint + endpointKey: "" # Use the Stackgres Helm chart to deploy an instance of a Stackgres cluster. # The Stackgres Operator must already be installed within the target @@ -206,6 +208,10 @@ migrationJob: disableSchemaUpdate: false # Skip schema migrations for specific environments. When True, the job will exit with code 0. annotations: {} ttlSecondsAfterFinished: 120 + resources: {} + # requests: + # cpu: 100m + # memory: 100Mi extraContainers: [] # Hook configuration diff --git a/docker/Dockerfile.database b/docker/Dockerfile.database index 956ec76dbe7..351c4f6bc48 100644 --- a/docker/Dockerfile.database +++ b/docker/Dockerfile.database @@ -57,8 +57,8 @@ COPY --from=builder /wheels/ /wheels/ # Install the built wheel using pip; again using a wildcard if it's the only file RUN pip install *.whl /wheels/* --no-index --find-links=/wheels/ && rm -f *.whl && rm -rf /wheels -# Install semantic_router without dependencies -RUN pip install semantic_router --no-deps +# Install semantic_router and aurelio-sdk using script +RUN chmod +x docker/install_auto_router.sh && ./docker/install_auto_router.sh # ensure pyjwt is used, not jwt RUN pip uninstall jwt -y diff --git a/docker/Dockerfile.non_root b/docker/Dockerfile.non_root index 388dc6d0766..bc2bf169c28 100644 --- a/docker/Dockerfile.non_root +++ b/docker/Dockerfile.non_root @@ -47,8 +47,8 @@ RUN pip install *.whl /wheels/* --no-index --find-links=/wheels/ \ && rm -f *.whl \ && rm -rf /wheels -# Install semantic_router without dependencies -RUN pip install semantic_router --no-deps +# Install semantic_router and aurelio-sdk using script +RUN chmod +x docker/install_auto_router.sh && ./docker/install_auto_router.sh # Ensure correct JWT library is used (pyjwt not jwt) RUN pip uninstall jwt -y && \ @@ -70,7 +70,9 @@ RUN mkdir -p /nonexistent /.npm && \ chown -R nobody:nogroup /app && \ chown -R nobody:nogroup /nonexistent /.npm && \ PRISMA_PATH=$(python -c "import os, prisma; print(os.path.dirname(prisma.__file__))") && \ - chown -R nobody:nogroup $PRISMA_PATH + chown -R nobody:nogroup $PRISMA_PATH && \ + LITELLM_PKG_MIGRATIONS_PATH="$(python -c 'import os, litellm_proxy_extras; print(os.path.dirname(litellm_proxy_extras.__file__))' 2>/dev/null || echo '')/migrations" && \ + [ -n "$LITELLM_PKG_MIGRATIONS_PATH" ] && chown -R nobody:nogroup $LITELLM_PKG_MIGRATIONS_PATH # --- OpenShift Compatibility: Apply Red Hat recommended pattern --- # Get paths for directories that need write access at runtime diff --git a/docker/build_from_pip/requirements.txt b/docker/build_from_pip/requirements.txt index 71e038b6267..cc14b99727f 100644 --- a/docker/build_from_pip/requirements.txt +++ b/docker/build_from_pip/requirements.txt @@ -2,4 +2,5 @@ litellm[proxy]==1.67.4.dev1 # Specify the litellm version you want to use prometheus_client langfuse prisma +openai==1.99.9 ddtrace==2.19.0 # for advanced DD tracing / profiling diff --git a/docker/install_auto_router.sh b/docker/install_auto_router.sh new file mode 100755 index 00000000000..794f9a2bbce --- /dev/null +++ b/docker/install_auto_router.sh @@ -0,0 +1,3 @@ +#!/bin/bash +pip install semantic_router==0.1.11 --no-deps +pip install aurelio-sdk==0.0.19 \ No newline at end of file diff --git a/docs/my-website/docs/completion/document_understanding.md b/docs/my-website/docs/completion/document_understanding.md index b831a7b9da2..172e0792801 100644 --- a/docs/my-website/docs/completion/document_understanding.md +++ b/docs/my-website/docs/completion/document_understanding.md @@ -10,6 +10,7 @@ Works for: - Bedrock Models - Anthropic API Models - OpenAI API Models +- Mistral (Only using file ID of already uploaded file, similar to OpenAI file_id input) ## Quick Start @@ -279,6 +280,71 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \ +## Mistral Example + +Here is a sample payload for using the Mistral model for document understanding: + + + + + +```python +from litellm.utils import completion + +# pdf file_id received from files endpoint +file_id = "fa778e5e-46ec-4562-8418-36623fe25a71" + +# model +model = "mistral/mistral-large-latest" + +file_content = [ + {"type": "text", "text": "What's this file about?"}, + { + "type": "file", + "file": { + "file_id": file_id, + } + }, +] + +response = completion( + model=model, + messages=[{"role": "user", "content": file_content}], +) +assert response is not None +``` + + + + +```bash +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-d '{ + "model": "mistral/mistral-large-latest", + "messages": [ + { + "role": "user", + "content": [ + { + "type": "text", + "text": "What is the content of the file?" + }, + { + "type": "file", + "file": { + "file_id": "fa778e5e-46ec-4562-8418-36623fe25a71" + } + } + ] + } + ] +} +``` + + + ## Checking if a model supports pdf input diff --git a/docs/my-website/docs/mcp.md b/docs/my-website/docs/mcp.md index 380a3b2be9c..684c2e6ca74 100644 --- a/docs/my-website/docs/mcp.md +++ b/docs/my-website/docs/mcp.md @@ -1134,6 +1134,91 @@ When MCP tools are called, your custom hook will: 2. Modify the response if needed 3. Track costs in LiteLLM's logging system +## MCP Guardrails + +LiteLLM supports applying guardrails to MCP tool calls to ensure security and compliance. You can configure guardrails to run before or during MCP calls to validate inputs and block or mask sensitive information. + +### Supported MCP Guardrail Modes + +MCP guardrails support the following modes: + +- `pre_mcp_call`: Run **before** MCP call, on **input**. Use this mode when you want to apply validation/masking/blocking for MCP requests +- `during_mcp_call`: Run **during** MCP call execution. Use this mode for real-time monitoring and intervention + +### Configuration Examples + +Configure guardrails to run before MCP tool calls to validate and sanitize inputs: + +```yaml title="config.yaml" showLineNumbers +guardrails: + - guardrail_name: "mcp-input-validation" + litellm_params: + guardrail: presidio # or other supported guardrails + mode: "pre_mcp_call" # or during_mcp_call + pii_entities_config: + CREDIT_CARD: "BLOCK" # Will block requests containing credit card numbers + EMAIL_ADDRESS: "MASK" # Will mask email addresses + PHONE_NUMBER: "MASK" # Will mask phone numbers + default_on: true +``` + + +### Usage Examples + +#### Testing Pre-MCP Call Guardrails + +Test your MCP guardrails with a request that includes sensitive information: + +```bash title="Test MCP Guardrail" showLineNumbers +curl http://localhost:4000/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "gpt-3.5-turbo", + "messages": [ + {"role": "user", "content": "My credit card is 4111-1111-1111-1111 and my email is john@example.com"} + ], + "guardrails": ["mcp-input-validation"] + }' +``` + +The request will be processed as follows: +1. Credit card number will be blocked (request rejected) +2. Email address will be masked (e.g., replaced with ``) + +#### Using with MCP Tools + +When using MCP tools, guardrails will be applied to the tool inputs: + +```python title="Python Example with MCP Guardrails" showLineNumbers +import openai + +client = openai.OpenAI( + api_key="your-api-key", + base_url="http://localhost:4000" +) + +# This request will trigger MCP guardrails +response = client.chat.completions.create( + model="gpt-3.5-turbo", + messages=[ + {"role": "user", "content": "Send an email to 555-123-4567 with my SSN 123-45-6789"} + ], + tools=[{"type": "mcp", "server_label": "litellm", "server_url": "litellm_proxy"}], + guardrails=["mcp-input-validation"] +) +``` + +### Supported Guardrail Providers + +MCP guardrails work with all LiteLLM-supported guardrail providers: + +- **Presidio**: PII detection and masking +- **Bedrock**: AWS Bedrock guardrails +- **Lakera**: Content moderation +- **Aporia**: Custom guardrails +- **Custom**: Your own guardrail implementations + ## MCP Permission Management LiteLLM supports managing permissions for MCP Servers by Keys, Teams, Organizations (entities) on LiteLLM. When a MCP client attempts to list tools, LiteLLM will only return the tools the entity has permissions to access. diff --git a/docs/my-website/docs/providers/aiml.md b/docs/my-website/docs/providers/aiml.md index 1343cbf8d8e..9d763daf7d7 100644 --- a/docs/my-website/docs/providers/aiml.md +++ b/docs/my-website/docs/providers/aiml.md @@ -1,5 +1,23 @@ # AI/ML API +https://aimlapi.com/ +## Overview + +| Property | Details | +|-------|-------| +| Description | AI/ML API provides access to state-of-the-art AI models including flux-pro/v1.1 for high-quality image generation. | +| Provider Route on LiteLLM | `aiml/` | +| Link to Provider Doc | [AI/ML API ↗](https://docs.aimlapi.com/) | +| Supported Operations | [`/chat/completions`], [`/images/generations`](#image-generation) | + +LiteLLM supports AI/ML API Image Generation calls. + +## API Base, Key +```python +# env variable +os.environ['AIML_API_KEY'] = "your-api-key" +os.environ['AIML_API_BASE'] = "https://api.aimlapi.com" # [optional] +``` Getting started with the AI/ML API is simple. Follow these steps to set up your integration: ### 1. Get Your API Key @@ -24,7 +42,7 @@ You can choose from LLama, Qwen, Flux, and 200+ other open and closed-source mod import litellm response = litellm.completion( - model="openai/meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo", # The model name must include prefix "openai" + the model name from ai/ml api + model="aiml/meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo", # The model name must include prefix "openai" + the model name from ai/ml api api_key="", # your aiml api-key api_base="https://api.aimlapi.com/v2", messages=[ @@ -42,7 +60,7 @@ response = litellm.completion( import litellm response = litellm.completion( - model="openai/Qwen/Qwen2-72B-Instruct", # The model name must include prefix "openai" + the model name from ai/ml api + model="aiml/Qwen/Qwen2-72B-Instruct", # The model name must include prefix "openai" + the model name from ai/ml api api_key="", # your aiml api-key api_base="https://api.aimlapi.com/v2", messages=[ @@ -67,7 +85,7 @@ import litellm async def main(): response = await litellm.acompletion( - model="openai/anthropic/claude-3-5-haiku", # The model name must include prefix "openai" + the model name from ai/ml api + model="aiml/anthropic/claude-3-5-haiku", # The model name must include prefix "openai" + the model name from ai/ml api api_key="", # your aiml api-key api_base="https://api.aimlapi.com/v2", messages=[ @@ -97,7 +115,7 @@ async def main(): try: print("test acompletion + streaming") response = await litellm.acompletion( - model="openai/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF", # The model name must include prefix "openai" + the model name from ai/ml api + model="aiml/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF", # The model name must include prefix "openai" + the model name from ai/ml api api_key="", # your aiml api-key api_base="https://api.aimlapi.com/v2", messages=[{"content": "Hey, how's it going?", "role": "user"}], @@ -125,7 +143,7 @@ import litellm async def main(): response = await litellm.aembedding( - model="openai/text-embedding-3-small", # The model name must include prefix "openai" + the model name from ai/ml api + model="aiml/text-embedding-3-small", # The model name must include prefix "openai" + the model name from ai/ml api api_key="", # your aiml api-key api_base="https://api.aimlapi.com/v1", # 👈 the URL has changed from v2 to v1 input="Your text string", @@ -147,7 +165,7 @@ import litellm async def main(): response = await litellm.aimage_generation( - model="openai/dall-e-3", # The model name must include prefix "openai" + the model name from ai/ml api + model="aiml/dall-e-3", # The model name must include prefix "openai" + the model name from ai/ml api api_key="", # your aiml api-key api_base="https://api.aimlapi.com/v1", # 👈 the URL has changed from v2 to v1 prompt="A cute baby sea otter", diff --git a/docs/my-website/docs/providers/azure/azure.md b/docs/my-website/docs/providers/azure/azure.md index 69f8db538e0..8471ca94066 100644 --- a/docs/my-website/docs/providers/azure/azure.md +++ b/docs/my-website/docs/providers/azure/azure.md @@ -9,8 +9,8 @@ import TabItem from '@theme/TabItem'; | Property | Details | |-------|-------| -| Description | Azure OpenAI Service provides REST API access to OpenAI's powerful language models including o1, o1-mini, GPT-4o, GPT-4o mini, GPT-4 Turbo with Vision, GPT-4, GPT-3.5-Turbo, and Embeddings model series | -| Provider Route on LiteLLM | `azure/`, [`azure/o_series/`](#azure-o-series-models) | +| Description | Azure OpenAI Service provides REST API access to OpenAI's powerful language models including o1, o1-mini, GPT-5, GPT-4o, GPT-4o mini, GPT-4 Turbo with Vision, GPT-4, GPT-3.5-Turbo, and Embeddings model series | +| Provider Route on LiteLLM | `azure/`, [`azure/o_series/`](#o-series-models), [`azure/gpt5_series/`](#gpt-5-models) | | Supported Operations | [`/chat/completions`](#azure-openai-chat-completion-models), [`/responses`](./azure_responses), [`/completions`](#azure-instruct-models), [`/embeddings`](./azure_embedding), [`/audio/speech`](#azure-text-to-speech-tts), [`/audio/transcriptions`](../audio_transcription), `/fine_tuning`, [`/batches`](#azure-batches-api), `/files`, [`/images`](../image_generation#azure-openai-image-generation-models) | | Link to Provider Doc | [Azure OpenAI ↗](https://learn.microsoft.com/en-us/azure/ai-services/openai/overview) @@ -207,6 +207,7 @@ model_list: |------------------|----------------------------------------| | o1-mini | `response = completion(model="azure/", messages=messages)` | | o1-preview | `response = completion(model="azure/", messages=messages)` | +| gpt-5 | `response = completion(model="azure/", messages=messages)` | | gpt-4o-mini | `completion('azure/', messages)` | | gpt-4o | `completion('azure/', messages)` | | gpt-4 | `completion('azure/', messages)` | @@ -368,6 +369,82 @@ model_list: +## GPT-5 Models + +| Property | Details | +|-------|-------| +| Description | Azure OpenAI GPT-5 models | +| Provider Route on LiteLLM | `azure/gpt5_series/` or `azure/gpt-5-deployment-name` | + +LiteLLM supports using Azure GPT-5 models in one of the two ways: +1. Explicit Routing: `model = azure/gpt5_series/`. In this scenario the model onboarded to litellm follows the format `model=azure/gpt5_series/`. +2. Inferred Routing (If the azure deployment name contains `gpt-5` in the name): `model = azure/gpt-5-mini`. In this scenario the model onboarded to litellm follows the format `model=azure/gpt-5-mini`. + +#### Explicit Routing +Use `azure/gpt5_series/` for explicit GPT-5 model routing. + + + + +```python +import litellm + +response = litellm.completion( + model="azure/gpt5_series/my-gpt-5-deployment", + messages=[{"role": "user", "content": "Hello, world!"}] +) +``` + + + +```yaml +model_list: + - model_name: gpt-5 + litellm_params: + model: azure/gpt5_series/my-gpt-5-deployment + api_base: os.environ/AZURE_API_BASE + api_key: os.environ/AZURE_API_KEY +``` + + + + +#### Inferred Routing (gpt-5 in the deployment name) +If your Azure deployment name contains `gpt-5`, LiteLLM automatically recognizes it as a GPT-5 model. + + + + +```python +import litellm + +# Deployment name contains 'gpt-5' - automatically inferred +response = litellm.completion( + model="azure/my-gpt-5-deployment", + messages=[{"role": "user", "content": "Hello, world!"}] +) +``` + + + + +```yaml +model_list: + - model_name: gpt-5-mini + litellm_params: + model: azure/my-gpt-5-deployment # deployment name contains 'gpt-5' + api_base: os.environ/AZURE_API_BASE + api_key: os.environ/AZURE_API_KEY +``` + + + + + + + + + ## Azure Audio Model diff --git a/docs/my-website/docs/providers/azure_ai_img.md b/docs/my-website/docs/providers/azure_ai_img.md new file mode 100644 index 00000000000..8e2f5226866 --- /dev/null +++ b/docs/my-website/docs/providers/azure_ai_img.md @@ -0,0 +1,266 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Azure AI Image Generation + +Azure AI provides powerful image generation capabilities using FLUX models from Black Forest Labs to create high-quality images from text descriptions. + +## Overview + +| Property | Details | +|----------|---------| +| Description | Azure AI Image Generation uses FLUX models to generate high-quality images from text descriptions. | +| Provider Route on LiteLLM | `azure_ai/` | +| Provider Doc | [Azure AI FLUX Models ↗](https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/black-forest-labs-flux-1-kontext-pro-and-flux1-1-pro-now-available-in-azure-ai-f/4434659) | +| Supported Operations | [`/images/generations`](#image-generation) | + +## Setup + +### API Key & Base URL + +```python showLineNumbers +# Set your Azure AI API credentials +import os +os.environ["AZURE_AI_API_KEY"] = "your-api-key-here" +os.environ["AZURE_AI_API_BASE"] = "your-azure-ai-endpoint" # e.g., https://your-endpoint.eastus2.inference.ai.azure.com/ +``` + +Get your API key and endpoint from [Azure AI Studio](https://ai.azure.com/). + +## Supported Models + +| Model Name | Description | Cost per Image | +|------------|-------------|----------------| +| `azure_ai/FLUX-1.1-pro` | Latest FLUX 1.1 Pro model for high-quality image generation | $0.04 | +| `azure_ai/FLUX.1-Kontext-pro` | FLUX 1 Kontext Pro model with enhanced context understanding | $0.04 | + +## Image Generation + +### Usage - LiteLLM Python SDK + + + + +```python showLineNumbers title="Basic Image Generation" +import litellm +import os + +# Set your API credentials +os.environ["AZURE_AI_API_KEY"] = "your-api-key-here" +os.environ["AZURE_AI_API_BASE"] = "your-azure-ai-endpoint" + +# Generate a single image +response = litellm.image_generation( + model="azure_ai/FLUX.1-Kontext-pro", + prompt="A cute baby sea otter swimming in crystal clear water", + api_base=os.environ["AZURE_AI_API_BASE"], + api_key=os.environ["AZURE_AI_API_KEY"] +) + +print(response.data[0].url) +``` + + + + + +```python showLineNumbers title="FLUX 1.1 Pro Image Generation" +import litellm +import os + +# Set your API credentials +os.environ["AZURE_AI_API_KEY"] = "your-api-key-here" +os.environ["AZURE_AI_API_BASE"] = "your-azure-ai-endpoint" + +# Generate image with FLUX 1.1 Pro +response = litellm.image_generation( + model="azure_ai/FLUX-1.1-pro", + prompt="A futuristic cityscape at night with neon lights and flying cars", + api_base=os.environ["AZURE_AI_API_BASE"], + api_key=os.environ["AZURE_AI_API_KEY"] +) + +print(response.data[0].url) +``` + + + + + +```python showLineNumbers title="Async Image Generation" +import litellm +import asyncio +import os + +async def generate_image(): + # Set your API credentials + os.environ["AZURE_AI_API_KEY"] = "your-api-key-here" + os.environ["AZURE_AI_API_BASE"] = "your-azure-ai-endpoint" + + # Generate image asynchronously + response = await litellm.aimage_generation( + model="azure_ai/FLUX.1-Kontext-pro", + prompt="A beautiful sunset over mountains with vibrant colors", + api_base=os.environ["AZURE_AI_API_BASE"], + api_key=os.environ["AZURE_AI_API_KEY"], + n=1, + ) + + print(response.data[0].url) + return response + +# Run the async function +asyncio.run(generate_image()) +``` + + + + + +```python showLineNumbers title="Advanced Image Generation with Parameters" +import litellm +import os + +# Set your API credentials +os.environ["AZURE_AI_API_KEY"] = "your-api-key-here" +os.environ["AZURE_AI_API_BASE"] = "your-azure-ai-endpoint" + +# Generate image with additional parameters +response = litellm.image_generation( + model="azure_ai/FLUX-1.1-pro", + prompt="A majestic dragon soaring over a medieval castle at dawn", + api_base=os.environ["AZURE_AI_API_BASE"], + api_key=os.environ["AZURE_AI_API_KEY"], + n=1, + size="1024x1024", + quality="standard" +) + +for image in response.data: + print(f"Generated image URL: {image.url}") +``` + + + + +### Usage - LiteLLM Proxy Server + +#### 1. Configure your config.yaml + +```yaml showLineNumbers title="Azure AI Image Generation Configuration" +model_list: + - model_name: azure-flux-kontext + litellm_params: + model: azure_ai/FLUX.1-Kontext-pro + api_key: os.environ/AZURE_AI_API_KEY + api_base: os.environ/AZURE_AI_API_BASE + model_info: + mode: image_generation + + - model_name: azure-flux-11-pro + litellm_params: + model: azure_ai/FLUX-1.1-pro + api_key: os.environ/AZURE_AI_API_KEY + api_base: os.environ/AZURE_AI_API_BASE + model_info: + mode: image_generation + +general_settings: + master_key: sk-1234 +``` + +#### 2. Start LiteLLM Proxy Server + +```bash showLineNumbers title="Start LiteLLM Proxy Server" +litellm --config /path/to/config.yaml + +# RUNNING on http://0.0.0.0:4000 +``` + +#### 3. Make requests with OpenAI Python SDK + + + + +```python showLineNumbers title="Azure AI Image Generation via Proxy - OpenAI SDK" +from openai import OpenAI + +# Initialize client with your proxy URL +client = OpenAI( + base_url="http://localhost:4000", # Your proxy URL + api_key="sk-1234" # Your proxy API key +) + +# Generate image with FLUX Kontext Pro +response = client.images.generate( + model="azure-flux-kontext", + prompt="A serene Japanese garden with cherry blossoms and a peaceful pond", + n=1, + size="1024x1024" +) + +print(response.data[0].url) +``` + + + + + +```python showLineNumbers title="Azure AI Image Generation via Proxy - LiteLLM SDK" +import litellm + +# Configure LiteLLM to use your proxy +response = litellm.image_generation( + model="litellm_proxy/azure-flux-11-pro", + prompt="A cyberpunk warrior in a neon-lit alleyway", + api_base="http://localhost:4000", + api_key="sk-1234" +) + +print(response.data[0].url) +``` + + + + + +```bash showLineNumbers title="Azure AI Image Generation via Proxy - cURL" +curl --location 'http://localhost:4000/v1/images/generations' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer sk-1234' \ +--data '{ + "model": "azure-flux-kontext", + "prompt": "A cozy coffee shop interior with warm lighting and rustic wooden furniture", + "n": 1, + "size": "1024x1024" +}' +``` + + + + +## Supported Parameters + +Azure AI Image Generation supports the following OpenAI-compatible parameters: + +| Parameter | Type | Description | Default | Example | +|-----------|------|-------------|---------|---------| +| `prompt` | string | Text description of the image to generate | Required | `"A sunset over the ocean"` | +| `model` | string | The FLUX model to use for generation | Required | `"azure_ai/FLUX.1-Kontext-pro"` | +| `n` | integer | Number of images to generate (1-4) | `1` | `2` | +| `size` | string | Image dimensions | `"1024x1024"` | `"512x512"`, `"1024x1024"` | +| `api_base` | string | Your Azure AI endpoint URL | Required | `"https://your-endpoint.eastus2.inference.ai.azure.com/"` | +| `api_key` | string | Your Azure AI API key | Required | Environment variable or direct value | + +## Getting Started + +1. Create an account at [Azure AI Studio](https://ai.azure.com/) +2. Deploy a FLUX model in your Azure AI Studio workspace +3. Get your API key and endpoint from the deployment details +4. Set your `AZURE_AI_API_KEY` and `AZURE_AI_API_BASE` environment variables +5. Start generating images using LiteLLM + +## Additional Resources + +- [Azure AI Studio Documentation](https://docs.microsoft.com/en-us/azure/ai-services/) +- [FLUX Models Announcement](https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/black-forest-labs-flux-1-kontext-pro-and-flux1-1-pro-now-available-in-azure-ai-f/4434659) diff --git a/docs/my-website/docs/providers/baseten.md b/docs/my-website/docs/providers/baseten.md index 902b1548faa..4e42cdf0447 100644 --- a/docs/my-website/docs/providers/baseten.md +++ b/docs/my-website/docs/providers/baseten.md @@ -1,23 +1,106 @@ -# Baseten -LiteLLM supports any Text-Gen-Interface models on Baseten. +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; -[Here's a tutorial on deploying a huggingface TGI model (Llama2, CodeLlama, WizardCoder, Falcon, etc.) on Baseten](https://truss.baseten.co/examples/performance/tgi-server) +# Baseten + +LiteLLM supports both Baseten Model APIs and dedicated deployments with automatic routing. + +## API Types + +### Model API (Default) +- **URL**: `https://inference.baseten.co/v1` +- **Format**: `baseten/` (e.g., `baseten/openai/gpt-oss-120b`) +- **Best for**: Quick access to popular models + +### Dedicated Deployments +- **URL**: `https://model-{id}.api.baseten.co/environments/production/sync/v1` +- **Format**: `baseten/{8-digit-alphanumeric-code}` (e.g., `baseten/abcd1234`) +- **Best for**: Custom models, latency SLAs + +:::tip +**Automatic Routing**: LiteLLM detects the type based on model format: +- 8-digit alphanumeric codes → Dedicated deployment +- All other formats → Model API +::: + + +## Quick Start -### API KEYS ```python -import os -os.environ["BASETEN_API_KEY"] = "" +import os +from litellm import completion + +os.environ['BASETEN_API_KEY'] = "your-api-key" + +# Model API (default) +response = completion( + model="baseten/openai/gpt-oss-120b", + messages=[{"role": "user", "content": "Hello!"}] +) + +# Dedicated deployment (8-digit ID) +response = completion( + model="baseten/abcd1234", + messages=[{"role": "user", "content": "Hello!"}] +) ``` -### Baseten Models -Baseten provides infrastructure to deploy and serve ML models https://www.baseten.co/. Use liteLLM to easily call models deployed on Baseten. +## Examples -Example Baseten Usage - Note: liteLLM supports all models deployed on Baseten +### Basic Usage +```python +# Model API +response = completion( + model="baseten/openai/gpt-oss-120b", + messages=[{"role": "user", "content": "Explain quantum computing"}], + max_tokens=500, + temperature=0.7 +) -Usage: Pass `model=baseten/` +# Dedicated deployment +response = completion( + model="baseten/abcd1234", + messages=[{"role": "user", "content": "Explain quantum computing"}], + max_tokens=500, + temperature=0.7 +) +``` -| Model Name | Function Call | Required OS Variables | -|------------------|--------------------------------------------|------------------------------------| -| Falcon 7B | `completion(model='baseten/qvv0xeq', messages=messages)` | `os.environ['BASETEN_API_KEY']` | -| Wizard LM | `completion(model='baseten/q841o8w', messages=messages)` | `os.environ['BASETEN_API_KEY']` | -| MPT 7B Base | `completion(model='baseten/31dxrj3', messages=messages)` | `os.environ['BASETEN_API_KEY']` | +### Streaming (Model API only) +```python +response = completion( + model="baseten/openai/gpt-oss-120b", + messages=[{"role": "user", "content": "Write a poem"}], + stream=True, + stream_options={"include_usage": True} +) + +for chunk in response: + if chunk.choices and chunk.choices[0].delta.content: + print(chunk.choices[0].delta.content, end="") +``` + +## Usage with LiteLLM Proxy + +1. **Config**: +```yaml +model_list: + - model_name: baseten-model + litellm_params: + model: baseten/openai/gpt-oss-120b + api_key: your-baseten-api-key +``` + +2. **Request**: +```python +import openai +client = openai.OpenAI( + api_key="sk-1234", + base_url="http://0.0.0.0:4000" +) + +response = client.chat.completions.create( + model="baseten-model", + messages=[{"role": "user", "content": "Hello!"}] +) +``` diff --git a/docs/my-website/docs/providers/bedrock.md b/docs/my-website/docs/providers/bedrock.md index 21eb3ee6862..1356ec1744e 100644 --- a/docs/my-website/docs/providers/bedrock.md +++ b/docs/my-website/docs/providers/bedrock.md @@ -584,6 +584,150 @@ Same as [Anthropic API response](../providers/anthropic#usage---thinking--reason Same as [Anthropic API response](../providers/anthropic#usage---thinking--reasoning_content). +## Usage - Anthropic Beta Features + +LiteLLM supports Anthropic's beta features on AWS Bedrock through the `anthropic-beta` header. This enables access to experimental features like: + +- **1M Context Window** - Up to 1 million tokens of context (Claude Sonnet 4) +- **Computer Use Tools** - AI that can interact with computer interfaces +- **Token-Efficient Tools** - More efficient tool usage patterns +- **Extended Output** - Up to 128K output tokens +- **Enhanced Thinking** - Advanced reasoning capabilities + +### Supported Beta Features + +| Beta Feature | Header Value | Compatible Models | Description | +|--------------|-------------|------------------|-------------| +| 1M Context Window | `context-1m-2025-08-07` | Claude Sonnet 4 | Enable 1 million token context window | +| Computer Use (Latest) | `computer-use-2025-01-24` | Claude 3.7 Sonnet | Latest computer use tools | +| Computer Use (Legacy) | `computer-use-2024-10-22` | Claude 3.5 Sonnet v2 | Computer use tools for Claude 3.5 | +| Token-Efficient Tools | `token-efficient-tools-2025-02-19` | Claude 3.7 Sonnet | More efficient tool usage | +| Interleaved Thinking | `interleaved-thinking-2025-05-14` | Claude 4 models | Enhanced thinking capabilities | +| Extended Output | `output-128k-2025-02-19` | Claude 3.7 Sonnet | Up to 128K output tokens | +| Developer Thinking | `dev-full-thinking-2025-05-14` | Claude 4 models | Raw thinking mode for developers | + + + + +**Single Beta Feature** + +```python +from litellm import completion +import os + +# set env +os.environ["AWS_ACCESS_KEY_ID"] = "" +os.environ["AWS_SECRET_ACCESS_KEY"] = "" +os.environ["AWS_REGION_NAME"] = "" + +# Use 1M context window with Claude Sonnet 4 +response = completion( + model="bedrock/anthropic.claude-sonnet-4-20250115-v1:0", + messages=[{"role": "user", "content": "Hello! Testing 1M context window."}], + max_tokens=100, + extra_headers={ + "anthropic-beta": "context-1m-2025-08-07" # 👈 Enable 1M context + } +) +``` + +**Multiple Beta Features** + +```python +from litellm import completion + +# Combine multiple beta features (comma-separated) +response = completion( + model="bedrock/converse/anthropic.claude-3-5-sonnet-20241022-v2:0", + messages=[{"role": "user", "content": "Testing multiple beta features"}], + max_tokens=100, + extra_headers={ + "anthropic-beta": "computer-use-2024-10-22,context-1m-2025-08-07" + } +) +``` + +**Computer Use Tools with Beta Features** + +```python +from litellm import completion + +# Computer use tools automatically add computer-use-2024-10-22 +# You can add additional beta features +response = completion( + model="bedrock/converse/anthropic.claude-3-5-sonnet-20241022-v2:0", + messages=[{"role": "user", "content": "Take a screenshot"}], + tools=[{ + "type": "computer_20241022", + "name": "computer", + "display_width_px": 1920, + "display_height_px": 1080 + }], + extra_headers={ + "anthropic-beta": "context-1m-2025-08-07" # Additional beta feature + } +) +``` + + + + +**Set on YAML Config** + +```yaml +model_list: + - model_name: claude-sonnet-4-1m + litellm_params: + model: bedrock/anthropic.claude-sonnet-4-20250115-v1:0 + extra_headers: + anthropic-beta: "context-1m-2025-08-07" # 👈 Enable 1M context + + - model_name: claude-computer-use + litellm_params: + model: bedrock/converse/anthropic.claude-3-5-sonnet-20241022-v2:0 + extra_headers: + anthropic-beta: "computer-use-2024-10-22,context-1m-2025-08-07" + +general_settings: + forward_client_headers_to_llm_api: true # 👈 Required for client-side header forwarding +``` + +**Set on Request** + +```python +import openai + +client = openai.OpenAI( + api_key="anything", + base_url="http://0.0.0.0:4000" +) + +response = client.chat.completions.create( + model="claude-sonnet-4-1m", + messages=[{ + "role": "user", + "content": "Testing 1M context window" + }], + extra_headers={ + "anthropic-beta": "context-1m-2025-08-07" + } +) +``` + +:::info +**For client-side header forwarding**: When using the proxy and sending `anthropic-beta` headers from the client (like the OpenAI SDK), you need to enable `forward_client_headers_to_llm_api: true` in your proxy's `general_settings`. This tells the proxy to extract headers from HTTP requests and forward them to the underlying LLM provider. +::: + + + + +:::info + +Beta features may require special access or permissions in your AWS account. Some features are only available in specific AWS regions. Check the [AWS Bedrock documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages-request-response.html) for availability and access requirements. + +::: + + ## Usage - Structured Output / JSON mode @@ -1488,6 +1632,91 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ +### OpenAI GPT OSS + +| Property | Details | +|----------|---------| +| Provider Route | `bedrock/converse/openai.gpt-oss-20b-1:0`, `bedrock/converse/openai.gpt-oss-120b-1:0` | +| Provider Documentation | [Amazon Bedrock ↗](https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html) | + + + + +```python title="GPT OSS SDK Usage" showLineNumbers +from litellm import completion +import os + +# Set AWS credentials +os.environ["AWS_ACCESS_KEY_ID"] = "your-aws-access-key" +os.environ["AWS_SECRET_ACCESS_KEY"] = "your-aws-secret-key" +os.environ["AWS_REGION_NAME"] = "us-east-1" + +# GPT OSS 20B model +response = completion( + model="bedrock/converse/openai.gpt-oss-20b-1:0", + messages=[{"role": "user", "content": "Hello, how are you?"}], +) +print(response.choices[0].message.content) + +# GPT OSS 120B model +response = completion( + model="bedrock/converse/openai.gpt-oss-120b-1:0", + messages=[{"role": "user", "content": "Explain machine learning in simple terms"}], +) +print(response.choices[0].message.content) +``` + + + + + +**1. Add to config** + +```yaml title="config.yaml" showLineNumbers +model_list: + - model_name: gpt-oss-20b + litellm_params: + model: bedrock/converse/openai.gpt-oss-20b-1:0 + aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID + aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY + aws_region_name: os.environ/AWS_REGION_NAME + + - model_name: gpt-oss-120b + litellm_params: + model: bedrock/converse/openai.gpt-oss-120b-1:0 + aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID + aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY + aws_region_name: os.environ/AWS_REGION_NAME +``` + +**2. Start proxy** + +```bash title="Start LiteLLM Proxy" showLineNumbers +litellm --config /path/to/config.yaml + +# RUNNING at http://0.0.0.0:4000 +``` + +**3. Test it!** + +```bash title="Test GPT OSS via Proxy" showLineNumbers +curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "gpt-oss-20b", + "messages": [ + { + "role": "user", + "content": "What are the key benefits of open source AI?" + } + ] + }' +``` + + + + ## Provisioned throughput models To use provisioned throughput Bedrock models pass - `model=bedrock/`, example `model=bedrock/anthropic.claude-v2`. Set `model` to any of the [Supported AWS models](#supported-aws-bedrock-models) @@ -1522,6 +1751,8 @@ Here's an example of using a bedrock model with LiteLLM. For a complete list, re | Model Name | Command | |----------------------------|------------------------------------------------------------------| +| GPT-OSS 20B | `completion(model='bedrock/converse/openai.gpt-oss-20b-1:0', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | +| GPT-OSS 120B | `completion(model='bedrock/converse/openai.gpt-oss-120b-1:0', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | | Deepseek R1 | `completion(model='bedrock/us.deepseek.r1-v1:0', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']` | | Anthropic Claude-V3.5 Sonnet | `completion(model='bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']` | | Anthropic Claude-V3 sonnet | `completion(model='bedrock/anthropic.claude-3-sonnet-20240229-v1:0', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']` | diff --git a/docs/my-website/docs/providers/deepinfra.md b/docs/my-website/docs/providers/deepinfra.md index 1360117445f..ddf6122cac8 100644 --- a/docs/my-website/docs/providers/deepinfra.md +++ b/docs/my-website/docs/providers/deepinfra.md @@ -1,3 +1,6 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + # DeepInfra https://deepinfra.com/ @@ -7,6 +10,11 @@ https://deepinfra.com/ ::: +## Table of Contents + +- [API Key](#api-key) +- [Chat Models](#chat-models) +- [Rerank Endpoint](#rerank-endpoint) ## API Key ```python @@ -53,3 +61,135 @@ for chunk in response: | codellama/CodeLlama-34b-Instruct-hf | `completion(model="deepinfra/codellama/CodeLlama-34b-Instruct-hf", messages)` | | mistralai/Mistral-7B-Instruct-v0.1 | `completion(model="deepinfra/mistralai/Mistral-7B-Instruct-v0.1", messages)` | | jondurbin/airoboros-l2-70b-gpt4-1.4.1 | `completion(model="deepinfra/jondurbin/airoboros-l2-70b-gpt4-1.4.1", messages)` | + +## Rerank Endpoint + +LiteLLM provides a Cohere API compatible `/rerank` endpoint for DeepInfra rerank models. + +### Supported Rerank Models + +| Model Name | Description | +|------------|-------------| +| `deepinfra/Qwen/Qwen3-Reranker-0.6B` | Lightweight rerank model (0.6B parameters) | +| `deepinfra/Qwen/Qwen3-Reranker-4B` | Medium rerank model (4B parameters) | +| `deepinfra/Qwen/Qwen3-Reranker-8B` | Large rerank model (8B parameters) | + +### Usage - LiteLLM Python SDK + + + + +```python +from litellm import rerank +import os + +os.environ["DEEPINFRA_API_KEY"] = "your-api-key" + +response = rerank( + model="deepinfra/Qwen/Qwen3-Reranker-0.6B", + query="What is the capital of France?", + documents=[ + "Paris is the capital of France.", + "London is the capital of the United Kingdom.", + "Berlin is the capital of Germany.", + "Madrid is the capital of Spain.", + "Rome is the capital of Italy." + ] +) +print(response) +``` + + + + +1. Add to config.yaml +```yaml +model_list: + - model_name: Qwen/Qwen3-Reranker-0.6B + litellm_params: + model: deepinfra/Qwen/Qwen3-Reranker-0.6B + api_key: os.environ/DEEPINFRA_API_KEY +``` + +2. Start proxy + +```bash +litellm --config /path/to/config.yaml + +# RUNNING on http://0.0.0.0:4000/ +``` + +3. Test it! + +```bash +curl -L -X POST 'http://0.0.0.0:4000/rerank' \ +-H 'Authorization: Bearer sk-1234' \ +-H 'Content-Type: application/json' \ +-d '{ + "model": "Qwen/Qwen3-Reranker-0.6B", + "query": "What is the capital of France?", + "documents": [ + "Paris is the capital of France.", + "London is the capital of the United Kingdom.", + "Berlin is the capital of Germany.", + "Madrid is the capital of Spain.", + "Rome is the capital of Italy." + ] +}' +``` + + + + +### Supported Cohere Rerank API Params + +| Param | Type | Description | +| ------------------ | ----------- | ----------------------------------------------- | +| `query` | `str` | The query to rerank the documents against | +| `documents` | `list[str]` | The documents to rerank | + + +### Provider-specific parameters +Pass any deepinfra specific parameters as a keyword argument to the rerank function, e.g. + +``` +response = rerank( + model="deepinfra/Qwen/Qwen3-Reranker-0.6B", + query="What is the capital of France?", + documents=[ + "Paris is the capital of France.", + "London is the capital of the United Kingdom.", + "Berlin is the capital of Germany.", + "Madrid is the capital of Spain.", + "Rome is the capital of Italy." + ], + my_custom_param="my_custom_value", # any other deepinfra specific parameters +) +``` + +### Response Format + +```json +{ + "id": "request-id", + "results": [ + { + "index": 0, + "relevance_score": 0.9975274205207825 + }, + { + "index": 1, + "relevance_score": 0.011687257327139378 + } + ], + "meta": { + "billed_units": { + "total_tokens": 427 + }, + "tokens": { + "input_tokens": 427, + "output_tokens": 0 + } + } +} +``` diff --git a/docs/my-website/docs/providers/gradient_ai.md b/docs/my-website/docs/providers/gradient_ai.md new file mode 100644 index 00000000000..7b5eef04dcd --- /dev/null +++ b/docs/my-website/docs/providers/gradient_ai.md @@ -0,0 +1,79 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# GradientAI +https://digitalocean.com/products/gradientai + + +LiteLLM provides native support for GradientAI models. +To use a GradientAI model, specify it as `gradient_ai/` in your LiteLLM requests. + + +## API Key & Endpoint + +Set your credentials and endpoint as environment variables: + +```python +import os +os.environ['GRADIENT_AI_API_KEY'] = "your-api-key" +os.environ['GRADIENT_AI_AGENT_ENDPOINT'] = "https://api.gradient_ai.com/api/v1/chat" # default endpoint +``` + +## Sample Usage + +```python +from litellm import completion +import os + +os.environ['GRADIENT_AI_API_KEY'] = "your-api-key" +response = completion( + model="gradient_ai/model-name", + messages=[ + {"role": "user", "content": "Hello, how are you?"} + ], +) +print(response.choices[0].message.content) +``` + +## Streaming Example + +```python +from litellm import completion +import os + +os.environ['GRADIENT_AI_API_KEY'] = "your-api-key" +response = completion( + model="gradient_ai/model-name", + messages=[ + {"role": "user", "content": "Write a story about a robot learning to love"} + ], + stream=True, +) + +for chunk in response: + print(chunk.choices[0].delta.content or "", end="") +``` + +## Supported Parameters + +| Parameter | Type | Description | +|-----------------------------------|--------------|--------------------------------------------------------------------| +| `temperature` | float | Controls randomness (0.0-2.0) | +| `top_p` | float | Nucleus sampling parameter (0.0-1.0) | +| `max_tokens` | int | Maximum tokens to generate | +| `max_completion_tokens` | int | Alternative to max_tokens | +| `stream` | bool | Whether to stream the response | +| `k` | int | Top results to return from knowledge bases | +| `retrieval_method` | string | Retrieval strategy (rewrite/step_back/sub_queries/none) | +| `frequency_penalty` | float | Penalizes repeated tokens (-2.0 to 2.0) | +| `presence_penalty` | float | Penalizes tokens based on presence (-2.0 to 2.0) | +| `stop` | string/list | Sequences to stop generation | +| `kb_filters` | List[Dict] | Filters for knowledge base retrieval | +| `instruction_override` | string | Override agent's default instruction | +| `include_retrieval_info` | bool | Include document retrieval metadata | +| `include_guardrails_info` | bool | Include guardrail trigger metadata | +| `provide_citations` | bool | Include citations in response | + +--- + +For more details, see [DigitalOcean GradientAI documentation](https://digitalocean.com/products/gradientai). \ No newline at end of file diff --git a/docs/my-website/docs/providers/litellm_proxy.md b/docs/my-website/docs/providers/litellm_proxy.md index d0441d4fb4f..bfefc8a787c 100644 --- a/docs/my-website/docs/providers/litellm_proxy.md +++ b/docs/my-website/docs/providers/litellm_proxy.md @@ -9,7 +9,7 @@ import TabItem from '@theme/TabItem'; | Description | LiteLLM Proxy is an OpenAI-compatible gateway that allows you to interact with multiple LLM providers through a unified API. Simply use the `litellm_proxy/` prefix before the model name to route your requests through the proxy. | | Provider Route on LiteLLM | `litellm_proxy/` (add this prefix to the model name, to route any requests to litellm_proxy - e.g. `litellm_proxy/your-model-name`) | | Setup LiteLLM Gateway | [LiteLLM Gateway ↗](../simple_proxy) | -| Supported Endpoints |`/chat/completions`, `/completions`, `/embeddings`, `/audio/speech`, `/audio/transcriptions`, `/images`, `/rerank` | +| Supported Endpoints |`/chat/completions`, `/completions`, `/embeddings`, `/audio/speech`, `/audio/transcriptions`, `/images`, `/images/edits`, `/rerank` | @@ -111,6 +111,21 @@ response = litellm.image_generation( ) ``` +## Image Edit + +```python +import litellm + +with open("your-image.png", "rb") as f: + response = litellm.image_edit( + model="litellm_proxy/gpt-image-1", + prompt="Make this image a watercolor painting", + image=[f], + api_base="your-litellm-proxy-url", + api_key="your-litellm-proxy-api-key", + ) +``` + ## Audio Transcription ```python @@ -211,3 +226,38 @@ response = litellm.completion( use_litellm_proxy=True ) ``` + +## Sending `tags` to LiteLLM Proxy + +Tags allow you to categorize and track your API requests for monitoring, debugging, and analytics purposes. You can send tags as a list of strings to the LiteLLM Proxy using the `extra_body` parameter. + +### Usage + +Send tags by including them in the `extra_body` parameter of your completion request: + +```python showLineNumbers title="Usage" +import litellm + +response = litellm.completion( + model="gpt-4", + messages=[{"role": "user", "content": "What is the capital of France?"}], + api_base="http://localhost:4000", + api_key="sk-1234", + extra_body={"tags": ["user:ishaan", "department:engineering", "priority:high"]} +) +``` + +### Async Usage + +```python showLineNumbers title="Async Usage" +import litellm + +response = await litellm.acompletion( + model="gpt-4", + messages=[{"role": "user", "content": "What is the capital of France?"}], + api_base="http://localhost:4000", + api_key="sk-1234", + extra_body={"tags": ["user:ishaan", "department:engineering"]} +) +``` + diff --git a/docs/my-website/docs/providers/openai/responses_api.md b/docs/my-website/docs/providers/openai/responses_api.md index db2d781ca15..e96a2f95225 100644 --- a/docs/my-website/docs/providers/openai/responses_api.md +++ b/docs/my-website/docs/providers/openai/responses_api.md @@ -492,3 +492,355 @@ print(response_with_mcp_call) +## Verbosity Parameter + +The `verbosity` parameter is supported for the `responses` API. + + + + +```python showLineNumbers title="Verbosity Parameter" +from litellm import responses + +question = "Write a poem about a boy and his first pet dog." + +for verbosity in ["low", "medium", "high"]: + response = responses( + model="gpt-5-mini", + input=question, + text={"verbosity": verbosity} + ) + + print(response) +``` + + + + +```python +from openai import OpenAI +import pandas as pd +from IPython.display import display + +client = OpenAI( + base_url="http://localhost:4000", # Your proxy URL + api_key="your-api-key" # Your proxy API key +) + +question = "Write a poem about a boy and his first pet dog." + +data = [] + +for verbosity in ["low", "medium", "high"]: + response = client.responses.create( + model="gpt-5-mini", + input=question, + text={"verbosity": verbosity} + ) + + # Extract text + output_text = "" + for item in response.output: + if hasattr(item, "content"): + for content in item.content: + if hasattr(content, "text"): + output_text += content.text + + usage = response.usage + data.append({ + "Verbosity": verbosity, + "Sample Output": output_text, + "Output Tokens": usage.output_tokens + }) + +# Create DataFrame +df = pd.DataFrame(data) + +# Display nicely with centered headers +pd.set_option('display.max_colwidth', None) +styled_df = df.style.set_table_styles( + [ + {'selector': 'th', 'props': [('text-align', 'center')]}, # Center column headers + {'selector': 'td', 'props': [('text-align', 'left')]} # Left-align table cells + ] +) + +display(styled_df) + +``` + + + + + +## Free-form Function Calling + + + + + +```python showLineNumbers title="Free-form Function Calling" +import litellm + +response = litellm.responses( + response = client.responses.create( + model="gpt-5-mini", + input="Please use the code_exec tool to calculate the area of a circle with radius equal to the number of 'r's in strawberry", + text={"format": {"type": "text"}}, + tools=[ + { + "type": "custom", + "name": "code_exec", + "description": "Executes arbitrary python code", + } + ] +) +print(response.output) +``` + + + + +```python showLineNumbers title="Free-form Function Calling" +from openai import OpenAI + +client = OpenAI( + base_url="http://localhost:4000", # Your proxy URL + api_key="your-api-key" # Your proxy API key +) + +response = client.responses.create( + model="gpt-5-mini", + input="Please use the code_exec tool to calculate the area of a circle with radius equal to the number of 'r's in strawberry", + text={"format": {"type": "text"}}, + tools=[ + { + "type": "custom", + "name": "code_exec", + "description": "Executes arbitrary python code", + } + ] +) +print(response.output) +``` + + + + + +## Context-Free Grammar + + + + +```python showLineNumbers title="Context-Free Grammar" +import litellm + +import textwrap + +# ----------------- grammars for MS SQL dialect ----------------- +mssql_grammar = textwrap.dedent(r""" + // ---------- Punctuation & operators ---------- + SP: " " + COMMA: "," + GT: ">" + EQ: "=" + SEMI: ";" + + // ---------- Start ---------- + start: "SELECT" SP "TOP" SP NUMBER SP select_list SP "FROM" SP table SP "WHERE" SP amount_filter SP "AND" SP date_filter SP "ORDER" SP "BY" SP sort_cols SEMI + + // ---------- Projections ---------- + select_list: column (COMMA SP column)* + column: IDENTIFIER + + // ---------- Tables ---------- + table: IDENTIFIER + + // ---------- Filters ---------- + amount_filter: "total_amount" SP GT SP NUMBER + date_filter: "order_date" SP GT SP DATE + + // ---------- Sorting ---------- + sort_cols: "order_date" SP "DESC" + + // ---------- Terminals ---------- + IDENTIFIER: /[A-Za-z_][A-Za-z0-9_]*/ + NUMBER: /[0-9]+/ + DATE: /'[0-9]{4}-[0-9]{2}-[0-9]{2}'/ + """) + +sql_prompt_mssql = ( + "Call the mssql_grammar to generate a query for Microsoft SQL Server that retrieve the " + "five most recent orders per customer, showing customer_id, order_id, order_date, and total_amount, " + "where total_amount > 500 and order_date is after '2025-01-01'. " +) + + +response = litellm.responses( + model="gpt-5", + input=sql_prompt_mssql, + text={"format": {"type": "text"}}, + tools=[ + { + "type": "custom", + "name": "mssql_grammar", + "description": "Executes read-only Microsoft SQL Server queries limited to SELECT statements with TOP and basic WHERE/ORDER BY. YOU MUST REASON HEAVILY ABOUT THE QUERY AND MAKE SURE IT OBEYS THE GRAMMAR.", + "format": { + "type": "grammar", + "syntax": "lark", + "definition": mssql_grammar + } + }, + ], + parallel_tool_calls=False +) + +print("--- MS SQL Query ---") +print(response_mssql.output[1].input) +``` + + + + +```python showLineNumbers title="Context-Free Grammar" +from openai import OpenAI + +client = OpenAI( + base_url="http://localhost:4000", # Your proxy URL + api_key="your-api-key" # Your proxy API key +) + +import textwrap + +# ----------------- grammars for MS SQL dialect ----------------- +mssql_grammar = textwrap.dedent(r""" + // ---------- Punctuation & operators ---------- + SP: " " + COMMA: "," + GT: ">" + EQ: "=" + SEMI: ";" + + // ---------- Start ---------- + start: "SELECT" SP "TOP" SP NUMBER SP select_list SP "FROM" SP table SP "WHERE" SP amount_filter SP "AND" SP date_filter SP "ORDER" SP "BY" SP sort_cols SEMI + + // ---------- Projections ---------- + select_list: column (COMMA SP column)* + column: IDENTIFIER + + // ---------- Tables ---------- + table: IDENTIFIER + + // ---------- Filters ---------- + amount_filter: "total_amount" SP GT SP NUMBER + date_filter: "order_date" SP GT SP DATE + + // ---------- Sorting ---------- + sort_cols: "order_date" SP "DESC" + + // ---------- Terminals ---------- + IDENTIFIER: /[A-Za-z_][A-Za-z0-9_]*/ + NUMBER: /[0-9]+/ + DATE: /'[0-9]{4}-[0-9]{2}-[0-9]{2}'/ + """) + +sql_prompt_mssql = ( + "Call the mssql_grammar to generate a query for Microsoft SQL Server that retrieve the " + "five most recent orders per customer, showing customer_id, order_id, order_date, and total_amount, " + "where total_amount > 500 and order_date is after '2025-01-01'. " +) + + +response = client.responses.create( + model="gpt-5", + input=sql_prompt_mssql, + text={"format": {"type": "text"}}, + tools=[ + { + "type": "custom", + "name": "mssql_grammar", + "description": "Executes read-only Microsoft SQL Server queries limited to SELECT statements with TOP and basic WHERE/ORDER BY. YOU MUST REASON HEAVILY ABOUT THE QUERY AND MAKE SURE IT OBEYS THE GRAMMAR.", + "format": { + "type": "grammar", + "syntax": "lark", + "definition": mssql_grammar + } + }, + ], + parallel_tool_calls=False +) + +print("--- MS SQL Query ---") +print(response_mssql.output[1].input) +``` + + + + +## Minimal Reasoning + + + + + +```python showLineNumbers title="Minimal Reasoning" +import litellm + +response = litellm.responses( + model="gpt-5", + input= [{ 'role': 'developer', 'content': prompt }, + { 'role': 'user', 'content': 'The food that the restaurant was great! I recommend it to everyone.' }], + reasoning = { + "effort": "minimal" + }, +) + +print(response) +``` + + + +```python showLineNumbers title="Minimal Reasoning" +from openai import OpenAI + +client = OpenAI( + base_url="http://localhost:4000", # Your proxy URL + api_key="your-api-key" # Your proxy API key +) + + +prompt = "Classify sentiment of the review as positive|neutral|negative. Return one word only." + + +response = client.responses.create( + model="gpt-5", + input= [{ 'role': 'developer', 'content': prompt }, + { 'role': 'user', 'content': 'The food that the restaurant was great! I recommend it to everyone.' }], + reasoning = { + "effort": "minimal" + }, +) + +# Extract model's text output +output_text = "" +for item in response.output: + if hasattr(item, "content"): + for content in item.content: + if hasattr(content, "text"): + output_text += content.text + +# Token usage details +usage = response.usage + +print("--------------------------------") +print("Output:") +print(output_text) + + + +``` + + + + diff --git a/docs/my-website/docs/providers/sambanova.md b/docs/my-website/docs/providers/sambanova.md index 290b64a1f09..f7be5d3ce77 100644 --- a/docs/my-website/docs/providers/sambanova.md +++ b/docs/my-website/docs/providers/sambanova.md @@ -307,3 +307,16 @@ response = litellm.completion( print(response.choices[0].message.content)) ``` + +## SambaNova - Embeddings + +```python +import litellm + +response = litellm.embedding( + model="sambanova/E5-Mistral-7B-Instruct", + input=["sample text to embed", "another sample text to embed"] +) + +print(response.data) +``` diff --git a/docs/my-website/docs/providers/vertex_partner.md b/docs/my-website/docs/providers/vertex_partner.md index c6e324f2958..cf780e35dbd 100644 --- a/docs/my-website/docs/providers/vertex_partner.md +++ b/docs/my-website/docs/providers/vertex_partner.md @@ -14,6 +14,7 @@ import TabItem from '@theme/TabItem'; | Meta/Llama | `vertex_ai/meta/{MODEL}` | [Vertex AI - Meta Models](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/llama) | | Mistral | `vertex_ai/mistral-*` | [Vertex AI - Mistral Models](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/mistral) | | AI21 (Jamba) | `vertex_ai/jamba-*` | [Vertex AI - AI21 Models](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/ai21) | +| Qwen | `vertex_ai/qwen/*` | [Vertex AI - Qwen Models](https://cloud.google.com/vertex-ai/generative-ai/docs/maas/qwen) | | Model Garden | `vertex_ai/openai/{MODEL_ID}` or `vertex_ai/{MODEL_ID}` | [Vertex Model Garden](https://cloud.google.com/model-garden?hl=en) | ## Vertex AI - Anthropic (Claude) @@ -571,6 +572,92 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ +## VertexAI Qwen API + +| Property | Details | +|----------|---------| +| Provider Route | `vertex_ai/qwen/{MODEL}` | +| Vertex Documentation | [Vertex AI - Qwen Models](https://cloud.google.com/vertex-ai/generative-ai/docs/maas/qwen) | + +**LiteLLM Supports all Vertex AI Qwen Models.** Ensure you use the `vertex_ai/qwen/` prefix for all Vertex AI Qwen models. + +| Model Name | Usage | +|------------------|------------------------------| +| vertex_ai/qwen/qwen3-coder-480b-a35b-instruct-maas | `completion('vertex_ai/qwen/qwen3-coder-480b-a35b-instruct-maas', messages)` | +| vertex_ai/qwen/qwen3-235b-a22b-instruct-2507-maas | `completion('vertex_ai/qwen/qwen3-235b-a22b-instruct-2507-maas', messages)` | + +#### Usage + + + + +```python +from litellm import completion +import os + +os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "" + +model = "qwen/qwen3-coder-480b-a35b-instruct-maas" + +vertex_ai_project = "your-vertex-project" # can also set this as os.environ["VERTEXAI_PROJECT"] +vertex_ai_location = "your-vertex-location" # can also set this as os.environ["VERTEXAI_LOCATION"] + +response = completion( + model="vertex_ai/" + model, + messages=[{"role": "user", "content": "hi"}], + vertex_ai_project=vertex_ai_project, + vertex_ai_location=vertex_ai_location, +) +print("\nModel Response", response) +``` + + + +**1. Add to config** + +```yaml +model_list: + - model_name: vertex-qwen + litellm_params: + model: vertex_ai/qwen/qwen3-coder-480b-a35b-instruct-maas + vertex_ai_project: "my-test-project" + vertex_ai_location: "us-east-1" + - model_name: vertex-qwen + litellm_params: + model: vertex_ai/qwen/qwen3-coder-480b-a35b-instruct-maas + vertex_ai_project: "my-test-project" + vertex_ai_location: "us-west-1" +``` + +**2. Start proxy** + +```bash +litellm --config /path/to/config.yaml + +# RUNNING at http://0.0.0.0:4000 +``` + +**3. Test it!** + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "vertex-qwen", # 👈 the 'model_name' in config + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ], + }' +``` + + + + + ## Model Garden :::tip diff --git a/docs/my-website/docs/providers/vllm.md b/docs/my-website/docs/providers/vllm.md index d8b201956e2..5472f0602f4 100644 --- a/docs/my-website/docs/providers/vllm.md +++ b/docs/my-website/docs/providers/vllm.md @@ -104,6 +104,52 @@ Here's how to call an OpenAI-Compatible Endpoint with the LiteLLM Proxy Server + ## Reasoning Effort + + + + + ```python + from litellm import completion + + response = completion( + model="hosted_vllm/gpt-oss-120b", + messages=[{"role": "user", "content": "whats 2 + 2"}], + reasoning_effort="high", + api_base="https://hosted-vllm-api.co", + ) + print(response) + ``` + + + + 1. Setup config.yaml + + ```yaml + model_list: + - model_name: gpt-oss-120b + litellm_params: + model: hosted_vllm/gpt-oss-120b + api_base: https://hosted-vllm-api.co + ``` + + 2. Start the proxy + + ```bash + litellm --config /path/to/config.yaml + ``` + + 3. Test it! + + ```bash + curl http://0.0.0.0:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{"model": "gpt-oss-120b", "messages": [{"role": "user", "content": "whats 2 + 2"}], "reasoning_effort": "high"}' + ``` + + + + ## Embeddings diff --git a/docs/my-website/docs/proxy/config_settings.md b/docs/my-website/docs/proxy/config_settings.md index 3b903935a04..73b16af2f95 100644 --- a/docs/my-website/docs/proxy/config_settings.md +++ b/docs/my-website/docs/proxy/config_settings.md @@ -335,12 +335,16 @@ router_settings: | ANTHROPIC_API_KEY | API key for Anthropic service | ANTHROPIC_API_BASE | Base URL for Anthropic API. Default is https://api.anthropic.com | AWS_ACCESS_KEY_ID | Access Key ID for AWS services +| AWS_DEFAULT_REGION | Default AWS region for service interactions when AWS_REGION is not set | AWS_PROFILE_NAME | AWS CLI profile name to be used +| AWS_REGION | AWS region for service interactions (takes precedence over AWS_DEFAULT_REGION) | AWS_REGION_NAME | Default AWS region for service interactions +| AWS_ROLE_ARN | ARN of the AWS IAM role to assume for authentication | AWS_ROLE_NAME | Role name for AWS IAM usage | AWS_SECRET_ACCESS_KEY | Secret Access Key for AWS services | AWS_SESSION_NAME | Name for AWS session | AWS_WEB_IDENTITY_TOKEN | Web identity token for AWS +| AWS_WEB_IDENTITY_TOKEN_FILE | Path to file containing web identity token for AWS | AZURE_API_VERSION | Version of the Azure API being used | AZURE_AUTHORITY_HOST | Azure authority host URL | AZURE_CERTIFICATE_PASSWORD | Password for Azure OpenAI certificate @@ -349,6 +353,7 @@ router_settings: | AZURE_CODE_INTERPRETER_COST_PER_SESSION | Cost per session for Azure Code Interpreter service | AZURE_COMPUTER_USE_INPUT_COST_PER_1K_TOKENS | Input cost per 1K tokens for Azure Computer Use service | AZURE_COMPUTER_USE_OUTPUT_COST_PER_1K_TOKENS | Output cost per 1K tokens for Azure Computer Use service +| AZURE_DEFAULT_RESPONSES_API_VERSION | Version of the Azure Default Responses API being used. Default is "preview" | AZURE_TENANT_ID | Tenant ID for Azure Active Directory | AZURE_USERNAME | Username for Azure services, use in conjunction with AZURE_PASSWORD for azure ad token with basic username/password workflow | AZURE_PASSWORD | Password for Azure services, use in conjunction with AZURE_USERNAME for azure ad token with basic username/password workflow diff --git a/docs/my-website/docs/proxy/deploy.md b/docs/my-website/docs/proxy/deploy.md index ddd88bb2904..7f893068645 100644 --- a/docs/my-website/docs/proxy/deploy.md +++ b/docs/my-website/docs/proxy/deploy.md @@ -127,6 +127,8 @@ CMD ["--port", "4000", "--config", "config.yaml", "--detailed_debug"] Follow these instructions to build a docker container from the litellm pip package. If your company has a strict requirement around security / building images you can follow these steps. +**Note:** You'll need to copy the `schema.prisma` file from the [litellm repository](https://github.com/BerriAI/litellm/blob/main/schema.prisma) to your build directory alongside the Dockerfile and requirements.txt. + Dockerfile ```shell @@ -149,6 +151,12 @@ COPY requirements.txt . RUN --mount=type=cache,target=${HOME}/.cache/pip \ ${HOME}/venv/bin/pip install -r requirements.txt +# Copy Prisma schema file +COPY schema.prisma . + +# Generate prisma client +RUN prisma generate + EXPOSE 4000/tcp ENTRYPOINT ["litellm"] @@ -1002,5 +1010,13 @@ User-agent: * Disallow: / ``` +## Deployment FAQ + +**Q: Is Postgres the only supported database, or do you support other ones (like Mongo)?** + +A: We explored MySQL but that was hard to maintain and led to bugs for customers. Currently, PostgreSQL is our primary supported database for production deployments. +**Q: If there is Postgres downtime, how does LiteLLM react? Does it fail-open or is there API downtime?** + +A: You can gracefully handle DB unavailability if it's on your VPC. See our production guide for more details: [Gracefully Handle DB Unavailability](https://docs.litellm.ai/docs/proxy/prod#6-if-running-litellm-on-vpc-gracefully-handle-db-unavailability) \ No newline at end of file diff --git a/docs/my-website/docs/proxy/docker_quick_start.md b/docs/my-website/docs/proxy/docker_quick_start.md index 99bf618b5a4..09f7dfbaf78 100644 --- a/docs/my-website/docs/proxy/docker_quick_start.md +++ b/docs/my-website/docs/proxy/docker_quick_start.md @@ -2,7 +2,7 @@ import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; -# Getting Started - E2E Tutorial +# E2E Tutorial End-to-End tutorial for LiteLLM Proxy to: - Add an Azure OpenAI model diff --git a/docs/my-website/docs/proxy/native_litellm_prompt.md b/docs/my-website/docs/proxy/native_litellm_prompt.md new file mode 100644 index 00000000000..1e1df999db9 --- /dev/null +++ b/docs/my-website/docs/proxy/native_litellm_prompt.md @@ -0,0 +1,162 @@ +import Image from '@theme/IdealImage'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# LiteLLM Prompt Management (GitOps) + +Store prompts as `.prompt` files in your repository and use them directly with LiteLLM. No external services required. + +## Quick Start + + + + + +**1. Create a .prompt file** + +Create `prompts/hello.prompt`: + +```yaml +--- +model: gpt-4 +temperature: 0.7 +--- +System: You are a helpful assistant. + +User: {{user_message}} +``` + +**2. Use with LiteLLM** + +```python +import litellm + +# Set the global prompt directory +litellm.global_prompt_directory = "prompts/" + +response = litellm.completion( + model="dotprompt/gpt-4", + prompt_id="hello", + prompt_variables={"user_message": "What is the capital of France?"} +) +``` + + + + +**1. Create a .prompt file** + +Create `prompts/hello.prompt`: + +```yaml +--- +model: gpt-4 +temperature: 0.7 +--- +System: You are a helpful assistant. + +User: {{user_message}} +``` + +**2. Setup config.yaml** + +```yaml +model_list: + - model_name: my-dotprompt-model + litellm_params: + model: dotprompt/gpt-4 + prompt_id: "hello" + api_key: os.environ/OPENAI_API_KEY + +litellm_settings: + global_prompt_directory: "./prompts" +``` + +**3. Start the proxy** + +```bash +litellm --config config.yaml --detailed_debug +``` + +**4. Test it!** + +```bash +curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-d '{ + "model": "my-dotprompt-model", + "messages": [{"role": "user", "content": "IGNORED"}], + "prompt_variables": { + "user_message": "What is the capital of France?" + } +}' +``` + + + + +### .prompt File Format + +`.prompt` files use YAML frontmatter for metadata and support Jinja2 templating: + +```yaml +--- +model: gpt-4 # Model to use +temperature: 0.7 # Optional parameters +max_tokens: 1000 +input: + schema: + user_message: string # Input validation (optional) +--- +System: You are a helpful {{role}} assistant. + +User: {{user_message}} +``` + +### Advanced Features + +**Multi-role conversations:** + +```yaml +--- +model: gpt-4 +temperature: 0.3 +--- +System: You are a helpful coding assistant. + +User: {{user_question}} +``` + +**Dynamic model selection:** + +```yaml +--- +model: "{{preferred_model}}" # Model can be a variable +temperature: 0.7 +--- +System: You are a helpful assistant specialized in {{domain}}. + +User: {{user_message}} +``` + +### API Reference + +For dotprompt integration, use these parameters: + +``` +model: dotprompt/ # required (e.g., dotprompt/gpt-4) +prompt_id: str # required - the .prompt filename without extension +prompt_variables: Optional[dict] # optional - variables for template rendering +``` + +**Example API call:** + +```python +response = litellm.completion( + model="dotprompt/gpt-4", + prompt_id="hello", + prompt_variables={"user_message": "Hello world"}, + messages=[{"role": "user", "content": "This will be ignored"}] +) +``` diff --git a/docs/my-website/docs/proxy/prod.md b/docs/my-website/docs/proxy/prod.md index 3a24a3427bd..fb2acf230c1 100644 --- a/docs/my-website/docs/proxy/prod.md +++ b/docs/my-website/docs/proxy/prod.md @@ -199,7 +199,7 @@ USE_PRISMA_MIGRATE="True" ```bash -litellm --use_prisma_migrate +litellm ``` diff --git a/docs/my-website/docs/proxy/prompt_management.md b/docs/my-website/docs/proxy/prompt_management.md index fc35fc5ef38..5a52c8c6c0d 100644 --- a/docs/my-website/docs/proxy/prompt_management.md +++ b/docs/my-website/docs/proxy/prompt_management.md @@ -8,6 +8,7 @@ Run experiments or change the specific model (e.g. from gpt-4o to gpt4o-mini fin | Supported Integrations | Link | |------------------------|------| +| Native LiteLLM GitOps (.prompt files) | [Get Started](native_litellm_prompt) | | Langfuse | [Get Started](https://langfuse.com/docs/prompts/get-started) | | Humanloop | [Get Started](../observability/humanloop) | diff --git a/docs/my-website/docs/proxy/quick_start.md b/docs/my-website/docs/proxy/quick_start.md index 8f8de2a9fae..a343bb00e9b 100644 --- a/docs/my-website/docs/proxy/quick_start.md +++ b/docs/my-website/docs/proxy/quick_start.md @@ -2,8 +2,9 @@ import Image from '@theme/IdealImage'; import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; -# Quick Start -Quick start CLI, Config, Docker +# CLI - Quick Start + +Setup LiteLLM Proxy quickly via CLI. LiteLLM Server (LLM Gateway) manages: diff --git a/docs/my-website/docs/proxy/team_budgets.md b/docs/my-website/docs/proxy/team_budgets.md index 854d6edf304..66ba679c65e 100644 --- a/docs/my-website/docs/proxy/team_budgets.md +++ b/docs/my-website/docs/proxy/team_budgets.md @@ -4,6 +4,12 @@ import TabItem from '@theme/TabItem'; # Setting Team Budgets + +# Pre-Requisites + +- You must set up a Postgres database (e.g. Supabase, Neon, etc.) +- To enable team member rate limits, set the environment variable `EXPERIMENTAL_MULTI_INSTANCE_RATE_LIMITING=true` **before starting the proxy server**. Without this, team member rate limits will not be enforced. + Track spend, set budgets for your Internal Team ## Setting Monthly Team Budgets diff --git a/docs/my-website/docs/proxy/users.md b/docs/my-website/docs/proxy/users.md index c812dccb199..d098e38de4a 100644 --- a/docs/my-website/docs/proxy/users.md +++ b/docs/my-website/docs/proxy/users.md @@ -58,6 +58,9 @@ You can: **Step-by step tutorial on setting, resetting budgets on Teams here (API or using Admin UI)** +> **Prerequisite:** +> To enable team member rate limits, you must set the environment variable `EXPERIMENTAL_MULTI_INSTANCE_RATE_LIMITING=true` before starting the proxy server. Without this, team member rate limits will not be enforced. + 👉 [https://docs.litellm.ai/docs/proxy/team_budgets](https://docs.litellm.ai/docs/proxy/team_budgets) ::: @@ -793,6 +796,11 @@ Expected Response: Enable multi-instance rate limiting with the env var `EXPERIMENTAL_MULTI_INSTANCE_RATE_LIMITING="True"` +**Important Notes:** +- Setting `EXPERIMENTAL_MULTI_INSTANCE_RATE_LIMITING="True"` is required for team member rate limits to function, not just for multi-instance scenarios. +- **Rate limits do not apply to proxy admin users.** +- When testing rate limits, use internal user roles (non-admin) to ensure limits are enforced as expected. + Changes: - This moves to using async_increment instead of async_set_cache when updating current requests/tokens. - The in-memory cache is synced with redis every 0.01s, to avoid calling redis for every request. diff --git a/docs/my-website/docs/rerank.md b/docs/my-website/docs/rerank.md index 11dcae777e4..c57eacbb224 100644 --- a/docs/my-website/docs/rerank.md +++ b/docs/my-website/docs/rerank.md @@ -118,4 +118,5 @@ curl http://0.0.0.0:4000/rerank \ | AWS Bedrock| [Usage](../docs/providers/bedrock#rerank-api) | | HuggingFace| [Usage](../docs/providers/huggingface_rerank) | | Infinity| [Usage](../docs/providers/infinity) | -| vLLM| [Usage](../docs/providers/vllm#rerank-endpoint) | \ No newline at end of file +| vLLM| [Usage](../docs/providers/vllm#rerank-endpoint) | +| DeepInfra| [Usage](../docs/providers/deepinfra#rerank-endpoint) | \ No newline at end of file diff --git a/docs/my-website/docs/response_api.md b/docs/my-website/docs/response_api.md index e64f922ac80..94d7c73be05 100644 --- a/docs/my-website/docs/response_api.md +++ b/docs/my-website/docs/response_api.md @@ -803,10 +803,18 @@ LiteLLM Proxy supports session management for non-OpenAI models. This allows you 1. Enable storing request / response content in the database -Set `store_prompts_in_spend_logs: true` in your proxy config.yaml. When this is enabled, LiteLLM will store the request and response content in the database. +Set `store_prompts_in_cold_storage: true` in your proxy config.yaml. When this is enabled, LiteLLM will store the request and response content in the s3 bucket you specify. + +```yaml showLineNumbers title="config.yaml with Session Continuity" +litellm_settings: + callbacks: ["s3_v2"] + cold_storage_custom_logger: s3_v2 + s3_callback_params: # learn more https://docs.litellm.ai/docs/proxy/logging#s3-buckets + s3_bucket_name: litellm-logs # AWS Bucket Name for S3 + s3_region_name: us-west-2 -```yaml general_settings: + store_prompts_in_cold_storage: true store_prompts_in_spend_logs: true ``` diff --git a/docs/my-website/docs/routing.md b/docs/my-website/docs/routing.md index fa784a719c2..fbb069895d8 100644 --- a/docs/my-website/docs/routing.md +++ b/docs/my-website/docs/routing.md @@ -1000,6 +1000,102 @@ router_settings: +### How Cooldowns Work + +Cooldowns apply to individual deployments, not entire model groups. The router isolates failures to specific deployments while keeping healthy alternatives available. + +#### What is a deployment? + +A deployment is a single entry in your `config.yaml` model list. Each deployment represents a unique configuration with its own `litellm_params`. + +LiteLLM generates a unique `model_id` for each deployment by creating a deterministic hash of all the `litellm_params`. This allows the router to track and manage each deployment independently. + +**Example: Multiple deployments for the same model** + +```yaml showLineNumbers title="Load Balancing config.yaml" +model_list: + - model_name: sonnet-4 # Deployment 1 + litellm_params: + model: anthropic/claude-sonnet-4-20250514 + api_key: + + - model_name: byok-sonnet-4 # Deployment 2 + litellm_params: + model: anthropic/claude-sonnet-4-20250514 + api_key: + api_base: https://proxy.litellm.ai/api.anthropic.com + + - model_name: sonnet-4 # Deployment 3 + litellm_params: + model: vertex_ai/claude-sonnet-4-20250514 + vertex_project: my-project +``` + +Each deployment gets a unique `model_id` (e.g., `1234567890`, `9129922`, `4982929292`) that the router uses for tracking health and cooldown status. + +#### When are deployments cooled down? + +The router automatically cools down deployments based on the following conditions: + +| Condition | Trigger | Cooldown Duration | +|-----------|---------|-------------------| +| **Rate Limiting (429)** | Immediate on 429 response | 5 seconds (default) | +| **High Failure Rate** | >50% failures in current minute | 5 seconds (default) | +| **Non-Retryable Errors** | 401 (Auth), 404 (Not Found), 408 (Timeout) | 5 seconds (default) | + +During cooldown, the specific deployment is temporarily removed from the available pool, while other healthy deployments continue serving requests. + +#### Cooldown Recovery + +Deployments automatically recover from cooldown after the cooldown period expires. The router will: + +1. **Monitor cooldown timers** for each deployment +2. **Automatically re-enable** deployments when cooldown expires +3. **Gradually reintroduce** cooled-down deployments to the rotation +4. **Reset failure counters** once the deployment is healthy again + +#### Real-World Example + +Consider this high-availability setup with multiple providers: + +```yaml showLineNumbers title="Load Balancing config.yaml" +model_list: + - model_name: sonnet-4 # Primary: Anthropic Direct + litellm_params: + model: anthropic/claude-sonnet-4-20250514 + api_key: + + - model_name: byok-sonnet-4 # BYOK: Customer-managed keys + litellm_params: + model: anthropic/claude-sonnet-4-20250514 + api_key: + api_base: https://proxy.litellm.ai/api.anthropic.com + + - model_name: sonnet-4 # Fallback: Vertex AI + litellm_params: + model: vertex_ai/claude-sonnet-4-20250514 + vertex_project: my-project +``` + +**Failure Scenario:** +```mermaid +flowchart TD + A["Request for 'sonnet-4'"] --> B["Router finds available deployments"] + B --> C["Available:
• Anthropic Direct
• Vertex AI"] + C --> D["Selects Anthropic Direct"] + D --> E{"Request fails with 429?"} + E -->|No| F["Success ✅"] + E -->|Yes| G["Cooldown Anthropic Direct
for 5 seconds"] + G --> H["Next request for 'sonnet-4'"] + H --> I["Route to Vertex AI
(only available deployment for model_name='sonnet-4')"] + I --> J["Success ✅"] + + style G fill:#ffcccc + style I fill:#ccffcc +``` + + + ### Retries For both async + sync functions, we support retrying failed requests. diff --git a/docs/my-website/docs/simple_proxy_old_doc.md b/docs/my-website/docs/simple_proxy_old_doc.md deleted file mode 100644 index 730fd0aab42..00000000000 --- a/docs/my-website/docs/simple_proxy_old_doc.md +++ /dev/null @@ -1,1353 +0,0 @@ -import Image from '@theme/IdealImage'; -import Tabs from '@theme/Tabs'; -import TabItem from '@theme/TabItem'; - -# 💥 LiteLLM Proxy Server - -LiteLLM Server manages: - -* **Unified Interface**: Calling 100+ LLMs [Huggingface/Bedrock/TogetherAI/etc.](#other-supported-models) in the OpenAI `ChatCompletions` & `Completions` format -* **Load Balancing**: between [Multiple Models](#multiple-models---quick-start) + [Deployments of the same model](#multiple-instances-of-1-model) - LiteLLM proxy can handle 1.5k+ requests/second during load tests. -* **Cost tracking**: Authentication & Spend Tracking [Virtual Keys](#managing-auth---virtual-keys) - -[**See LiteLLM Proxy code**](https://github.com/BerriAI/litellm/tree/main/litellm/proxy) - -## Quick Start -View all the supported args for the Proxy CLI [here](https://docs.litellm.ai/docs/simple_proxy#proxy-cli-arguments) - -```shell -$ pip install 'litellm[proxy]' -``` - -```shell -$ litellm --model huggingface/bigcode/starcoder - -#INFO: Proxy running on http://0.0.0.0:4000 -``` - -### Test -In a new shell, run, this will make an `openai.chat.completions` request. Ensure you're using openai v1.0.0+ -```shell -litellm --test -``` - -This will now automatically route any requests for gpt-3.5-turbo to bigcode starcoder, hosted on huggingface inference endpoints. - -### Using LiteLLM Proxy - Curl Request, OpenAI Package - - - - -```shell -curl --location 'http://0.0.0.0:4000/chat/completions' \ ---header 'Content-Type: application/json' \ ---data ' { - "model": "gpt-3.5-turbo", - "messages": [ - { - "role": "user", - "content": "what llm are you" - } - ], - } -' -``` - - - -```python -import openai -client = openai.OpenAI( - api_key="anything", - base_url="http://0.0.0.0:4000" -) - -# request sent to model set on litellm proxy, `litellm --model` -response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [ - { - "role": "user", - "content": "this is a test request, write a short poem" - } -]) - -print(response) - -``` - - - - -### Server Endpoints -- POST `/chat/completions` - chat completions endpoint to call 100+ LLMs -- POST `/completions` - completions endpoint -- POST `/embeddings` - embedding endpoint for Azure, OpenAI, Huggingface endpoints -- GET `/models` - available models on server -- POST `/key/generate` - generate a key to access the proxy - -### Supported LLMs -All LiteLLM supported LLMs are supported on the Proxy. Seel all [supported llms](https://docs.litellm.ai/docs/providers) - - - -```shell -$ export AWS_ACCESS_KEY_ID= -$ export AWS_REGION_NAME= -$ export AWS_SECRET_ACCESS_KEY= -``` - -```shell -$ litellm --model bedrock/anthropic.claude-v2 -``` - - - -```shell -$ export AZURE_API_KEY=my-api-key -$ export AZURE_API_BASE=my-api-base -``` -``` -$ litellm --model azure/my-deployment-name -``` - - - - -```shell -$ export OPENAI_API_KEY=my-api-key -``` - -```shell -$ litellm --model gpt-3.5-turbo -``` - - - -```shell -$ export HUGGINGFACE_API_KEY=my-api-key #[OPTIONAL] -``` -```shell -$ litellm --model huggingface/ --api_base https://k58ory32yinf1ly0.us-east-1.aws.endpoints.huggingface.cloud -``` - - - - -```shell -$ litellm --model huggingface/ --api_base http://0.0.0.0:8001 -``` - - - - -```shell -export AWS_ACCESS_KEY_ID= -export AWS_REGION_NAME= -export AWS_SECRET_ACCESS_KEY= -``` - -```shell -$ litellm --model sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b -``` - - - - -```shell -$ export ANTHROPIC_API_KEY=my-api-key -``` -```shell -$ litellm --model claude-instant-1 -``` - - - -Assuming you're running vllm locally - -```shell -$ litellm --model vllm/facebook/opt-125m -``` - - - -```shell -$ export TOGETHERAI_API_KEY=my-api-key -``` -```shell -$ litellm --model together_ai/lmsys/vicuna-13b-v1.5-16k -``` - - - - - -```shell -$ export REPLICATE_API_KEY=my-api-key -``` -```shell -$ litellm \ - --model replicate/meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3 -``` - - - - - -```shell -$ litellm --model petals/meta-llama/Llama-2-70b-chat-hf -``` - - - - - -```shell -$ export PALM_API_KEY=my-palm-key -``` -```shell -$ litellm --model palm/chat-bison -``` - - - - - -```shell -$ export AI21_API_KEY=my-api-key -``` - -```shell -$ litellm --model j2-light -``` - - - - - -```shell -$ export COHERE_API_KEY=my-api-key -``` - -```shell -$ litellm --model command-nightly -``` - - - - - - -## Using with OpenAI compatible projects -Set `base_url` to the LiteLLM Proxy server - - - - -```python -import openai -client = openai.OpenAI( - api_key="anything", - base_url="http://0.0.0.0:4000" -) - -# request sent to model set on litellm proxy, `litellm --model` -response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [ - { - "role": "user", - "content": "this is a test request, write a short poem" - } -]) - -print(response) - -``` - - - -#### Start the LiteLLM proxy -```shell -litellm --model gpt-3.5-turbo - -#INFO: Proxy running on http://0.0.0.0:4000 -``` - -#### 1. Clone the repo - -```shell -git clone https://github.com/danny-avila/LibreChat.git -``` - - -#### 2. Modify Librechat's `docker-compose.yml` -LiteLLM Proxy is running on port `4000`, set `4000` as the proxy below -```yaml -OPENAI_REVERSE_PROXY=http://host.docker.internal:4000/v1/chat/completions -``` - -#### 3. Save fake OpenAI key in Librechat's `.env` - -Copy Librechat's `.env.example` to `.env` and overwrite the default OPENAI_API_KEY (by default it requires the user to pass a key). -```env -OPENAI_API_KEY=sk-1234 -``` - -#### 4. Run LibreChat: -```shell -docker compose up -``` - - - - -Continue-Dev brings ChatGPT to VSCode. See how to [install it here](https://continue.dev/docs/quickstart). - -In the [config.py](https://continue.dev/docs/reference/Models/openai) set this as your default model. -```python - default=OpenAI( - api_key="IGNORED", - model="fake-model-name", - context_length=2048, # customize if needed for your model - api_base="http://localhost:4000" # your proxy server url - ), -``` - -Credits [@vividfog](https://github.com/ollama/ollama/issues/305#issuecomment-1751848077) for this tutorial. - - - - -```shell -$ pip install aider - -$ aider --openai-api-base http://0.0.0.0:4000 --openai-api-key fake-key -``` - - - -```python -pip install pyautogen -``` - -```python -from autogen import AssistantAgent, UserProxyAgent, oai -config_list=[ - { - "model": "my-fake-model", - "api_base": "http://localhost:4000", #litellm compatible endpoint - "api_type": "open_ai", - "api_key": "NULL", # just a placeholder - } -] - -response = oai.Completion.create(config_list=config_list, prompt="Hi") -print(response) # works fine - -llm_config={ - "config_list": config_list, -} - -assistant = AssistantAgent("assistant", llm_config=llm_config) -user_proxy = UserProxyAgent("user_proxy") -user_proxy.initiate_chat(assistant, message="Plot a chart of META and TESLA stock price change YTD.", config_list=config_list) -``` - -Credits [@victordibia](https://github.com/microsoft/autogen/issues/45#issuecomment-1749921972) for this tutorial. - - - -A guidance language for controlling large language models. -https://github.com/guidance-ai/guidance - -**NOTE:** Guidance sends additional params like `stop_sequences` which can cause some models to fail if they don't support it. - -**Fix**: Start your proxy using the `--drop_params` flag - -```shell -litellm --model ollama/codellama --temperature 0.3 --max_tokens 2048 --drop_params -``` - -```python -import guidance - -# set api_base to your proxy -# set api_key to anything -gpt4 = guidance.llms.OpenAI("gpt-4", api_base="http://0.0.0.0:4000", api_key="anything") - -experts = guidance(''' -{{#system~}} -You are a helpful and terse assistant. -{{~/system}} - -{{#user~}} -I want a response to the following question: -{{query}} -Name 3 world-class experts (past or present) who would be great at answering this? -Don't answer the question yet. -{{~/user}} - -{{#assistant~}} -{{gen 'expert_names' temperature=0 max_tokens=300}} -{{~/assistant}} -''', llm=gpt4) - -result = experts(query='How can I be more productive?') -print(result) -``` - - - -## Proxy Configs -The Config allows you to set the following params - -| Param Name | Description | -|----------------------|---------------------------------------------------------------| -| `model_list` | List of supported models on the server, with model-specific configs | -| `litellm_settings` | litellm Module settings, example `litellm.drop_params=True`, `litellm.set_verbose=True`, `litellm.api_base`, `litellm.cache` | -| `general_settings` | Server settings, example setting `master_key: sk-my_special_key` | -| `environment_variables` | Environment Variables example, `REDIS_HOST`, `REDIS_PORT` | - -#### Example Config -```yaml -model_list: - - model_name: gpt-3.5-turbo - litellm_params: - model: azure/gpt-turbo-small-eu - api_base: https://my-endpoint-europe-berri-992.openai.azure.com/ - api_key: - rpm: 6 # Rate limit for this deployment: in requests per minute (rpm) - - model_name: gpt-3.5-turbo - litellm_params: - model: azure/gpt-turbo-small-ca - api_base: https://my-endpoint-canada-berri992.openai.azure.com/ - api_key: - rpm: 6 - - model_name: gpt-3.5-turbo - litellm_params: - model: azure/gpt-turbo-large - api_base: https://openai-france-1234.openai.azure.com/ - api_key: - rpm: 1440 - -litellm_settings: - drop_params: True - set_verbose: True - -general_settings: - master_key: sk-1234 # [OPTIONAL] Only use this if you to require all calls to contain this key (Authorization: Bearer sk-1234) - - -environment_variables: - OPENAI_API_KEY: sk-123 - REPLICATE_API_KEY: sk-cohere-is-okay - REDIS_HOST: redis-16337.c322.us-east-1-2.ec2.cloud.redislabs.com - REDIS_PORT: "16337" - REDIS_PASSWORD: -``` - -### Config for Multiple Models - GPT-4, Claude-2 - -Here's how you can use multiple llms with one proxy `config.yaml`. - -#### Step 1: Setup Config -```yaml -model_list: - - model_name: zephyr-alpha # the 1st model is the default on the proxy - litellm_params: # params for litellm.completion() - https://docs.litellm.ai/docs/completion/input#input---request-body - model: huggingface/HuggingFaceH4/zephyr-7b-alpha - api_base: http://0.0.0.0:8001 - - model_name: gpt-4 - litellm_params: - model: gpt-4 - api_key: sk-1233 - - model_name: claude-2 - litellm_params: - model: claude-2 - api_key: sk-claude -``` - -:::info - -The proxy uses the first model in the config as the default model - in this config the default model is `zephyr-alpha` -::: - - -#### Step 2: Start Proxy with config - -```shell -$ litellm --config /path/to/config.yaml -``` - -#### Step 3: Use proxy -Curl Command -```shell -curl --location 'http://0.0.0.0:4000/chat/completions' \ ---header 'Content-Type: application/json' \ ---data ' { - "model": "zephyr-alpha", - "messages": [ - { - "role": "user", - "content": "what llm are you" - } - ], - } -' -``` - -### Load Balancing - Multiple Instances of 1 model -Use this config to load balance between multiple instances of the same model. The proxy will handle routing requests (using LiteLLM's Router). **Set `rpm` in the config if you want maximize throughput** - -#### Example config -requests with `model=gpt-3.5-turbo` will be routed across multiple instances of `azure/gpt-3.5-turbo` -```yaml -model_list: - - model_name: gpt-3.5-turbo - litellm_params: - model: azure/gpt-turbo-small-eu - api_base: https://my-endpoint-europe-berri-992.openai.azure.com/ - api_key: - rpm: 6 # Rate limit for this deployment: in requests per minute (rpm) - - model_name: gpt-3.5-turbo - litellm_params: - model: azure/gpt-turbo-small-ca - api_base: https://my-endpoint-canada-berri992.openai.azure.com/ - api_key: - rpm: 6 - - model_name: gpt-3.5-turbo - litellm_params: - model: azure/gpt-turbo-large - api_base: https://openai-france-1234.openai.azure.com/ - api_key: - rpm: 1440 -``` - -#### Step 2: Start Proxy with config - -```shell -$ litellm --config /path/to/config.yaml -``` - -#### Step 3: Use proxy -Curl Command -```shell -curl --location 'http://0.0.0.0:4000/chat/completions' \ ---header 'Content-Type: application/json' \ ---data ' { - "model": "gpt-3.5-turbo", - "messages": [ - { - "role": "user", - "content": "what llm are you" - } - ], - } -' -``` - -### Fallbacks + Cooldowns + Retries + Timeouts - -If a call fails after num_retries, fall back to another model group. - -If the error is a context window exceeded error, fall back to a larger model group (if given). - -[**See Code**](https://github.com/BerriAI/litellm/blob/main/litellm/router.py) - -**Set via config** -```yaml -model_list: - - model_name: zephyr-beta - litellm_params: - model: huggingface/HuggingFaceH4/zephyr-7b-beta - api_base: http://0.0.0.0:8001 - - model_name: zephyr-beta - litellm_params: - model: huggingface/HuggingFaceH4/zephyr-7b-beta - api_base: http://0.0.0.0:8002 - - model_name: zephyr-beta - litellm_params: - model: huggingface/HuggingFaceH4/zephyr-7b-beta - api_base: http://0.0.0.0:8003 - - model_name: gpt-3.5-turbo - litellm_params: - model: gpt-3.5-turbo - api_key: - - model_name: gpt-3.5-turbo-16k - litellm_params: - model: gpt-3.5-turbo-16k - api_key: - -litellm_settings: - num_retries: 3 # retry call 3 times on each model_name (e.g. zephyr-beta) - request_timeout: 10 # raise Timeout error if call takes longer than 10s - fallbacks: [{"zephyr-beta": ["gpt-3.5-turbo"]}] # fallback to gpt-3.5-turbo if call fails num_retries - context_window_fallbacks: [{"zephyr-beta": ["gpt-3.5-turbo-16k"]}, {"gpt-3.5-turbo": ["gpt-3.5-turbo-16k"]}] # fallback to gpt-3.5-turbo-16k if context window error - allowed_fails: 3 # cooldown model if it fails > 1 call in a minute. -``` - -**Set dynamically** - -```bash -curl --location 'http://0.0.0.0:4000/chat/completions' \ ---header 'Content-Type: application/json' \ ---data ' { - "model": "zephyr-beta", - "messages": [ - { - "role": "user", - "content": "what llm are you" - } - ], - "fallbacks": [{"zephyr-beta": ["gpt-3.5-turbo"]}], - "context_window_fallbacks": [{"zephyr-beta": ["gpt-3.5-turbo"]}], - "num_retries": 2, - "request_timeout": 10 - } -' -``` - -### Config for Embedding Models - xorbitsai/inference - -Here's how you can use multiple llms with one proxy `config.yaml`. -Here is how [LiteLLM calls OpenAI Compatible Embedding models](https://docs.litellm.ai/docs/embedding/supported_embedding#openai-compatible-embedding-models) - -#### Config -```yaml -model_list: - - model_name: custom_embedding_model - litellm_params: - model: openai/custom_embedding # the `openai/` prefix tells litellm it's openai compatible - api_base: http://0.0.0.0:4000/ - - model_name: custom_embedding_model - litellm_params: - model: openai/custom_embedding # the `openai/` prefix tells litellm it's openai compatible - api_base: http://0.0.0.0:8001/ -``` - -Run the proxy using this config -```shell -$ litellm --config /path/to/config.yaml -``` - - -### Managing Auth - Virtual Keys - -Grant other's temporary access to your proxy, with keys that expire after a set duration. - -Requirements: - -- Need to a postgres database (e.g. [Supabase](https://supabase.com/), [Neon](https://neon.tech/), etc) - -You can then generate temporary keys by hitting the `/key/generate` endpoint. - -[**See code**](https://github.com/BerriAI/litellm/blob/7a669a36d2689c7f7890bc9c93e04ff3c2641299/litellm/proxy/proxy_server.py#L672) - -**Step 1: Save postgres db url** - -```yaml -model_list: - - model_name: gpt-4 - litellm_params: - model: ollama/llama2 - - model_name: gpt-3.5-turbo - litellm_params: - model: ollama/llama2 - -general_settings: - master_key: sk-1234 # [OPTIONAL] if set all calls to proxy will require either this key or a valid generated token - database_url: "postgresql://:@:/" -``` - -**Step 2: Start litellm** - -```shell -litellm --config /path/to/config.yaml -``` - -**Step 3: Generate temporary keys** - -```shell -curl 'http://0.0.0.0:4000/key/generate' \ ---h 'Authorization: Bearer sk-1234' \ ---d '{"models": ["gpt-3.5-turbo", "gpt-4", "claude-2"], "duration": "20m"}' -``` - -- `models`: *list or null (optional)* - Specify the models a token has access too. If null, then token has access to all models on server. - -- `duration`: *str or null (optional)* Specify the length of time the token is valid for. If null, default is set to 1 hour. You can set duration as seconds ("30s"), minutes ("30m"), hours ("30h"), days ("30d"). - -Expected response: - -```python -{ - "key": "sk-kdEXbIqZRwEeEiHwdg7sFA", # Bearer token - "expires": "2023-11-19T01:38:25.838000+00:00" # datetime object -} -``` - -### Managing Auth - Upgrade/Downgrade Models - -If a user is expected to use a given model (i.e. gpt3-5), and you want to: - -- try to upgrade the request (i.e. GPT4) -- or downgrade it (i.e. Mistral) -- OR rotate the API KEY (i.e. open AI) -- OR access the same model through different end points (i.e. openAI vs openrouter vs Azure) - -Here's how you can do that: - -**Step 1: Create a model group in config.yaml (save model name, api keys, etc.)** - -```yaml -model_list: - - model_name: my-free-tier - litellm_params: - model: huggingface/HuggingFaceH4/zephyr-7b-beta - api_base: http://0.0.0.0:8001 - - model_name: my-free-tier - litellm_params: - model: huggingface/HuggingFaceH4/zephyr-7b-beta - api_base: http://0.0.0.0:8002 - - model_name: my-free-tier - litellm_params: - model: huggingface/HuggingFaceH4/zephyr-7b-beta - api_base: http://0.0.0.0:8003 - - model_name: my-paid-tier - litellm_params: - model: gpt-4 - api_key: my-api-key -``` - -**Step 2: Generate a user key - enabling them access to specific models, custom model aliases, etc.** - -```bash -curl -X POST "https://0.0.0.0:4000/key/generate" \ --H "Authorization: Bearer sk-1234" \ --H "Content-Type: application/json" \ --d '{ - "models": ["my-free-tier"], - "aliases": {"gpt-3.5-turbo": "my-free-tier"}, - "duration": "30min" -}' -``` - -- **How to upgrade / downgrade request?** Change the alias mapping -- **How are routing between diff keys/api bases done?** litellm handles this by shuffling between different models in the model list with the same model_name. [**See Code**](https://github.com/BerriAI/litellm/blob/main/litellm/router.py) - -### Managing Auth - Tracking Spend - -You can get spend for a key by using the `/key/info` endpoint. - -```bash -curl 'http://0.0.0.0:4000/key/info?key=' \ - -X GET \ - -H 'Authorization: Bearer ' -``` - -This is automatically updated (in USD) when calls are made to /completions, /chat/completions, /embeddings using litellm's completion_cost() function. [**See Code**](https://github.com/BerriAI/litellm/blob/1a6ea20a0bb66491968907c2bfaabb7fe45fc064/litellm/utils.py#L1654). - -**Sample response** - -```python -{ - "key": "sk-tXL0wt5-lOOVK9sfY2UacA", - "info": { - "token": "sk-tXL0wt5-lOOVK9sfY2UacA", - "spend": 0.0001065, - "expires": "2023-11-24T23:19:11.131000Z", - "models": [ - "gpt-3.5-turbo", - "gpt-4", - "claude-2" - ], - "aliases": { - "mistral-7b": "gpt-3.5-turbo" - }, - "config": {} - } -} -``` - -### Save Model-specific params (API Base, API Keys, Temperature, Headers etc.) -You can use the config to save model-specific information like api_base, api_key, temperature, max_tokens, etc. - -**Step 1**: Create a `config.yaml` file -```yaml -model_list: - - model_name: gpt-4-team1 - litellm_params: # params for litellm.completion() - https://docs.litellm.ai/docs/completion/input#input---request-body - model: azure/chatgpt-v-2 - api_base: https://openai-gpt-4-test-v-1.openai.azure.com/ - api_version: "2023-05-15" - azure_ad_token: eyJ0eXAiOiJ - - model_name: gpt-4-team2 - litellm_params: - model: azure/gpt-4 - api_key: sk-123 - api_base: https://openai-gpt-4-test-v-2.openai.azure.com/ - - model_name: mistral-7b - litellm_params: - model: ollama/mistral - api_base: your_ollama_api_base -``` - -**Step 2**: Start server with config - -```shell -$ litellm --config /path/to/config.yaml -``` - -### Load API Keys from Vault - -If you have secrets saved in Azure Vault, etc. and don't want to expose them in the config.yaml, here's how to load model-specific keys from the environment. - -```python -os.environ["AZURE_NORTH_AMERICA_API_KEY"] = "your-azure-api-key" -``` - -```yaml -model_list: - - model_name: gpt-4-team1 - litellm_params: # params for litellm.completion() - https://docs.litellm.ai/docs/completion/input#input---request-body - model: azure/chatgpt-v-2 - api_base: https://openai-gpt-4-test-v-1.openai.azure.com/ - api_version: "2023-05-15" - api_key: os.environ/AZURE_NORTH_AMERICA_API_KEY -``` - -[**See Code**](https://github.com/BerriAI/litellm/blob/c12d6c3fe80e1b5e704d9846b246c059defadce7/litellm/utils.py#L2366) - -s/o to [@David Manouchehri](https://www.linkedin.com/in/davidmanouchehri/) for helping with this. - -### Config for setting Model Aliases - -Set a model alias for your deployments. - -In the `config.yaml` the model_name parameter is the user-facing name to use for your deployment. - -In the config below requests with `model=gpt-4` will route to `ollama/llama2` - -```yaml -model_list: - - model_name: text-davinci-003 - litellm_params: - model: ollama/zephyr - - model_name: gpt-4 - litellm_params: - model: ollama/llama2 - - model_name: gpt-3.5-turbo - litellm_params: - model: ollama/llama2 -``` -### Caching Responses -Caching can be enabled by adding the `cache` key in the `config.yaml` -#### Step 1: Add `cache` to the config.yaml -```yaml -model_list: - - model_name: gpt-3.5-turbo - litellm_params: - model: gpt-3.5-turbo - -litellm_settings: - set_verbose: True - cache: # init cache - type: redis # tell litellm to use redis caching -``` - -#### Step 2: Add Redis Credentials to .env -LiteLLM requires the following REDIS credentials in your env to enable caching - - ```shell - REDIS_HOST = "" # REDIS_HOST='redis-18841.c274.us-east-1-3.ec2.cloud.redislabs.com' - REDIS_PORT = "" # REDIS_PORT='18841' - REDIS_PASSWORD = "" # REDIS_PASSWORD='liteLlmIsAmazing' - ``` -#### Step 3: Run proxy with config -```shell -$ litellm --config /path/to/config.yaml -``` - -#### Using Caching -Send the same request twice: -```shell -curl http://0.0.0.0:4000/v1/chat/completions \ - -H "Content-Type: application/json" \ - -d '{ - "model": "gpt-3.5-turbo", - "messages": [{"role": "user", "content": "write a poem about litellm!"}], - "temperature": 0.7 - }' - -curl http://0.0.0.0:4000/v1/chat/completions \ - -H "Content-Type: application/json" \ - -d '{ - "model": "gpt-3.5-turbo", - "messages": [{"role": "user", "content": "write a poem about litellm!"}], - "temperature": 0.7 - }' -``` - -#### Control caching per completion request -Caching can be switched on/off per `/chat/completions` request -- Caching **on** for completion - pass `caching=True`: - ```shell - curl http://0.0.0.0:4000/v1/chat/completions \ - -H "Content-Type: application/json" \ - -d '{ - "model": "gpt-3.5-turbo", - "messages": [{"role": "user", "content": "write a poem about litellm!"}], - "temperature": 0.7, - "caching": true - }' - ``` -- Caching **off** for completion - pass `caching=False`: - ```shell - curl http://0.0.0.0:4000/v1/chat/completions \ - -H "Content-Type: application/json" \ - -d '{ - "model": "gpt-3.5-turbo", - "messages": [{"role": "user", "content": "write a poem about litellm!"}], - "temperature": 0.7, - "caching": false - }' - ``` - -### Set Custom Prompt Templates - -LiteLLM by default checks if a model has a [prompt template and applies it](./completion/prompt_formatting.md) (e.g. if a huggingface model has a saved chat template in it's tokenizer_config.json). However, you can also set a custom prompt template on your proxy in the `config.yaml`: - -**Step 1**: Save your prompt template in a `config.yaml` -```yaml -# Model-specific parameters -model_list: - - model_name: mistral-7b # model alias - litellm_params: # actual params for litellm.completion() - model: "huggingface/mistralai/Mistral-7B-Instruct-v0.1" - api_base: "" - api_key: "" # [OPTIONAL] for hf inference endpoints - initial_prompt_value: "\n" - roles: {"system":{"pre_message":"<|im_start|>system\n", "post_message":"<|im_end|>"}, "assistant":{"pre_message":"<|im_start|>assistant\n","post_message":"<|im_end|>"}, "user":{"pre_message":"<|im_start|>user\n","post_message":"<|im_end|>"}} - final_prompt_value: "\n" - bos_token: "" - eos_token: "" - max_tokens: 4096 -``` - -**Step 2**: Start server with config - -```shell -$ litellm --config /path/to/config.yaml -``` - -## Debugging Proxy -Run the proxy with `--debug` to easily view debug logs -```shell -litellm --model gpt-3.5-turbo --debug -``` - -### Detailed Debug Logs - -Run the proxy with `--detailed_debug` to view detailed debug logs -```shell -litellm --model gpt-3.5-turbo --detailed_debug -``` - -When making requests you should see the POST request sent by LiteLLM to the LLM on the Terminal output -```shell -POST Request Sent from LiteLLM: -curl -X POST \ -https://api.openai.com/v1/chat/completions \ --H 'content-type: application/json' -H 'Authorization: Bearer sk-qnWGUIW9****************************************' \ --d '{"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "this is a test request, write a short poem"}]}' -``` - -## Health Check LLMs on Proxy -Use this to health check all LLMs defined in your config.yaml -#### Request -```shell -curl --location 'http://0.0.0.0:4000/health' -``` - -You can also run `litellm -health` it makes a `get` request to `http://0.0.0.0:4000/health` for you -``` -litellm --health -``` -#### Response -```shell -{ - "healthy_endpoints": [ - { - "model": "azure/gpt-35-turbo", - "api_base": "https://my-endpoint-canada-berri992.openai.azure.com/" - }, - { - "model": "azure/gpt-35-turbo", - "api_base": "https://my-endpoint-europe-berri-992.openai.azure.com/" - } - ], - "unhealthy_endpoints": [ - { - "model": "azure/gpt-35-turbo", - "api_base": "https://openai-france-1234.openai.azure.com/" - } - ] -} -``` - -## Logging Proxy Input/Output - OpenTelemetry - -### Step 1 Start OpenTelemetry Collector Docker Container -This container sends logs to your selected destination - -#### Install OpenTelemetry Collector Docker Image -```shell -docker pull otel/opentelemetry-collector:0.90.0 -docker run -p 127.0.0.1:4317:4317 -p 127.0.0.1:55679:55679 otel/opentelemetry-collector:0.90.0 -``` - -#### Set Destination paths on OpenTelemetry Collector - -Here's the OpenTelemetry yaml config to use with Elastic Search -```yaml -receivers: - otlp: - protocols: - grpc: - endpoint: 0.0.0.0:4317 - -processors: - batch: - timeout: 1s - send_batch_size: 1024 - -exporters: - logging: - loglevel: debug - otlphttp/elastic: - endpoint: "" - headers: - Authorization: "Bearer " - -service: - pipelines: - metrics: - receivers: [otlp] - exporters: [logging, otlphttp/elastic] - traces: - receivers: [otlp] - exporters: [logging, otlphttp/elastic] - logs: - receivers: [otlp] - exporters: [logging,otlphttp/elastic] -``` - -#### Start the OpenTelemetry container with config -Run the following command to start your docker container. We pass `otel_config.yaml` from the previous step - -```shell -docker run -p 4317:4317 \ - -v $(pwd)/otel_config.yaml:/etc/otel-collector-config.yaml \ - otel/opentelemetry-collector:latest \ - --config=/etc/otel-collector-config.yaml -``` - -### Step 2 Configure LiteLLM proxy to log on OpenTelemetry - -#### Pip install opentelemetry -```shell -pip install opentelemetry-api opentelemetry-sdk opentelemetry-exporter-otlp -U -``` - -#### Set (OpenTelemetry) `otel=True` on the proxy `config.yaml` -**Example config.yaml** - -```yaml -model_list: - - model_name: gpt-3.5-turbo - litellm_params: - model: azure/gpt-turbo-small-eu - api_base: https://my-endpoint-europe-berri-992.openai.azure.com/ - api_key: - rpm: 6 # Rate limit for this deployment: in requests per minute (rpm) - -general_settings: - otel: True # set OpenTelemetry=True, on litellm Proxy - -``` - -#### Set OTEL collector endpoint -LiteLLM will read the `OTEL_ENDPOINT` environment variable to send data to your OTEL collector - -```python -os.environ['OTEL_ENDPOINT'] # defaults to 127.0.0.1:4317 if not provided -``` - -#### Start LiteLLM Proxy -```shell -litellm -config config.yaml -``` - -#### Run a test request to Proxy -```shell -curl --location 'http://0.0.0.0:4000/chat/completions' \ - --header 'Authorization: Bearer sk-1244' \ - --data ' { - "model": "gpt-3.5-turbo", - "messages": [ - { - "role": "user", - "content": "request from LiteLLM testing" - } - ] - }' -``` - - -#### Test & View Logs on OpenTelemetry Collector -On successful logging you should be able to see this log on your `OpenTelemetry Collector` Docker Container -```shell -Events: -SpanEvent #0 - -> Name: LiteLLM: Request Input - -> Timestamp: 2023-12-02 05:05:53.71063 +0000 UTC - -> DroppedAttributesCount: 0 - -> Attributes:: - -> type: Str(http) - -> asgi: Str({'version': '3.0', 'spec_version': '2.3'}) - -> http_version: Str(1.1) - -> server: Str(('127.0.0.1', 8000)) - -> client: Str(('127.0.0.1', 62796)) - -> scheme: Str(http) - -> method: Str(POST) - -> root_path: Str() - -> path: Str(/chat/completions) - -> raw_path: Str(b'/chat/completions') - -> query_string: Str(b'') - -> headers: Str([(b'host', b'0.0.0.0:8000'), (b'user-agent', b'curl/7.88.1'), (b'accept', b'*/*'), (b'authorization', b'Bearer sk-1244'), (b'content-length', b'147'), (b'content-type', b'application/x-www-form-urlencoded')]) - -> state: Str({}) - -> app: Str() - -> fastapi_astack: Str() - -> router: Str() - -> endpoint: Str() - -> path_params: Str({}) - -> route: Str(APIRoute(path='/chat/completions', name='chat_completion', methods=['POST'])) -SpanEvent #1 - -> Name: LiteLLM: Request Headers - -> Timestamp: 2023-12-02 05:05:53.710652 +0000 UTC - -> DroppedAttributesCount: 0 - -> Attributes:: - -> host: Str(0.0.0.0:8000) - -> user-agent: Str(curl/7.88.1) - -> accept: Str(*/*) - -> authorization: Str(Bearer sk-1244) - -> content-length: Str(147) - -> content-type: Str(application/x-www-form-urlencoded) -SpanEvent #2 -``` - -### View Log on Elastic Search -Here's the log view on Elastic Search. You can see the request `input`, `output` and `headers` - - - -## Logging Proxy Input/Output - Langfuse -We will use the `--config` to set `litellm.success_callback = ["langfuse"]` this will log all successful LLM calls to langfuse - -**Step 1** Install langfuse - -```shell -pip install langfuse -``` - -**Step 2**: Create a `config.yaml` file and set `litellm_settings`: `success_callback` -```yaml -model_list: - - model_name: gpt-3.5-turbo - litellm_params: - model: gpt-3.5-turbo -litellm_settings: - success_callback: ["langfuse"] -``` - -**Step 3**: Start the proxy, make a test request - -Start proxy -```shell -litellm --config config.yaml --debug -``` - -Test Request -``` -litellm --test -``` - -Expected output on Langfuse - - - -## Deploying LiteLLM Proxy - -### Deploy on Render https://render.com/ - - - -## LiteLLM Proxy Performance - -### Throughput - 30% Increase -LiteLLM proxy + Load Balancer gives **30% increase** in throughput compared to Raw OpenAI API - - -### Latency Added - 0.00325 seconds -LiteLLM proxy adds **0.00325 seconds** latency as compared to using the Raw OpenAI API - - - - - -## Proxy CLI Arguments - -#### --host - - **Default:** `'0.0.0.0'` - - The host for the server to listen on. - - **Usage:** - ```shell - litellm --host 127.0.0.1 - ``` - -#### --port - - **Default:** `4000` - - The port to bind the server to. - - **Usage:** - ```shell - litellm --port 8080 - ``` - -#### --num_workers - - **Default:** `1` - - The number of uvicorn workers to spin up. - - **Usage:** - ```shell - litellm --num_workers 4 - ``` - -#### --api_base - - **Default:** `None` - - The API base for the model litellm should call. - - **Usage:** - ```shell - litellm --model huggingface/tinyllama --api_base https://k58ory32yinf1ly0.us-east-1.aws.endpoints.huggingface.cloud - ``` - -#### --api_version - - **Default:** `None` - - For Azure services, specify the API version. - - **Usage:** - ```shell - litellm --model azure/gpt-deployment --api_version 2023-08-01 --api_base https://" - ``` - -#### --model or -m - - **Default:** `None` - - The model name to pass to Litellm. - - **Usage:** - ```shell - litellm --model gpt-3.5-turbo - ``` - -#### --test - - **Type:** `bool` (Flag) - - Proxy chat completions URL to make a test request. - - **Usage:** - ```shell - litellm --test - ``` - -#### --health - - **Type:** `bool` (Flag) - - Runs a health check on all models in config.yaml - - **Usage:** - ```shell - litellm --health - ``` - -#### --alias - - **Default:** `None` - - An alias for the model, for user-friendly reference. - - **Usage:** - ```shell - litellm --alias my-gpt-model - ``` - -#### --debug - - **Default:** `False` - - **Type:** `bool` (Flag) - - Enable debugging mode for the input. - - **Usage:** - ```shell - litellm --debug - ``` -#### --detailed_debug - - **Default:** `False` - - **Type:** `bool` (Flag) - - Enable debugging mode for the input. - - **Usage:** - ```shell - litellm --detailed_debug - ``` - -#### --temperature - - **Default:** `None` - - **Type:** `float` - - Set the temperature for the model. - - **Usage:** - ```shell - litellm --temperature 0.7 - ``` - -#### --max_tokens - - **Default:** `None` - - **Type:** `int` - - Set the maximum number of tokens for the model output. - - **Usage:** - ```shell - litellm --max_tokens 50 - ``` - -#### --request_timeout - - **Default:** `6000` - - **Type:** `int` - - Set the timeout in seconds for completion calls. - - **Usage:** - ```shell - litellm --request_timeout 300 - ``` - -#### --drop_params - - **Type:** `bool` (Flag) - - Drop any unmapped params. - - **Usage:** - ```shell - litellm --drop_params - ``` - -#### --add_function_to_prompt - - **Type:** `bool` (Flag) - - If a function passed but unsupported, pass it as a part of the prompt. - - **Usage:** - ```shell - litellm --add_function_to_prompt - ``` - -#### --config - - Configure Litellm by providing a configuration file path. - - **Usage:** - ```shell - litellm --config path/to/config.yaml - ``` - -#### --telemetry - - **Default:** `True` - - **Type:** `bool` - - Help track usage of this feature. - - **Usage:** - ```shell - litellm --telemetry False - ``` diff --git a/docs/my-website/docs/tutorials/claude_responses_api.md b/docs/my-website/docs/tutorials/claude_responses_api.md index a06be87409b..09b352a7663 100644 --- a/docs/my-website/docs/tutorials/claude_responses_api.md +++ b/docs/my-website/docs/tutorials/claude_responses_api.md @@ -4,11 +4,11 @@ import TabItem from '@theme/TabItem'; # Claude Code -This tutorial shows how to call the Responses API models like `codex-mini` and `o3-pro` from the Claude Code endpoint on LiteLLM. +This tutorial shows how to call Claude models through LiteLLM proxy from Claude Code. :::info -This tutorial is based on [Anthropic's official LiteLLM configuration documentation](https://docs.anthropic.com/en/docs/claude-code/llm-gateway#litellm-configuration). This integration allows you to use any LiteLLM supported model through Claude Code. +This tutorial is based on [Anthropic's official LiteLLM configuration documentation](https://docs.anthropic.com/en/docs/claude-code/llm-gateway#litellm-configuration). This integration allows you to use any LiteLLM supported model through Claude Code with centralized authentication, usage tracking, and cost controls. ::: @@ -31,19 +31,18 @@ Create a secure configuration using environment variables: ```yaml model_list: - # Responses API models - - model_name: codex-mini + # Claude models + - model_name: claude-3-5-sonnet-20241022 litellm_params: - model: openai/codex-mini - api_key: os.environ/OPENAI_API_KEY - api_base: https://api.openai.com/v1 + model: anthropic/claude-3-5-sonnet-20241022 + api_key: os.environ/ANTHROPIC_API_KEY - - model_name: o3-pro + - model_name: claude-3-5-haiku-20241022 litellm_params: - model: openai/o3-pro - api_key: os.environ/OPENAI_API_KEY - api_base: https://api.openai.com/v1 + model: anthropic/claude-3-5-haiku-20241022 + api_key: os.environ/ANTHROPIC_API_KEY + litellm_settings: master_key: os.environ/LITELLM_MASTER_KEY ``` @@ -51,7 +50,7 @@ litellm_settings: Set your environment variables: ```bash -export OPENAI_API_KEY="your-openai-api-key" +export ANTHROPIC_API_KEY="your-anthropic-api-key" export LITELLM_MASTER_KEY="sk-1234567890" # Generate a secure key ``` @@ -72,31 +71,43 @@ curl -X POST http://0.0.0.0:4000/v1/messages \ -H "Authorization: Bearer $LITELLM_MASTER_KEY" \ -H "Content-Type: application/json" \ -d '{ - "model": "codex-mini", + "model": "claude-3-5-sonnet-20241022", + "max_tokens": 1000, "messages": [{"role": "user", "content": "What is the capital of France?"}] }' ``` ### 4. Configure Claude Code -Setup Claude Code to use your LiteLLM proxy: +#### Method 1: Unified Endpoint (Recommended) + +Configure Claude Code to use LiteLLM's unified endpoint: ```bash export ANTHROPIC_BASE_URL="http://0.0.0.0:4000" export ANTHROPIC_AUTH_TOKEN="$LITELLM_MASTER_KEY" ``` -### 5. Use Claude Code +#### Method 2: Provider-specific Pass-through Endpoint -Start Claude Code with any configured model: +Alternatively, use the Anthropic pass-through endpoint: ```bash -# Use Responses API models -claude --model codex-mini -claude --model o3-pro +export ANTHROPIC_BASE_URL="http://0.0.0.0:4000/anthropic" +export ANTHROPIC_AUTH_TOKEN="$LITELLM_MASTER_KEY" +``` -# Or use the latest model alias -claude --model codex-mini-latest +### 5. Use Claude Code + +Start Claude Code and it will automatically use your configured models: + +```bash +# Claude Code will use the models configured in your LiteLLM proxy +claude + +# Or specify a model if you have multiple configured +claude --model claude-3-5-sonnet-20241022 +claude --model claude-3-5-haiku-20241022 ``` Example conversation: @@ -112,7 +123,8 @@ Common issues and solutions: **Authentication errors:** - Verify your environment variables are set: `echo $LITELLM_MASTER_KEY` -- Check that your OpenAI API key is valid and has sufficient credits +- Check that your API keys are valid and have sufficient credits +- Ensure the `ANTHROPIC_AUTH_TOKEN` matches your LiteLLM master key **Model not found:** - Ensure the model name in Claude Code matches exactly with your `config.yaml` @@ -123,33 +135,47 @@ Common issues and solutions: Expand your configuration to support multiple providers and models: - + ```yaml model_list: - # Responses API models + # OpenAI models - model_name: codex-mini - litellm_params: + litellm_params: model: openai/codex-mini api_key: os.environ/OPENAI_API_KEY api_base: https://api.openai.com/v1 - + - model_name: o3-pro litellm_params: model: openai/o3-pro api_key: os.environ/OPENAI_API_KEY api_base: https://api.openai.com/v1 - # Standard models - model_name: gpt-4o litellm_params: model: openai/gpt-4o api_key: os.environ/OPENAI_API_KEY + api_base: https://api.openai.com/v1 - - model_name: claude-3-5-sonnet + # Anthropic models + - model_name: claude-3-5-sonnet-20241022 litellm_params: model: anthropic/claude-3-5-sonnet-20241022 api_key: os.environ/ANTHROPIC_API_KEY + + - model_name: claude-3-5-haiku-20241022 + litellm_params: + model: anthropic/claude-3-5-haiku-20241022 + api_key: os.environ/ANTHROPIC_API_KEY + + # AWS Bedrock + - model_name: claude-bedrock + litellm_params: + model: bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0 + aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID + aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY + aws_region_name: us-east-1 litellm_settings: master_key: os.environ/LITELLM_MASTER_KEY @@ -158,13 +184,14 @@ litellm_settings: Switch between models seamlessly: ```bash -# Use Responses API models for advanced reasoning -claude --model o3-pro -claude --model codex-mini +# Use Claude for complex reasoning +claude --model claude-3-5-sonnet-20241022 -# Use standard models for general tasks -claude --model gpt-4o -claude --model claude-3-5-sonnet +# Use Haiku for fast responses +claude --model claude-3-5-haiku-20241022 + +# Use Bedrock deployment +claude --model claude-bedrock ``` diff --git a/docs/my-website/docusaurus.config.js b/docs/my-website/docusaurus.config.js index cab1669824c..cec0479f673 100644 --- a/docs/my-website/docusaurus.config.js +++ b/docs/my-website/docusaurus.config.js @@ -136,6 +136,11 @@ const config = { ], ], + themes: ['@docusaurus/theme-mermaid'], + markdown: { + mermaid: true, + }, + scripts: [ { async: true, diff --git a/docs/my-website/img/release_notes/faster_caching_calls.png b/docs/my-website/img/release_notes/faster_caching_calls.png new file mode 100644 index 00000000000..fb7409aec28 Binary files /dev/null and b/docs/my-website/img/release_notes/faster_caching_calls.png differ diff --git a/docs/my-website/img/release_notes/responses_api_session_mgt_images.jpg b/docs/my-website/img/release_notes/responses_api_session_mgt_images.jpg new file mode 100644 index 00000000000..852d2fdd6d0 Binary files /dev/null and b/docs/my-website/img/release_notes/responses_api_session_mgt_images.jpg differ diff --git a/docs/my-website/package-lock.json b/docs/my-website/package-lock.json index da4687e0e40..4b37e2be11d 100644 --- a/docs/my-website/package-lock.json +++ b/docs/my-website/package-lock.json @@ -12,6 +12,7 @@ "@docusaurus/plugin-google-gtag": "3.8.1", "@docusaurus/plugin-ideal-image": "3.8.1", "@docusaurus/preset-classic": "3.8.1", + "@docusaurus/theme-mermaid": "^3.8.1", "@inkeep/cxkit-docusaurus": "^0.5.89", "@mdx-js/react": "^3.0.0", "clsx": "^1.2.1", @@ -254,6 +255,26 @@ "node": ">=6.0.0" } }, + "node_modules/@antfu/install-pkg": { + "version": "1.1.0", + "resolved": "https://registry.npmjs.org/@antfu/install-pkg/-/install-pkg-1.1.0.tgz", + "integrity": "sha512-MGQsmw10ZyI+EJo45CdSER4zEb+p31LpDAFp2Z3gkSd1yqVZGi0Ebx++YTEMonJy4oChEMLsxZ64j8FH6sSqtQ==", + "dependencies": { + "package-manager-detector": "^1.3.0", + "tinyexec": "^1.0.1" + }, + "funding": { + "url": "https://github.com/sponsors/antfu" + } + }, + "node_modules/@antfu/utils": { + "version": "8.1.1", + "resolved": "https://registry.npmjs.org/@antfu/utils/-/utils-8.1.1.tgz", + "integrity": "sha512-Mex9nXf9vR6AhcXmMrlz/HVgYYZpVGJ6YlPgwl7UnaFpnshXs6EK/oa5Gpf3CzENMjkvEx2tQtntGnb7UtSTOQ==", + "funding": { + "url": "https://github.com/sponsors/antfu" + } + }, "node_modules/@babel/code-frame": { "version": "7.27.1", "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.27.1.tgz", @@ -1819,6 +1840,45 @@ 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"node_modules/vscode-uri": { + "version": "3.0.8", + "resolved": "https://registry.npmjs.org/vscode-uri/-/vscode-uri-3.0.8.tgz", + "integrity": "sha512-AyFQ0EVmsOZOlAnxoFOGOq1SQDWAB7C6aqMGS23svWAllfOaxbuFvcT8D1i8z3Gyn8fraVeZNNmN6e9bxxXkKw==" + }, "node_modules/watchpack": { "version": "2.4.4", "resolved": "https://registry.npmjs.org/watchpack/-/watchpack-2.4.4.tgz", diff --git a/docs/my-website/package.json b/docs/my-website/package.json index 24d212ea2c6..955e63c2d84 100644 --- a/docs/my-website/package.json +++ b/docs/my-website/package.json @@ -18,6 +18,7 @@ "@docusaurus/plugin-google-gtag": "3.8.1", "@docusaurus/plugin-ideal-image": "3.8.1", "@docusaurus/preset-classic": "3.8.1", + "@docusaurus/theme-mermaid": "^3.8.1", "@inkeep/cxkit-docusaurus": "^0.5.89", "@mdx-js/react": "^3.0.0", "clsx": "^1.2.1", @@ -48,6 +49,7 @@ }, "overrides": { "webpack-dev-server": ">=5.2.1", - "form-data": ">=4.0.4" + "form-data": ">=4.0.4", + "mermaid": ">=11.10.0" } } diff --git a/docs/my-website/release_notes/v1.74.15-stable/index.md b/docs/my-website/release_notes/v1.74.15-stable/index.md index 4fbb76bdbc4..dd748f18ffa 100644 --- a/docs/my-website/release_notes/v1.74.15-stable/index.md +++ b/docs/my-website/release_notes/v1.74.15-stable/index.md @@ -1,5 +1,5 @@ --- -title: "[Pre-Release] v1.74.15-stable" +title: "v1.74.15-stable" slug: "v1-74-15" date: 2025-08-02T10:00:00 authors: @@ -28,14 +28,14 @@ import TabItem from '@theme/TabItem'; docker run \ -e STORE_MODEL_IN_DB=True \ -p 4000:4000 \ -ghcr.io/berriai/litellm:1.74.15.rc.1 +ghcr.io/berriai/litellm:v1.74.15-stable ``` ``` showLineNumbers title="pip install litellm" -pip install litellm==1.74.15.post1 +pip install litellm==1.74.15.post2 ``` diff --git a/docs/my-website/release_notes/v1.75.5-stable/index.md b/docs/my-website/release_notes/v1.75.5-stable/index.md new file mode 100644 index 00000000000..270be64190e --- /dev/null +++ b/docs/my-website/release_notes/v1.75.5-stable/index.md @@ -0,0 +1,299 @@ +--- +title: "v1.75.5-stable - Redis latency improvements" +slug: "v1-75-5" +date: 2025-08-10T10:00:00 +authors: + - name: Krrish Dholakia + title: CEO, LiteLLM + url: https://www.linkedin.com/in/krish-d/ + image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg + - name: Ishaan Jaffer + title: CTO, LiteLLM + url: https://www.linkedin.com/in/reffajnaahsi/ + image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg + +hide_table_of_contents: false +--- + +import Image from '@theme/IdealImage'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +## Deploy this version + + + + +``` showLineNumbers title="docker run litellm" +docker run \ +-e STORE_MODEL_IN_DB=True \ +-p 4000:4000 \ +ghcr.io/berriai/litellm:v1.75.5-stable +``` + + + + +``` showLineNumbers title="pip install litellm" +pip install litellm==1.75.5.post2 +``` + + + + +--- + +## Key Highlights + +- **Redis - Latency Improvements** - Reduces P99 latency by 50% with Redis enabled. +- **Responses API Session Management** - Support for managing responses API sessions with images. +- **Oracle Cloud Infrastructure** - New LLM provider for calling models on Oracle Cloud Infrastructure. +- **Digital Ocean's Gradient AI** - New LLM provider for calling models on Digital Ocean's Gradient AI platform. + + +### Risk of Upgrade + +If you build the proxy from the pip package, you should hold off on upgrading. This version makes `prisma migrate deploy` our default for managing the DB. This is safer, as it doesn't reset the DB, but it requires a manual `prisma generate` step. + +Users of our Docker image, are **not** affected by this change. + +--- + +## Redis Latency Improvements + + + +
+ +This release adds in-memory caching for Redis requests, enabling faster response times in high-traffic. Now, LiteLLM instances will check their in-memory cache for a cache hit, before checking Redis. This reduces caching-related latency from 100ms for LLM API calls to sub-1ms, on cache hits. + +--- + +## Responses API Session Management w/ Images + + + +
+ +LiteLLM now supports session management for Responses API requests with images. This is great for use-cases like chatbots, that are using the Responses API to track the state of a conversation. LiteLLM session management works across **ALL** LLM API's (including Anthropic, Bedrock, OpenAI, etc). LiteLLM session management works by storing the request and response content in an s3 bucket, you can specify. + +--- + + +## New Models / Updated Models + +#### New Model Support + +| Provider | Model | Context Window | Input ($/1M tokens) | Output ($/1M tokens) | +| ----------- | -------------------------------------- | -------------- | ------------------- | -------------------- | +| Bedrock | `bedrock/us.anthropic.claude-opus-4-1-20250805-v1:0` | 200k | $15 | $75 | +| Bedrock | `bedrock/openai.gpt-oss-20b-1:0` | 200k | 0.07 | 0.3 | +| Bedrock | `bedrock/openai.gpt-oss-120b-1:0` | 200k | 0.15 | 0.6 | +| Fireworks AI | `fireworks_ai/accounts/fireworks/models/glm-4p5` | 128k | 0.55 | 2.19 | +| Fireworks AI | `fireworks_ai/accounts/fireworks/models/glm-4p5-air` | 128k | 0.22 | 0.88 | +| Fireworks AI | `fireworks_ai/accounts/fireworks/models/gpt-oss-120b` | 131072 | 0.15 | 0.6 | +| Fireworks AI | `fireworks_ai/accounts/fireworks/models/gpt-oss-20b` | 131072 | 0.05 | 0.2 | +| Groq | `groq/openai/gpt-oss-20b` | 131072 | 0.1 | 0.5 | +| Groq | `groq/openai/gpt-oss-120b` | 131072 | 0.15 | 0.75 | +| OpenAI | `openai/gpt-5` | 400k | 1.25 | 10 | +| OpenAI | `openai/gpt-5-2025-08-07` | 400k | 1.25 | 10 | +| OpenAI | `openai/gpt-5-mini` | 400k | 0.25 | 2 | +| OpenAI | `openai/gpt-5-mini-2025-08-07` | 400k | 0.25 | 2 | +| OpenAI | `openai/gpt-5-nano` | 400k | 0.05 | 0.4 | +| OpenAI | `openai/gpt-5-nano-2025-08-07` | 400k | 0.05 | 0.4 | +| OpenAI | `openai/gpt-5-chat` | 400k | 1.25 | 10 | +| OpenAI | `openai/gpt-5-chat-latest` | 400k | 1.25 | 10 | +| Azure | `azure/gpt-5` | 400k | 1.25 | 10 | +| Azure | `azure/gpt-5-2025-08-07` | 400k | 1.25 | 10 | +| Azure | `azure/gpt-5-mini` | 400k | 0.25 | 2 | +| Azure | `azure/gpt-5-mini-2025-08-07` | 400k | 0.25 | 2 | +| Azure | `azure/gpt-5-nano-2025-08-07` | 400k | 0.05 | 0.4 | +| Azure | `azure/gpt-5-nano` | 400k | 0.05 | 0.4 | +| Azure | `azure/gpt-5-chat` | 400k | 1.25 | 10 | +| Azure | `azure/gpt-5-chat-latest` | 400k | 1.25 | 10 | + +#### Features + +- **[OCI](../../docs/providers/oci)** + - New LLM provider - [PR #13206](https://github.com/BerriAI/litellm/pull/13206) +- **[JinaAI](../../docs/providers/jina_ai)** + - support multimodal embedding models - [PR #13181](https://github.com/BerriAI/litellm/pull/13181) +- **GPT-5 ([OpenAI](../../docs/providers/openai)/[Azure](../../docs/providers/azure))** + - Support drop_params for temperature - [PR #13390](https://github.com/BerriAI/litellm/pull/13390) + - Map max_tokens to max_completion_tokens - [PR #13390](https://github.com/BerriAI/litellm/pull/13390) +- **[Anthropic](../../docs/providers/anthropic)** + - Add claude-opus-4-1 on model cost map - [PR #13384](https://github.com/BerriAI/litellm/pull/13384) +- **[OpenRouter](../../docs/providers/openrouter)** + - Add gpt-oss to model cost map - [PR #13442](https://github.com/BerriAI/litellm/pull/13442) +- **[Cerebras](../../docs/providers/cerebras)** + - Add gpt-oss to model cost map - [PR #13442](https://github.com/BerriAI/litellm/pull/13442) +- **[Azure](../../docs/providers/azure)** + - Support drop params for ‘temperature’ on o-series models - [PR #13353](https://github.com/BerriAI/litellm/pull/13353) +- **[GradientAI](../../docs/providers/gradient_ai)** + - New LLM Provider - [PR #12169](https://github.com/BerriAI/litellm/pull/12169) + +#### Bugs + +- **[OpenAI](../../docs/providers/openai)** + - Add ‘service_tier’ and ‘safety_identifier’ as supported responses api params - [PR #13258](https://github.com/BerriAI/litellm/pull/13258) + - Correct pricing for web search on 4o-mini - [PR #13269](https://github.com/BerriAI/litellm/pull/13269) +- **[Mistral](../../docs/providers/mistral)** + - Handle $id and $schema fields when calling mistral - [PR #13389](https://github.com/BerriAI/litellm/pull/13389) +--- + +## LLM API Endpoints + +#### Features + +- `/responses` + - Responses API Session Handling w/ support for images - [PR #13347](https://github.com/BerriAI/litellm/pull/13347) + - failed if input containing ResponseReasoningItem - [PR #13465](https://github.com/BerriAI/litellm/pull/13465) + - Support custom tools - [PR #13418](https://github.com/BerriAI/litellm/pull/13418) + +#### Bugs + +- `/chat/completions` + - Fix completion_token_details usage object missing ‘text’ tokens - [PR #13234](https://github.com/BerriAI/litellm/pull/13234) + - (SDK) handle tool being a pydantic object - [PR #13274](https://github.com/BerriAI/litellm/pull/13274) + - include cost in streaming usage object - [PR #13418](https://github.com/BerriAI/litellm/pull/13418) + - Exclude none fields on /chat/completion - allows usage with n8n - [PR #13320](https://github.com/BerriAI/litellm/pull/13320) +- `/responses` + - Transform function call in response for non-openai models (gemini/anthropic) - [PR #13260](https://github.com/BerriAI/litellm/pull/13260) + - Fix unsupported operand error with model groups - [PR #13293](https://github.com/BerriAI/litellm/pull/13293) + - Responses api session management for streaming responses - [PR #13396](https://github.com/BerriAI/litellm/pull/13396) +- `/v1/messages` + - Added litellm claude code count tokens - [PR #13261](https://github.com/BerriAI/litellm/pull/13261) +- `/vector_stores` + - Fix create/search vector store errors - [PR #13285](https://github.com/BerriAI/litellm/pull/13285) +--- + +## [MCP Gateway](../../docs/mcp) + +#### Features + +- Add route check for internal users - [PR #13350](https://github.com/BerriAI/litellm/pull/13350) +- MCP Guardrails - docs - [PR #13392](https://github.com/BerriAI/litellm/pull/13392) + + +#### Bugs + +- Fix auth on UI for bearer token servers - [PR #13312](https://github.com/BerriAI/litellm/pull/13312) +- allow access group on mcp tool retrieval - [PR #13425](https://github.com/BerriAI/litellm/pull/13425) + + +--- + +## Management Endpoints / UI + +#### Features + +- **Teams** + - Add team deletion check for teams with keys - [PR #12953](https://github.com/BerriAI/litellm/pull/12953) +- **Models** + - Add ability to set model alias per key/team - [PR #13276](https://github.com/BerriAI/litellm/pull/13276) + - New button to reload model pricing from model cost map - [PR #13464](https://github.com/BerriAI/litellm/pull/13464), [PR #13470](https://github.com/BerriAI/litellm/pull/13470) +- **Keys** + - Make ‘team’ field required when creating service account keys - [PR #13302](https://github.com/BerriAI/litellm/pull/13302) + - Gray out key-based logging settings for non-enterprise users - prevents confusion on if ‘logging’ all up is supported - [PR #13431](https://github.com/BerriAI/litellm/pull/13431) +- **Navbar** + - Add logo customization for LiteLLM admin UI - [PR #12958](https://github.com/BerriAI/litellm/pull/12958) +- **Logs** + - Add token breakdowns on logs + session page - [PR #13357](https://github.com/BerriAI/litellm/pull/13357) +- **Usage** + - Ensure Usage Page loads after the DB has large entries - [PR #13400](https://github.com/BerriAI/litellm/pull/13400) +- **Test Key Page** + - allow uploading images for /chat/completions and /responses - [PR #13445](https://github.com/BerriAI/litellm/pull/13445) +- **MCP** + - Add auth tokens to local storage auth - [PR #13473](https://github.com/BerriAI/litellm/pull/13473) + +#### Bugs + +- **Custom Root Path** + - Fix login route when SSO is enabled - [PR #13267](https://github.com/BerriAI/litellm/pull/13267) +- **Customers/End-users** + - Allow calling /v1/models when end user over budget - allows model listing to work on OpenWebUI when customer over budget - [PR #13320](https://github.com/BerriAI/litellm/pull/13320) +- **Teams** + - Remove user - team membership, when user removed from team - [PR #13433](https://github.com/BerriAI/litellm/pull/13433) +- **Errors** + - Bubble up network errors to user for Logging and Alerts page - [PR #13427](https://github.com/BerriAI/litellm/pull/13427) +- **Model Hub** + - Show pricing for azure models, when base model is set - [PR #13418](https://github.com/BerriAI/litellm/pull/13418) +--- + +## Logging / Guardrail Integrations + +#### Features + +- **Bedrock Guardrails** + - Redacted sensitive information in bedrock guardrails error message - [PR #13356](https://github.com/BerriAI/litellm/pull/13356) +- **Standard Logging Payload** + - Fix ‘can’t register atextexit’ bug - [PR #13436](https://github.com/BerriAI/litellm/pull/13436) + +#### Bugs + +- **Braintrust** + - Allow setting of braintrust callback base url - [PR #13368](https://github.com/BerriAI/litellm/pull/13368) +- **OTEL** + - Track pre_call hook latency - [PR #13362](https://github.com/BerriAI/litellm/pull/13362) + +--- + +## Performance / Loadbalancing / Reliability improvements + +#### Features + +- **Team-BYOK models** + - Add wildcard model support - [PR #13278](https://github.com/BerriAI/litellm/pull/13278) +- **Caching** + - GCP IAM auth support for caching - [PR #13275](https://github.com/BerriAI/litellm/pull/13275) +- **Latency** + - reduce p99 latency w/ redis enabled by 50% - only updates model usage if tpm/rpm limits set - [PR #13362](https://github.com/BerriAI/litellm/pull/13362) + +--- + +## General Proxy Improvements + +#### Features + +- **Models** + - Support /v1/models/\{model_id\} retrieval - [PR #13268](https://github.com/BerriAI/litellm/pull/13268) +- **Multi-instance** + - Ensure disable_llm_api_endpoints works - [PR #13278](https://github.com/BerriAI/litellm/pull/13278) +- **Logs** + - Add apscheduler log suppress - [PR #13299](https://github.com/BerriAI/litellm/pull/13299) +- **Helm** + - Add labels to migrations job template - [PR #13343](https://github.com/BerriAI/litellm/pull/13343) s/o [@unique-jakub](https://github.com/unique-jakub) + +#### Bugs + +- **Non-root image** + - Fix non-root image for migration - [PR #13379](https://github.com/BerriAI/litellm/pull/13379) +- **Get Routes** + - Load get routes when using fastapi-offline - [PR #13466](https://github.com/BerriAI/litellm/pull/13466) +- **Health checks** + - Generate unique trace IDs for Langfuse health checks - [PR #13468](https://github.com/BerriAI/litellm/pull/13468) +- **Swagger** + - Allow using Swagger for /chat/completions - [PR #13469](https://github.com/BerriAI/litellm/pull/13469) +- **Auth** + - Fix JWTs access not working with model access groups - [PR #13474](https://github.com/BerriAI/litellm/pull/13474) + +--- + +## New Contributors + +* @bbartels made their first contribution in https://github.com/BerriAI/litellm/pull/13244 +* @breno-aumo made their first contribution in https://github.com/BerriAI/litellm/pull/13206 +* @pascalwhoop made their first contribution in https://github.com/BerriAI/litellm/pull/13122 +* @ZPerling made their first contribution in https://github.com/BerriAI/litellm/pull/13045 +* @zjx20 made their first contribution in https://github.com/BerriAI/litellm/pull/13181 +* @edwarddamato made their first contribution in https://github.com/BerriAI/litellm/pull/13368 +* @msannan2 made their first contribution in https://github.com/BerriAI/litellm/pull/12169 + + +## **[Full Changelog](https://github.com/BerriAI/litellm/compare/v1.74.15-stable...v1.75.5-stable.rc-draft)** \ No newline at end of file diff --git a/docs/my-website/release_notes/v1.75.8/index.md b/docs/my-website/release_notes/v1.75.8/index.md new file mode 100644 index 00000000000..2e90f26c3ad --- /dev/null +++ b/docs/my-website/release_notes/v1.75.8/index.md @@ -0,0 +1,231 @@ +--- +title: "[PRE-RELEASE]v1.75.8" +slug: "v1-75-8" +date: 2025-08-16T10:00:00 +authors: + - name: Krrish Dholakia + title: CEO, LiteLLM + url: https://www.linkedin.com/in/krish-d/ + image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg + - name: Ishaan Jaffer + title: CTO, LiteLLM + url: https://www.linkedin.com/in/reffajnaahsi/ + image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg + +hide_table_of_contents: false +--- + +import Image from '@theme/IdealImage'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +## Deploy this version + + + + +``` showLineNumbers title="docker run litellm" +docker run \ +-e STORE_MODEL_IN_DB=True \ +-p 4000:4000 \ +ghcr.io/berriai/litellm:v1.75.8 +``` + + + + +``` showLineNumbers title="pip install litellm" +pip install litellm==1.75.8 +``` + + + + +--- + +## Key Highlights + +- **Team Member Rate Limits** - Individual rate limiting for team members with JWT authentication support. +- **Performance Improvements** - New experimental HTTP handler flag for 100+ RPS improvement on OpenAI calls. +- **GPT-5 Model Family Support** - Full support for OpenAI's GPT-5 models with `reasoning_effort` parameter and Azure OpenAI integration. +- **Azure AI Flux Image Generation** - Support for Azure AI's Flux image generation models. + +--- + +## New Models / Updated Models + +#### New Model Support + +| Provider | Model | Context Window | Input ($/1M tokens) | Output ($/1M tokens) | Features | +| ----------- | -------------------------------------- | -------------- | ------------------- | -------------------- | -------- | +| Azure AI | `azure_ai/FLUX-1.1-pro` | - | - | $40/image | Image generation | +| Azure AI | `azure_ai/FLUX.1-Kontext-pro` | - | - | $40/image | Image generation | +| Vertex AI | `vertex_ai/deepseek-ai/deepseek-r1-0528-maas` | 65k | $1.35 | $5.4 | Chat completions + reasoning | +| OpenRouter | `openrouter/deepseek/deepseek-chat-v3-0324` | 65k | $0.14 | $0.28 | Chat completions | + + +#### Features + +- **[OpenAI](../../docs/providers/openai)** + - Added `reasoning_effort` parameter support for GPT-5 model family - [PR #13475](https://github.com/BerriAI/litellm/pull/13475), [Get Started](../../docs/providers/openai#openai-chat-completion-models) + - Support for `reasoning` parameter in Responses API - [PR #13475](https://github.com/BerriAI/litellm/pull/13475), [Get Started](../../docs/response_api) +- **[Azure OpenAI](../../docs/providers/azure/azure)** + - GPT-5 support with max_tokens and `reasoning` parameter - [PR #13510](https://github.com/BerriAI/litellm/pull/13510), [Get Started](../../docs/providers/azure/azure#gpt-5-models) +- **[AWS Bedrock](../../docs/providers/bedrock)** + - Streaming support for bedrock gpt-oss model family - [PR #13346](https://github.com/BerriAI/litellm/pull/13346), [Get Started](../../docs/providers/bedrock#openai-gpt-oss) + - `/messages` endpoint compatibility with `bedrock/converse/` - [PR #13627](https://github.com/BerriAI/litellm/pull/13627) + - Cache point support for assistant and tool messages - [PR #13640](https://github.com/BerriAI/litellm/pull/13640) +- **[Azure AI](../../docs/providers/azure)** + - New Azure AI Flux Image Generation provider - [PR #13592](https://github.com/BerriAI/litellm/pull/13592), [Get Started](../../docs/providers/azure_ai_img) + - Fixed Content-Type header for image generation - [PR #13584](https://github.com/BerriAI/litellm/pull/13584) +- **[CometAPI](../../docs/providers/comet)** + - New provider support with chat completions and streaming - [PR #13458](https://github.com/BerriAI/litellm/pull/13458) +- **[SambaNova](../../docs/providers/sambanova)** + - Added embedding model support - [PR #13308](https://github.com/BerriAI/litellm/pull/13308), [Get Started](../../docs/providers/sambanova#sambanova---embeddings) +- **[Vertex AI](../../docs/providers/vertex)** + - Added `/countTokens` endpoint support for Gemini CLI integration - [PR #13545](https://github.com/BerriAI/litellm/pull/13545) + - Token counter support for VertexAI models - [PR #13558](https://github.com/BerriAI/litellm/pull/13558) +- **[hosted_vllm](../../docs/providers/vllm)** + - Added `reasoning_effort` parameter support - [PR #13620](https://github.com/BerriAI/litellm/pull/13620), [Get Started](../../docs/providers/vllm#reasoning-effort) + +#### Bugs + +- **[OCI](../../docs/providers/oci)** + - Fixed streaming issues - [PR #13437](https://github.com/BerriAI/litellm/pull/13437) +- **[Ollama](../../docs/providers/ollama)** + - Fixed GPT-OSS streaming with 'thinking' field - [PR #13375](https://github.com/BerriAI/litellm/pull/13375) +- **[VolcEngine](../../docs/providers/volcengine)** + - Fixed thinking disabled parameter handling - [PR #13598](https://github.com/BerriAI/litellm/pull/13598) +- **[Streaming](../../docs/completion/stream)** + - Consistent 'finish_reason' chunk indexing - [PR #13560](https://github.com/BerriAI/litellm/pull/13560) +--- + +## LLM API Endpoints + +#### Features + +- **[/messages](../../docs/anthropic/messages)** + - Tool use arguments properly returned for non-anthropic models - [PR #13638](https://github.com/BerriAI/litellm/pull/13638) + +#### Bugs + +- **[Real-time API](../../docs/realtime)** + - Fixed endpoint for no intent scenarios - [PR #13476](https://github.com/BerriAI/litellm/pull/13476) +- **[Responses API](../../docs/response_api)** + - Fixed `stream=True` + `background=True` with Responses API - [PR #13654](https://github.com/BerriAI/litellm/pull/13654) + +--- + +## [MCP Gateway](../../docs/mcp) + +#### Features + +- **Access Control & Configuration** + - Enhanced MCPServerManager with access groups and description support - [PR #13549](https://github.com/BerriAI/litellm/pull/13549) + +#### Bugs + +- **Authentication** + - Fixed MCP gateway key authentication - [PR #13630](https://github.com/BerriAI/litellm/pull/13630) + +[Read More](../../docs/mcp) + +--- + +## Management Endpoints / UI + +#### Features + +- **Team Management** + - Team Member Rate Limits implementation - [PR #13601](https://github.com/BerriAI/litellm/pull/13601) + - JWT authentication support for team member rate limits - [PR #13601](https://github.com/BerriAI/litellm/pull/13601) + - Show team member TPM/RPM limits in UI - [PR #13662](https://github.com/BerriAI/litellm/pull/13662) + - Allow editing team member RPM/TPM limits - [PR #13669](https://github.com/BerriAI/litellm/pull/13669) + - Allow unsetting TPM and RPM in Teams Settings - [PR #13430](https://github.com/BerriAI/litellm/pull/13430) + - Team Member Permissions Page access column changes - [PR #13145](https://github.com/BerriAI/litellm/pull/13145) +- **Key Management** + - Display errors from backend on the UI Keys page - [PR #13435](https://github.com/BerriAI/litellm/pull/13435) + - Added confirmation modal before deleting keys - [PR #13655](https://github.com/BerriAI/litellm/pull/13655) + - Support for `user` parameter in LiteLLM SDK to Proxy communication - [PR #13555](https://github.com/BerriAI/litellm/pull/13555) +- **UI Improvements** + - Fixed internal users table overflow - [PR #12736](https://github.com/BerriAI/litellm/pull/12736) + - Enhanced chart readability with short-form notation for large numbers - [PR #12370](https://github.com/BerriAI/litellm/pull/12370) + - Fixed image overflow in LiteLLM model display - [PR #13639](https://github.com/BerriAI/litellm/pull/13639) + - Removed ambiguous network response errors - [PR #13582](https://github.com/BerriAI/litellm/pull/13582) +- **Credentials** + - Added CredentialDeleteModal component and integration with CredentialsPanel - [PR #13550](https://github.com/BerriAI/litellm/pull/13550) +- **Admin & Permissions** + - Allow routes for admin viewer - [PR #13588](https://github.com/BerriAI/litellm/pull/13588) + +#### Bugs + +- **SCIM Integration** + - Fixed SCIM Team Memberships metadata handling - [PR #13553](https://github.com/BerriAI/litellm/pull/13553) +- **Authentication** + - Fixed incorrect key info endpoint - [PR #13633](https://github.com/BerriAI/litellm/pull/13633) + +--- + +## Logging / Guardrail Integrations + +#### Features + +- **[Langfuse OTEL](../../docs/proxy/logging#langfuse)** + - Added key/team logging for Langfuse OTEL Logger - [PR #13512](https://github.com/BerriAI/litellm/pull/13512) + - Fixed LangfuseOtelSpanAttributes constants to match expected values - [PR #13659](https://github.com/BerriAI/litellm/pull/13659) +- **[MLflow](../../docs/proxy/logging#mlflow)** + - Updated MLflow logger usage span attributes - [PR #13561](https://github.com/BerriAI/litellm/pull/13561) + +#### Bugs + +- **Security** + - Hide sensitive data in `/model/info` - azure entra client_secret - [PR #13577](https://github.com/BerriAI/litellm/pull/13577) + - Fixed trivy/secrets false positives - [PR #13631](https://github.com/BerriAI/litellm/pull/13631) + +--- + +## Performance / Loadbalancing / Reliability improvements + +#### Features + +- **HTTP Performance** + - New 'EXPERIMENTAL_OPENAI_BASE_LLM_HTTP_HANDLER' flag for +100 RPS improvement on OpenAI calls - [PR #13625](https://github.com/BerriAI/litellm/pull/13625) +- **Database Monitoring** + - Added DB metrics to Prometheus - [PR #13626](https://github.com/BerriAI/litellm/pull/13626) +- **Error Handling** + - Added safe divide by 0 protection to prevent crashes - [PR #13624](https://github.com/BerriAI/litellm/pull/13624) + +#### Bugs + +- **Dependencies** + - Updated boto3 to 1.36.0 and aioboto3 to 13.4.0 - [PR #13665](https://github.com/BerriAI/litellm/pull/13665) + +--- + +## General Proxy Improvements + +#### Features + +- **Database** + - Removed redundant `use_prisma_migrate` flag - now default - [PR #13555](https://github.com/BerriAI/litellm/pull/13555) +- **LLM Translation** + - Added model ID check - [PR #13507](https://github.com/BerriAI/litellm/pull/13507) + - Refactored Anthropic configurations and added support for `anthropic_beta` headers - [PR #13590](https://github.com/BerriAI/litellm/pull/13590) + + +--- + +## New Contributors +* @TensorNull made their first contribution in [PR #13458](https://github.com/BerriAI/litellm/pull/13458) +* @MajorD00m made their first contribution in [PR #13577](https://github.com/BerriAI/litellm/pull/13577) +* @VerunicaM made their first contribution in [PR #13584](https://github.com/BerriAI/litellm/pull/13584) +* @huangyafei made their first contribution in [PR #13607](https://github.com/BerriAI/litellm/pull/13607) +* @TomeHirata made their first contribution in [PR #13561](https://github.com/BerriAI/litellm/pull/13561) +* @willfinnigan made their first contribution in [PR #13659](https://github.com/BerriAI/litellm/pull/13659) +* @dcbark01 made their first contribution in [PR #13633](https://github.com/BerriAI/litellm/pull/13633) +* @javacruft made their first contribution in [PR #13631](https://github.com/BerriAI/litellm/pull/13631) + +--- + +## **[Full Changelog](https://github.com/BerriAI/litellm/compare/v1.75.5-stable.rc-draft...v1.75.8-nightly)** + diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index bffa8a91b6d..7827649d2d9 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -65,6 +65,7 @@ const sidebars = { label: "[Beta] Prompt Management", items: [ "proxy/prompt_management", + "proxy/native_litellm_prompt", "proxy/custom_prompt_management" ].sort() }, @@ -81,12 +82,12 @@ const sidebars = { "tutorials/cost_tracking_coding", ] }, - + ], // But you can create a sidebar manually tutorialSidebar: [ { type: "doc", id: "index" }, // NEW - + { type: "category", label: "LiteLLM Proxy Server", @@ -107,6 +108,7 @@ const sidebars = { type: "category", label: "Setup & Deployment", items: [ + "proxy/quick_start", "proxy/deploy", "proxy/prod", "proxy/cli", @@ -213,7 +215,7 @@ const sidebars = { "proxy/dynamic_logging" ], }, - + { type: "category", label: "Secret Managers", @@ -371,7 +373,14 @@ const sidebars = { "providers/azure/azure_embedding", ] }, - "providers/azure_ai", + { + type: "category", + label: "Azure AI", + items: [ + "providers/azure_ai", + "providers/azure_ai_img", + ] + }, { type: "category", label: "Vertex AI", @@ -466,6 +475,7 @@ const sidebars = { "providers/custom_llm_server", "providers/petals", "providers/snowflake", + "providers/gradient_ai", "providers/featherless_ai", "providers/nebius", "providers/dashscope", @@ -504,7 +514,7 @@ const sidebars = { ] }, - + { type: "category", label: "Routing, Loadbalancing & Fallbacks", @@ -535,7 +545,7 @@ const sidebars = { }, ], }, - + { type: "category", label: "Load Testing", diff --git a/enterprise/litellm_enterprise/proxy/management_endpoints/internal_user_endpoints.py b/enterprise/litellm_enterprise/proxy/management_endpoints/internal_user_endpoints.py index d17946171bb..e60b4d69905 100644 --- a/enterprise/litellm_enterprise/proxy/management_endpoints/internal_user_endpoints.py +++ b/enterprise/litellm_enterprise/proxy/management_endpoints/internal_user_endpoints.py @@ -2,6 +2,8 @@ Enterprise internal user management endpoints """ +import os + from fastapi import APIRouter, Depends, HTTPException from litellm.proxy._types import UserAPIKeyAuth @@ -21,7 +23,7 @@ async def available_enterprise_users( """ For keys with `max_users` set, return the list of users that are allowed to use the key. """ - from litellm.proxy._types import CommonProxyErrors + from litellm.proxy._types import CommonProxyErrors, EnterpriseLicenseData from litellm.proxy.proxy_server import ( premium_user, premium_user_data, @@ -34,10 +36,14 @@ async def available_enterprise_users( detail={"error": CommonProxyErrors.db_not_connected_error.value}, ) - if premium_user is None: - raise HTTPException( - status_code=500, detail={"error": CommonProxyErrors.not_premium_user.value} - ) + if not premium_user: + # check if SSO is enabled - show 5 user limit + from litellm.proxy.auth.auth_utils import _has_user_setup_sso + + if _has_user_setup_sso(): + premium_user_data = EnterpriseLicenseData( + max_users=5, + ) # Count number of rows in LiteLLM_UserTable user_count = await prisma_client.db.litellm_usertable.count() diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.17-py3-none-any.whl b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.17-py3-none-any.whl new file mode 100644 index 00000000000..71160d51a7e Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.17-py3-none-any.whl differ diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.17.tar.gz b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.17.tar.gz new file mode 100644 index 00000000000..7bab2b9c8b6 Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.17.tar.gz differ diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.18-py3-none-any.whl b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.18-py3-none-any.whl new file mode 100644 index 00000000000..fca66b532ff Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.18-py3-none-any.whl differ diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.18.tar.gz b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.18.tar.gz new file mode 100644 index 00000000000..ddd00e8439e Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.18.tar.gz differ diff --git a/litellm-proxy-extras/litellm_proxy_extras/utils.py b/litellm-proxy-extras/litellm_proxy_extras/utils.py index 21c9131887b..ece2b496bf6 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/utils.py +++ b/litellm-proxy-extras/litellm_proxy_extras/utils.py @@ -243,7 +243,6 @@ class ProxyExtrasDBManager: bool: True if setup was successful, False otherwise """ schema_path = ProxyExtrasDBManager._get_prisma_dir() + "/schema.prisma" - use_migrate = str_to_bool(os.getenv("USE_PRISMA_MIGRATE")) or use_migrate for attempt in range(4): original_dir = os.getcwd() migrations_dir = ProxyExtrasDBManager._get_prisma_dir() @@ -299,7 +298,7 @@ class ProxyExtrasDBManager: and "database schema is not empty" in e.stderr ): logger.info( - "Database schema is not empty, creating baseline migration" + "Database schema is not empty, creating baseline migration. In read-only file system, please set an environment variable `LITELLM_MIGRATION_DIR` to a writable directory to enable migrations. Learn more - https://docs.litellm.ai/docs/proxy/prod#read-only-file-system" ) ProxyExtrasDBManager._create_baseline_migration(schema_path) logger.info( diff --git a/litellm-proxy-extras/pyproject.toml b/litellm-proxy-extras/pyproject.toml index 86307e08238..0cb9c35fa62 100644 --- a/litellm-proxy-extras/pyproject.toml +++ b/litellm-proxy-extras/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "litellm-proxy-extras" -version = "0.2.16" +version = "0.2.18" description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package." authors = ["BerriAI"] readme = "README.md" @@ -22,7 +22,7 @@ requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" [tool.commitizen] -version = "0.2.16" +version = "0.2.18" version_files = [ "pyproject.toml:version", "../requirements.txt:litellm-proxy-extras==", diff --git a/litellm/__init__.py b/litellm/__init__.py index f7e1fb8f24d..c405d3cdeb2 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -146,6 +146,7 @@ _custom_logger_compatible_callbacks_literal = Literal[ "vector_store_pre_call_hook", "dotprompt", ] +configured_cold_storage_logger: Optional[_custom_logger_compatible_callbacks_literal] = None logged_real_time_event_types: Optional[Union[List[str], Literal["*"]]] = None _known_custom_logger_compatible_callbacks: List = list( get_args(_custom_logger_compatible_callbacks_literal) @@ -231,7 +232,9 @@ aleph_alpha_key: Optional[str] = None nlp_cloud_key: Optional[str] = None novita_api_key: Optional[str] = None snowflake_key: Optional[str] = None +gradient_ai_api_key: Optional[str] = None nebius_key: Optional[str] = None +cometapi_key: Optional[str] = None common_cloud_provider_auth_params: dict = { "params": ["project", "region_name", "token"], "providers": ["vertex_ai", "bedrock", "watsonx", "azure", "vertex_ai_beta"], @@ -301,7 +304,6 @@ default_in_memory_ttl: Optional[float] = None default_redis_ttl: Optional[float] = None default_redis_batch_cache_expiry: Optional[float] = None model_alias_map: Dict[str, str] = {} -model_group_alias_map: Dict[str, str] = {} model_group_settings: Optional["ModelGroupSettings"] = None max_budget: float = 0.0 # set the max budget across all providers budget_duration: Optional[str] = ( @@ -465,75 +467,81 @@ BEDROCK_CONVERSE_MODELS = [ ] ####### COMPLETION MODELS ################### -open_ai_chat_completion_models: List = [] -open_ai_text_completion_models: List = [] -cohere_models: List = [] -cohere_chat_models: List = [] -mistral_chat_models: List = [] -text_completion_codestral_models: List = [] -anthropic_models: List = [] -openrouter_models: List = [] -datarobot_models: List = [] -vertex_language_models: List = [] -vertex_vision_models: List = [] -vertex_chat_models: List = [] -vertex_code_chat_models: List = [] -vertex_ai_image_models: List = [] -vertex_text_models: List = [] -vertex_code_text_models: List = [] -vertex_embedding_models: List = [] -vertex_anthropic_models: List = [] -vertex_llama3_models: List = [] -vertex_ai_ai21_models: List = [] -vertex_mistral_models: List = [] -ai21_models: List = [] -ai21_chat_models: List = [] -nlp_cloud_models: List = [] -aleph_alpha_models: List = [] -bedrock_models: List = [] -bedrock_converse_models: List = BEDROCK_CONVERSE_MODELS -fireworks_ai_models: List = [] -fireworks_ai_embedding_models: List = [] -deepinfra_models: List = [] -perplexity_models: List = [] -watsonx_models: List = [] -gemini_models: List = [] -xai_models: List = [] -deepseek_models: List = [] -azure_ai_models: List = [] -jina_ai_models: List = [] -voyage_models: List = [] -infinity_models: List = [] -databricks_models: List = [] -cloudflare_models: List = [] -codestral_models: List = [] -friendliai_models: List = [] -featherless_ai_models: List = [] -palm_models: List = [] -groq_models: List = [] -azure_models: List = [] -azure_text_models: List = [] -anyscale_models: List = [] -cerebras_models: List = [] -galadriel_models: List = [] -sambanova_models: List = [] -novita_models: List = [] -assemblyai_models: List = [] -snowflake_models: List = [] -llama_models: List = [] -nscale_models: List = [] -nebius_models: List = [] -nebius_embedding_models: List = [] -deepgram_models: List = [] -elevenlabs_models: List = [] -dashscope_models: List = [] -moonshot_models: List = [] -v0_models: List = [] -morph_models: List = [] -lambda_ai_models: List = [] -hyperbolic_models: List = [] -recraft_models: List = [] -oci_models: List = [] +from typing import Set +open_ai_chat_completion_models: Set = set() +open_ai_text_completion_models: Set = set() +cohere_models: Set = set() +cohere_chat_models: Set = set() +mistral_chat_models: Set = set() +text_completion_codestral_models: Set = set() +anthropic_models: Set = set() +openrouter_models: Set = set() +datarobot_models: Set = set() +vertex_language_models: Set = set() +vertex_vision_models: Set = set() +vertex_chat_models: Set = set() +vertex_code_chat_models: Set = set() +vertex_ai_image_models: Set = set() +vertex_text_models: Set = set() +vertex_code_text_models: Set = set() +vertex_embedding_models: Set = set() +vertex_anthropic_models: Set = set() +vertex_llama3_models: Set = set() +vertex_deepseek_models: Set = set() +vertex_ai_ai21_models: Set = set() +vertex_mistral_models: Set = set() +ai21_models: Set = set() +ai21_chat_models: Set = set() +nlp_cloud_models: Set = set() +aleph_alpha_models: Set = set() +bedrock_models: Set = set() +bedrock_converse_models: Set = set(BEDROCK_CONVERSE_MODELS) +fireworks_ai_models: Set = set() +fireworks_ai_embedding_models: Set = set() +deepinfra_models: Set = set() +perplexity_models: Set = set() +watsonx_models: Set = set() +gemini_models: Set = set() +xai_models: Set = set() +deepseek_models: Set = set() +azure_ai_models: Set = set() +jina_ai_models: Set = set() +voyage_models: Set = set() +infinity_models: Set = set() +databricks_models: Set = set() +cloudflare_models: Set = set() +codestral_models: Set = set() +friendliai_models: Set = set() +featherless_ai_models: Set = set() +palm_models: Set = set() +groq_models: Set = set() +azure_models: Set = set() +azure_text_models: Set = set() +anyscale_models: Set = set() +cerebras_models: Set = set() +galadriel_models: Set = set() +sambanova_models: Set = set() +sambanova_embedding_models: Set = set() +novita_models: Set = set() +assemblyai_models: Set = set() +snowflake_models: Set = set() +gradient_ai_models: Set = set() +llama_models: Set = set() +nscale_models: Set = set() +nebius_models: Set = set() +nebius_embedding_models: Set = set() +aiml_models: Set = set() +deepgram_models: Set = set() +elevenlabs_models: Set = set() +dashscope_models: Set = set() +moonshot_models: Set = set() +v0_models: Set = set() +morph_models: Set = set() +lambda_ai_models: Set = set() +hyperbolic_models: Set = set() +recraft_models: Set = set() +cometapi_models: Set = set() +oci_models: Set = set() def is_bedrock_pricing_only_model(key: str) -> bool: @@ -574,157 +582,168 @@ def add_known_models(): if value.get("litellm_provider") == "openai" and not is_openai_finetune_model( key ): - open_ai_chat_completion_models.append(key) + open_ai_chat_completion_models.add(key) elif value.get("litellm_provider") == "text-completion-openai": - open_ai_text_completion_models.append(key) + open_ai_text_completion_models.add(key) elif value.get("litellm_provider") == "azure_text": - azure_text_models.append(key) + azure_text_models.add(key) elif value.get("litellm_provider") == "cohere": - cohere_models.append(key) + cohere_models.add(key) elif value.get("litellm_provider") == "cohere_chat": - cohere_chat_models.append(key) + cohere_chat_models.add(key) elif value.get("litellm_provider") == "mistral": - mistral_chat_models.append(key) + mistral_chat_models.add(key) elif value.get("litellm_provider") == "anthropic": - anthropic_models.append(key) + anthropic_models.add(key) elif value.get("litellm_provider") == "empower": - empower_models.append(key) + empower_models.add(key) elif value.get("litellm_provider") == "openrouter": - openrouter_models.append(key) + openrouter_models.add(key) elif value.get("litellm_provider") == "datarobot": - datarobot_models.append(key) + datarobot_models.add(key) elif value.get("litellm_provider") == "vertex_ai-text-models": - vertex_text_models.append(key) + vertex_text_models.add(key) elif value.get("litellm_provider") == "vertex_ai-code-text-models": - vertex_code_text_models.append(key) + vertex_code_text_models.add(key) elif value.get("litellm_provider") == "vertex_ai-language-models": - vertex_language_models.append(key) + vertex_language_models.add(key) elif value.get("litellm_provider") == "vertex_ai-vision-models": - vertex_vision_models.append(key) + vertex_vision_models.add(key) elif value.get("litellm_provider") == "vertex_ai-chat-models": - vertex_chat_models.append(key) + vertex_chat_models.add(key) elif value.get("litellm_provider") == "vertex_ai-code-chat-models": - vertex_code_chat_models.append(key) + vertex_code_chat_models.add(key) elif value.get("litellm_provider") == "vertex_ai-embedding-models": - vertex_embedding_models.append(key) + vertex_embedding_models.add(key) elif value.get("litellm_provider") == "vertex_ai-anthropic_models": key = key.replace("vertex_ai/", "") - vertex_anthropic_models.append(key) + vertex_anthropic_models.add(key) elif value.get("litellm_provider") == "vertex_ai-llama_models": key = key.replace("vertex_ai/", "") - vertex_llama3_models.append(key) + vertex_llama3_models.add(key) + elif value.get("litellm_provider") == "vertex_ai-deepseek_models": + key = key.replace("vertex_ai/", "") + vertex_deepseek_models.add(key) elif value.get("litellm_provider") == "vertex_ai-mistral_models": key = key.replace("vertex_ai/", "") - vertex_mistral_models.append(key) + vertex_mistral_models.add(key) elif value.get("litellm_provider") == "vertex_ai-ai21_models": key = key.replace("vertex_ai/", "") - vertex_ai_ai21_models.append(key) + vertex_ai_ai21_models.add(key) elif value.get("litellm_provider") == "vertex_ai-image-models": key = key.replace("vertex_ai/", "") - vertex_ai_image_models.append(key) + vertex_ai_image_models.add(key) elif value.get("litellm_provider") == "ai21": if value.get("mode") == "chat": - ai21_chat_models.append(key) + ai21_chat_models.add(key) else: - ai21_models.append(key) + ai21_models.add(key) elif value.get("litellm_provider") == "nlp_cloud": - nlp_cloud_models.append(key) + nlp_cloud_models.add(key) elif value.get("litellm_provider") == "aleph_alpha": - aleph_alpha_models.append(key) + aleph_alpha_models.add(key) elif value.get( "litellm_provider" ) == "bedrock" and not is_bedrock_pricing_only_model(key): - bedrock_models.append(key) + bedrock_models.add(key) elif value.get("litellm_provider") == "bedrock_converse": - bedrock_converse_models.append(key) + bedrock_converse_models.add(key) elif value.get("litellm_provider") == "deepinfra": - deepinfra_models.append(key) + deepinfra_models.add(key) elif value.get("litellm_provider") == "perplexity": - perplexity_models.append(key) + perplexity_models.add(key) elif value.get("litellm_provider") == "watsonx": - watsonx_models.append(key) + watsonx_models.add(key) elif value.get("litellm_provider") == "gemini": - gemini_models.append(key) + gemini_models.add(key) elif value.get("litellm_provider") == "fireworks_ai": # ignore the 'up-to', '-to-' model names -> not real models. just for cost tracking based on model params. if "-to-" not in key and "fireworks-ai-default" not in key: - fireworks_ai_models.append(key) + fireworks_ai_models.add(key) elif value.get("litellm_provider") == "fireworks_ai-embedding-models": # ignore the 'up-to', '-to-' model names -> not real models. just for cost tracking based on model params. if "-to-" not in key: - fireworks_ai_embedding_models.append(key) + fireworks_ai_embedding_models.add(key) elif value.get("litellm_provider") == "text-completion-codestral": - text_completion_codestral_models.append(key) + text_completion_codestral_models.add(key) elif value.get("litellm_provider") == "xai": - xai_models.append(key) + xai_models.add(key) elif value.get("litellm_provider") == "deepseek": - deepseek_models.append(key) + deepseek_models.add(key) elif value.get("litellm_provider") == "meta_llama": - llama_models.append(key) + llama_models.add(key) elif value.get("litellm_provider") == "nscale": - nscale_models.append(key) + nscale_models.add(key) elif value.get("litellm_provider") == "azure_ai": - azure_ai_models.append(key) + azure_ai_models.add(key) elif value.get("litellm_provider") == "voyage": - voyage_models.append(key) + voyage_models.add(key) elif value.get("litellm_provider") == "infinity": - infinity_models.append(key) + infinity_models.add(key) elif value.get("litellm_provider") == "databricks": - databricks_models.append(key) + databricks_models.add(key) elif value.get("litellm_provider") == "cloudflare": - cloudflare_models.append(key) + cloudflare_models.add(key) elif value.get("litellm_provider") == "codestral": - codestral_models.append(key) + codestral_models.add(key) elif value.get("litellm_provider") == "friendliai": - friendliai_models.append(key) + friendliai_models.add(key) elif value.get("litellm_provider") == "palm": - palm_models.append(key) + palm_models.add(key) elif value.get("litellm_provider") == "groq": - groq_models.append(key) + groq_models.add(key) elif value.get("litellm_provider") == "azure": - azure_models.append(key) + azure_models.add(key) elif value.get("litellm_provider") == "anyscale": - anyscale_models.append(key) + anyscale_models.add(key) elif value.get("litellm_provider") == "cerebras": - cerebras_models.append(key) + cerebras_models.add(key) elif value.get("litellm_provider") == "galadriel": - galadriel_models.append(key) + galadriel_models.add(key) elif value.get("litellm_provider") == "sambanova": - sambanova_models.append(key) + sambanova_models.add(key) + elif value.get("litellm_provider") == "sambanova-embedding-models": + sambanova_embedding_models.add(key) elif value.get("litellm_provider") == "novita": - novita_models.append(key) + novita_models.add(key) elif value.get("litellm_provider") == "nebius-chat-models": - nebius_models.append(key) + nebius_models.add(key) elif value.get("litellm_provider") == "nebius-embedding-models": - nebius_embedding_models.append(key) + nebius_embedding_models.add(key) + elif value.get("litellm_provider") == "aiml": + aiml_models.add(key) elif value.get("litellm_provider") == "assemblyai": - assemblyai_models.append(key) + assemblyai_models.add(key) elif value.get("litellm_provider") == "jina_ai": - jina_ai_models.append(key) + jina_ai_models.add(key) elif value.get("litellm_provider") == "snowflake": - snowflake_models.append(key) + snowflake_models.add(key) + elif value.get("litellm_provider") == "gradient_ai": + gradient_ai_models.add(key) elif value.get("litellm_provider") == "featherless_ai": - featherless_ai_models.append(key) + featherless_ai_models.add(key) elif value.get("litellm_provider") == "deepgram": - deepgram_models.append(key) + deepgram_models.add(key) elif value.get("litellm_provider") == "elevenlabs": - elevenlabs_models.append(key) + elevenlabs_models.add(key) elif value.get("litellm_provider") == "dashscope": - dashscope_models.append(key) + dashscope_models.add(key) elif value.get("litellm_provider") == "moonshot": - moonshot_models.append(key) + moonshot_models.add(key) elif value.get("litellm_provider") == "v0": - v0_models.append(key) + v0_models.add(key) elif value.get("litellm_provider") == "morph": - morph_models.append(key) + morph_models.add(key) elif value.get("litellm_provider") == "lambda_ai": - lambda_ai_models.append(key) + lambda_ai_models.add(key) elif value.get("litellm_provider") == "hyperbolic": - hyperbolic_models.append(key) + hyperbolic_models.add(key) elif value.get("litellm_provider") == "recraft": - recraft_models.append(key) + recraft_models.add(key) + elif value.get("litellm_provider") == "cometapi": + cometapi_models.add(key) elif value.get("litellm_provider") == "oci": - oci_models.append(key) + oci_models.add(key) add_known_models() @@ -754,66 +773,68 @@ ollama_models = ["llama2"] maritalk_models = ["maritalk"] -model_list = ( +model_list = list( open_ai_chat_completion_models - + open_ai_text_completion_models - + cohere_models - + cohere_chat_models - + anthropic_models - + replicate_models - + openrouter_models - + datarobot_models - + huggingface_models - + vertex_chat_models - + vertex_text_models - + ai21_models - + ai21_chat_models - + together_ai_models - + baseten_models - + aleph_alpha_models - + nlp_cloud_models - + ollama_models - + bedrock_models - + deepinfra_models - + perplexity_models - + maritalk_models - + vertex_language_models - + watsonx_models - + gemini_models - + text_completion_codestral_models - + xai_models - + deepseek_models - + azure_ai_models - + voyage_models - + infinity_models - + databricks_models - + cloudflare_models - + codestral_models - + friendliai_models - + palm_models - + groq_models - + azure_models - + anyscale_models - + cerebras_models - + galadriel_models - + sambanova_models - + azure_text_models - + novita_models - + assemblyai_models - + jina_ai_models - + snowflake_models - + llama_models - + featherless_ai_models - + nscale_models - + deepgram_models - + elevenlabs_models - + dashscope_models - + moonshot_models - + v0_models - + morph_models - + lambda_ai_models - + recraft_models - + oci_models + | open_ai_text_completion_models + | cohere_models + | cohere_chat_models + | anthropic_models + | set(replicate_models) + | openrouter_models + | datarobot_models + | set(huggingface_models) + | vertex_chat_models + | vertex_text_models + | ai21_models + | ai21_chat_models + | set(together_ai_models) + | set(baseten_models) + | aleph_alpha_models + | nlp_cloud_models + | set(ollama_models) + | bedrock_models + | deepinfra_models + | perplexity_models + | set(maritalk_models) + | vertex_language_models + | watsonx_models + | gemini_models + | text_completion_codestral_models + | xai_models + | deepseek_models + | azure_ai_models + | voyage_models + | infinity_models + | databricks_models + | cloudflare_models + | codestral_models + | friendliai_models + | palm_models + | groq_models + | azure_models + | anyscale_models + | cerebras_models + | galadriel_models + | sambanova_models + | azure_text_models + | novita_models + | assemblyai_models + | jina_ai_models + | snowflake_models + | gradient_ai_models + | llama_models + | featherless_ai_models + | nscale_models + | deepgram_models + | elevenlabs_models + | dashscope_models + | moonshot_models + | v0_models + | morph_models + | lambda_ai_models + | recraft_models + | cometapi_models + | oci_models ) model_list_set = set(model_list) @@ -822,9 +843,9 @@ provider_list: List[Union[LlmProviders, str]] = list(LlmProviders) models_by_provider: dict = { - "openai": open_ai_chat_completion_models + open_ai_text_completion_models, + "openai": open_ai_chat_completion_models | open_ai_text_completion_models, "text-completion-openai": open_ai_text_completion_models, - "cohere": cohere_models + cohere_chat_models, + "cohere": cohere_models | cohere_chat_models, "cohere_chat": cohere_chat_models, "anthropic": anthropic_models, "replicate": replicate_models, @@ -833,13 +854,9 @@ models_by_provider: dict = { "baseten": baseten_models, "openrouter": openrouter_models, "datarobot": datarobot_models, - "vertex_ai": vertex_chat_models - + vertex_text_models - + vertex_anthropic_models - + vertex_vision_models - + vertex_language_models, + "vertex_ai": vertex_chat_models | vertex_text_models | vertex_anthropic_models | vertex_vision_models | vertex_language_models | vertex_deepseek_models, "ai21": ai21_models, - "bedrock": bedrock_models + bedrock_converse_models, + "bedrock": bedrock_models | bedrock_converse_models, "petals": petals_models, "ollama": ollama_models, "ollama_chat": ollama_models, @@ -848,7 +865,7 @@ models_by_provider: dict = { "maritalk": maritalk_models, "watsonx": watsonx_models, "gemini": gemini_models, - "fireworks_ai": fireworks_ai_models + fireworks_ai_embedding_models, + "fireworks_ai": fireworks_ai_models | fireworks_ai_embedding_models, "aleph_alpha": aleph_alpha_models, "text-completion-codestral": text_completion_codestral_models, "xai": xai_models, @@ -864,17 +881,19 @@ models_by_provider: dict = { "friendliai": friendliai_models, "palm": palm_models, "groq": groq_models, - "azure": azure_models + azure_text_models, + "azure": azure_models | azure_text_models, "azure_text": azure_text_models, "anyscale": anyscale_models, "cerebras": cerebras_models, "galadriel": galadriel_models, - "sambanova": sambanova_models, + "sambanova": sambanova_models | sambanova_embedding_models, "novita": novita_models, - "nebius": nebius_models + nebius_embedding_models, + "nebius": nebius_models | nebius_embedding_models, + "aiml": aiml_models, "assemblyai": assemblyai_models, "jina_ai": jina_ai_models, "snowflake": snowflake_models, + "gradient_ai": gradient_ai_models, "meta_llama": llama_models, "nscale": nscale_models, "featherless_ai": featherless_ai_models, @@ -887,6 +906,7 @@ models_by_provider: dict = { "lambda_ai": lambda_ai_models, "hyperbolic": hyperbolic_models, "recraft": recraft_models, + "cometapi": cometapi_models, "oci": oci_models, } @@ -916,11 +936,12 @@ longer_context_model_fallback_dict: dict = { all_embedding_models = ( open_ai_embedding_models - + cohere_embedding_models - + bedrock_embedding_models - + vertex_embedding_models - + fireworks_ai_embedding_models - + nebius_embedding_models + | set(cohere_embedding_models) + | set(bedrock_embedding_models) + | vertex_embedding_models + | fireworks_ai_embedding_models + | nebius_embedding_models + | sambanova_embedding_models ) ####### IMAGE GENERATION MODELS ################### @@ -1018,6 +1039,7 @@ from .llms.cohere.rerank_v2.transformation import CohereRerankV2Config from .llms.azure_ai.rerank.transformation import AzureAIRerankConfig from .llms.infinity.rerank.transformation import InfinityRerankConfig from .llms.jina_ai.rerank.transformation import JinaAIRerankConfig +from .llms.deepinfra.rerank.transformation import DeepinfraRerankConfig from .llms.clarifai.chat.transformation import ClarifaiConfig from .llms.ai21.chat.transformation import AI21ChatConfig, AI21ChatConfig as AI21Config from .llms.meta_llama.chat.transformation import LlamaAPIConfig @@ -1025,7 +1047,7 @@ from .llms.anthropic.experimental_pass_through.messages.transformation import ( AnthropicMessagesConfig, ) from .llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation import ( - AmazonAnthropicClaude3MessagesConfig, + AmazonAnthropicClaudeMessagesConfig, ) from .llms.together_ai.chat import TogetherAIConfig from .llms.together_ai.completion.transformation import TogetherAITextCompletionConfig @@ -1085,7 +1107,7 @@ from .llms.bedrock.chat.invoke_transformations.anthropic_claude2_transformation AmazonAnthropicConfig, ) from .llms.bedrock.chat.invoke_transformations.anthropic_claude3_transformation import ( - AmazonAnthropicClaude3Config, + AmazonAnthropicClaudeConfig, ) from .llms.bedrock.chat.invoke_transformations.amazon_cohere_transformation import ( AmazonCohereConfig, @@ -1129,17 +1151,22 @@ from .llms.topaz.image_variations.transformation import TopazImageVariationConfi from litellm.llms.openai.completion.transformation import OpenAITextCompletionConfig from .llms.groq.chat.transformation import GroqChatConfig from .llms.voyage.embedding.transformation import VoyageEmbeddingConfig +from .llms.voyage.embedding.transformation_contextual import VoyageContextualEmbeddingConfig from .llms.infinity.embedding.transformation import InfinityEmbeddingConfig from .llms.azure_ai.chat.transformation import AzureAIStudioConfig from .llms.mistral.chat.transformation import MistralConfig from .llms.openai.responses.transformation import OpenAIResponsesAPIConfig from .llms.azure.responses.transformation import AzureOpenAIResponsesAPIConfig +from .llms.azure.responses.o_series_transformation import ( + AzureOpenAIOSeriesResponsesAPIConfig, +) from .llms.openai.chat.o_series_transformation import ( OpenAIOSeriesConfig as OpenAIO1Config, # maintain backwards compatibility OpenAIOSeriesConfig, ) from .llms.snowflake.chat.transformation import SnowflakeConfig +from .llms.gradient_ai.chat.transformation import GradientAIConfig openaiOSeriesConfig = OpenAIOSeriesConfig() from .llms.openai.chat.gpt_transformation import ( @@ -1171,7 +1198,9 @@ nvidiaNimEmbeddingConfig = NvidiaNimEmbeddingConfig() from .llms.featherless_ai.chat.transformation import FeatherlessAIConfig from .llms.cerebras.chat import CerebrasConfig +from .llms.baseten.chat import BasetenConfig from .llms.sambanova.chat import SambanovaConfig +from .llms.sambanova.embedding.transformation import SambaNovaEmbeddingConfig from .llms.ai21.chat.transformation import AI21ChatConfig from .llms.fireworks_ai.chat.transformation import FireworksAIConfig from .llms.fireworks_ai.completion.transformation import FireworksAITextCompletionConfig @@ -1185,14 +1214,16 @@ from .llms.friendliai.chat.transformation import FriendliaiChatConfig from .llms.jina_ai.embedding.transformation import JinaAIEmbeddingConfig from .llms.xai.chat.transformation import XAIChatConfig from .llms.xai.common_utils import XAIModelInfo +from .llms.aiml.chat.transformation import AIMLChatConfig from .llms.volcengine import VolcEngineConfig from .llms.codestral.completion.transformation import CodestralTextCompletionConfig from .llms.azure.azure import ( AzureOpenAIError, AzureOpenAIAssistantsAPIConfig, ) - +from .llms.cometapi.chat.transformation import CometAPIConfig from .llms.azure.chat.gpt_transformation import AzureOpenAIConfig +from .llms.azure.chat.gpt_5_transformation import AzureOpenAIGPT5Config from .llms.azure.completion.transformation import AzureOpenAITextConfig from .llms.hosted_vllm.chat.transformation import HostedVLLMChatConfig from .llms.llamafile.chat.transformation import LlamafileChatConfig diff --git a/litellm/caching/caching.py b/litellm/caching/caching.py index 1455e011bc5..82fc37e0cb4 100644 --- a/litellm/caching/caching.py +++ b/litellm/caching/caching.py @@ -28,13 +28,13 @@ from .azure_blob_cache import AzureBlobCache from .base_cache import BaseCache from .disk_cache import DiskCache from .dual_cache import DualCache # noqa +from .gcs_cache import GCSCache from .in_memory_cache import InMemoryCache from .qdrant_semantic_cache import QdrantSemanticCache from .redis_cache import RedisCache from .redis_cluster_cache import RedisClusterCache from .redis_semantic_cache import RedisSemanticCache from .s3_cache import S3Cache -from .gcs_cache import GCSCache def print_verbose(print_statement): @@ -177,7 +177,7 @@ class Cache: cluster_kwargs["gcp_service_account"] = gcp_service_account if gcp_ssl_ca_certs is not None: cluster_kwargs["gcp_ssl_ca_certs"] = gcp_ssl_ca_certs - + self.cache: BaseCache = RedisClusterCache(**cluster_kwargs) else: self.cache = RedisCache( @@ -481,7 +481,7 @@ class Cache: return cached_response return cached_result - def get_cache(self, **kwargs): + def get_cache(self, dynamic_cache_object: Optional[BaseCache] = None, **kwargs): """ Retrieves the cached result for the given arguments. @@ -507,8 +507,12 @@ class Cache: or cache_control_args.get("s-max-age") or float("inf") ) - cached_result = self.cache.get_cache(cache_key, messages=messages) - cached_result = self.cache.get_cache(cache_key, messages=messages) + if dynamic_cache_object is not None: + cached_result = dynamic_cache_object.get_cache( + cache_key, messages=messages + ) + else: + cached_result = self.cache.get_cache(cache_key, messages=messages) return self._get_cache_logic( cached_result=cached_result, max_age=max_age ) @@ -516,7 +520,9 @@ class Cache: print_verbose(f"An exception occurred: {traceback.format_exc()}") return None - async def async_get_cache(self, **kwargs): + async def async_get_cache( + self, dynamic_cache_object: Optional[BaseCache] = None, **kwargs + ): """ Async get cache implementation. @@ -537,7 +543,14 @@ class Cache: max_age = cache_control_args.get( "s-max-age", cache_control_args.get("s-maxage", float("inf")) ) - cached_result = await self.cache.async_get_cache(cache_key, **kwargs) + if dynamic_cache_object is not None: + cached_result = await dynamic_cache_object.async_get_cache( + cache_key, **kwargs + ) + else: + cached_result = await self.cache.async_get_cache( + cache_key, **kwargs + ) return self._get_cache_logic( cached_result=cached_result, max_age=max_age ) @@ -596,7 +609,9 @@ class Cache: except Exception as e: verbose_logger.exception(f"LiteLLM Cache: Excepton add_cache: {str(e)}") - async def async_add_cache(self, result, **kwargs): + async def async_add_cache( + self, result, dynamic_cache_object: Optional[BaseCache] = None, **kwargs + ): """ Async implementation of add_cache """ @@ -610,12 +625,18 @@ class Cache: cache_key, cached_data, kwargs = self._add_cache_logic( result=result, **kwargs ) - - await self.cache.async_set_cache(cache_key, cached_data, **kwargs) + if dynamic_cache_object is not None: + await dynamic_cache_object.async_set_cache( + cache_key, cached_data, **kwargs + ) + else: + await self.cache.async_set_cache(cache_key, cached_data, **kwargs) except Exception as e: verbose_logger.exception(f"LiteLLM Cache: Excepton add_cache: {str(e)}") - def _convert_to_cached_embedding(self, embedding_response: Any, model: Optional[str]) -> CachedEmbedding: + def _convert_to_cached_embedding( + self, embedding_response: Any, model: Optional[str] + ) -> CachedEmbedding: """ Convert any embedding response into the standardized CachedEmbedding TypedDict format. """ @@ -627,7 +648,7 @@ class Cache: "object": embedding_response.get("object"), "model": model, } - elif hasattr(embedding_response, 'model_dump'): + elif hasattr(embedding_response, "model_dump"): data = embedding_response.model_dump() return { "embedding": data.get("embedding"), @@ -646,7 +667,6 @@ class Cache: except KeyError as e: raise ValueError(f"Missing expected key in embedding response: {e}") - def add_embedding_response_to_cache( self, result: EmbeddingResponse, @@ -657,18 +677,22 @@ class Cache: preset_cache_key = self.get_cache_key(**{**kwargs, "input": input}) kwargs["cache_key"] = preset_cache_key embedding_response = result.data[idx_in_result_data] - + # Always convert to properly typed CachedEmbedding model_name = result.model - embedding_dict: CachedEmbedding = self._convert_to_cached_embedding(embedding_response, model_name) - + embedding_dict: CachedEmbedding = self._convert_to_cached_embedding( + embedding_response, model_name + ) + cache_key, cached_data, kwargs = self._add_cache_logic( result=embedding_dict, **kwargs, ) return cache_key, cached_data, kwargs - async def async_add_cache_pipeline(self, result, **kwargs): + async def async_add_cache_pipeline( + self, result, dynamic_cache_object: Optional[BaseCache] = None, **kwargs + ): """ Async implementation of add_cache for Embedding calls @@ -697,14 +721,14 @@ class Cache: ) cache_list.append((cache_key, cached_data)) - await self.cache.async_set_cache_pipeline(cache_list=cache_list, **kwargs) - # if async_set_cache_pipeline: - # await async_set_cache_pipeline(cache_list=cache_list, **kwargs) - # else: - # tasks = [] - # for val in cache_list: - # tasks.append(self.cache.async_set_cache(val[0], val[1], **kwargs)) - # await asyncio.gather(*tasks) + if dynamic_cache_object is not None: + await dynamic_cache_object.async_set_cache_pipeline( + cache_list=cache_list, **kwargs + ) + else: + await self.cache.async_set_cache_pipeline( + cache_list=cache_list, **kwargs + ) except Exception as e: verbose_logger.exception(f"LiteLLM Cache: Excepton add_cache: {str(e)}") @@ -750,11 +774,9 @@ class Cache: """ Internal method to check if the cache type supports async get/set operations - Only S3 Cache Does NOT support async operations + All cache types now support async operations """ - if self.type and self.type == LiteLLMCacheType.S3: - return False return True diff --git a/litellm/caching/caching_handler.py b/litellm/caching/caching_handler.py index dcc59b20714..1dcc0f1fdb2 100644 --- a/litellm/caching/caching_handler.py +++ b/litellm/caching/caching_handler.py @@ -1,5 +1,5 @@ """ -This contains LLMCachingHandler +This contains LLMCachingHandler This exposes two methods: - async_get_cache @@ -35,11 +35,12 @@ from pydantic import BaseModel import litellm from litellm._logging import print_verbose, verbose_logger +from litellm.caching import InMemoryCache from litellm.caching.caching import S3Cache -from litellm.types.caching import CachedEmbedding from litellm.litellm_core_utils.logging_utils import ( _assemble_complete_response_from_streaming_chunks, ) +from litellm.types.caching import CachedEmbedding from litellm.types.rerank import RerankResponse from litellm.types.utils import ( CallTypes, @@ -68,7 +69,12 @@ class CachingHandlerResponse(BaseModel): cached_result: Optional[Any] = None final_embedding_cached_response: Optional[EmbeddingResponse] = None - embedding_all_elements_cache_hit: bool = False # this is set to True when all elements in the list have a cache hit in the embedding cache, if true return the final_embedding_cached_response no need to make an API call + embedding_all_elements_cache_hit: bool = ( + False # this is set to True when all elements in the list have a cache hit in the embedding cache, if true return the final_embedding_cached_response no need to make an API call + ) + + +in_memory_cache_obj = InMemoryCache() class LLMCachingHandler: @@ -78,11 +84,20 @@ class LLMCachingHandler: request_kwargs: Dict[str, Any], start_time: datetime.datetime, ): + from litellm.caching import DualCache, RedisCache + self.async_streaming_chunks: List[ModelResponse] = [] self.sync_streaming_chunks: List[ModelResponse] = [] self.request_kwargs = request_kwargs self.original_function = original_function self.start_time = start_time + if litellm.cache is not None and isinstance(litellm.cache.cache, RedisCache): + self.dual_cache: Optional[DualCache] = DualCache( + redis_cache=litellm.cache.cache, + in_memory_cache=in_memory_cache_obj, + ) + else: + self.dual_cache = None pass async def _async_get_cache( @@ -115,10 +130,16 @@ class LLMCachingHandler: Raises: None """ + from litellm.litellm_core_utils.core_helpers import ( + _get_parent_otel_span_from_kwargs, + ) from litellm.utils import CustomStreamWrapper + kwargs = kwargs.copy() args = args or () + parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs) + kwargs["parent_otel_span"] = parent_otel_span final_embedding_cached_response: Optional[EmbeddingResponse] = None embedding_all_elements_cache_hit: bool = False cached_result: Optional[Any] = None @@ -306,13 +327,15 @@ class LLMCachingHandler: else: raise ValueError("input must be a string or a list") - def _extract_model_from_cached_results(self, non_null_list: List[Tuple[int, CachedEmbedding]]) -> Optional[str]: + def _extract_model_from_cached_results( + self, non_null_list: List[Tuple[int, CachedEmbedding]] + ) -> Optional[str]: """ Helper method to extract the model name from cached results. - + Args: non_null_list: List of (idx, cr) tuples where cr is the cached result dict - + Returns: Optional[str]: The model name if found, None otherwise """ @@ -558,7 +581,12 @@ class LLMCachingHandler: preset_cache_key = litellm.cache.get_cache_key( **{**new_kwargs, "input": i} ) - tasks.append(litellm.cache.async_get_cache(cache_key=preset_cache_key)) + tasks.append( + litellm.cache.async_get_cache( + cache_key=preset_cache_key, + dynamic_cache_object=self.dual_cache, + ) + ) cached_result = await asyncio.gather(*tasks) ## check if cached result is None ## if cached_result is not None and isinstance(cached_result, list): @@ -567,9 +595,14 @@ class LLMCachingHandler: cached_result = None else: if litellm.cache._supports_async() is True: - cached_result = await litellm.cache.async_get_cache(**new_kwargs) - else: # for s3 caching. [NOT RECOMMENDED IN PROD - this will slow down responses since boto3 is sync] - cached_result = litellm.cache.get_cache(**new_kwargs) + ## check if dual cache is supported ## + cached_result = await litellm.cache.async_get_cache( + dynamic_cache_object=self.dual_cache, **new_kwargs + ) + else: # fallback for caches that don't support async + cached_result = litellm.cache.get_cache( + dynamic_cache_object=self.dual_cache, **new_kwargs + ) return cached_result def _convert_cached_result_to_model_response( @@ -735,6 +768,9 @@ class LLMCachingHandler: Raises: None """ + from litellm.litellm_core_utils.core_helpers import ( + _get_parent_otel_span_from_kwargs, + ) if litellm.cache is None: return @@ -746,6 +782,8 @@ class LLMCachingHandler: args, ) ) + parent_otel_span = _get_parent_otel_span_from_kwargs(new_kwargs) + new_kwargs["parent_otel_span"] = parent_otel_span # [OPTIONAL] ADD TO CACHE if self._should_store_result_in_cache( original_function=original_function, kwargs=new_kwargs @@ -764,18 +802,16 @@ class LLMCachingHandler: ) # s3 doesn't support bulk writing. Exclude. ): asyncio.create_task( - litellm.cache.async_add_cache_pipeline(result, **new_kwargs) + litellm.cache.async_add_cache_pipeline( + result, dynamic_cache_object=self.dual_cache, **new_kwargs + ) ) - elif isinstance(litellm.cache.cache, S3Cache): - threading.Thread( - target=litellm.cache.add_cache, - args=(result,), - kwargs=new_kwargs, - ).start() else: asyncio.create_task( litellm.cache.async_add_cache( - result.model_dump_json(), **new_kwargs + result.model_dump_json(), + dynamic_cache_object=self.dual_cache, + **new_kwargs, ) ) else: @@ -933,9 +969,9 @@ class LLMCachingHandler: } if litellm.cache is not None: - litellm_params[ - "preset_cache_key" - ] = litellm.cache._get_preset_cache_key_from_kwargs(**kwargs) + litellm_params["preset_cache_key"] = ( + litellm.cache._get_preset_cache_key_from_kwargs(**kwargs) + ) else: litellm_params["preset_cache_key"] = None diff --git a/litellm/caching/redis_cache.py b/litellm/caching/redis_cache.py index b8091187bfa..47bc0222ed5 100644 --- a/litellm/caching/redis_cache.py +++ b/litellm/caching/redis_cache.py @@ -43,6 +43,45 @@ else: Span = Any +def _get_call_stack_info(num_frames: int = 2) -> str: + """ + Get the function names from the previous 1-2 functions in the call stack. + + Args: + num_frames: Number of previous frames to include (default: 2) + + Returns: + A string with format "current_function <- caller_function [<- grandparent_function]" + """ + try: + current_frame = inspect.currentframe() + if current_frame is None: + return "unknown" + + # Skip this function and the immediate caller (which sets call_type) + f_back = current_frame.f_back + if f_back is None: + return "unknown" + frame = f_back.f_back + if frame is None: + return "unknown" + function_names = [] + + for _ in range(num_frames): + if frame is None: + break + func_name = frame.f_code.co_name + function_names.append(func_name) + frame = frame.f_back + + if not function_names: + return "unknown" + + return " <- ".join(function_names) + except Exception: + return "unknown" + + class RedisCache(BaseCache): # if users don't provider one, use the default litellm cache @@ -181,7 +220,7 @@ class RedisCache(BaseCache): self.service_logger_obj.service_success_hook( service=ServiceTypes.REDIS, duration=_duration, - call_type="set_cache", + call_type=f"set_cache <- {_get_call_stack_info()}", start_time=start_time, end_time=end_time, ) @@ -205,7 +244,7 @@ class RedisCache(BaseCache): self.service_logger_obj.service_success_hook( service=ServiceTypes.REDIS, duration=_duration, - call_type="increment_cache", + call_type=f"increment_cache <- {_get_call_stack_info()}", start_time=start_time, end_time=end_time, ) @@ -219,7 +258,7 @@ class RedisCache(BaseCache): self.service_logger_obj.service_success_hook( service=ServiceTypes.REDIS, duration=_duration, - call_type="increment_cache_ttl", + call_type=f"increment_cache_ttl <- {_get_call_stack_info()}", start_time=start_time, end_time=end_time, ) @@ -232,7 +271,7 @@ class RedisCache(BaseCache): self.service_logger_obj.service_success_hook( service=ServiceTypes.REDIS, duration=_duration, - call_type="increment_cache_expire", + call_type=f"increment_cache_expire <- {_get_call_stack_info()}", start_time=start_time, end_time=end_time, ) @@ -271,7 +310,7 @@ class RedisCache(BaseCache): self.service_logger_obj.async_service_success_hook( service=ServiceTypes.REDIS, duration=_duration, - call_type="async_scan_iter", + call_type=f"async_scan_iter <- {_get_call_stack_info()}", start_time=start_time, end_time=end_time, ) @@ -287,7 +326,7 @@ class RedisCache(BaseCache): service=ServiceTypes.REDIS, duration=_duration, error=e, - call_type="async_scan_iter", + call_type=f"async_scan_iter <- {_get_call_stack_info()}", start_time=start_time, end_time=end_time, ) @@ -341,7 +380,7 @@ class RedisCache(BaseCache): start_time=start_time, end_time=end_time, parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs), - call_type="async_set_cache", + call_type=f"async_set_cache <- {_get_call_stack_info()}", ) ) verbose_logger.error( @@ -374,7 +413,7 @@ class RedisCache(BaseCache): self.service_logger_obj.async_service_success_hook( service=ServiceTypes.REDIS, duration=_duration, - call_type="async_set_cache", + call_type=f"async_set_cache <- {_get_call_stack_info()}", start_time=start_time, end_time=end_time, parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs), @@ -390,7 +429,7 @@ class RedisCache(BaseCache): service=ServiceTypes.REDIS, duration=_duration, error=e, - call_type="async_set_cache", + call_type=f"async_set_cache <- {_get_call_stack_info()}", start_time=start_time, end_time=end_time, parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs), @@ -463,7 +502,7 @@ class RedisCache(BaseCache): self.service_logger_obj.async_service_success_hook( service=ServiceTypes.REDIS, duration=_duration, - call_type="async_set_cache_pipeline", + call_type=f"async_set_cache_pipeline <- {_get_call_stack_info()}", start_time=start_time, end_time=end_time, parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs), @@ -479,7 +518,7 @@ class RedisCache(BaseCache): service=ServiceTypes.REDIS, duration=_duration, error=e, - call_type="async_set_cache_pipeline", + call_type=f"async_set_cache_pipeline <- {_get_call_stack_info()}", start_time=start_time, end_time=end_time, parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs), @@ -528,7 +567,7 @@ class RedisCache(BaseCache): start_time=start_time, end_time=end_time, parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs), - call_type="async_set_cache_sadd", + call_type=f"async_set_cache_sadd <- {_get_call_stack_info()}", ) ) # NON blocking - notify users Redis is throwing an exception @@ -554,7 +593,7 @@ class RedisCache(BaseCache): self.service_logger_obj.async_service_success_hook( service=ServiceTypes.REDIS, duration=_duration, - call_type="async_set_cache_sadd", + call_type=f"async_set_cache_sadd <- {_get_call_stack_info()}", start_time=start_time, end_time=end_time, parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs), @@ -568,7 +607,7 @@ class RedisCache(BaseCache): service=ServiceTypes.REDIS, duration=_duration, error=e, - call_type="async_set_cache_sadd", + call_type=f"async_set_cache_sadd <- {_get_call_stack_info()}", start_time=start_time, end_time=end_time, parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs), @@ -620,7 +659,7 @@ class RedisCache(BaseCache): self.service_logger_obj.async_service_success_hook( service=ServiceTypes.REDIS, duration=_duration, - call_type="async_increment", + call_type=f"async_increment <- {_get_call_stack_info()}", start_time=start_time, end_time=end_time, parent_otel_span=parent_otel_span, @@ -636,7 +675,7 @@ class RedisCache(BaseCache): service=ServiceTypes.REDIS, duration=_duration, error=e, - call_type="async_increment", + call_type=f"async_increment <- {_get_call_stack_info()}", start_time=start_time, end_time=end_time, parent_otel_span=parent_otel_span, @@ -683,7 +722,7 @@ class RedisCache(BaseCache): self.service_logger_obj.service_success_hook( service=ServiceTypes.REDIS, duration=_duration, - call_type="get_cache", + call_type=f"get_cache <- {_get_call_stack_info()}", start_time=start_time, end_time=end_time, parent_otel_span=parent_otel_span, @@ -745,7 +784,7 @@ class RedisCache(BaseCache): self.service_logger_obj.service_success_hook( service=ServiceTypes.REDIS, duration=_duration, - call_type="batch_get_cache", + call_type=f"batch_get_cache <- {_get_call_stack_info()}", start_time=start_time, end_time=end_time, parent_otel_span=parent_otel_span, @@ -790,7 +829,7 @@ class RedisCache(BaseCache): self.service_logger_obj.async_service_success_hook( service=ServiceTypes.REDIS, duration=_duration, - call_type="async_get_cache", + call_type=f"async_get_cache <- {_get_call_stack_info()}", start_time=start_time, end_time=end_time, parent_otel_span=parent_otel_span, @@ -806,7 +845,7 @@ class RedisCache(BaseCache): service=ServiceTypes.REDIS, duration=_duration, error=e, - call_type="async_get_cache", + call_type=f"async_get_cache <- {_get_call_stack_info()}", start_time=start_time, end_time=end_time, parent_otel_span=parent_otel_span, @@ -851,7 +890,7 @@ class RedisCache(BaseCache): self.service_logger_obj.async_service_success_hook( service=ServiceTypes.REDIS, duration=_duration, - call_type="async_batch_get_cache", + call_type=f"async_batch_get_cache <- {_get_call_stack_info()}", start_time=start_time, end_time=end_time, parent_otel_span=parent_otel_span, @@ -879,7 +918,7 @@ class RedisCache(BaseCache): service=ServiceTypes.REDIS, duration=_duration, error=e, - call_type="async_batch_get_cache", + call_type=f"async_batch_get_cache <- {_get_call_stack_info()}", start_time=start_time, end_time=end_time, parent_otel_span=parent_otel_span, @@ -903,7 +942,7 @@ class RedisCache(BaseCache): self.service_logger_obj.service_success_hook( service=ServiceTypes.REDIS, duration=_duration, - call_type="sync_ping", + call_type=f"sync_ping <- {_get_call_stack_info()}", start_time=start_time, end_time=end_time, ) @@ -917,7 +956,7 @@ class RedisCache(BaseCache): service=ServiceTypes.REDIS, duration=_duration, error=e, - call_type="sync_ping", + call_type=f"sync_ping <- {_get_call_stack_info()}", ) verbose_logger.error( f"LiteLLM Redis Cache PING: - Got exception from REDIS : {str(e)}" @@ -938,7 +977,7 @@ class RedisCache(BaseCache): self.service_logger_obj.async_service_success_hook( service=ServiceTypes.REDIS, duration=_duration, - call_type="async_ping", + call_type=f"async_ping <- {_get_call_stack_info()}", ) ) return response @@ -952,7 +991,7 @@ class RedisCache(BaseCache): service=ServiceTypes.REDIS, duration=_duration, error=e, - call_type="async_ping", + call_type=f"async_ping <- {_get_call_stack_info()}", ) ) verbose_logger.error( @@ -1051,7 +1090,7 @@ class RedisCache(BaseCache): self.service_logger_obj.async_service_success_hook( service=ServiceTypes.REDIS, duration=_duration, - call_type="async_increment_pipeline", + call_type=f"async_increment_pipeline <- {_get_call_stack_info()}", start_time=start_time, end_time=end_time, parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs), @@ -1067,7 +1106,7 @@ class RedisCache(BaseCache): service=ServiceTypes.REDIS, duration=_duration, error=e, - call_type="async_increment_pipeline", + call_type=f"async_increment_pipeline <- {_get_call_stack_info()}", start_time=start_time, end_time=end_time, parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs), @@ -1131,7 +1170,7 @@ class RedisCache(BaseCache): self.service_logger_obj.async_service_success_hook( service=ServiceTypes.REDIS, duration=_duration, - call_type="async_rpush", + call_type=f"async_rpush <- {_get_call_stack_info()}", ) ) return response @@ -1145,7 +1184,7 @@ class RedisCache(BaseCache): service=ServiceTypes.REDIS, duration=_duration, error=e, - call_type="async_rpush", + call_type=f"async_rpush <- {_get_call_stack_info()}", ) ) verbose_logger.error( @@ -1202,7 +1241,7 @@ class RedisCache(BaseCache): self.service_logger_obj.async_service_success_hook( service=ServiceTypes.REDIS, duration=_duration, - call_type="async_lpop", + call_type=f"async_lpop <- {_get_call_stack_info()}", ) ) @@ -1230,7 +1269,7 @@ class RedisCache(BaseCache): service=ServiceTypes.REDIS, duration=_duration, error=e, - call_type="async_lpop", + call_type=f"async_lpop <- {_get_call_stack_info()}", ) ) verbose_logger.error( diff --git a/litellm/caching/s3_cache.py b/litellm/caching/s3_cache.py index c02e1091369..15f7a5c1e16 100644 --- a/litellm/caching/s3_cache.py +++ b/litellm/caching/s3_cache.py @@ -1,17 +1,17 @@ """ S3 Cache implementation -WARNING: DO NOT USE THIS IN PRODUCTION - This is not ASYNC Has 4 methods: - set_cache - get_cache - - async_set_cache - - async_get_cache + - async_set_cache (uses run_in_executor) + - async_get_cache (uses run_in_executor) """ import ast import asyncio import json +from functools import partial from typing import Optional from litellm._logging import print_verbose, verbose_logger @@ -55,20 +55,24 @@ class S3Cache(BaseCache): **kwargs, ) + def _to_s3_key(self, key: str) -> str: + """Convert cache key to S3 key""" + return self.key_prefix + key.replace(":", "/") + def set_cache(self, key, value, **kwargs): try: print_verbose(f"LiteLLM SET Cache - S3. Key={key}. Value={value}") ttl = kwargs.get("ttl", None) # Convert value to JSON before storing in S3 serialized_value = json.dumps(value) - key = self.key_prefix + key + key = self._to_s3_key(key) if ttl is not None: cache_control = f"immutable, max-age={ttl}, s-maxage={ttl}" import datetime # Calculate expiration time - expiration_time = datetime.datetime.now() + ttl + expiration_time = datetime.datetime.now() + datetime.timedelta(seconds=ttl) # Upload the data to S3 with the calculated expiration time self.s3_client.put_object( @@ -94,17 +98,26 @@ class S3Cache(BaseCache): ContentDisposition=f'inline; filename="{key}.json"', ) except Exception as e: - # NON blocking - notify users S3 is throwing an exception print_verbose(f"S3 Caching: set_cache() - Got exception from S3: {e}") async def async_set_cache(self, key, value, **kwargs): - self.set_cache(key=key, value=value, **kwargs) + """ + Asynchronously set cache using run_in_executor to avoid blocking the event loop. + Compatible with Python 3.8+. + """ + try: + verbose_logger.debug(f"Set ASYNC S3 Cache: Key={key}. Value={value}") + loop = asyncio.get_event_loop() + func = partial(self.set_cache, key, value, **kwargs) + await loop.run_in_executor(None, func) + except Exception as e: + verbose_logger.error(f"S3 Caching: async_set_cache() - Got exception from S3: {e}") def get_cache(self, key, **kwargs): import botocore try: - key = self.key_prefix + key + key = self._to_s3_key(key) print_verbose(f"Get S3 Cache: key: {key}") # Download the data from S3 @@ -138,13 +151,26 @@ class S3Cache(BaseCache): return None except Exception as e: - # NON blocking - notify users S3 is throwing an exception verbose_logger.error( f"S3 Caching: get_cache() - Got exception from S3: {e}" ) async def async_get_cache(self, key, **kwargs): - return self.get_cache(key=key, **kwargs) + """ + Asynchronously get cache using run_in_executor to avoid blocking the event loop. + Compatible with Python 3.8+. + """ + try: + verbose_logger.debug(f"Get ASYNC S3 Cache: key: {key}") + loop = asyncio.get_event_loop() + func = partial(self.get_cache, key, **kwargs) + result = await loop.run_in_executor(None, func) + return result + except Exception as e: + verbose_logger.error( + f"S3 Caching: async_get_cache() - Got exception from S3: {e}" + ) + return None def flush_cache(self): pass diff --git a/litellm/completion_extras/litellm_responses_transformation/transformation.py b/litellm/completion_extras/litellm_responses_transformation/transformation.py index f35510e41ba..5f732fc5219 100644 --- a/litellm/completion_extras/litellm_responses_transformation/transformation.py +++ b/litellm/completion_extras/litellm_responses_transformation/transformation.py @@ -460,6 +460,8 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): return Reasoning(effort="medium", summary="auto") elif reasoning_effort == "low": return Reasoning(effort="low", summary="auto") + elif reasoning_effort == "minimal": + return Reasoning(effort="minimal", summary="auto") return None def _map_responses_status_to_finish_reason(self, status: Optional[str]) -> str: diff --git a/litellm/constants.py b/litellm/constants.py index c7404f10a78..72cc52fdf21 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -1,6 +1,9 @@ import os from typing import List, Literal +AZURE_DEFAULT_RESPONSES_API_VERSION = str( + os.getenv("AZURE_DEFAULT_RESPONSES_API_VERSION", "preview") +) ROUTER_MAX_FALLBACKS = int(os.getenv("ROUTER_MAX_FALLBACKS", 5)) DEFAULT_BATCH_SIZE = int(os.getenv("DEFAULT_BATCH_SIZE", 512)) DEFAULT_FLUSH_INTERVAL_SECONDS = int(os.getenv("DEFAULT_FLUSH_INTERVAL_SECONDS", 5)) @@ -224,6 +227,7 @@ LITELLM_CHAT_PROVIDERS = [ "together_ai", "datarobot", "openrouter", + "cometapi", "vertex_ai", "vertex_ai_beta", "gemini", @@ -247,6 +251,7 @@ LITELLM_CHAT_PROVIDERS = [ "groq", "nvidia_nim", "cerebras", + "baseten", "ai21_chat", "volcengine", "codestral", @@ -270,6 +275,7 @@ LITELLM_CHAT_PROVIDERS = [ "llamafile", "lm_studio", "galadriel", + "gradient_ai", "github_copilot", # GitHub Copilot Chat API "novita", "meta_llama", @@ -422,6 +428,7 @@ openai_compatible_providers: List = [ "groq", "nvidia_nim", "cerebras", + "baseten", "sambanova", "ai21_chat", "ai21", @@ -455,6 +462,7 @@ openai_compatible_providers: List = [ "morph", "lambda_ai", "hyperbolic", + "aiml", ] openai_text_completion_compatible_providers: List = ( [ # providers that support `/v1/completions` @@ -478,7 +486,7 @@ _openai_like_providers: List = [ "watsonx", ] # private helper. similar to openai but require some custom auth / endpoint handling, so can't use the openai sdk # well supported replicate llms -replicate_models: List = [ +replicate_models: set = set([ # llama replicate supported LLMs "replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf", "a16z-infra/llama-2-13b-chat:2a7f981751ec7fdf87b5b91ad4db53683a98082e9ff7bfd12c8cd5ea85980a52", @@ -491,9 +499,9 @@ replicate_models: List = [ # Others "replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5", "replit/replit-code-v1-3b:b84f4c074b807211cd75e3e8b1589b6399052125b4c27106e43d47189e8415ad", -] +]) -clarifai_models: List = [ +clarifai_models: set = set([ "clarifai/meta.Llama-3.Llama-3-8B-Instruct", "clarifai/gcp.generate.gemma-1_1-7b-it", "clarifai/mistralai.completion.mixtral-8x22B", @@ -557,10 +565,10 @@ clarifai_models: List = [ "clarifai/gcp.generate.gemini-1_5-pro", "clarifai/gcp.generate.imagen-2", "clarifai/salesforce.blip.general-english-image-caption-blip-2", -] +]) -huggingface_models: List = [ +huggingface_models: set = set([ "meta-llama/Llama-2-7b-hf", "meta-llama/Llama-2-7b-chat-hf", "meta-llama/Llama-2-13b-hf", @@ -573,13 +581,13 @@ huggingface_models: List = [ "meta-llama/Llama-2-13b-chat", "meta-llama/Llama-2-70b", "meta-llama/Llama-2-70b-chat", -] # these have been tested on extensively. But by default all text2text-generation and text-generation models are supported by liteLLM. - https://docs.litellm.ai/docs/providers -empower_models = [ +]) # these have been tested on extensively. But by default all text2text-generation and text-generation models are supported by liteLLM. - https://docs.litellm.ai/docs/providers +empower_models = set([ "empower/empower-functions", "empower/empower-functions-small", -] +]) -together_ai_models: List = [ +together_ai_models: set = set([ # llama llms - chat "togethercomputer/llama-2-70b-chat", # llama llms - language / instruct @@ -607,16 +615,17 @@ together_ai_models: List = [ "Austism/chronos-hermes-13b", "upstage/SOLAR-0-70b-16bit", "WizardLM/WizardLM-70B-V1.0", -] # supports all together ai models, just pass in the model id e.g. completion(model="together_computer/replit_code_3b",...) +]) + # supports all together ai models, just pass in the model id e.g. completion(model="together_computer/replit_code_3b",...) -baseten_models: List = [ +baseten_models: set = set([ "qvv0xeq", "q841o8w", "31dxrj3", -] # FALCON 7B # WizardLM # Mosaic ML +]) # FALCON 7B # WizardLM # Mosaic ML -featherless_ai_models: List = [ +featherless_ai_models: set = set([ "featherless-ai/Qwerky-72B", "featherless-ai/Qwerky-QwQ-32B", "Qwen/Qwen2.5-72B-Instruct", @@ -626,9 +635,9 @@ featherless_ai_models: List = [ "mistralai/Mistral-Small-24B-Instruct-2501", "mistralai/Mistral-Nemo-Instruct-2407", "ProdeusUnity/Stellar-Odyssey-12b-v0.0", -] +]) -nebius_models: List = [ +nebius_models: set = set([ "Qwen/Qwen3-235B-A22B", "Qwen/Qwen3-30B-A3B-fast", "Qwen/Qwen3-32B", @@ -641,9 +650,9 @@ nebius_models: List = [ "meta-llama/Llama-3.3-70B-Instruct-fast", "Qwen/Qwen2.5-32B-Instruct-fast", "Qwen/Qwen2.5-Coder-32B-Instruct-fast", -] +]) -dashscope_models: List = [ +dashscope_models: set = set([ "qwen-turbo", "qwen-plus", "qwen-max", @@ -654,13 +663,13 @@ dashscope_models: List = [ "qwen3-235b-a22b", "qwen3-32b", "qwen3-30b-a3b", -] +]) -nebius_embedding_models: List = [ +nebius_embedding_models: set = set([ "BAAI/bge-en-icl", "BAAI/bge-multilingual-gemma2", "intfloat/e5-mistral-7b-instruct", -] +]) BEDROCK_INVOKE_PROVIDERS_LITERAL = Literal[ "cohere", @@ -674,8 +683,8 @@ BEDROCK_INVOKE_PROVIDERS_LITERAL = Literal[ "deepseek_r1", ] -open_ai_embedding_models: List = ["text-embedding-ada-002"] -cohere_embedding_models: List = [ +open_ai_embedding_models: set = set(["text-embedding-ada-002"]) +cohere_embedding_models: set = set([ "embed-v4.0", "embed-english-v3.0", "embed-english-light-v3.0", @@ -683,12 +692,12 @@ cohere_embedding_models: List = [ "embed-english-v2.0", "embed-english-light-v2.0", "embed-multilingual-v2.0", -] -bedrock_embedding_models: List = [ +]) +bedrock_embedding_models: set = set([ "amazon.titan-embed-text-v1", "cohere.embed-english-v3", "cohere.embed-multilingual-v3", -] +]) known_tokenizer_config = { "mistralai/Mistral-7B-Instruct-v0.1": { @@ -877,6 +886,7 @@ SENTRY_DENYLIST = [ "CLOUDFLARE_API_KEY", "BASETEN_KEY", "OPENROUTER_KEY", + "COMETAPI_KEY", "DATAROBOT_API_TOKEN", "FIREWORKS_API_KEY", "FIREWORKS_AI_API_KEY", diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py index 9956a9d314a..6c6a09cd73e 100644 --- a/litellm/cost_calculator.py +++ b/litellm/cost_calculator.py @@ -32,9 +32,6 @@ from litellm.llms.azure.cost_calculation import ( from litellm.llms.bedrock.cost_calculation import ( cost_per_token as bedrock_cost_per_token, ) -from litellm.llms.bedrock.image.cost_calculator import ( - cost_calculator as bedrock_image_cost_calculator, -) from litellm.llms.databricks.cost_calculator import ( cost_per_token as databricks_cost_per_token, ) @@ -60,9 +57,6 @@ from litellm.llms.vertex_ai.cost_calculator import ( cost_per_token as google_cost_per_token, ) from litellm.llms.vertex_ai.cost_calculator import cost_router as google_cost_router -from litellm.llms.vertex_ai.image_generation.cost_calculator import ( - cost_calculator as vertex_ai_image_cost_calculator, -) from litellm.responses.utils import ResponseAPILoggingUtils from litellm.types.llms.openai import ( HttpxBinaryResponseContent, @@ -768,50 +762,15 @@ def completion_cost( # noqa: PLR0915 ) if CostCalculatorUtils._call_type_has_image_response(call_type): ### IMAGE GENERATION COST CALCULATION ### - if custom_llm_provider == "vertex_ai": - if isinstance(completion_response, ImageResponse): - return vertex_ai_image_cost_calculator( - model=model, - image_response=completion_response, - ) - elif custom_llm_provider == "bedrock": - if isinstance(completion_response, ImageResponse): - return bedrock_image_cost_calculator( - model=model, - size=size, - image_response=completion_response, - optional_params=optional_params, - ) - raise TypeError( - "completion_response must be of type ImageResponse for bedrock image cost calculation" - ) - elif custom_llm_provider == litellm.LlmProviders.RECRAFT.value: - from litellm.llms.recraft.cost_calculator import ( - cost_calculator as recraft_image_cost_calculator, - ) - - return recraft_image_cost_calculator( - model=model, - image_response=completion_response, - ) - elif custom_llm_provider == litellm.LlmProviders.GEMINI.value: - from litellm.llms.gemini.image_generation.cost_calculator import ( - cost_calculator as gemini_image_cost_calculator, - ) - - return gemini_image_cost_calculator( - model=model, - image_response=completion_response, - ) - else: - return default_image_cost_calculator( - model=model, - quality=quality, - custom_llm_provider=custom_llm_provider, - n=n, - size=size, - optional_params=optional_params, - ) + return CostCalculatorUtils.route_image_generation_cost_calculator( + model=model, + custom_llm_provider=custom_llm_provider, + completion_response=completion_response, + quality=quality, + n=n, + size=size, + optional_params=optional_params, + ) elif ( call_type == CallTypes.speech.value or call_type == CallTypes.aspeech.value diff --git a/litellm/images/main.py b/litellm/images/main.py index b808388d83e..953a8012e49 100644 --- a/litellm/images/main.py +++ b/litellm/images/main.py @@ -311,7 +311,7 @@ def image_generation( # noqa: PLR0915 ) or get_secret_str("AZURE_AD_TOKEN") default_headers = { - "Content-Type": "application/json;", + "Content-Type": "application/json", "api-key": api_key, } for k, v in default_headers.items(): @@ -335,8 +335,63 @@ def image_generation( # noqa: PLR0915 headers=headers, litellm_params=litellm_params_dict, ) + ######################################################### + # Providers using llm_http_handler + ######################################################### + elif custom_llm_provider in ( + litellm.LlmProviders.RECRAFT, + litellm.LlmProviders.AIML, + litellm.LlmProviders.GEMINI, + ): + if image_generation_config is None: + raise ValueError(f"image generation config is not supported for {custom_llm_provider}") + + return llm_http_handler.image_generation_handler( + model=model, + prompt=prompt, + image_generation_provider_config=image_generation_config, + image_generation_optional_request_params=optional_params, + custom_llm_provider=custom_llm_provider, + litellm_params=litellm_params_dict, + logging_obj=litellm_logging_obj, + timeout=timeout, + client=client, + ) + elif custom_llm_provider == "azure_ai": + from litellm.llms.azure_ai.common_utils import AzureFoundryModelInfo + api_base = AzureFoundryModelInfo.get_api_base(api_base) + api_key = AzureFoundryModelInfo.get_api_key(api_key) + if extra_headers is not None: + optional_params["extra_headers"] = extra_headers + + default_headers = { + "Content-Type": "application/json", + "api-key": api_key, + } + for k, v in default_headers.items(): + if k not in headers: + headers[k] = v + + model_response = azure_chat_completions.image_generation( + model=model, + prompt=prompt, + timeout=timeout, + api_key=api_key, + api_base=api_base, + azure_ad_token=None, + azure_ad_token_provider=azure_ad_token_provider, + logging_obj=litellm_logging_obj, + optional_params=optional_params, + model_response=model_response, + api_version=api_version, + aimg_generation=aimg_generation, + client=client, + headers=headers, + litellm_params=litellm_params_dict, + ) elif ( custom_llm_provider == "openai" + or custom_llm_provider == LlmProviders.LITELLM_PROXY.value or custom_llm_provider in litellm.openai_compatible_providers ): model_response = openai_chat_completions.image_generation( @@ -406,28 +461,6 @@ def image_generation( # noqa: PLR0915 api_base=api_base, client=client, ) - ######################################################### - # Providers using llm_http_handler - ######################################################### - elif custom_llm_provider in ( - litellm.LlmProviders.RECRAFT, - litellm.LlmProviders.GEMINI, - - ): - if image_generation_config is None: - raise ValueError(f"image generation config is not supported for {custom_llm_provider}") - - return llm_http_handler.image_generation_handler( - model=model, - prompt=prompt, - image_generation_provider_config=image_generation_config, - image_generation_optional_request_params=optional_params, - custom_llm_provider=custom_llm_provider, - litellm_params=litellm_params_dict, - logging_obj=litellm_logging_obj, - timeout=timeout, - client=client, - ) elif ( custom_llm_provider in litellm._custom_providers ): # Assume custom LLM provider diff --git a/litellm/integrations/braintrust_logging.py b/litellm/integrations/braintrust_logging.py index 8149a6131e8..5cf403adee4 100644 --- a/litellm/integrations/braintrust_logging.py +++ b/litellm/integrations/braintrust_logging.py @@ -19,10 +19,6 @@ from litellm.llms.custom_httpx.http_handler import ( ) from litellm.utils import print_verbose -global_braintrust_http_handler = get_async_httpx_client( - llm_provider=httpxSpecialProvider.LoggingCallback -) -global_braintrust_sync_http_handler = HTTPHandler() API_BASE = "https://api.braintrustdata.com/v1" @@ -52,6 +48,10 @@ class BraintrustLogger(CustomLogger): self._project_id_cache: Dict[ str, str ] = {} # Cache mapping project names to IDs + self.global_braintrust_http_handler = get_async_httpx_client( + llm_provider=httpxSpecialProvider.LoggingCallback + ) + self.global_braintrust_sync_http_handler = HTTPHandler() def validate_environment(self, api_key: Optional[str]): """ @@ -76,7 +76,7 @@ class BraintrustLogger(CustomLogger): return self._project_id_cache[project_name] try: - response = global_braintrust_sync_http_handler.post( + response = self.global_braintrust_sync_http_handler.post( f"{self.api_base}/project", headers=self.headers, json={"name": project_name}, @@ -96,7 +96,7 @@ class BraintrustLogger(CustomLogger): return self._project_id_cache[project_name] try: - response = await global_braintrust_http_handler.post( + response = await self.global_braintrust_http_handler.post( f"{self.api_base}/project/register", headers=self.headers, json={"name": project_name}, @@ -146,7 +146,7 @@ class BraintrustLogger(CustomLogger): return metadata async def create_default_project_and_experiment(self): - project = await global_braintrust_http_handler.post( + project = await self.global_braintrust_http_handler.post( f"{self.api_base}/project", headers=self.headers, json={"name": "litellm"} ) @@ -155,7 +155,7 @@ class BraintrustLogger(CustomLogger): self.default_project_id = project_dict["id"] def create_sync_default_project_and_experiment(self): - project = global_braintrust_sync_http_handler.post( + project = self.global_braintrust_sync_http_handler.post( f"{self.api_base}/project", headers=self.headers, json={"name": "litellm"} ) @@ -291,9 +291,9 @@ class BraintrustLogger(CustomLogger): try: print_verbose( - f"global_braintrust_sync_http_handler.post: {global_braintrust_sync_http_handler.post}" + f"self.global_braintrust_sync_http_handler.post: {self.global_braintrust_sync_http_handler.post}" ) - global_braintrust_sync_http_handler.post( + self.global_braintrust_sync_http_handler.post( url=f"{self.api_base}/project_logs/{project_id}/insert", json={"events": [request_data]}, headers=self.headers, @@ -446,7 +446,7 @@ class BraintrustLogger(CustomLogger): request_data["metrics"] = metrics try: - await global_braintrust_http_handler.post( + await self.global_braintrust_http_handler.post( url=f"{self.api_base}/project_logs/{project_id}/insert", json={"events": [request_data]}, headers=self.headers, diff --git a/litellm/integrations/datadog/datadog_llm_obs.py b/litellm/integrations/datadog/datadog_llm_obs.py index 2577ed3ddf0..4f9c6409770 100644 --- a/litellm/integrations/datadog/datadog_llm_obs.py +++ b/litellm/integrations/datadog/datadog_llm_obs.py @@ -27,7 +27,12 @@ from litellm.llms.custom_httpx.http_handler import ( httpxSpecialProvider, ) from litellm.types.integrations.datadog_llm_obs import * -from litellm.types.utils import CallTypes, StandardLoggingPayload +from litellm.types.utils import ( + CallTypes, + StandardLoggingGuardrailInformation, + StandardLoggingPayload, + StandardLoggingPayloadErrorInformation, +) class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): @@ -102,6 +107,24 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): verbose_logger.exception( f"DataDogLLMObs: Error logging success event - {str(e)}" ) + + async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): + try: + verbose_logger.debug( + f"DataDogLLMObs: Logging failure event for model {kwargs.get('model', 'unknown')}" + ) + payload = self.create_llm_obs_payload( + kwargs, start_time, end_time + ) + verbose_logger.debug(f"DataDogLLMObs: Payload: {payload}") + self.log_queue.append(payload) + + if len(self.log_queue) >= self.batch_size: + await self.async_send_batch() + except Exception as e: + verbose_logger.exception( + f"DataDogLLMObs: Error logging failure event - {str(e)}" + ) async def async_send_batch(self): try: @@ -174,11 +197,14 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): call_type=standard_logging_payload.get("call_type") )) + error_info = self._assemble_error_info(standard_logging_payload) + meta = Meta( kind=self._get_datadog_span_kind(standard_logging_payload.get("call_type")), input=input_meta, output=output_meta, metadata=self._get_dd_llm_obs_payload_metadata(standard_logging_payload), + error=error_info, ) # Calculate metrics (you may need to adjust these based on available data) @@ -199,11 +225,31 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): start_ns=int(start_time.timestamp() * 1e9), duration=int((end_time - start_time).total_seconds() * 1e9), metrics=metrics, + status="error" if error_info else "ok", tags=[ self._get_datadog_tags(standard_logging_object=standard_logging_payload) ], ) + def _assemble_error_info(self, standard_logging_payload: StandardLoggingPayload) -> Optional[DDLLMObsError]: + """ + Assemble error information for failure cases according to DD LLM Obs API spec + """ + # Handle error information for failure cases according to DD LLM Obs API spec + error_info: Optional[DDLLMObsError] = None + + if standard_logging_payload.get("status") == "failure": + # Try to get structured error information first + error_information: Optional[StandardLoggingPayloadErrorInformation] = standard_logging_payload.get("error_information") + + if error_information: + error_info = DDLLMObsError( + message=error_information.get("error_message") or standard_logging_payload.get("error_str") or "Unknown error", + type=error_information.get("error_class"), + stack=error_information.get("traceback") + ) + return error_info + def _get_time_to_first_token_seconds(self, standard_logging_payload: StandardLoggingPayload) -> float: """ Get the time to first token in seconds @@ -232,8 +278,20 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): for now this handles logging /chat/completions responses """ + if response_obj is None: + return [] + if call_type in [CallTypes.completion.value, CallTypes.acompletion.value]: - return [response_obj["choices"][0]["message"]] + try: + # Safely extract message from response_obj, handle failure cases + if isinstance(response_obj, dict) and "choices" in response_obj: + choices = response_obj["choices"] + if choices and len(choices) > 0 and "message" in choices[0]: + return [choices[0]["message"]] + return [] + except (KeyError, IndexError, TypeError): + # In case of any error accessing the response structure, return empty list + return [] return [] def _get_datadog_span_kind(self, call_type: Optional[str]) -> Literal["llm", "tool", "task", "embedding", "retrieval"]: @@ -350,11 +408,11 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): def _get_dd_llm_obs_payload_metadata( self, standard_logging_payload: StandardLoggingPayload - ) -> Dict: + ) -> Dict[str, Any]: """ Fields to track in DD LLM Observability metadata from litellm standard logging payload """ - _metadata = { + _metadata: Dict[str, Any] = { "model_name": standard_logging_payload.get("model", "unknown"), "model_provider": standard_logging_payload.get( "custom_llm_provider", "unknown" @@ -364,9 +422,44 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): "cache_hit": standard_logging_payload.get("cache_hit", "unknown"), "cache_key": standard_logging_payload.get("cache_key", "unknown"), "saved_cache_cost": standard_logging_payload.get("saved_cache_cost", 0), + "guardrail_information": standard_logging_payload.get("guardrail_information", None), } + + ######################################################### + # Add latency metrics to metadata + ######################################################### + latency_metrics = self._get_latency_metrics(standard_logging_payload) + _metadata.update({"latency_metrics": dict(latency_metrics)}) + _standard_logging_metadata: dict = ( dict(standard_logging_payload.get("metadata", {})) or {} ) _metadata.update(_standard_logging_metadata) return _metadata + + def _get_latency_metrics(self, standard_logging_payload: StandardLoggingPayload) -> DDLLMObsLatencyMetrics: + """ + Get the latency metrics from the standard logging payload + """ + latency_metrics: DDLLMObsLatencyMetrics = DDLLMObsLatencyMetrics() + # Add latency metrics to metadata + # Time to first token (convert from seconds to milliseconds for consistency) + time_to_first_token_seconds = self._get_time_to_first_token_seconds(standard_logging_payload) + if time_to_first_token_seconds > 0: + latency_metrics["time_to_first_token_ms"] = time_to_first_token_seconds * 1000 + + # LiteLLM overhead time + hidden_params = standard_logging_payload.get("hidden_params", {}) + litellm_overhead_ms = hidden_params.get("litellm_overhead_time_ms") + if litellm_overhead_ms is not None: + latency_metrics["litellm_overhead_time_ms"] = litellm_overhead_ms + + # Guardrail overhead latency + guardrail_info: Optional[StandardLoggingGuardrailInformation] = standard_logging_payload.get("guardrail_information") + if guardrail_info is not None: + _guardrail_duration_seconds: Optional[float] = guardrail_info.get("duration") + if _guardrail_duration_seconds is not None: + # Convert from seconds to milliseconds for consistency + latency_metrics["guardrail_overhead_time_ms"] = _guardrail_duration_seconds * 1000 + + return latency_metrics \ No newline at end of file diff --git a/litellm/integrations/langfuse/langfuse_otel.py b/litellm/integrations/langfuse/langfuse_otel.py index 4072be2a256..8b90e123371 100644 --- a/litellm/integrations/langfuse/langfuse_otel.py +++ b/litellm/integrations/langfuse/langfuse_otel.py @@ -1,15 +1,16 @@ import base64 -import os import json # <--- NEW -from typing import TYPE_CHECKING, Any, Union -from urllib.parse import quote +import os +from typing import TYPE_CHECKING, Any, Optional, Union from litellm._logging import verbose_logger from litellm.integrations.arize import _utils +from litellm.integrations.opentelemetry import OpenTelemetry from litellm.types.integrations.langfuse_otel import ( LangfuseOtelConfig, LangfuseSpanAttributes, ) +from litellm.types.utils import StandardCallbackDynamicParams if TYPE_CHECKING: from opentelemetry.trace import Span as _Span @@ -33,7 +34,11 @@ LANGFUSE_CLOUD_US_ENDPOINT = "https://us.cloud.langfuse.com/api/public/otel" -class LangfuseOtelLogger: +class LangfuseOtelLogger(OpenTelemetry): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + @staticmethod def set_langfuse_otel_attributes(span: Span, kwargs, response_obj): """ @@ -174,11 +179,11 @@ class LangfuseOtelLogger: endpoint = LANGFUSE_CLOUD_US_ENDPOINT verbose_logger.debug(f"Using Langfuse US cloud endpoint: {endpoint}") - # Create Basic Auth header - auth_string = f"{public_key}:{secret_key}" - auth_header = base64.b64encode(auth_string.encode()).decode() - # URL encode the entire header value as required by OpenTelemetry specification - otlp_auth_headers = f"Authorization={quote(f'Basic {auth_header}')}" + auth_header = LangfuseOtelLogger._get_langfuse_authorization_header( + public_key=public_key, + secret_key=secret_key + ) + otlp_auth_headers = f"Authorization={auth_header}" # Set standard OTEL environment variables os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = endpoint @@ -187,3 +192,37 @@ class LangfuseOtelLogger: return LangfuseOtelConfig( otlp_auth_headers=otlp_auth_headers, protocol="otlp_http" ) + + @staticmethod + def _get_langfuse_authorization_header(public_key: str, secret_key: str) -> str: + """ + Get the authorization header for Langfuse OpenTelemetry. + """ + auth_string = f"{public_key}:{secret_key}" + auth_header = base64.b64encode(auth_string.encode()).decode() + return f'Basic {auth_header}' + + def construct_dynamic_otel_headers( + self, + standard_callback_dynamic_params: StandardCallbackDynamicParams + ) -> Optional[dict]: + """ + Construct dynamic Langfuse headers from standard callback dynamic params + + This is used for team/key based logging. + + Returns: + dict: A dictionary of dynamic Langfuse headers + """ + dynamic_headers = {} + + dynamic_langfuse_public_key = standard_callback_dynamic_params.get("langfuse_public_key") + dynamic_langfuse_secret_key = standard_callback_dynamic_params.get("langfuse_secret_key") + if dynamic_langfuse_public_key and dynamic_langfuse_secret_key: + auth_header = LangfuseOtelLogger._get_langfuse_authorization_header( + public_key=dynamic_langfuse_public_key, + secret_key=dynamic_langfuse_secret_key + ) + dynamic_headers["Authorization"] = auth_header + + return dynamic_headers diff --git a/litellm/integrations/mlflow.py b/litellm/integrations/mlflow.py index ea9051db4de..86af800d732 100644 --- a/litellm/integrations/mlflow.py +++ b/litellm/integrations/mlflow.py @@ -60,10 +60,7 @@ class MlflowLogger(CustomLogger): inputs = self._construct_input(kwargs) input_messages = inputs.get("messages", []) - output_messages = [ - c.message.model_dump(exclude_none=True) - for c in getattr(response_obj, "choices", []) - ] + output_messages = [c.message.model_dump(exclude_none=True) for c in getattr(response_obj, "choices", [])] if messages := [*input_messages, *output_messages]: set_span_chat_messages(span, messages) if tools := inputs.get("tools"): @@ -168,6 +165,10 @@ class MlflowLogger(CustomLogger): for key in ["functions", "tools", "stream", "tool_choice", "user"]: if value := kwargs.get("optional_params", {}).pop(key, None): inputs[key] = value + + if prediction := kwargs.get("prediction"): + inputs["prediction"] = prediction + return inputs def _extract_attributes(self, kwargs): @@ -189,9 +190,9 @@ class MlflowLogger(CustomLogger): { "api_base": standard_obj.get("api_base"), "cache_hit": standard_obj.get("cache_hit"), - "usage": { - "completion_tokens": standard_obj.get("completion_tokens"), - "prompt_tokens": standard_obj.get("prompt_tokens"), + "mlflow.chat.tokenUsage": { + "input_tokens": standard_obj.get("prompt_tokens"), + "output_tokens": standard_obj.get("completion_tokens"), "total_tokens": standard_obj.get("total_tokens"), }, "raw_llm_response": standard_obj.get("response"), @@ -232,7 +233,6 @@ class MlflowLogger(CustomLogger): """ import mlflow - call_type = kwargs.get("call_type", "completion") span_name = f"litellm-{call_type}" span_type = self._get_span_type(call_type) @@ -260,6 +260,7 @@ class MlflowLogger(CustomLogger): tags=self._transform_tag_list_to_dict(attributes.get("request_tags", [])), start_time_ns=start_time_ns, ) + def _transform_tag_list_to_dict(self, tag_list: list) -> dict: return {tag: "" for tag in tag_list} diff --git a/litellm/litellm_core_utils/core_helpers.py b/litellm/litellm_core_utils/core_helpers.py index 13a2e554f12..4aeb9d4d640 100644 --- a/litellm/litellm_core_utils/core_helpers.py +++ b/litellm/litellm_core_utils/core_helpers.py @@ -37,6 +37,27 @@ def safe_divide_seconds( return float(seconds / denominator) +def safe_divide( + numerator: Union[int, float], + denominator: Union[int, float], + default: Union[int, float] = 0 +) -> Union[int, float]: + """ + Safely divide two numbers, returning a default value if denominator is zero. + + Args: + numerator: The number to divide + denominator: The number to divide by + default: Value to return if denominator is zero (defaults to 0) + + Returns: + The result of numerator/denominator, or default if denominator is zero + """ + if denominator == 0: + return default + return numerator / denominator + + def map_finish_reason( finish_reason: str, ): # openai supports 5 stop sequences - 'stop', 'length', 'function_call', 'content_filter', 'null' diff --git a/litellm/litellm_core_utils/get_llm_provider_logic.py b/litellm/litellm_core_utils/get_llm_provider_logic.py index 702196a7f05..2d511741ec6 100644 --- a/litellm/litellm_core_utils/get_llm_provider_logic.py +++ b/litellm/litellm_core_utils/get_llm_provider_logic.py @@ -196,6 +196,9 @@ def get_llm_provider( # noqa: PLR0915 elif endpoint == "https://api.cerebras.ai/v1": custom_llm_provider = "cerebras" dynamic_api_key = get_secret_str("CEREBRAS_API_KEY") + elif endpoint == "https://inference.baseten.co/v1": + custom_llm_provider = "baseten" + dynamic_api_key = get_secret_str("BASETEN_API_KEY") elif endpoint == "https://api.sambanova.ai/v1": custom_llm_provider = "sambanova" dynamic_api_key = get_secret_str("SAMBANOVA_API_KEY") @@ -351,11 +354,16 @@ def get_llm_provider( # noqa: PLR0915 custom_llm_provider = "openai" elif model in litellm.empower_models: custom_llm_provider = "empower" + elif model in litellm.gradient_ai_models: + custom_llm_provider = "gradient_ai" elif model == "*": custom_llm_provider = "openai" # bytez models elif model.startswith("bytez/"): custom_llm_provider = "bytez" + # cometapi models + elif model.startswith("cometapi/"): + custom_llm_provider = "cometapi" elif model.startswith("oci/"): custom_llm_provider = "oci" if not custom_llm_provider: @@ -473,6 +481,13 @@ def _get_openai_compatible_provider_info( # noqa: PLR0915 api_base or get_secret("CEREBRAS_API_BASE") or "https://api.cerebras.ai/v1" ) # type: ignore dynamic_api_key = api_key or get_secret_str("CEREBRAS_API_KEY") + elif custom_llm_provider == "baseten": + # Use BasetenConfig to determine the appropriate API base URL + if api_base is None: + api_base = litellm.BasetenConfig.get_api_base_for_model(model) + else: + api_base = api_base or get_secret_str("BASETEN_API_BASE") or "https://inference.baseten.co/v1" + dynamic_api_key = api_key or get_secret_str("BASETEN_API_KEY") elif custom_llm_provider == "sambanova": api_base = ( api_base @@ -664,6 +679,13 @@ def _get_openai_compatible_provider_info( # noqa: PLR0915 or f"https://{get_secret('SNOWFLAKE_ACCOUNT_ID')}.snowflakecomputing.com/api/v2/cortex/inference:complete" ) # type: ignore dynamic_api_key = api_key or get_secret_str("SNOWFLAKE_JWT") + elif custom_llm_provider == "gradient_ai": + ( + api_base, + dynamic_api_key, + ) = litellm.GradientAIConfig()._get_openai_compatible_provider_info( + api_base, api_key + ) elif custom_llm_provider == "featherless_ai": ( api_base, @@ -720,6 +742,13 @@ def _get_openai_compatible_provider_info( # noqa: PLR0915 ) = litellm.HyperbolicChatConfig()._get_openai_compatible_provider_info( api_base, api_key ) + elif custom_llm_provider == "aiml": + ( + api_base, + dynamic_api_key, + ) = litellm.AIMLChatConfig()._get_openai_compatible_provider_info( + api_base, api_key + ) if api_base is not None and not isinstance(api_base, str): raise Exception("api base needs to be a string. api_base={}".format(api_base)) diff --git a/litellm/litellm_core_utils/get_supported_openai_params.py b/litellm/litellm_core_utils/get_supported_openai_params.py index b2a2c364240..6584defe5b7 100644 --- a/litellm/litellm_core_utils/get_supported_openai_params.py +++ b/litellm/litellm_core_utils/get_supported_openai_params.py @@ -78,6 +78,8 @@ def get_supported_openai_params( # noqa: PLR0915 return litellm.nvidiaNimEmbeddingConfig.get_supported_openai_params() elif custom_llm_provider == "cerebras": return litellm.CerebrasConfig().get_supported_openai_params(model=model) + elif custom_llm_provider == "baseten": + return litellm.BasetenConfig().get_supported_openai_params(model=model) elif custom_llm_provider == "xai": return litellm.XAIChatConfig().get_supported_openai_params(model=model) elif custom_llm_provider == "ai21_chat" or custom_llm_provider == "ai21": @@ -121,6 +123,10 @@ def get_supported_openai_params( # noqa: PLR0915 return litellm.AzureOpenAIO1Config().get_supported_openai_params( model=model ) + elif litellm.AzureOpenAIGPT5Config.is_model_gpt_5_model(model=model): + return litellm.AzureOpenAIGPT5Config().get_supported_openai_params( + model=model + ) else: return litellm.AzureOpenAIConfig().get_supported_openai_params(model=model) elif custom_llm_provider == "openrouter": @@ -136,7 +142,10 @@ def get_supported_openai_params( # noqa: PLR0915 model=model ) elif custom_llm_provider == "sambanova": - return litellm.SambanovaConfig().get_supported_openai_params(model=model) + if request_type == "embeddings": + litellm.SambaNovaEmbeddingConfig().get_supported_openai_params(model=model) + else: + return litellm.SambanovaConfig().get_supported_openai_params(model=model) elif custom_llm_provider == "nebius": if request_type == "chat_completion": return litellm.NebiusConfig().get_supported_openai_params(model=model) diff --git a/litellm/litellm_core_utils/health_check_helpers.py b/litellm/litellm_core_utils/health_check_helpers.py index 7a2c005e8f6..2f412479937 100644 --- a/litellm/litellm_core_utils/health_check_helpers.py +++ b/litellm/litellm_core_utils/health_check_helpers.py @@ -1,12 +1,13 @@ - """ Helper functions for health check calls. """ + from typing import TYPE_CHECKING if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging + class HealthCheckHelpers: @staticmethod @@ -38,10 +39,9 @@ class HealthCheckHelpers: model_params["model"] = cheapest_models[0] model_params["litellm_logging_obj"] = litellm_logging_obj model_params["fallbacks"] = fallback_models - model_params["max_tokens"] = 1 + model_params["max_tokens"] = 10 # gpt-5-nano throws errors for max_tokens=1 await acompletion(**model_params) return {} - @staticmethod def _update_model_params_with_health_check_tracking_information( @@ -57,6 +57,7 @@ class HealthCheckHelpers: """ from litellm.proxy._types import UserAPIKeyAuth from litellm.proxy.litellm_pre_call_utils import LiteLLMProxyRequestSetup + _metadata_variable_name = "litellm_metadata" litellm_metadata = HealthCheckHelpers._get_metadata_for_health_check_call() model_params[_metadata_variable_name] = litellm_metadata @@ -66,13 +67,14 @@ class HealthCheckHelpers: _metadata_variable_name=_metadata_variable_name, ) return model_params - + @staticmethod def _get_metadata_for_health_check_call(): """ Returns the metadata for the health check call. """ from litellm.constants import LITTELM_INTERNAL_HEALTH_SERVICE_ACCOUNT_NAME + return { "tags": [LITTELM_INTERNAL_HEALTH_SERVICE_ACCOUNT_NAME], - } \ No newline at end of file + } diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 5462257c9b7..7bc7702684d 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -131,7 +131,6 @@ from ..integrations.humanloop import HumanloopLogger from ..integrations.lago import LagoLogger from ..integrations.langfuse.langfuse import LangFuseLogger from ..integrations.langfuse.langfuse_handler import LangFuseHandler -from ..integrations.langfuse.langfuse_otel import LangfuseOtelLogger from ..integrations.langfuse.langfuse_prompt_management import LangfusePromptManagement from ..integrations.langsmith import LangsmithLogger from ..integrations.literal_ai import LiteralAILogger @@ -812,7 +811,7 @@ class Logging(LiteLLMLoggingBaseClass): str(e) ) ) - if self.logger_fn and callable(self.logger_fn): + if getattr(self, "logger_fn", None) and callable(self.logger_fn): try: self.logger_fn( self.model_call_details @@ -1000,7 +999,7 @@ class Logging(LiteLLMLoggingBaseClass): ) ) ) - if self.logger_fn and callable(self.logger_fn): + if getattr(self, "logger_fn", None) and callable(self.logger_fn): try: self.logger_fn( self.model_call_details @@ -3457,6 +3456,7 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 _in_memory_loggers.append(langfuse_logger) return langfuse_logger # type: ignore elif logging_integration == "langfuse_otel": + from litellm.integrations.langfuse.langfuse_otel import LangfuseOtelLogger from litellm.integrations.opentelemetry import ( OpenTelemetry, OpenTelemetryConfig, @@ -3467,15 +3467,16 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 # The endpoint and headers are now set as environment variables by get_langfuse_otel_config() otel_config = OpenTelemetryConfig( exporter=langfuse_otel_config.protocol, + headers=langfuse_otel_config.otlp_auth_headers, ) for callback in _in_memory_loggers: if ( - isinstance(callback, OpenTelemetry) + isinstance(callback, LangfuseOtelLogger) and callback.callback_name == "langfuse_otel" ): return callback # type: ignore - _otel_logger = OpenTelemetry( + _otel_logger = LangfuseOtelLogger( config=otel_config, callback_name="langfuse_otel" ) _in_memory_loggers.append(_otel_logger) @@ -3918,10 +3919,12 @@ class StandardLoggingPayloadSetup: # Generate cold storage object key if cold storage is configured if start_time is not None and response_id is not None: - cold_storage_object_key = StandardLoggingPayloadSetup._generate_cold_storage_object_key( - start_time=start_time, - response_id=response_id, - team_alias=clean_metadata.get("user_api_key_team_alias"), + cold_storage_object_key = ( + StandardLoggingPayloadSetup._generate_cold_storage_object_key( + start_time=start_time, + response_id=response_id, + team_alias=clean_metadata.get("user_api_key_team_alias"), + ) ) if cold_storage_object_key: clean_metadata["cold_storage_object_key"] = cold_storage_object_key @@ -4092,36 +4095,33 @@ class StandardLoggingPayloadSetup: ) -> Optional[str]: """ Generate cold storage object key in the same format as S3Logger. - + Args: start_time: The start time of the request - response_id: The response ID + response_id: The response ID team_alias: Optional team alias for team-based prefixing - + Returns: Optional[str]: The generated object key or None if cold storage not configured """ # Generate object key in same format as S3Logger from litellm.integrations.s3 import get_s3_object_key - from litellm.proxy.spend_tracking.cold_storage_handler import ColdStorageHandler # Only generate object key if cold storage is configured - configured_cold_storage_logger = ColdStorageHandler._get_configured_cold_storage_custom_logger() - if configured_cold_storage_logger is None: + if litellm.configured_cold_storage_logger is None: return None - + try: # Generate file name in same format as litellm.utils.get_logging_id s3_file_name = f"time-{start_time.strftime('%H-%M-%S-%f')}_{response_id}" - s3_object_key = get_s3_object_key( - s3_path="", # Use empty path as default - team_alias_prefix="", # Don't split by team alias for cold storage + s3_path="", # Use empty path as default + team_alias_prefix="", # Don't split by team alias for cold storage start_time=start_time, s3_file_name=s3_file_name, ) - + return s3_object_key except Exception: # If any error occurs in generating the key, return None diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py index 737e3f7f982..c851ec06a6b 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/utils.py +++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py @@ -1,11 +1,17 @@ # What is this? ## Helper utilities for cost_per_token() -from typing import Literal, Optional, Tuple, cast +from typing import Any, Literal, Optional, Tuple, cast import litellm from litellm._logging import verbose_logger -from litellm.types.utils import CallTypes, ModelInfo, PassthroughCallTypes, Usage +from litellm.types.utils import ( + CallTypes, + ImageResponse, + ModelInfo, + PassthroughCallTypes, + Usage, +) from litellm.utils import get_model_info @@ -107,15 +113,20 @@ def _generic_cost_per_character( return prompt_cost, completion_cost -def _get_token_base_cost(model_info: ModelInfo, usage: Usage) -> Tuple[float, float]: +def _get_token_base_cost(model_info: ModelInfo, usage: Usage) -> Tuple[float, float, float, float]: """ - Return prompt cost for a given model and usage. + Return prompt cost, completion cost, and cache costs for a given model and usage. If input_tokens > threshold and `input_cost_per_token_above_[x]k_tokens` or `input_cost_per_token_above_[x]_tokens` is set, - then we use the corresponding threshold cost. + then we use the corresponding threshold cost for all token types. + + Returns: + Tuple[float, float, float, float] - (prompt_cost, completion_cost, cache_creation_cost, cache_read_cost) """ prompt_base_cost = cast(float, _get_cost_per_unit(model_info, "input_cost_per_token")) completion_base_cost = cast(float, _get_cost_per_unit(model_info, "output_cost_per_token")) + cache_creation_cost = cast(float, _get_cost_per_unit(model_info, "cache_creation_input_token_cost")) + cache_read_cost = cast(float, _get_cost_per_unit(model_info, "cache_read_input_token_cost")) ## CHECK IF ABOVE THRESHOLD threshold: Optional[float] = None @@ -135,13 +146,28 @@ def _get_token_base_cost(model_info: ModelInfo, usage: Usage) -> Tuple[float, fl f"output_cost_per_token_above_{threshold_str}_tokens", completion_base_cost, )) + + # Apply tiered pricing to cache costs + cache_creation_tiered_key = f"cache_creation_input_token_cost_above_{threshold_str}_tokens" + cache_read_tiered_key = f"cache_read_input_token_cost_above_{threshold_str}_tokens" + + if cache_creation_tiered_key in model_info: + cache_creation_cost = cast(float, _get_cost_per_unit( + model_info, cache_creation_tiered_key, cache_creation_cost + )) + + if cache_read_tiered_key in model_info: + cache_read_cost = cast(float, _get_cost_per_unit( + model_info, cache_read_tiered_key, cache_read_cost + )) + break except (IndexError, ValueError): continue except Exception: continue - return prompt_base_cost, completion_base_cost + return prompt_base_cost, completion_base_cost, cache_creation_cost, cache_read_cost def calculate_cost_component( @@ -256,28 +282,22 @@ def generic_cost_per_token( if text_tokens == 0: text_tokens = usage.prompt_tokens - cache_hit_tokens - audio_tokens - prompt_base_cost, completion_base_cost = _get_token_base_cost( + prompt_base_cost, completion_base_cost, cache_creation_cost, cache_read_cost = _get_token_base_cost( model_info=model_info, usage=usage ) prompt_cost = float(text_tokens) * prompt_base_cost - ### CACHE READ COST - prompt_cost += calculate_cost_component( - model_info, "cache_read_input_token_cost", cache_hit_tokens - ) + ### CACHE READ COST - Now uses tiered pricing + prompt_cost += float(cache_hit_tokens) * cache_read_cost ### AUDIO COST prompt_cost += calculate_cost_component( model_info, "input_cost_per_audio_token", audio_tokens ) - ### CACHE WRITING COST - prompt_cost += calculate_cost_component( - model_info, - "cache_creation_input_token_cost", - usage._cache_creation_input_tokens, - ) + ### CACHE WRITING COST - Now uses tiered pricing + prompt_cost += float(usage._cache_creation_input_tokens or 0) * cache_creation_cost ### CHARACTER COST @@ -377,3 +397,93 @@ class CostCalculatorUtils: ]: return True return False + + @staticmethod + def route_image_generation_cost_calculator( + model: str, + completion_response: Any, + custom_llm_provider: Optional[str] = None, + quality: Optional[str] = None, + n: Optional[int] = None, + size: Optional[str] = None, + optional_params: Optional[dict] = None, + ) -> float: + """ + Route the image generation cost calculator based on the custom_llm_provider + """ + from litellm.cost_calculator import default_image_cost_calculator + from litellm.llms.azure_ai.image_generation.cost_calculator import ( + cost_calculator as azure_ai_image_cost_calculator, + ) + from litellm.llms.bedrock.image.cost_calculator import ( + cost_calculator as bedrock_image_cost_calculator, + ) + from litellm.llms.gemini.image_generation.cost_calculator import ( + cost_calculator as gemini_image_cost_calculator, + ) + from litellm.llms.recraft.cost_calculator import ( + cost_calculator as recraft_image_cost_calculator, + ) + from litellm.llms.vertex_ai.image_generation.cost_calculator import ( + cost_calculator as vertex_ai_image_cost_calculator, + ) + + if custom_llm_provider == litellm.LlmProviders.VERTEX_AI.value: + if isinstance(completion_response, ImageResponse): + return vertex_ai_image_cost_calculator( + model=model, + image_response=completion_response, + ) + elif custom_llm_provider == litellm.LlmProviders.BEDROCK.value: + if isinstance(completion_response, ImageResponse): + return bedrock_image_cost_calculator( + model=model, + size=size, + image_response=completion_response, + optional_params=optional_params, + ) + raise TypeError( + "completion_response must be of type ImageResponse for bedrock image cost calculation" + ) + elif custom_llm_provider == litellm.LlmProviders.RECRAFT.value: + from litellm.llms.recraft.cost_calculator import ( + cost_calculator as recraft_image_cost_calculator, + ) + + return recraft_image_cost_calculator( + model=model, + image_response=completion_response, + ) + elif custom_llm_provider == litellm.LlmProviders.AIML.value: + from litellm.llms.aiml.image_generation.cost_calculator import ( + cost_calculator as aiml_image_cost_calculator, + ) + + return aiml_image_cost_calculator( + model=model, + image_response=completion_response, + ) + elif custom_llm_provider == litellm.LlmProviders.GEMINI.value: + from litellm.llms.gemini.image_generation.cost_calculator import ( + cost_calculator as gemini_image_cost_calculator, + ) + + return gemini_image_cost_calculator( + model=model, + image_response=completion_response, + ) + elif custom_llm_provider == litellm.LlmProviders.AZURE_AI.value: + return azure_ai_image_cost_calculator( + model=model, + image_response=completion_response, + ) + else: + return default_image_cost_calculator( + model=model, + quality=quality, + custom_llm_provider=custom_llm_provider, + n=n, + size=size, + optional_params=optional_params, + ) + return 0.0 diff --git a/litellm/litellm_core_utils/logging_utils.py b/litellm/litellm_core_utils/logging_utils.py index c7512ea146b..bf43519afc6 100644 --- a/litellm/litellm_core_utils/logging_utils.py +++ b/litellm/litellm_core_utils/logging_utils.py @@ -1,5 +1,6 @@ import asyncio import functools +import time from datetime import datetime from typing import TYPE_CHECKING, Any, List, Optional, Union @@ -11,15 +12,19 @@ from litellm.types.utils import ( ) if TYPE_CHECKING: + from opentelemetry.trace import Span as _Span + from litellm import ModelResponse as _ModelResponse from litellm.litellm_core_utils.litellm_logging import ( Logging as LiteLLMLoggingObject, ) LiteLLMModelResponse = _ModelResponse + Span = Union[_Span, Any] else: LiteLLMModelResponse = Any LiteLLMLoggingObject = Any + Span = Any import litellm @@ -28,9 +33,52 @@ import litellm Helper utils used for logging callbacks """ +# Global service logger instance to avoid recreating it +_service_logger = None + + +def _get_service_logger(): + """Get or create the global ServiceLogging instance""" + global _service_logger + if _service_logger is None: + from litellm._service_logger import ServiceLogging + + _service_logger = ServiceLogging() + return _service_logger + + +def _get_parent_otel_span_from_logging_obj( + logging_obj: Optional[LiteLLMLoggingObject] = None, +) -> Optional[Span]: + """ + Extract the parent OTEL span from the logging object using existing helper. + + Args: + logging_obj: The LiteLLM logging object containing model call details + + Returns: + The parent OTEL span if found, None otherwise + """ + try: + if logging_obj is None or not hasattr(logging_obj, "model_call_details"): + return None + + # Reuse existing function by passing model_call_details as kwargs + from litellm.litellm_core_utils.core_helpers import ( + _get_parent_otel_span_from_kwargs, + ) + + return _get_parent_otel_span_from_kwargs(logging_obj.model_call_details) + + except Exception as e: + verbose_logger.exception( + f"Error in _get_parent_otel_span_from_logging_obj: {str(e)}" + ) + return None + def convert_litellm_response_object_to_str( - response_obj: Union[Any, LiteLLMModelResponse] + response_obj: Union[Any, LiteLLMModelResponse], ) -> Optional[str]: """ Get the string of the response object from LiteLLM @@ -125,37 +173,102 @@ def track_llm_api_timing(): """ Decorator to track LLM API call timing for both sync and async functions. The logging_obj is expected to be passed as an argument to the decorated function. + Logs timing using ServiceLogging similar to Redis cache. """ def decorator(func): @functools.wraps(func) async def async_wrapper(*args, **kwargs): start_time = datetime.now() + start_time_float = time.time() + logging_obj = kwargs.get("logging_obj", None) + + # Extract parent OTEL span from logging object + parent_otel_span = _get_parent_otel_span_from_logging_obj(logging_obj) + try: result = await func(*args, **kwargs) return result finally: end_time = datetime.now() + end_time_float = time.time() + duration = end_time_float - start_time_float + + # Set duration in model call details _set_duration_in_model_call_details( - logging_obj=kwargs.get("logging_obj", None), + logging_obj=logging_obj, start_time=start_time, end_time=end_time, ) + # Log timing using ServiceLogging (like Redis cache) + try: + from litellm.types.services import ServiceTypes + + service_logger = _get_service_logger() + + # Get function name for call_type + call_type = f"{func.__name__} <- track_llm_api_timing" + + # Create async task for service logging (similar to Redis cache pattern) + asyncio.create_task( + service_logger.async_service_success_hook( + service=ServiceTypes.LITELLM, + duration=duration, + call_type=call_type, + start_time=start_time_float, + end_time=end_time_float, + parent_otel_span=parent_otel_span, + ) + ) + except Exception as e: + verbose_logger.debug(f"Error in service logging: {str(e)}") + @functools.wraps(func) def sync_wrapper(*args, **kwargs): start_time = datetime.now() + start_time_float = time.time() + logging_obj = kwargs.get("logging_obj", None) + + # Extract parent OTEL span from logging object + parent_otel_span = _get_parent_otel_span_from_logging_obj(logging_obj) + try: result = func(*args, **kwargs) return result finally: end_time = datetime.now() + end_time_float = time.time() + duration = end_time_float - start_time_float + + # Set duration in model call details _set_duration_in_model_call_details( - logging_obj=kwargs.get("logging_obj", None), + logging_obj=logging_obj, start_time=start_time, end_time=end_time, ) + # Log timing using ServiceLogging (like Redis cache) + try: + from litellm.types.services import ServiceTypes + + service_logger = _get_service_logger() + + # Get function name for call_type + call_type = f"{func.__name__} <- track_llm_api_timing" + + # Use sync service logging for sync functions + service_logger.service_success_hook( + service=ServiceTypes.LITELLM, + duration=duration, + call_type=call_type, + start_time=start_time_float, + end_time=end_time_float, + parent_otel_span=parent_otel_span, + ) + except Exception as e: + verbose_logger.debug(f"Error in service logging: {str(e)}") + # Check if the function is async or sync if asyncio.iscoroutinefunction(func): return async_wrapper diff --git a/litellm/litellm_core_utils/logging_worker.py b/litellm/litellm_core_utils/logging_worker.py new file mode 100644 index 00000000000..faf8413bd57 --- /dev/null +++ b/litellm/litellm_core_utils/logging_worker.py @@ -0,0 +1,131 @@ +import asyncio +import contextlib +from typing import Coroutine, Optional + +from litellm._logging import verbose_logger + + +class LoggingWorker: + """ + A simple, async logging worker that processes log coroutines in the background. + Designed to be best-effort with bounded queues to prevent backpressure. + + This leads to a +200 RPS performance improvement when using LiteLLM Python SDK or Proxy Server. + - Use this to queue coroutine tasks that are not critical to the main flow of the application. e.g Success/Error callbacks, logging, etc. + """ + LOGGING_WORKER_MAX_QUEUE_SIZE = 50_000 + LOGGING_WORKER_MAX_TIME_PER_COROUTINE = 20.0 + + MAX_ITERATIONS_TO_CLEAR_QUEUE = 200 + MAX_TIME_TO_CLEAR_QUEUE = 5.0 + + def __init__( + self, + timeout: float = LOGGING_WORKER_MAX_TIME_PER_COROUTINE, + max_queue_size: int = LOGGING_WORKER_MAX_QUEUE_SIZE, + ): + self.timeout = timeout + self.max_queue_size = max_queue_size + self._queue: Optional[asyncio.Queue] = None + self._worker_task: Optional[asyncio.Task] = None + + def _ensure_queue(self) -> None: + """Initialize the queue if it doesn't exist.""" + if self._queue is None: + self._queue = asyncio.Queue(maxsize=self.max_queue_size) + + def start(self) -> None: + """Start the logging worker. Idempotent - safe to call multiple times.""" + self._ensure_queue() + if self._worker_task is None or self._worker_task.done(): + self._worker_task = asyncio.create_task(self._worker_loop()) + + async def _worker_loop(self) -> None: + """Main worker loop that processes log coroutines sequentially.""" + try: + if self._queue is None: + return + + while True: + # Process one coroutine at a time to keep event loop load predictable + coroutine = await self._queue.get() + try: + await asyncio.wait_for(coroutine, timeout=self.timeout) + except Exception as e: + verbose_logger.exception(f"LoggingWorker error: {e}") + pass + finally: + self._queue.task_done() + + except asyncio.CancelledError as e: + verbose_logger.exception(f"LoggingWorker cancelled: {e}") + pass + + def enqueue(self, coroutine: Coroutine) -> None: + """ + Add a coroutine to the logging queue. + Hot path: never blocks, drops logs if queue is full. + """ + if self._queue is None: + return + + try: + self._queue.put_nowait(coroutine) + except asyncio.QueueFull as e: + verbose_logger.exception(f"LoggingWorker queue is full: {e}") + # Drop logs on overload to protect request throughput + pass + + def ensure_initialized_and_enqueue(self, async_coroutine: Coroutine): + """ + Ensure the logging worker is initialized and enqueue the coroutine. + """ + self.start() + self.enqueue(async_coroutine) + + async def stop(self) -> None: + """Stop the logging worker and clean up resources.""" + if self._worker_task: + self._worker_task.cancel() + with contextlib.suppress(Exception): + await self._worker_task + self._worker_task = None + + async def flush(self) -> None: + """Flush the logging queue.""" + if self._queue is None: + return + while not self._queue.empty(): + await self._queue.join() + + async def clear_queue(self): + """ + Clear the queue with a maximum time limit. + """ + if self._queue is None: + return + + start_time = asyncio.get_event_loop().time() + + for _ in range(self.MAX_ITERATIONS_TO_CLEAR_QUEUE): + # Check if we've exceeded the maximum time + if asyncio.get_event_loop().time() - start_time >= self.MAX_TIME_TO_CLEAR_QUEUE: + verbose_logger.warning(f"clear_queue exceeded max_time of {self.MAX_TIME_TO_CLEAR_QUEUE}s, stopping early") + break + + try: + coroutine = self._queue.get_nowait() + # Await the coroutine to properly execute and avoid "never awaited" warnings + try: + await asyncio.wait_for(coroutine, timeout=self.timeout) + except Exception: + # Suppress errors during cleanup + pass + self._queue.task_done() # If you're using join() elsewhere + except asyncio.QueueEmpty: + break + + +# Global instance for backward compatibility +GLOBAL_LOGGING_WORKER = LoggingWorker() + diff --git a/litellm/litellm_core_utils/model_response_utils.py b/litellm/litellm_core_utils/model_response_utils.py new file mode 100644 index 00000000000..5f6fced9d44 --- /dev/null +++ b/litellm/litellm_core_utils/model_response_utils.py @@ -0,0 +1,213 @@ +""" +Utility functions for ModelResponse and ModelResponseStream objects. +""" + +from typing import Any + +from litellm.types.utils import Delta, ModelResponseBase, ModelResponseStream + + +def is_model_response_stream_empty(model_response: ModelResponseStream) -> bool: + """ + Check if a ModelResponseStream is empty based on: + - If finish_reason is set -> it's non empty + - If any field in choices is set (e.g. content, tool calls, etc.) it's non empty + - If usage exists -> it's non empty + + This function is robust and ignores fields that are always set (from ModelResponseBase) + and checks for any meaningful content in other fields. + + Args: + model_response: The ModelResponseStream to check + + Returns: + bool: True if the stream is empty, False if it contains meaningful data + """ + # Fields that are always set in ModelResponseBase and should be ignored + # These are structural fields that don't indicate content + BASE_FIELDS = ModelResponseBase.model_fields.keys() + + # Check if usage exists - this indicates meaningful data + if getattr(model_response, "usage", None) is not None: + return False + + # Check provider_specific_fields at the top level + if ( + hasattr(model_response, "provider_specific_fields") + and model_response.provider_specific_fields is not None + and model_response.provider_specific_fields != {} + ): + return False + + # Check model_extra for dynamically added fields (this is where Pydantic stores them) + if hasattr(model_response, "model_extra") and model_response.model_extra: + for extra_field_name, extra_field_value in model_response.model_extra.items(): + if _has_meaningful_content(extra_field_value): + return False + + # Check for any non-base fields that are set + for model_response_field in model_response.model_fields.keys(): + # Skip base fields that are always set + if model_response_field in BASE_FIELDS: + continue + + # Skip choices - we'll handle them separately with deep inspection + if model_response_field == "choices": + continue + + # Check if any other field has meaningful content + model_response_value = getattr(model_response, model_response_field, None) + if _has_meaningful_content(model_response_value): + return False + + # Deep check of choices for any meaningful content + if hasattr(model_response, "choices") and model_response.choices: + for choice in model_response.choices: + if _is_choice_non_empty(choice): + return False + + # If we get here, the stream is empty + return True + + +def _has_meaningful_content(value: Any) -> bool: + """ + Check if a value contains meaningful content. + + Args: + value: The value to check + + Returns: + bool: True if the value has meaningful content, False otherwise + """ + if value is None: + return False + + if isinstance(value, str): + return len(value.strip()) > 0 + + if isinstance(value, (list, dict)): + return len(value) > 0 + + if isinstance(value, bool): + return True # Any boolean value is meaningful + + if isinstance(value, (int, float)): + return True # Any numeric value is meaningful + + # For other types (objects), consider them meaningful if they exist + return True + + +def _is_choice_non_empty(choice: Any) -> bool: + """ + Deep check if a choice contains any meaningful content. + + Args: + choice: The choice object to check + + Returns: + bool: True if the choice has meaningful content, False otherwise + """ + # Check finish_reason + if hasattr(choice, "finish_reason") and choice.finish_reason is not None: + + return True + + # Check logprobs + if hasattr(choice, "logprobs") and choice.logprobs is not None: + + return True + + # Check enhancements (if present) + if hasattr(choice, "enhancements") and choice.enhancements is not None: + + return True + + # Deep check delta object + if hasattr(choice, "delta") and choice.delta is not None: + if _is_delta_non_empty(choice.delta): + + return True + + # Check model_extra for dynamically added fields on the choice + if hasattr(choice, "model_extra") and choice.model_extra: + for extra_field_name, extra_field_value in choice.model_extra.items(): + # Skip certain structural fields that are just default/None placeholders + if extra_field_name == "index" and extra_field_value == 0: + + continue + if ( + extra_field_name in {"finish_reason", "logprobs"} + and extra_field_value is None + ): + + continue + if extra_field_name == "delta": + + continue + if _has_meaningful_content(extra_field_value): + + return True + + # Check for any other non-standard fields on the choice + for attr_name in dir(choice): + # Skip private attributes, methods, and known empty fields + if ( + attr_name.startswith("_") + or callable(getattr(choice, attr_name)) + or attr_name.startswith("model_") + or attr_name + in { + "finish_reason", + "index", + "delta", + "logprobs", + "enhancements", + } + ): + + continue + + attr_value = getattr(choice, attr_name, None) + if _has_meaningful_content(attr_value): + + return True + + return False + + +def _is_delta_non_empty(delta: Delta) -> bool: + """ + Deep check if a delta object contains any meaningful content. + + Args: + delta: The delta object to check + + Returns: + bool: True if the delta has meaningful content, False otherwise + """ + # Check model_extra for dynamically added fields (this is where Pydantic stores them) + if hasattr(delta, "model_extra") and delta.model_extra: + for extra_field_name, extra_field_value in delta.model_extra.items(): + # Even structural fields are meaningful if they have actual content + if _has_meaningful_content(extra_field_value): + + return True + + # Check all regular attributes of the delta object + for attr_name in dir(delta): + # Skip private attributes, methods, and Pydantic-specific fields + if ( + attr_name.startswith("_") + or callable(getattr(delta, attr_name)) + or attr_name.startswith("model_") + ): + continue + + attr_value = getattr(delta, attr_name, None) + if _has_meaningful_content(attr_value): + + return True + + return False diff --git a/litellm/litellm_core_utils/prompt_templates/common_utils.py b/litellm/litellm_core_utils/prompt_templates/common_utils.py index 9ba547b3600..a99883ef7b6 100644 --- a/litellm/litellm_core_utils/prompt_templates/common_utils.py +++ b/litellm/litellm_core_utils/prompt_templates/common_utils.py @@ -18,6 +18,7 @@ from typing import ( cast, ) +from litellm.router_utils.batch_utils import InMemoryFile from litellm.types.llms.openai import ( AllMessageValues, ChatCompletionAssistantMessage, @@ -453,6 +454,10 @@ def extract_file_data(file_data: FileTypes) -> ExtractedFileData: filename, file_content, content_type = file_data elif len(file_data) == 4: filename, file_content, content_type, file_headers = file_data + elif isinstance(file_data, InMemoryFile): + filename = file_data.name + file_content = file_data + content_type = file_data.content_type else: file_content = file_data # Convert content to bytes diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py index 26388dc2362..77cbe4c9a8e 100644 --- a/litellm/litellm_core_utils/prompt_templates/factory.py +++ b/litellm/litellm_core_utils/prompt_templates/factory.py @@ -17,6 +17,7 @@ from litellm.llms.custom_httpx.http_handler import HTTPHandler, get_async_httpx_ from litellm.types.files import get_file_extension_from_mime_type from litellm.types.llms.anthropic import * from litellm.types.llms.bedrock import MessageBlock as BedrockMessageBlock +from litellm.types.llms.bedrock import CachePointBlock from litellm.types.llms.custom_http import httpxSpecialProvider from litellm.types.llms.ollama import OllamaVisionModelObject from litellm.types.llms.openai import ( @@ -2685,6 +2686,11 @@ def _convert_to_bedrock_tool_call_invoke( ) bedrock_content_block = BedrockContentBlock(toolUse=bedrock_tool) _parts_list.append(bedrock_content_block) + + # Check for cache_control and add a separate cachePoint block + if tool.get("cache_control", None) is not None: + cache_point_block = BedrockContentBlock(cachePoint=CachePointBlock(type="default")) + _parts_list.append(cache_point_block) return _parts_list except Exception as e: raise Exception( @@ -2745,6 +2751,7 @@ def _convert_to_bedrock_tool_call_result( for content in content_list: if content["type"] == "text": content_str += content["text"] + message.get("name", "") id = str(message.get("tool_call_id", str(uuid.uuid4()))) @@ -2753,6 +2760,7 @@ def _convert_to_bedrock_tool_call_result( content=[tool_result_content_block], toolUseId=id, ) + content_block = BedrockContentBlock(toolResult=tool_result) return content_block @@ -3185,9 +3193,30 @@ class BedrockConverseMessagesProcessor: ## MERGE CONSECUTIVE TOOL CALL MESSAGES ## tool_content: List[BedrockContentBlock] = [] while msg_i < len(messages) and messages[msg_i]["role"] == "tool": - tool_call_result = _convert_to_bedrock_tool_call_result(messages[msg_i]) - + current_message = messages[msg_i] + tool_call_result = _convert_to_bedrock_tool_call_result(current_message) tool_content.append(tool_call_result) + + # Check if we need to add a separate cachePoint block + has_cache_control = False + + # Check for message-level cache_control + if current_message.get("cache_control", None) is not None: + has_cache_control = True + # Check for content-level cache_control in list content + elif isinstance(current_message.get("content"), list): + for content_element in current_message["content"]: + if (isinstance(content_element, dict) and + content_element.get("cache_control", None) is not None): + has_cache_control = True + break + + # Add a separate cachePoint block if cache_control is present + if has_cache_control: + cache_point_block = BedrockContentBlock(cachePoint=CachePointBlock(type="default")) + tool_content.append(cache_point_block) + + msg_i += 1 if tool_content: # if last message was a 'user' message, then add a blank assistant message (bedrock requires alternating roles) @@ -3267,13 +3296,29 @@ class BedrockConverseMessagesProcessor: image_url=image_url ) assistants_parts.append(assistants_part) + # Add cache point block for assistant content elements + _cache_point_block = ( + litellm.AmazonConverseConfig()._get_cache_point_block( + message_block=cast( + OpenAIMessageContentListBlock, element + ), + block_type="content_block", + ) + ) + if _cache_point_block is not None: + assistants_parts.append(_cache_point_block) assistant_content.extend(assistants_parts) - elif _assistant_content is not None and isinstance( - _assistant_content, str - ): - assistant_content.append( - BedrockContentBlock(text=_assistant_content) + elif _assistant_content is not None and isinstance(_assistant_content, str): + assistant_content.append(BedrockContentBlock(text=_assistant_content)) + # Add cache point block for assistant string content + _cache_point_block = ( + litellm.AmazonConverseConfig()._get_cache_point_block( + assistant_message_block, block_type="content_block" + ) ) + if _cache_point_block is not None: + assistant_content.append(_cache_point_block) + _tool_calls = assistant_message_block.get("tool_calls", []) if _tool_calls: assistant_content.extend( @@ -3516,8 +3561,30 @@ def _bedrock_converse_messages_pt( # noqa: PLR0915 tool_content: List[BedrockContentBlock] = [] while msg_i < len(messages) and messages[msg_i]["role"] == "tool": tool_call_result = _convert_to_bedrock_tool_call_result(messages[msg_i]) - + current_message = messages[msg_i] + + # Add the tool result first tool_content.append(tool_call_result) + + # Check if we need to add a separate cachePoint block + has_cache_control = False + + # Check for message-level cache_control + if current_message.get("cache_control", None) is not None: + has_cache_control = True + # Check for content-level cache_control in list content + elif isinstance(current_message.get("content"), list): + for content_element in current_message["content"]: + if (isinstance(content_element, dict) and + content_element.get("cache_control", None) is not None): + has_cache_control = True + break + + # Add a separate cachePoint block if cache_control is present + if has_cache_control: + cache_point_block = BedrockContentBlock(cachePoint=CachePointBlock(type="default")) + tool_content.append(cache_point_block) + msg_i += 1 if tool_content: # if last message was a 'user' message, then add a blank assistant message (bedrock requires alternating roles) @@ -3589,9 +3656,28 @@ def _bedrock_converse_messages_pt( # noqa: PLR0915 image_url=image_url ) assistants_parts.append(assistants_part) + # Add cache point block for assistant content elements + _cache_point_block = ( + litellm.AmazonConverseConfig()._get_cache_point_block( + message_block=cast( + OpenAIMessageContentListBlock, element + ), + block_type="content_block", + ) + ) + if _cache_point_block is not None: + assistants_parts.append(_cache_point_block) assistant_content.extend(assistants_parts) elif _assistant_content is not None and isinstance(_assistant_content, str): assistant_content.append(BedrockContentBlock(text=_assistant_content)) + # Add cache point block for assistant string content + _cache_point_block = ( + litellm.AmazonConverseConfig()._get_cache_point_block( + assistant_message_block, block_type="content_block" + ) + ) + if _cache_point_block is not None: + assistant_content.append(_cache_point_block) _tool_calls = assistant_message_block.get("tool_calls", []) if _tool_calls: assistant_content.extend( diff --git a/litellm/litellm_core_utils/sensitive_data_masker.py b/litellm/litellm_core_utils/sensitive_data_masker.py index 900239602df..07f652ecb9b 100644 --- a/litellm/litellm_core_utils/sensitive_data_masker.py +++ b/litellm/litellm_core_utils/sensitive_data_masker.py @@ -33,7 +33,12 @@ class SensitiveDataMasker: value_str = str(value) masked_length = len(value_str) - (self.visible_prefix + self.visible_suffix) - return f"{value_str[:self.visible_prefix]}{self.mask_char * masked_length}{value_str[-self.visible_suffix:]}" + + # Handle the case where visible_suffix is 0 to avoid showing the entire string + if self.visible_suffix == 0: + return f"{value_str[:self.visible_prefix]}{self.mask_char * masked_length}" + else: + return f"{value_str[:self.visible_prefix]}{self.mask_char * masked_length}{value_str[-self.visible_suffix:]}" def is_sensitive_key(self, key: str) -> bool: key_lower = str(key).lower() diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py index 3721851a38f..b9433239271 100644 --- a/litellm/litellm_core_utils/streaming_handler.py +++ b/litellm/litellm_core_utils/streaming_handler.py @@ -13,6 +13,9 @@ from pydantic import BaseModel import litellm from litellm import verbose_logger +from litellm.litellm_core_utils.model_response_utils import ( + is_model_response_stream_empty, +) from litellm.litellm_core_utils.redact_messages import LiteLLMLoggingObject from litellm.litellm_core_utils.thread_pool_executor import executor from litellm.types.llms.openai import ChatCompletionChunk @@ -1574,6 +1577,13 @@ class CustomStreamWrapper: response = self.model_response_creator( chunk=obj_dict, hidden_params=response._hidden_params ) + ## check if empty + is_empty = is_model_response_stream_empty( + model_response=cast(ModelResponseStream, response) + ) + + if is_empty: + continue # add usage as hidden param if self.sent_last_chunk is True and self.stream_options is None: usage = calculate_total_usage(chunks=self.chunks) @@ -1730,7 +1740,18 @@ class CustomStreamWrapper: # Create a new object without the removed attribute processed_chunk = self.model_response_creator(chunk=obj_dict) + is_empty = is_model_response_stream_empty( + model_response=cast(ModelResponseStream, processed_chunk) + ) + + if is_empty: + continue print_verbose(f"final returned processed chunk: {processed_chunk}") + + # add usage as hidden param + if self.sent_last_chunk is True and self.stream_options is None: + usage = calculate_total_usage(chunks=self.chunks) + processed_chunk._hidden_params["usage"] = usage return processed_chunk raise StopAsyncIteration else: # temporary patch for non-aiohttp async calls @@ -1774,6 +1795,7 @@ class CustomStreamWrapper: messages=self.messages, logging_obj=self.logging_obj, ) + response = self.model_response_creator() if complete_streaming_response is not None: setattr( diff --git a/litellm/llms/aiml/__init__.py b/litellm/llms/aiml/__init__.py new file mode 100644 index 00000000000..42482760cda --- /dev/null +++ b/litellm/llms/aiml/__init__.py @@ -0,0 +1,5 @@ +from .image_generation import get_aiml_image_generation_config + +__all__ = [ + "get_aiml_image_generation_config", +] diff --git a/litellm/llms/aiml/chat/transformation.py b/litellm/llms/aiml/chat/transformation.py new file mode 100644 index 00000000000..0f3e333343d --- /dev/null +++ b/litellm/llms/aiml/chat/transformation.py @@ -0,0 +1,23 @@ +from typing import Optional, Tuple + +from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig +from litellm.secret_managers.main import get_secret_str + + +class AIMLChatConfig(OpenAIGPTConfig): + @property + def custom_llm_provider(self) -> Optional[str]: + return "aiml" + + def _get_openai_compatible_provider_info( + self, api_base: Optional[str], api_key: Optional[str] + ) -> Tuple[Optional[str], Optional[str]]: + # AIML is openai compatible, we just need to set the api_base + api_base = ( + api_base + or get_secret_str("AIML_API_BASE") + or "https://api.aimlapi.com/v1" # Default AIML API base URL + ) # type: ignore + dynamic_api_key = api_key or get_secret_str("AIML_API_KEY") + return api_base, dynamic_api_key + pass \ No newline at end of file diff --git a/litellm/llms/aiml/image_generation/__init__.py b/litellm/llms/aiml/image_generation/__init__.py new file mode 100644 index 00000000000..4548bd1b3f8 --- /dev/null +++ b/litellm/llms/aiml/image_generation/__init__.py @@ -0,0 +1,13 @@ +from litellm.llms.base_llm.image_generation.transformation import ( + BaseImageGenerationConfig, +) + +from .transformation import AimlImageGenerationConfig + +__all__ = [ + "AimlImageGenerationConfig", +] + + +def get_aiml_image_generation_config(model: str) -> BaseImageGenerationConfig: + return AimlImageGenerationConfig() diff --git a/litellm/llms/aiml/image_generation/cost_calculator.py b/litellm/llms/aiml/image_generation/cost_calculator.py new file mode 100644 index 00000000000..1fecfb6a9a5 --- /dev/null +++ b/litellm/llms/aiml/image_generation/cost_calculator.py @@ -0,0 +1,25 @@ +from typing import Any + +import litellm +from litellm.types.utils import ImageResponse + + +def cost_calculator( + model: str, + image_response: Any, +) -> float: + """ + AI/ML flux image generation cost calculator + """ + _model_info = litellm.get_model_info( + model=model, + custom_llm_provider=litellm.LlmProviders.AIML.value, + ) + output_cost_per_image: float = _model_info.get("output_cost_per_image") or 0.0 + num_images: int = 0 + if isinstance(image_response, ImageResponse): + if image_response.data: + num_images = len(image_response.data) + return output_cost_per_image * num_images + else: + raise ValueError(f"image_response must be of type ImageResponse got type={type(image_response)}") diff --git a/litellm/llms/aiml/image_generation/transformation.py b/litellm/llms/aiml/image_generation/transformation.py new file mode 100644 index 00000000000..3b586689ea7 --- /dev/null +++ b/litellm/llms/aiml/image_generation/transformation.py @@ -0,0 +1,204 @@ +from typing import TYPE_CHECKING, Any, List, Optional + +import httpx + +from litellm.llms.base_llm.image_generation.transformation import ( + BaseImageGenerationConfig, +) +from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.aiml import AimlImageGenerationRequestParams +from litellm.types.llms.openai import ( + AllMessageValues, + OpenAIImageGenerationOptionalParams, +) +from litellm.types.utils import ImageObject, ImageResponse + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + + LiteLLMLoggingObj = _LiteLLMLoggingObj +else: + LiteLLMLoggingObj = Any + + +class AimlImageGenerationConfig(BaseImageGenerationConfig): + DEFAULT_BASE_URL: str = "https://api.aimlapi.com" + IMAGE_GENERATION_ENDPOINT: str = "v1/images/generations" + + def get_supported_openai_params( + self, model: str + ) -> List[OpenAIImageGenerationOptionalParams]: + """ + https://api.aimlapi.com/v1/images/generations + """ + return [ + "n", + "response_format", + "size" + ] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + supported_params = self.get_supported_openai_params(model) + + for k in non_default_params.keys(): + if k not in optional_params.keys(): + if k in supported_params: + # Map OpenAI params to AI/ML params + if k == "n": + optional_params["num_images"] = non_default_params[k] + elif k == "response_format": + optional_params["output_format"] = non_default_params[k] + elif k == "size": + # Map OpenAI size format to AI/ML image_size + size_value = non_default_params[k] + if isinstance(size_value, str): + # Handle standard OpenAI sizes like "1024x1024" + if "x" in size_value: + width, height = map(int, size_value.split("x")) + optional_params["image_size"] = {"width": width, "height": height} + else: + # Pass through predefined sizes + optional_params["image_size"] = size_value + else: + optional_params["image_size"] = size_value + else: + optional_params[k] = non_default_params[k] + elif drop_params: + pass + else: + raise ValueError( + f"Parameter {k} is not supported for model {model}. Supported parameters are {supported_params}. Set drop_params=True to drop unsupported parameters." + ) + + return optional_params + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + """ + Get the complete url for the request + """ + complete_url: str = ( + api_base + or get_secret_str("AIML_API_BASE") + or self.DEFAULT_BASE_URL + ) + + complete_url = complete_url.rstrip("/") + complete_url = f"{complete_url}/{self.IMAGE_GENERATION_ENDPOINT}" + return complete_url + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + final_api_key: Optional[str] = ( + api_key or + get_secret_str("AIML_API_KEY") or + get_secret_str("AIMLAPI_KEY") # Alternative name + ) + if not final_api_key: + raise ValueError("AIML_API_KEY or AIMLAPI_KEY is not set") + + headers["Authorization"] = f"Bearer {final_api_key}" + headers["Content-Type"] = "application/json" + return headers + + def transform_image_generation_request( + self, + model: str, + prompt: str, + optional_params: dict, + litellm_params: dict, + headers: dict, + ) -> dict: + """ + Transform the image generation request to the AI/ML flux image generation request body + + https://api.aimlapi.com/v1/images/generations + """ + aiml_image_generation_request_body: AimlImageGenerationRequestParams = AimlImageGenerationRequestParams( + prompt=prompt, + model=model, + **optional_params, + ) + return dict(aiml_image_generation_request_body) + + def transform_image_generation_response( + self, + model: str, + raw_response: httpx.Response, + model_response: ImageResponse, + logging_obj: LiteLLMLoggingObj, + request_data: dict, + optional_params: dict, + litellm_params: dict, + encoding: Any, + api_key: Optional[str] = None, + json_mode: Optional[bool] = None, + ) -> ImageResponse: + """ + Transform the image generation response to the litellm image response + + https://api.aimlapi.com/v1/images/generations + """ + try: + response_data = raw_response.json() + except Exception as e: + raise self.get_error_class( + error_message=f"Error transforming image generation response: {e}", + status_code=raw_response.status_code, + headers=raw_response.headers, + ) + + if not model_response.data: + model_response.data = [] + + # AI/ML API can return images in two different formats: + # 1. output.choices array with image_base64 + # 2. images array with url (and optional width, height, content_type) + + if "output" in response_data and "choices" in response_data["output"]: + for choice in response_data["output"]["choices"]: + if "image_base64" in choice: + model_response.data.append(ImageObject( + b64_json=choice["image_base64"], + url=None, # AI/ML API returns base64, not URLs + )) + elif "url" in choice: + model_response.data.append(ImageObject( + b64_json=None, + url=choice["url"], + )) + elif "images" in response_data: + # Handle alternative format: {"images": [{"url": "...", "width": 1024, "height": 768, "content_type": "image/jpeg"}]} + for image in response_data["images"]: + if "url" in image: + model_response.data.append(ImageObject( + b64_json=None, + url=image["url"], + )) + elif "image_base64" in image: + model_response.data.append(ImageObject( + b64_json=image["image_base64"], + url=None, + )) + return model_response diff --git a/litellm/llms/anthropic/common_utils.py b/litellm/llms/anthropic/common_utils.py index 3845dc5a46e..68b5341e954 100644 --- a/litellm/llms/anthropic/common_utils.py +++ b/litellm/llms/anthropic/common_utils.py @@ -10,10 +10,11 @@ import litellm from litellm.litellm_core_utils.prompt_templates.common_utils import ( get_file_ids_from_messages, ) -from litellm.llms.base_llm.base_utils import BaseLLMModelInfo +from litellm.llms.base_llm.base_utils import BaseLLMModelInfo, BaseTokenCounter from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.types.llms.anthropic import AllAnthropicToolsValues, AnthropicMcpServerTool from litellm.types.llms.openai import AllMessageValues +from litellm.types.utils import TokenCountResponse class AnthropicError(BaseLLMException): @@ -229,7 +230,7 @@ class AnthropicModelInfo(BaseLLMModelInfo): litellm_model_names.append(litellm_model_name) return litellm_model_names - def get_token_counter(self) -> Optional["AnthropicTokenCounter"]: + def get_token_counter(self) -> Optional[BaseTokenCounter]: """ Factory method to create an Anthropic token counter. @@ -239,32 +240,24 @@ class AnthropicModelInfo(BaseLLMModelInfo): return AnthropicTokenCounter() -class AnthropicTokenCounter: +class AnthropicTokenCounter(BaseTokenCounter): """Token counter implementation for Anthropic provider.""" - - def supports_provider( + + def should_use_token_counting_api( self, - deployment: Optional[Dict[str, Any]] = None, - from_endpoint: bool = False + custom_llm_provider: Optional[str] = None, ) -> bool: - if not from_endpoint: - return False - - if deployment is None: - return False - - full_model = deployment.get("litellm_params", {}).get("model", "") - is_anthropic_provider = full_model.startswith("anthropic/") or "anthropic" in full_model.lower() - - return is_anthropic_provider + from litellm.types.utils import LlmProviders + return custom_llm_provider == LlmProviders.ANTHROPIC.value async def count_tokens( self, model_to_use: str, messages: Optional[List[Dict[str, Any]]], + contents: Optional[List[Dict[str, Any]]], deployment: Optional[Dict[str, Any]] = None, request_model: str = "", - ) -> Optional[Dict[str, Any]]: + ) -> Optional[TokenCountResponse]: from litellm.proxy.utils import count_tokens_with_anthropic_api result = await count_tokens_with_anthropic_api( @@ -274,12 +267,13 @@ class AnthropicTokenCounter: ) if result is not None: - return { - "total_tokens": result["total_tokens"], - "request_model": request_model, - "model_used": model_to_use, - "tokenizer_type": result["tokenizer_used"], - } + return TokenCountResponse( + total_tokens=result.get("total_tokens", 0), + request_model=request_model, + model_used=model_to_use, + tokenizer_type=result.get("tokenizer_used", ""), + original_response=result, + ) return None diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py index 990d613ecf0..d38e7adc231 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py @@ -489,7 +489,7 @@ class LiteLLMAnthropicMessagesAdapter: text: str = "" partial_json: Optional[str] = None for choice in choices: - if choice.delta.content is not None: + if choice.delta.content is not None and len(choice.delta.content) > 0: text += choice.delta.content elif choice.delta.tool_calls is not None: partial_json = "" @@ -499,7 +499,6 @@ class LiteLLMAnthropicMessagesAdapter: and tool.function.arguments is not None ): partial_json += tool.function.arguments - if partial_json is not None: return "input_json_delta", ContentJsonBlockDelta( type="input_json_delta", partial_json=partial_json diff --git a/litellm/llms/azure/azure.py b/litellm/llms/azure/azure.py index 285f176026d..5ee9065f5e1 100644 --- a/litellm/llms/azure/azure.py +++ b/litellm/llms/azure/azure.py @@ -230,6 +230,14 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): ) data = {"model": None, "messages": messages, **optional_params} + elif litellm.AzureOpenAIGPT5Config.is_model_gpt_5_model(model=model): + data = litellm.AzureOpenAIGPT5Config().transform_request( + model=model, + messages=messages, + optional_params=optional_params, + litellm_params=litellm_params, + headers=headers or {}, + ) else: data = litellm.AzureOpenAIConfig().transform_request( model=model, diff --git a/litellm/llms/azure/chat/gpt_5_transformation.py b/litellm/llms/azure/chat/gpt_5_transformation.py new file mode 100644 index 00000000000..d563a2889ca --- /dev/null +++ b/litellm/llms/azure/chat/gpt_5_transformation.py @@ -0,0 +1,59 @@ +"""Support for Azure OpenAI gpt-5 model family.""" + +from typing import List + +from litellm.llms.openai.chat.gpt_5_transformation import OpenAIGPT5Config +from litellm.types.llms.openai import AllMessageValues + +from .gpt_transformation import AzureOpenAIConfig + + +class AzureOpenAIGPT5Config(AzureOpenAIConfig, OpenAIGPT5Config): + """Azure specific handling for gpt-5 models.""" + + GPT5_SERIES_ROUTE = "gpt5_series/" + + @classmethod + def is_model_gpt_5_model(cls, model: str) -> bool: + """Check if the Azure model string refers to a gpt-5 variant. + + Accepts both explicit gpt-5 model names and the ``gpt5_series/`` prefix + used for manual routing. + """ + return "gpt-5" in model or "gpt5_series" in model + + def get_supported_openai_params(self, model: str) -> List[str]: + return OpenAIGPT5Config.get_supported_openai_params(self, model=model) + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + api_version: str = "", + ) -> dict: + return OpenAIGPT5Config.map_openai_params( + self, + non_default_params=non_default_params, + optional_params=optional_params, + model=model, + drop_params=drop_params, + ) + + def transform_request( + self, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + headers: dict, + ) -> dict: + model = model.replace(self.GPT5_SERIES_ROUTE, "") + return super().transform_request( + model=model, + messages=messages, + optional_params=optional_params, + litellm_params=litellm_params, + headers=headers, + ) diff --git a/litellm/llms/azure/common_utils.py b/litellm/llms/azure/common_utils.py index 0ed4627908d..09b1888e04d 100644 --- a/litellm/llms/azure/common_utils.py +++ b/litellm/llms/azure/common_utils.py @@ -365,14 +365,16 @@ def get_azure_ad_token( azure_ad_token_provider = get_azure_ad_token_provider(azure_scope=scope) except ValueError: verbose_logger.debug("Azure AD Token Provider could not be used.") - + ######################################################### # If litellm.enable_azure_ad_token_refresh is True and no other token provider is available, # try to get DefaultAzureCredential provider ######################################################### if azure_ad_token_provider is None and azure_ad_token is None: - azure_ad_token_provider = BaseAzureLLM._try_get_default_azure_credential_provider( - scope=scope, + azure_ad_token_provider = ( + BaseAzureLLM._try_get_default_azure_credential_provider( + scope=scope, + ) ) # Execute the token provider to get the token if available @@ -403,27 +405,27 @@ class BaseAzureLLM(BaseOpenAILLM): ) -> Optional[Callable[[], str]]: """ Try to get DefaultAzureCredential provider - + Args: scope: Azure scope for the token - + Returns: Token provider callable if DefaultAzureCredential is enabled and available, None otherwise """ from litellm.types.secret_managers.get_azure_ad_token_provider import ( AzureCredentialType, ) - - verbose_logger.debug( - "Attempting to use DefaultAzureCredential for Azure Auth" - ) - + + verbose_logger.debug("Attempting to use DefaultAzureCredential for Azure Auth") + try: azure_ad_token_provider = get_azure_ad_token_provider( azure_scope=scope, azure_credential=AzureCredentialType.DefaultAzureCredential, ) - verbose_logger.debug("Successfully obtained Azure AD token provider using DefaultAzureCredential") + verbose_logger.debug( + "Successfully obtained Azure AD token provider using DefaultAzureCredential" + ) return azure_ad_token_provider except Exception as e: verbose_logger.debug(f"DefaultAzureCredential failed: {str(e)}") @@ -656,12 +658,17 @@ class BaseAzureLLM(BaseOpenAILLM): else: client = AzureOpenAI(**azure_client_params) # type: ignore return client - + @staticmethod def _base_validate_azure_environment( - headers: dict, litellm_params: Optional[GenericLiteLLMParams] + headers: dict, litellm_params: Optional[GenericLiteLLMParams] ) -> dict: litellm_params = litellm_params or GenericLiteLLMParams() + + # If api-key is already in headers, preserve it + if "api-key" in headers: + return headers + api_key = ( litellm_params.api_key or litellm.api_key @@ -681,13 +688,24 @@ class BaseAzureLLM(BaseOpenAILLM): headers["Authorization"] = f"Bearer {azure_ad_token}" return headers - + @staticmethod def _get_base_azure_url( api_base: Optional[str], litellm_params: Optional[Union[GenericLiteLLMParams, Dict[str, Any]]], - route: Literal["/openai/responses", "/openai/vector_stores"] + route: Literal["/openai/responses", "/openai/vector_stores"], + default_api_version: Optional[Union[str, Literal["latest", "preview"]]] = None, ) -> str: + """ + Get the base Azure URL for the given route and API version. + + Args: + api_base: The base URL of the Azure API. + litellm_params: The litellm parameters. + route: The route to the API. + default_api_version: The default API version to use if no api_version is provided. If 'latest', it will use `openai/v1/...` route. + """ + api_base = api_base or litellm.api_base or get_secret_str("AZURE_API_BASE") if api_base is None: raise ValueError( @@ -697,7 +715,10 @@ class BaseAzureLLM(BaseOpenAILLM): # Extract api_version or use default litellm_params = litellm_params or {} - api_version = cast(Optional[str], litellm_params.get("api_version")) + api_version = ( + cast(Optional[str], litellm_params.get("api_version")) + or default_api_version + ) # Create a new dictionary with existing params query_params = dict(original_url.params) @@ -705,27 +726,28 @@ class BaseAzureLLM(BaseOpenAILLM): # Add api_version if needed if "api-version" not in query_params and api_version: query_params["api-version"] = api_version - + # Add the path to the base URL if route not in api_base: - new_url = _add_path_to_api_base( - api_base=api_base, ending_path=route - ) + new_url = _add_path_to_api_base(api_base=api_base, ending_path=route) else: new_url = api_base - + if BaseAzureLLM._is_azure_v1_api_version(api_version): # ensure the request go to /openai/v1 and not just /openai if "/openai/v1" not in new_url: parsed_url = httpx.URL(new_url) - new_url = str(parsed_url.copy_with(path=parsed_url.path.replace("/openai", "/openai/v1"))) - + new_url = str( + parsed_url.copy_with( + path=parsed_url.path.replace("/openai", "/openai/v1") + ) + ) # Use the new query_params dictionary final_url = httpx.URL(new_url).copy_with(params=query_params) return str(final_url) - + @staticmethod def _is_azure_v1_api_version(api_version: Optional[str]) -> bool: if api_version is None: diff --git a/litellm/llms/azure/responses/o_series_transformation.py b/litellm/llms/azure/responses/o_series_transformation.py new file mode 100644 index 00000000000..a0b2ef16300 --- /dev/null +++ b/litellm/llms/azure/responses/o_series_transformation.py @@ -0,0 +1,93 @@ +""" +Support for Azure OpenAI O-series models (o1, o3, etc.) in Responses API + +https://platform.openai.com/docs/guides/reasoning + +Translations handled by LiteLLM: +- temperature => drop param (if user opts in to dropping param) +- Other parameters follow base Azure OpenAI Responses API behavior +""" + +from typing import TYPE_CHECKING, Any, Dict + +from litellm._logging import verbose_logger +from litellm.types.llms.openai import ResponsesAPIOptionalRequestParams +from litellm.utils import supports_reasoning + +from .transformation import AzureOpenAIResponsesAPIConfig + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + + LiteLLMLoggingObj = _LiteLLMLoggingObj +else: + LiteLLMLoggingObj = Any + + +class AzureOpenAIOSeriesResponsesAPIConfig(AzureOpenAIResponsesAPIConfig): + """ + Configuration for Azure OpenAI O-series models in Responses API. + + O-series models (o1, o3, etc.) do not support the temperature parameter + in the responses API, so we need to drop it when drop_params is enabled. + """ + + def get_supported_openai_params(self, model: str) -> list: + """ + Get supported parameters for Azure OpenAI O-series Responses API. + + O-series models don't support temperature parameter in responses API. + """ + # Get the base Azure supported params + base_supported_params = super().get_supported_openai_params(model) + + # O-series models don't support temperature parameter in responses API + o_series_unsupported_params = ["temperature"] + + # Filter out unsupported parameters for O-series models + o_series_supported_params = [ + param for param in base_supported_params + if param not in o_series_unsupported_params + ] + + return o_series_supported_params + + def map_openai_params( + self, + response_api_optional_params: ResponsesAPIOptionalRequestParams, + model: str, + drop_params: bool, + ) -> Dict: + """ + Map OpenAI parameters for Azure OpenAI O-series Responses API. + + Drops temperature parameter if drop_params is True since O-series models + don't support temperature in the responses API. + """ + mapped_params = dict(response_api_optional_params) + + # If drop_params is enabled, remove temperature parameter for O-series models + if drop_params and "temperature" in mapped_params: + verbose_logger.debug( + f"Dropping unsupported parameter 'temperature' for Azure OpenAI O-series responses API model {model}" + ) + mapped_params.pop("temperature", None) + + return mapped_params + + def is_o_series_model(self, model: str) -> bool: + """ + Check if the model is an O-series model. + + Args: + model: The model name to check + + Returns: + True if it's an O-series model, False otherwise + """ + # Check if model name contains o_series or if it's a known O-series model + if "o_series" in model.lower(): + return True + + # Check if the model supports reasoning (which is O-series specific) + return supports_reasoning(model) \ No newline at end of file diff --git a/litellm/llms/azure/responses/transformation.py b/litellm/llms/azure/responses/transformation.py index e3d37c8a15a..488a711669d 100644 --- a/litellm/llms/azure/responses/transformation.py +++ b/litellm/llms/azure/responses/transformation.py @@ -6,6 +6,7 @@ from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfi from litellm.types.llms.openai import * from litellm.types.responses.main import * from litellm.types.router import GenericLiteLLMParams +from litellm.types.utils import LlmProviders if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj @@ -16,6 +17,10 @@ else: class AzureOpenAIResponsesAPIConfig(OpenAIResponsesAPIConfig): + @property + def custom_llm_provider(self) -> LlmProviders: + return LlmProviders.AZURE + def validate_environment( self, headers: dict, model: str, litellm_params: Optional[GenericLiteLLMParams] ) -> dict: @@ -70,8 +75,13 @@ class AzureOpenAIResponsesAPIConfig(OpenAIResponsesAPIConfig): - A complete URL string, e.g., "https://litellm8397336933.openai.azure.com/openai/responses?api-version=2024-05-01-preview" """ + from litellm.constants import AZURE_DEFAULT_RESPONSES_API_VERSION + return BaseAzureLLM._get_base_azure_url( - api_base=api_base, litellm_params=litellm_params, route="/openai/responses" + api_base=api_base, + litellm_params=litellm_params, + route="/openai/responses", + default_api_version=AZURE_DEFAULT_RESPONSES_API_VERSION, ) ######################################################### diff --git a/litellm/llms/azure_ai/common_utils.py b/litellm/llms/azure_ai/common_utils.py new file mode 100644 index 00000000000..dcc9335e42d --- /dev/null +++ b/litellm/llms/azure_ai/common_utils.py @@ -0,0 +1,56 @@ +from typing import List, Optional + +import litellm +from litellm.llms.base_llm.base_utils import BaseLLMModelInfo +from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import AllMessageValues + + +class AzureFoundryModelInfo(BaseLLMModelInfo): + @staticmethod + def get_api_base(api_base: Optional[str] = None) -> Optional[str]: + return ( + api_base + or litellm.api_base + or get_secret_str("AZURE_AI_API_BASE") + ) + + @staticmethod + def get_api_key(api_key: Optional[str] = None) -> Optional[str]: + return ( + api_key + or litellm.api_key + or litellm.openai_key + or get_secret_str("AZURE_AI_API_KEY") + ) + + @property + def api_version(self, api_version: Optional[str] = None) -> Optional[str]: + api_version = ( + api_version + or litellm.api_version + or get_secret_str("AZURE_API_VERSION") + ) + return api_version + + ######################################################### + # Not implemented methods + ######################################################### + + + @staticmethod + def get_base_model(model: str) -> Optional[str]: + raise NotImplementedError("Azure Foundry does not support base model") + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + """Azure Foundry sends api key in query params""" + raise NotImplementedError("Azure Foundry does not support environment validation") diff --git a/litellm/llms/azure_ai/image_generation/__init__.py b/litellm/llms/azure_ai/image_generation/__init__.py new file mode 100644 index 00000000000..cebab3de16e --- /dev/null +++ b/litellm/llms/azure_ai/image_generation/__init__.py @@ -0,0 +1,33 @@ +from litellm._logging import verbose_logger +from litellm.llms.base_llm.image_generation.transformation import ( + BaseImageGenerationConfig, +) + +from .dall_e_2_transformation import AzureFoundryDallE2ImageGenerationConfig +from .dall_e_3_transformation import AzureFoundryDallE3ImageGenerationConfig +from .flux_transformation import AzureFoundryFluxImageGenerationConfig +from .gpt_transformation import AzureFoundryGPTImageGenerationConfig + +__all__ = [ + "AzureFoundryFluxImageGenerationConfig", + "AzureFoundryGPTImageGenerationConfig", + "AzureFoundryDallE2ImageGenerationConfig", + "AzureFoundryDallE3ImageGenerationConfig", +] + + +def get_azure_ai_image_generation_config(model: str) -> BaseImageGenerationConfig: + model = model.lower() + model = model.replace("-", "") + model = model.replace("_", "") + if model == "" or "dalle2" in model: # empty model is dall-e-2 + return AzureFoundryDallE2ImageGenerationConfig() + elif "dalle3" in model: + return AzureFoundryDallE3ImageGenerationConfig() + elif "flux" in model: + return AzureFoundryFluxImageGenerationConfig() + else: + verbose_logger.debug( + f"Using AzureGPTImageGenerationConfig for model: {model}. This follows the gpt-image-1 model format." + ) + return AzureFoundryGPTImageGenerationConfig() diff --git a/litellm/llms/azure_ai/image_generation/cost_calculator.py b/litellm/llms/azure_ai/image_generation/cost_calculator.py new file mode 100644 index 00000000000..2fc7c554a34 --- /dev/null +++ b/litellm/llms/azure_ai/image_generation/cost_calculator.py @@ -0,0 +1,25 @@ +from typing import Any + +import litellm +from litellm.types.utils import ImageResponse + + +def cost_calculator( + model: str, + image_response: Any, +) -> float: + """ + Recraft image generation cost calculator + """ + _model_info = litellm.get_model_info( + model=model, + custom_llm_provider=litellm.LlmProviders.AZURE_AI.value, + ) + output_cost_per_image: float = _model_info.get("output_cost_per_image") or 0.0 + num_images: int = 0 + if isinstance(image_response, ImageResponse): + if image_response.data: + num_images = len(image_response.data) + return output_cost_per_image * num_images + else: + raise ValueError(f"image_response must be of type ImageResponse got type={type(image_response)}") diff --git a/litellm/llms/azure_ai/image_generation/dall_e_2_transformation.py b/litellm/llms/azure_ai/image_generation/dall_e_2_transformation.py new file mode 100644 index 00000000000..1ef93366f71 --- /dev/null +++ b/litellm/llms/azure_ai/image_generation/dall_e_2_transformation.py @@ -0,0 +1,9 @@ +from litellm.llms.openai.image_generation import DallE2ImageGenerationConfig + + +class AzureFoundryDallE2ImageGenerationConfig(DallE2ImageGenerationConfig): + """ + Azure dall-e-2 image generation config + """ + + pass diff --git a/litellm/llms/azure_ai/image_generation/dall_e_3_transformation.py b/litellm/llms/azure_ai/image_generation/dall_e_3_transformation.py new file mode 100644 index 00000000000..4688a5c3caa --- /dev/null +++ b/litellm/llms/azure_ai/image_generation/dall_e_3_transformation.py @@ -0,0 +1,9 @@ +from litellm.llms.openai.image_generation import DallE3ImageGenerationConfig + + +class AzureFoundryDallE3ImageGenerationConfig(DallE3ImageGenerationConfig): + """ + Azure dall-e-3 image generation config + """ + + pass diff --git a/litellm/llms/azure_ai/image_generation/flux_transformation.py b/litellm/llms/azure_ai/image_generation/flux_transformation.py new file mode 100644 index 00000000000..5325f32ef63 --- /dev/null +++ b/litellm/llms/azure_ai/image_generation/flux_transformation.py @@ -0,0 +1,14 @@ +from litellm.llms.openai.image_generation import GPTImageGenerationConfig + + +class AzureFoundryFluxImageGenerationConfig(GPTImageGenerationConfig): + """ + Azure Foundry flux image generation config + + From manual testing it follows the gpt-image-1 image generation config + + (Azure Foundry does not have any docs on supported params at the time of writing) + + From our test suite - following GPTImageGenerationConfig is working for this model + """ + pass diff --git a/litellm/llms/azure_ai/image_generation/gpt_transformation.py b/litellm/llms/azure_ai/image_generation/gpt_transformation.py new file mode 100644 index 00000000000..3eead307463 --- /dev/null +++ b/litellm/llms/azure_ai/image_generation/gpt_transformation.py @@ -0,0 +1,9 @@ +from litellm.llms.openai.image_generation import GPTImageGenerationConfig + + +class AzureFoundryGPTImageGenerationConfig(GPTImageGenerationConfig): + """ + Azure gpt-image-1 image generation config + """ + + pass diff --git a/litellm/llms/base_llm/base_utils.py b/litellm/llms/base_llm/base_utils.py index 3961ee2b9e9..9172a05e385 100644 --- a/litellm/llms/base_llm/base_utils.py +++ b/litellm/llms/base_llm/base_utils.py @@ -5,14 +5,37 @@ Utility functions for base LLM classes. import copy import json from abc import ABC, abstractmethod -from typing import List, Optional, Type, Union +from typing import Any, Dict, List, Optional, Type, Union from openai.lib import _parsing, _pydantic from pydantic import BaseModel from litellm._logging import verbose_logger from litellm.types.llms.openai import AllMessageValues, ChatCompletionToolCallChunk -from litellm.types.utils import Message, ProviderSpecificModelInfo +from litellm.types.utils import Message, ProviderSpecificModelInfo, TokenCountResponse + + +class BaseTokenCounter(ABC): + @abstractmethod + async def count_tokens( + self, + model_to_use: str, + messages: Optional[List[Dict[str, Any]]], + contents: Optional[List[Dict[str, Any]]], + deployment: Optional[Dict[str, Any]] = None, + request_model: str = "", + ) -> Optional[TokenCountResponse]: + pass + + @abstractmethod + def should_use_token_counting_api( + self, + custom_llm_provider: Optional[str] = None, + ) -> bool: + """ + Returns True if we should the this API for token counting for the selected `custom_llm_provider` + """ + return False class BaseLLMModelInfo(ABC): @@ -70,7 +93,7 @@ class BaseLLMModelInfo(ABC): """ pass - def get_token_counter(self): + def get_token_counter(self) -> Optional[BaseTokenCounter]: """ Factory method to create a token counter for this provider. diff --git a/litellm/llms/base_llm/responses/transformation.py b/litellm/llms/base_llm/responses/transformation.py index e2f89da5e86..4da4f7652e0 100644 --- a/litellm/llms/base_llm/responses/transformation.py +++ b/litellm/llms/base_llm/responses/transformation.py @@ -12,6 +12,7 @@ from litellm.types.llms.openai import ( ) from litellm.types.responses.main import * from litellm.types.router import GenericLiteLLMParams +from litellm.types.utils import LlmProviders if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj @@ -29,6 +30,11 @@ class BaseResponsesAPIConfig(ABC): def __init__(self): pass + @property + @abstractmethod + def custom_llm_provider(self) -> LlmProviders: + pass + @classmethod def get_config(cls): return { diff --git a/litellm/llms/baseten.py b/litellm/llms/baseten.py deleted file mode 100644 index e1d513d6d11..00000000000 --- a/litellm/llms/baseten.py +++ /dev/null @@ -1,172 +0,0 @@ -import json -import time -from typing import Callable - -import litellm -from litellm.types.utils import ModelResponse, Usage - - -class BasetenError(Exception): - def __init__(self, status_code, message): - self.status_code = status_code - self.message = message - super().__init__( - self.message - ) # Call the base class constructor with the parameters it needs - - -def validate_environment(api_key): - headers = { - "accept": "application/json", - "content-type": "application/json", - } - if api_key: - headers["Authorization"] = f"Api-Key {api_key}" - return headers - - -def completion( - model: str, - messages: list, - model_response: ModelResponse, - print_verbose: Callable, - encoding, - api_key, - logging_obj, - optional_params: dict, - litellm_params=None, - logger_fn=None, -): - headers = validate_environment(api_key) - completion_url_fragment_1 = "https://app.baseten.co/models/" - completion_url_fragment_2 = "/predict" - model = model - prompt = "" - for message in messages: - if "role" in message: - if message["role"] == "user": - prompt += f"{message['content']}" - else: - prompt += f"{message['content']}" - else: - prompt += f"{message['content']}" - data = { - "inputs": prompt, - "prompt": prompt, - "parameters": optional_params, - "stream": ( - True - if "stream" in optional_params and optional_params["stream"] is True - else False - ), - } - - ## LOGGING - logging_obj.pre_call( - input=prompt, - api_key=api_key, - additional_args={"complete_input_dict": data}, - ) - ## COMPLETION CALL - response = litellm.module_level_client.post( - completion_url_fragment_1 + model + completion_url_fragment_2, - headers=headers, - data=json.dumps(data), - stream=( - True - if "stream" in optional_params and optional_params["stream"] is True - else False - ), - ) - if "text/event-stream" in response.headers["Content-Type"] or ( - "stream" in optional_params and optional_params["stream"] is True - ): - return response.iter_lines() - else: - ## LOGGING - logging_obj.post_call( - input=prompt, - api_key=api_key, - original_response=response.text, - additional_args={"complete_input_dict": data}, - ) - print_verbose(f"raw model_response: {response.text}") - ## RESPONSE OBJECT - completion_response = response.json() - if "error" in completion_response: - raise BasetenError( - message=completion_response["error"], - status_code=response.status_code, - ) - else: - if "model_output" in completion_response: - if ( - isinstance(completion_response["model_output"], dict) - and "data" in completion_response["model_output"] - and isinstance(completion_response["model_output"]["data"], list) - ): - model_response.choices[0].message.content = completion_response[ # type: ignore - "model_output" - ][ - "data" - ][ - 0 - ] - elif isinstance(completion_response["model_output"], str): - model_response.choices[0].message.content = completion_response[ # type: ignore - "model_output" - ] - elif "completion" in completion_response and isinstance( - completion_response["completion"], str - ): - model_response.choices[0].message.content = completion_response[ # type: ignore - "completion" - ] - elif isinstance(completion_response, list) and len(completion_response) > 0: - if "generated_text" not in completion_response: - raise BasetenError( - message=f"Unable to parse response. Original response: {response.text}", - status_code=response.status_code, - ) - model_response.choices[0].message.content = completion_response[0][ # type: ignore - "generated_text" - ] - ## GETTING LOGPROBS - if ( - "details" in completion_response[0] - and "tokens" in completion_response[0]["details"] - ): - model_response.choices[0].finish_reason = completion_response[0][ - "details" - ]["finish_reason"] - sum_logprob = 0 - for token in completion_response[0]["details"]["tokens"]: - sum_logprob += token["logprob"] - model_response.choices[0].logprobs = sum_logprob # type: ignore - else: - raise BasetenError( - message=f"Unable to parse response. Original response: {response.text}", - status_code=response.status_code, - ) - - ## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here. - prompt_tokens = len(encoding.encode(prompt)) - completion_tokens = len( - encoding.encode(model_response["choices"][0]["message"]["content"]) - ) - - model_response.created = int(time.time()) - model_response.model = model - usage = Usage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=prompt_tokens + completion_tokens, - ) - - setattr(model_response, "usage", usage) - return model_response - - -def embedding(): - # logic for parsing in - calling - parsing out model embedding calls - pass diff --git a/litellm/llms/baseten/chat.py b/litellm/llms/baseten/chat.py new file mode 100644 index 00000000000..05fc9961ac5 --- /dev/null +++ b/litellm/llms/baseten/chat.py @@ -0,0 +1,118 @@ +from typing import Optional +from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig + + +class BasetenConfig(OpenAIGPTConfig): + """ + Reference: https://inference.baseten.co/v1 + + Below are the parameters: + """ + + max_tokens: Optional[int] = None + response_format: Optional[dict] = None + seed: Optional[int] = None + stream: Optional[bool] = None + top_p: Optional[int] = None + tool_choice: Optional[str] = None + tools: Optional[list] = None + user: Optional[str] = None + presence_penalty: Optional[int] = None + frequency_penalty: Optional[int] = None + stream_options: Optional[dict] = None + + def __init__( + self, + max_tokens: Optional[int] = None, + response_format: Optional[dict] = None, + seed: Optional[int] = None, + stop: Optional[list] = None, + stream: Optional[bool] = None, + temperature: Optional[float] = None, + top_p: Optional[int] = None, + tool_choice: Optional[str] = None, + tools: Optional[list] = None, + user: Optional[str] = None, + presence_penalty: Optional[int] = None, + frequency_penalty: Optional[int] = None, + stream_options: Optional[dict] = None, + ) -> None: + locals_ = locals().copy() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return super().get_config() + + def get_supported_openai_params(self, model: str) -> list: + """ + Get the supported OpenAI params for the given model + """ + return [ + "max_tokens", + "max_completion_tokens", + "response_format", + "seed", + "stop", + "stream", + "temperature", + "top_p", + "tool_choice", + "tools", + "user", + "presence_penalty", + "frequency_penalty", + "stream_options", + ] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + supported_openai_params = self.get_supported_openai_params(model=model) + for param, value in non_default_params.items(): + if param == "max_completion_tokens": + optional_params["max_tokens"] = value + elif param in supported_openai_params: + optional_params[param] = value + return optional_params + + def _get_openai_compatible_provider_info(self, api_base: str, api_key: str) -> tuple: + """ + Get the OpenAI compatible provider info for Baseten + """ + # Default to Model API + default_api_base = "https://inference.baseten.co/v1" + default_api_key = api_key or "BASETEN_API_KEY" + + return default_api_base, default_api_key + + @staticmethod + def is_dedicated_deployment(model: str) -> bool: + """ + Check if the model is a dedicated deployment (8-digit alphanumeric code) + """ + # Remove 'baseten/' prefix if present + model_id = model.replace("baseten/", "") + + # Check if it's an 8-digit alphanumeric code + import re + return bool(re.match(r'^[a-zA-Z0-9]{8}$', model_id)) + + @staticmethod + def get_api_base_for_model(model: str) -> str: + """ + Get the appropriate API base URL for the given model + """ + if BasetenConfig.is_dedicated_deployment(model): + # Extract the model ID (remove 'baseten/' prefix if present) + model_id = model.replace("baseten/", "") + return f"https://model-{model_id}.api.baseten.co/environments/production/sync/v1" + else: + # Use Model API + return "https://inference.baseten.co/v1" \ No newline at end of file diff --git a/litellm/llms/bedrock/base_aws_llm.py b/litellm/llms/bedrock/base_aws_llm.py index cc205e62dc9..55aad673ced 100644 --- a/litellm/llms/bedrock/base_aws_llm.py +++ b/litellm/llms/bedrock/base_aws_llm.py @@ -179,15 +179,32 @@ class BaseAWSLLM: aws_sts_endpoint=aws_sts_endpoint, ) elif aws_role_name is not None: - # If aws_session_name is not provided, generate a default one - if aws_session_name is None: - aws_session_name = f"litellm-session-{int(datetime.now().timestamp())}" - credentials, _cache_ttl = self._auth_with_aws_role( - aws_access_key_id=aws_access_key_id, - aws_secret_access_key=aws_secret_access_key, - aws_role_name=aws_role_name, - aws_session_name=aws_session_name, - ) + # Check if we're in IRSA and trying to assume the same role we already have + current_role_arn = os.getenv("AWS_ROLE_ARN") + web_identity_token_file = os.getenv("AWS_WEB_IDENTITY_TOKEN_FILE") + + # In IRSA environments, we should skip role assumption if we're already running as the target role + # This is true when: + # 1. We have AWS_ROLE_ARN set (current role) + # 2. We have AWS_WEB_IDENTITY_TOKEN_FILE set (IRSA environment) + # 3. The current role matches the requested role + if (current_role_arn and web_identity_token_file and + current_role_arn == aws_role_name): + verbose_logger.debug("Using IRSA same-role optimization: calling _auth_with_env_vars") + # We're already running as this role via IRSA, no need to assume it again + # Use the default boto3 credentials (which will use the IRSA credentials) + credentials, _cache_ttl = self._auth_with_env_vars() + else: + verbose_logger.debug("Using role assumption: calling _auth_with_aws_role") + # If aws_session_name is not provided, generate a default one + if aws_session_name is None: + aws_session_name = f"litellm-session-{int(datetime.now().timestamp())}" + credentials, _cache_ttl = self._auth_with_aws_role( + aws_access_key_id=aws_access_key_id, + aws_secret_access_key=aws_secret_access_key, + aws_role_name=aws_role_name, + aws_session_name=aws_session_name, + ) elif aws_profile_name is not None: ### CHECK SESSION ### credentials, _cache_ttl = self._auth_with_aws_profile(aws_profile_name) @@ -446,6 +463,92 @@ class BaseAWSLLM: iam_creds = session.get_credentials() return iam_creds, self._get_default_ttl_for_boto3_credentials() + def _handle_irsa_cross_account(self, irsa_role_arn: str, aws_role_name: str, + aws_session_name: str, region: str, web_identity_token_file: str) -> dict: + """Handle cross-account role assumption for IRSA.""" + import boto3 + + verbose_logger.debug("Cross-account role assumption detected") + + # Read the web identity token + with open(web_identity_token_file, 'r') as f: + web_identity_token = f.read().strip() + + # Create an STS client without credentials + with tracer.trace("boto3.client(sts) for manual IRSA"): + sts_client = boto3.client('sts', region_name=region) + + # Manually assume the IRSA role with the session name + verbose_logger.debug(f"Manually assuming IRSA role {irsa_role_arn} with session {aws_session_name}") + irsa_response = sts_client.assume_role_with_web_identity( + RoleArn=irsa_role_arn, + RoleSessionName=aws_session_name, + WebIdentityToken=web_identity_token + ) + + # Extract the credentials from the IRSA assumption + irsa_creds = irsa_response["Credentials"] + + # Create a new STS client with the IRSA credentials + with tracer.trace("boto3.client(sts) with manual IRSA credentials"): + sts_client_with_creds = boto3.client( + 'sts', + region_name=region, + aws_access_key_id=irsa_creds["AccessKeyId"], + aws_secret_access_key=irsa_creds["SecretAccessKey"], + aws_session_token=irsa_creds["SessionToken"] + ) + + # Get current caller identity for debugging + try: + caller_identity = sts_client_with_creds.get_caller_identity() + verbose_logger.debug(f"Current identity after manual IRSA assumption: {caller_identity.get('Arn', 'unknown')}") + except Exception as e: + verbose_logger.debug(f"Failed to get caller identity: {e}") + + # Now assume the target role + verbose_logger.debug(f"Attempting to assume target role: {aws_role_name} with session: {aws_session_name}") + return sts_client_with_creds.assume_role( + RoleArn=aws_role_name, RoleSessionName=aws_session_name + ) + + def _handle_irsa_same_account(self, aws_role_name: str, aws_session_name: str, region: str) -> dict: + """Handle same-account role assumption for IRSA.""" + import boto3 + + verbose_logger.debug("Same account role assumption, using automatic IRSA") + with tracer.trace("boto3.client(sts) with automatic IRSA"): + sts_client = boto3.client("sts", region_name=region) + + # Get current caller identity for debugging + try: + caller_identity = sts_client.get_caller_identity() + verbose_logger.debug(f"Current IRSA identity: {caller_identity.get('Arn', 'unknown')}") + except Exception as e: + verbose_logger.debug(f"Failed to get caller identity: {e}") + + # Assume the role + verbose_logger.debug(f"Attempting to assume role: {aws_role_name} with session: {aws_session_name}") + return sts_client.assume_role( + RoleArn=aws_role_name, RoleSessionName=aws_session_name + ) + + def _extract_credentials_and_ttl(self, sts_response: dict) -> Tuple[Credentials, Optional[int]]: + """Extract credentials and TTL from STS response.""" + from botocore.credentials import Credentials + + sts_credentials = sts_response["Credentials"] + credentials = Credentials( + access_key=sts_credentials["AccessKeyId"], + secret_key=sts_credentials["SecretAccessKey"], + token=sts_credentials["SessionToken"], + ) + + expiration_time = sts_credentials["Expiration"] + ttl = int((expiration_time - datetime.now(expiration_time.tzinfo)).total_seconds()) + + return credentials, ttl + @tracer.wrap() def _auth_with_aws_role( self, @@ -460,12 +563,58 @@ class BaseAWSLLM: import boto3 from botocore.credentials import Credentials - with tracer.trace("boto3.client(sts)"): - sts_client = boto3.client( - "sts", - aws_access_key_id=aws_access_key_id, # [OPTIONAL] - aws_secret_access_key=aws_secret_access_key, # [OPTIONAL] - ) + # Check if we're in an EKS/IRSA environment + web_identity_token_file = os.getenv("AWS_WEB_IDENTITY_TOKEN_FILE") + irsa_role_arn = os.getenv("AWS_ROLE_ARN") + + # If we have IRSA environment variables and no explicit credentials, + # we need to use the web identity token flow + if (web_identity_token_file and irsa_role_arn and + aws_access_key_id is None and aws_secret_access_key is None): + # For cross-account role assumption with specific session names, + # we need to manually assume the IRSA role first with the correct session name + verbose_logger.debug(f"IRSA detected: using web identity token from {web_identity_token_file}") + + try: + # Get region from environment + region = os.getenv("AWS_REGION") or os.getenv("AWS_DEFAULT_REGION") or "us-east-1" + + # Check if we need to do cross-account role assumption + if aws_role_name != irsa_role_arn: + sts_response = self._handle_irsa_cross_account( + irsa_role_arn, aws_role_name, aws_session_name, region, web_identity_token_file + ) + else: + sts_response = self._handle_irsa_same_account( + aws_role_name, aws_session_name, region + ) + + return self._extract_credentials_and_ttl(sts_response) + + except Exception as e: + verbose_logger.debug(f"Failed to assume role via IRSA: {e}") + if "AccessDenied" in str(e) and "is not authorized to perform: sts:AssumeRole" in str(e): + # Provide a more helpful error message for trust policy issues + verbose_logger.error( + f"Access denied when trying to assume role {aws_role_name}. " + f"Please ensure the trust policy of {aws_role_name} allows " + f"the current role to assume it. Current identity: check logs with verbose mode." + ) + # Re-raise the exception instead of falling through + raise + + # In EKS/IRSA environments, use ambient credentials (no explicit keys needed) + # This allows the web identity token to work automatically + if aws_access_key_id is None and aws_secret_access_key is None: + with tracer.trace("boto3.client(sts)"): + sts_client = boto3.client("sts") + else: + with tracer.trace("boto3.client(sts)"): + sts_client = boto3.client( + "sts", + aws_access_key_id=aws_access_key_id, + aws_secret_access_key=aws_secret_access_key, + ) sts_response = sts_client.assume_role( RoleArn=aws_role_name, RoleSessionName=aws_session_name diff --git a/litellm/llms/bedrock/chat/converse_handler.py b/litellm/llms/bedrock/chat/converse_handler.py index 900fad3d043..15a5002f0e4 100644 --- a/litellm/llms/bedrock/chat/converse_handler.py +++ b/litellm/llms/bedrock/chat/converse_handler.py @@ -119,6 +119,7 @@ class BedrockConverseLLM(BaseAWSLLM): messages=messages, optional_params=optional_params, litellm_params=litellm_params, + headers=headers, ) data = json.dumps(request_data) @@ -185,8 +186,10 @@ class BedrockConverseLLM(BaseAWSLLM): messages=messages, optional_params=optional_params, litellm_params=litellm_params, + headers=headers, ) data = json.dumps(request_data) + prepped = self.get_request_headers( credentials=credentials, aws_region_name=litellm_params.get("aws_region_name") or "us-west-2", @@ -276,8 +279,13 @@ class BedrockConverseLLM(BaseAWSLLM): else: modelId = self.encode_model_id(model_id=model) - if stream is True and "ai21" in modelId: - fake_stream = True + fake_stream = litellm.AmazonConverseConfig().should_fake_stream( + fake_stream=fake_stream, + model=model, + stream=stream, + custom_llm_provider="bedrock", + ) + ### SET REGION NAME ### aws_region_name = self._get_aws_region_name( @@ -385,8 +393,10 @@ class BedrockConverseLLM(BaseAWSLLM): messages=messages, optional_params=optional_params, litellm_params=litellm_params, + headers=extra_headers, ) data = json.dumps(_data) + prepped = self.get_request_headers( credentials=credentials, aws_region_name=aws_region_name, diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index e961433b52e..b93ca94bed4 100644 --- a/litellm/llms/bedrock/chat/converse_transformation.py +++ b/litellm/llms/bedrock/chat/converse_transformation.py @@ -25,6 +25,7 @@ from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMExcepti from litellm.types.llms.bedrock import * from litellm.types.llms.openai import ( AllMessageValues, + ChatCompletionAssistantMessage, ChatCompletionRedactedThinkingBlock, ChatCompletionResponseMessage, ChatCompletionSystemMessage, @@ -47,7 +48,7 @@ from litellm.types.utils import ( ) from litellm.utils import add_dummy_tool, has_tool_call_blocks, supports_reasoning -from ..common_utils import BedrockError, BedrockModelInfo, get_bedrock_tool_name +from ..common_utils import BedrockError, BedrockModelInfo, get_bedrock_tool_name, get_anthropic_beta_from_headers # Computer use tool prefixes supported by Bedrock BEDROCK_COMPUTER_USE_TOOLS = [ @@ -505,6 +506,7 @@ class AmazonConverseConfig(BaseConfig): OpenAIMessageContentListBlock, ChatCompletionUserMessage, ChatCompletionSystemMessage, + ChatCompletionAssistantMessage, ], block_type: Literal["system"], ) -> Optional[SystemContentBlock]: @@ -517,6 +519,7 @@ class AmazonConverseConfig(BaseConfig): OpenAIMessageContentListBlock, ChatCompletionUserMessage, ChatCompletionSystemMessage, + ChatCompletionAssistantMessage, ], block_type: Literal["content_block"], ) -> Optional[ContentBlock]: @@ -528,6 +531,7 @@ class AmazonConverseConfig(BaseConfig): OpenAIMessageContentListBlock, ChatCompletionUserMessage, ChatCompletionSystemMessage, + ChatCompletionAssistantMessage, ], block_type: Literal["system", "content_block"], ) -> Optional[Union[SystemContentBlock, ContentBlock]]: @@ -593,12 +597,14 @@ class AmazonConverseConfig(BaseConfig): return {} + def _transform_request_helper( self, model: str, system_content_blocks: List[SystemContentBlock], optional_params: dict, messages: Optional[List[AllMessageValues]] = None, + headers: Optional[dict] = None, ) -> CommonRequestObject: ## VALIDATE REQUEST """ @@ -651,6 +657,12 @@ class AmazonConverseConfig(BaseConfig): # Initialize bedrock_tools bedrock_tools: List[ToolBlock] = [] + # Collect anthropic_beta values from user headers + anthropic_beta_list = [] + if headers: + user_betas = get_anthropic_beta_from_headers(headers) + anthropic_beta_list.extend(user_betas) + # Only separate tools if computer use tools are actually present if original_tools and self.is_computer_use_tool_used(original_tools, model): # Separate computer use tools from regular function tools @@ -663,7 +675,7 @@ class AmazonConverseConfig(BaseConfig): # Add computer use tools and anthropic_beta if needed (only when computer use tools are present) if computer_use_tools: - additional_request_params["anthropic_beta"] = ["computer-use-2024-10-22"] + anthropic_beta_list.append("computer-use-2024-10-22") # Transform computer use tools to proper Bedrock format transformed_computer_tools = self._transform_computer_use_tools(computer_use_tools) additional_request_params["tools"] = transformed_computer_tools @@ -671,6 +683,17 @@ class AmazonConverseConfig(BaseConfig): # No computer use tools, process all tools as regular tools bedrock_tools = _bedrock_tools_pt(original_tools) + # Set anthropic_beta in additional_request_params if we have any beta features + if anthropic_beta_list: + # Remove duplicates while preserving order + unique_betas = [] + seen = set() + for beta in anthropic_beta_list: + if beta not in seen: + unique_betas.append(beta) + seen.add(beta) + additional_request_params["anthropic_beta"] = unique_betas + bedrock_tool_config: Optional[ToolConfigBlock] = None if len(bedrock_tools) > 0: tool_choice_values: ToolChoiceValuesBlock = inference_params.pop( @@ -708,6 +731,7 @@ class AmazonConverseConfig(BaseConfig): messages: List[AllMessageValues], optional_params: dict, litellm_params: dict, + headers: Optional[dict] = None, ) -> RequestObject: messages, system_content_blocks = self._transform_system_message(messages) ## TRANSFORMATION ## @@ -717,6 +741,7 @@ class AmazonConverseConfig(BaseConfig): system_content_blocks=system_content_blocks, optional_params=optional_params, messages=messages, + headers=headers, ) bedrock_messages = ( @@ -747,6 +772,7 @@ class AmazonConverseConfig(BaseConfig): messages=messages, optional_params=optional_params, litellm_params=litellm_params, + headers=headers, ), ) @@ -756,6 +782,7 @@ class AmazonConverseConfig(BaseConfig): messages: List[AllMessageValues], optional_params: dict, litellm_params: dict, + headers: Optional[dict] = None, ) -> RequestObject: messages, system_content_blocks = self._transform_system_message(messages) @@ -764,6 +791,7 @@ class AmazonConverseConfig(BaseConfig): system_content_blocks=system_content_blocks, optional_params=optional_params, messages=messages, + headers=headers, ) ## TRANSFORMATION ## @@ -1154,3 +1182,37 @@ class AmazonConverseConfig(BaseConfig): if api_key: headers["Authorization"] = f"Bearer {api_key}" return headers + + + def should_fake_stream( + self, + model: Optional[str], + stream: Optional[bool], + custom_llm_provider: Optional[str] = None, + fake_stream: Optional[bool] = None, + ) -> bool: + """ + Returns True if the model/provider should fake stream + """ + ################################################################### + # If an upstream method already set fake_stream to True, return True + ################################################################### + if fake_stream is True: + return True + + ################################################################### + # Bedrock Converse Specific Logic + ################################################################### + if stream is True: + if model is not None: + ################################################################### + # GPT-OSS models do not support streaming + ################################################################### + if "gpt-oss" in model: + return True + ################################################################### + # AI21 models do not support streaming + ################################################################### + if "ai21" in model: + return True + return False diff --git a/litellm/llms/bedrock/chat/invoke_handler.py b/litellm/llms/bedrock/chat/invoke_handler.py index b8dac7c3cd7..42cdb34fc1a 100644 --- a/litellm/llms/bedrock/chat/invoke_handler.py +++ b/litellm/llms/bedrock/chat/invoke_handler.py @@ -831,7 +831,7 @@ class BedrockLLM(BaseAWSLLM): model=model, messages=messages, custom_llm_provider="anthropic_xml" ) # type: ignore ## LOAD CONFIG - config = litellm.AmazonAnthropicClaude3Config.get_config() + config = litellm.AmazonAnthropicClaudeConfig.get_config() for k, v in config.items(): if ( k not in inference_params diff --git a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py index 738490aa7bb..9b13d3df08e 100644 --- a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py +++ b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py @@ -6,6 +6,7 @@ from litellm.llms.anthropic.chat.transformation import AnthropicConfig from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation import ( AmazonInvokeConfig, ) +from litellm.llms.bedrock.common_utils import get_anthropic_beta_from_headers from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import ModelResponse @@ -17,13 +18,22 @@ else: LiteLLMLoggingObj = Any -class AmazonAnthropicClaude3Config(AmazonInvokeConfig, AnthropicConfig): +class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig): """ Reference: https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=claude https://docs.anthropic.com/claude/docs/models-overview#model-comparison + https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages-request-response.html - Supported Params for the Amazon / Anthropic Claude 3 models: + Supported Params for the Amazon / Anthropic Claude models (Claude 3, Claude 4, etc.): + Supports anthropic_beta parameter for beta features like: + - computer-use-2025-01-24 (Claude 3.7 Sonnet) + - computer-use-2024-10-22 (Claude 3.5 Sonnet v2) + - token-efficient-tools-2025-02-19 (Claude 3.7 Sonnet) + - interleaved-thinking-2025-05-14 (Claude 4 models) + - output-128k-2025-02-19 (Claude 3.7 Sonnet) + - dev-full-thinking-2025-05-14 (Claude 4 models) + - context-1m-2025-08-07 (Claude Sonnet 4) """ anthropic_version: str = "bedrock-2023-05-31" @@ -50,6 +60,7 @@ class AmazonAnthropicClaude3Config(AmazonInvokeConfig, AnthropicConfig): drop_params, ) + def transform_request( self, model: str, @@ -72,6 +83,11 @@ class AmazonAnthropicClaude3Config(AmazonInvokeConfig, AnthropicConfig): if "anthropic_version" not in _anthropic_request: _anthropic_request["anthropic_version"] = self.anthropic_version + # Handle anthropic_beta from user headers + anthropic_beta_list = get_anthropic_beta_from_headers(headers) + if anthropic_beta_list: + _anthropic_request["anthropic_beta"] = anthropic_beta_list + return _anthropic_request def transform_response( diff --git a/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py index 16f146206b1..08a0690716b 100644 --- a/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py +++ b/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py @@ -190,13 +190,15 @@ class AmazonInvokeConfig(BaseConfig, BaseAWSLLM): ] = True # cohere requires stream = True in inference params request_data = {"prompt": prompt, **inference_params} elif provider == "anthropic": - return litellm.AmazonAnthropicClaude3Config().transform_request( + transformed_request = litellm.AmazonAnthropicClaudeConfig().transform_request( model=model, messages=messages, optional_params=optional_params, litellm_params=litellm_params, headers=headers, ) + + return transformed_request elif provider == "nova": return litellm.AmazonInvokeNovaConfig().transform_request( model=model, @@ -293,7 +295,7 @@ class AmazonInvokeConfig(BaseConfig, BaseAWSLLM): completion_response["generations"][0]["finish_reason"] ) elif provider == "anthropic": - return litellm.AmazonAnthropicClaude3Config().transform_response( + return litellm.AmazonAnthropicClaudeConfig().transform_response( model=model, raw_response=raw_response, model_response=model_response, diff --git a/litellm/llms/bedrock/common_utils.py b/litellm/llms/bedrock/common_utils.py index 2a8fdc148bd..c76fc0a80c3 100644 --- a/litellm/llms/bedrock/common_utils.py +++ b/litellm/llms/bedrock/common_utils.py @@ -4,11 +4,14 @@ Common utilities used across bedrock chat/embedding/image generation import json import os -from typing import TYPE_CHECKING, List, Literal, Optional, Union +from typing import TYPE_CHECKING, Dict, List, Literal, Optional, Union import httpx import litellm +from litellm.llms.base_llm.anthropic_messages.transformation import ( + BaseAnthropicMessagesConfig, +) from litellm.llms.base_llm.base_utils import BaseLLMModelInfo from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.secret_managers.main import get_secret @@ -443,23 +446,82 @@ class BedrockModelInfo(BaseLLMModelInfo): """ Get the bedrock route for the given model. """ + route_mappings: Dict[str, Literal["invoke", "converse_like", "converse", "agent"]] = { + "invoke/": "invoke", + "converse_like/": "converse_like", + "converse/": "converse", + "agent/": "agent" + } + + # Check explicit routes first + for prefix, route_type in route_mappings.items(): + if prefix in model: + return route_type + base_model = BedrockModelInfo.get_base_model(model) alt_model = BedrockModelInfo.get_non_litellm_routing_model_name(model=model) - if "invoke/" in model: - return "invoke" - elif "converse_like" in model: - return "converse_like" - elif "converse/" in model: - return "converse" - elif "agent/" in model: - return "agent" - elif ( + if ( base_model in litellm.bedrock_converse_models or alt_model in litellm.bedrock_converse_models ): return "converse" return "invoke" + + @staticmethod + def _explicit_converse_route(model: str) -> bool: + """ + Check if the model is an explicit converse route. + """ + return "converse/" in model + + @staticmethod + def _explicit_invoke_route(model: str) -> bool: + """ + Check if the model is an explicit invoke route. + """ + return "invoke/" in model + + @staticmethod + def _explicit_agent_route(model: str) -> bool: + """ + Check if the model is an explicit agent route. + """ + return "agent/" in model + + @staticmethod + def _explicit_converse_like_route(model: str) -> bool: + """ + Check if the model is an explicit converse like route. + """ + return "converse_like/" in model + + @staticmethod + def get_bedrock_provider_config_for_messages_api(model: str) -> Optional[BaseAnthropicMessagesConfig]: + """ + Get the bedrock provider config for the given model. + + Only route to AmazonAnthropicClaude3MessagesConfig() for BaseMessagesConfig + + All other routes should return None since they will go through litellm.completion + """ + + ######################################################### + # Converse routes should go through litellm.completion() + if BedrockModelInfo._explicit_converse_route(model): + return None + + ######################################################### + # This goes through litellm.AmazonAnthropicClaude3MessagesConfig() + # Since bedrock Invoke supports Native Anthropic Messages API + ######################################################### + if "claude" in model: + return litellm.AmazonAnthropicClaudeMessagesConfig() + + ######################################################### + # These routes will go through litellm.completion() + ######################################################### + return None class BedrockEventStreamDecoderBase: """ @@ -524,3 +586,25 @@ class BedrockEventStreamDecoderBase: return None return chunk.decode() # type: ignore[no-any-return] + + +def get_anthropic_beta_from_headers(headers: dict) -> List[str]: + """ + Extract anthropic-beta header values and convert them to a list. + Supports comma-separated values from user headers. + + Used by both converse and invoke transformations for consistent handling + of anthropic-beta headers that should be passed to AWS Bedrock. + + Args: + headers (dict): Request headers dictionary + + Returns: + List[str]: List of anthropic beta feature strings, empty list if no header + """ + anthropic_beta_header = headers.get("anthropic-beta") + if not anthropic_beta_header: + return [] + + # Split comma-separated values and strip whitespace + return [beta.strip() for beta in anthropic_beta_header.split(",")] diff --git a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py index 09c6673cc5d..4fa8517a090 100644 --- a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py +++ b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py @@ -12,6 +12,7 @@ from litellm.llms.bedrock.chat.invoke_handler import AWSEventStreamDecoder from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation import ( AmazonInvokeConfig, ) +from litellm.llms.bedrock.common_utils import get_anthropic_beta_from_headers from litellm.types.router import GenericLiteLLMParams from litellm.types.utils import GenericStreamingChunk from litellm.types.utils import GenericStreamingChunk as GChunk @@ -25,12 +26,13 @@ else: LiteLLMLoggingObj = Any -class AmazonAnthropicClaude3MessagesConfig( +class AmazonAnthropicClaudeMessagesConfig( AnthropicMessagesConfig, AmazonInvokeConfig, ): """ Call Claude model family in the /v1/messages API spec + Supports anthropic_beta parameter for beta features. """ DEFAULT_BEDROCK_ANTHROPIC_API_VERSION = "bedrock-2023-05-31" @@ -127,6 +129,12 @@ class AmazonAnthropicClaude3MessagesConfig( # 3. `model` is not allowed in request body for bedrock invoke if "model" in anthropic_messages_request: anthropic_messages_request.pop("model", None) + + # 4. Handle anthropic_beta from user headers + anthropic_beta_list = get_anthropic_beta_from_headers(headers) + if anthropic_beta_list: + anthropic_messages_request["anthropic_beta"] = anthropic_beta_list + return anthropic_messages_request def get_async_streaming_response_iterator( diff --git a/litellm/llms/cometapi/chat/transformation.py b/litellm/llms/cometapi/chat/transformation.py new file mode 100644 index 00000000000..fedb8f61e5b --- /dev/null +++ b/litellm/llms/cometapi/chat/transformation.py @@ -0,0 +1,207 @@ +""" +Support for CometAPI's `/v1/chat/completions` endpoint. + +Based on OpenAI-compatible API interface implementation +Documentation: [CometAPI Documentation Link] +""" + +from typing import Any, AsyncIterator, Iterator, List, Optional, Tuple, Union + +import httpx + +from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.types.llms.openai import AllMessageValues, ChatCompletionToolParam +from litellm.types.utils import ModelResponse, ModelResponseStream + +from ...openai.chat.gpt_transformation import OpenAIGPTConfig +from ..common_utils import CometAPIException + + +class CometAPIConfig(OpenAIGPTConfig): + """ + CometAPI configuration class, inherits from OpenAIGPTConfig + + Since CometAPI is OpenAI-compatible API, we inherit from OpenAIGPTConfig + and only need to override necessary methods to handle CometAPI-specific features + """ + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + """ + Map OpenAI format parameters to CometAPI format + """ + mapped_openai_params = super().map_openai_params( + non_default_params, optional_params, model, drop_params + ) + + # CometAPI-specific parameters (if any) + extra_body: dict[str, Any] = {} + # TODO: Add CometAPI-specific parameter handling here + # Example: + # custom_param = non_default_params.pop("custom_param", None) + # if custom_param is not None: + # extra_body["custom_param"] = custom_param + + if extra_body: + mapped_openai_params["extra_body"] = extra_body + + return mapped_openai_params + + def remove_cache_control_flag_from_messages_and_tools( + self, + model: str, + messages: List[AllMessageValues], + tools: Optional[List["ChatCompletionToolParam"]] = None, + ) -> Tuple[List[AllMessageValues], Optional[List["ChatCompletionToolParam"]]]: + """ + Remove cache control flags from messages and tools if not supported + """ + # For CometAPI, use default behavior (remove cache control) + return super().remove_cache_control_flag_from_messages_and_tools( + model, messages, tools + ) + + def transform_request( + self, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + headers: dict, + ) -> dict: + """ + Transform the overall request to be sent to the API. + + Returns: + dict: The transformed request. Sent as the body of the API call. + """ + extra_body = optional_params.pop("extra_body", {}) + response = super().transform_request( + model, messages, optional_params, litellm_params, headers + ) + response.update(extra_body) + return response + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + """ + Get the complete URL for the CometAPI call. + + Returns: + str: The complete URL for the API call. + """ + # Default base + if api_base is None: + api_base = "https://api.cometapi.com/v1" + endpoint = "chat/completions" + + # Normalize + api_base = api_base.rstrip("/") + + # If endpoint already present, return as-is + if endpoint in api_base: + return api_base + + # Ensure we include /v1 prefix when missing + if api_base.endswith("/v1"): + return f"{api_base}/{endpoint}" + if api_base.endswith("/v1/"): + return f"{api_base}{endpoint}" + # If user provided https://api.cometapi.com, add /v1 + if api_base == "https://api.cometapi.com": + return f"{api_base}/v1/{endpoint}" + # Generic fallback: if '/v1' not in path, add it + if "/v1" not in api_base.split("//", 1)[-1]: + return f"{api_base}/v1/{endpoint}" + return f"{api_base}/{endpoint}" + + def get_error_class( + self, + error_message: str, + status_code: int, + headers: Union[dict, httpx.Headers] + ) -> BaseLLMException: + """ + Return CometAPI-specific error class + """ + return CometAPIException( + message=error_message, + status_code=status_code, + headers=headers, + ) + + def get_model_response_iterator( + self, + streaming_response: Union[Iterator[str], AsyncIterator[str], ModelResponse], + sync_stream: bool, + json_mode: Optional[bool] = False, + ) -> Any: + """ + Get model response iterator for streaming responses + """ + return CometAPIChatCompletionStreamingHandler( + streaming_response=streaming_response, + sync_stream=sync_stream, + json_mode=json_mode, + ) + + +class CometAPIChatCompletionStreamingHandler(BaseModelResponseIterator): + """ + Handler for CometAPI streaming chat completion responses + """ + + def chunk_parser(self, chunk: dict) -> ModelResponseStream: + """ + Parse individual chunks from streaming response + """ + try: + # Handle error in chunk + if "error" in chunk: + error_chunk = chunk["error"] + error_message = "CometAPI Error: {}".format( + error_chunk.get("message", "Unknown error") + ) + raise CometAPIException( + message=error_message, + status_code=error_chunk.get("code", 400), + headers={"Content-Type": "application/json"}, + ) + + # Process choices + new_choices = [] + for choice in chunk["choices"]: + # Handle reasoning content if present + if "delta" in choice and "reasoning" in choice["delta"]: + choice["delta"]["reasoning_content"] = choice["delta"].get("reasoning") + new_choices.append(choice) + + return ModelResponseStream( + id=chunk["id"], + object="chat.completion.chunk", + created=chunk["created"], + usage=chunk.get("usage"), + model=chunk["model"], + choices=new_choices, + ) + except KeyError as e: + raise CometAPIException( + message=f"KeyError: {e}, Got unexpected response from CometAPI: {chunk}", + status_code=400, + headers={"Content-Type": "application/json"}, + ) + except Exception as e: + raise e diff --git a/litellm/llms/cometapi/common_utils.py b/litellm/llms/cometapi/common_utils.py new file mode 100644 index 00000000000..2e5e3e5fab7 --- /dev/null +++ b/litellm/llms/cometapi/common_utils.py @@ -0,0 +1,6 @@ +from litellm.llms.base_llm.chat.transformation import BaseLLMException + + +class CometAPIException(BaseLLMException): + """CometAPI exception handling class""" + pass diff --git a/litellm/llms/custom_httpx/aiohttp_transport.py b/litellm/llms/custom_httpx/aiohttp_transport.py index 3ed7d04bde6..ab69ea1f8c3 100644 --- a/litellm/llms/custom_httpx/aiohttp_transport.py +++ b/litellm/llms/custom_httpx/aiohttp_transport.py @@ -3,7 +3,7 @@ import contextlib import os import typing import urllib.request -from typing import Callable, Dict, Union +from typing import Callable, Dict, Optional, Union import aiohttp import aiohttp.client_exceptions @@ -115,6 +115,12 @@ class AiohttpTransport(httpx.AsyncBaseTransport): ) -> None: self.client = client + ######################################################### + # Class variables for proxy settings + ######################################################### + self.proxy: Optional[str] = None + self.checked_proxy_env_settings: bool = False + async def aclose(self) -> None: if isinstance(self.client, ClientSession): await self.client.close() @@ -249,7 +255,22 @@ class LiteLLMAiohttpTransport(AiohttpTransport): def _proxy_from_env(self, url: httpx.URL) -> typing.Optional[str]: - """Return proxy URL from env for the given request URL.""" + """ + Return proxy URL from env for the given request URL + + Only check the proxy env settings once, this is a costly operation for CPU % usage + + .""" + ######################################################### + # Check if we've already checked the proxy env settings + ######################################################### + if self.checked_proxy_env_settings is True: + return self.proxy + + ######################################################### + # set self.checked_proxy_env_settings to True + ######################################################### + self.checked_proxy_env_settings = True proxies = urllib.request.getproxies() if urllib.request.proxy_bypass(url.host): return None @@ -257,4 +278,5 @@ class LiteLLMAiohttpTransport(AiohttpTransport): proxy = proxies.get(url.scheme) or proxies.get("all") if proxy and "://" not in proxy: proxy = f"http://{proxy}" - return proxy + self.proxy = proxy + return self.proxy diff --git a/litellm/llms/custom_httpx/http_handler.py b/litellm/llms/custom_httpx/http_handler.py index cf2187153a9..4d8781fff2a 100644 --- a/litellm/llms/custom_httpx/http_handler.py +++ b/litellm/llms/custom_httpx/http_handler.py @@ -40,7 +40,9 @@ headers = { _DEFAULT_TIMEOUT = httpx.Timeout(timeout=5.0, connect=5.0) -def get_ssl_configuration(ssl_verify: Optional[VerifyTypes] = None) -> Union[bool, str, ssl.SSLContext]: +def get_ssl_configuration( + ssl_verify: Optional[VerifyTypes] = None, +) -> Union[bool, str, ssl.SSLContext]: """ Unified SSL configuration function that handles ssl_context and ssl_verify logic. @@ -59,7 +61,7 @@ def get_ssl_configuration(ssl_verify: Optional[VerifyTypes] = None) -> Union[boo - False: Disable SSL verification - True: Enable SSL verification - str: Path to CA bundle file - + Returns: Union[bool, str, ssl.SSLContext]: Appropriate SSL configuration """ @@ -72,7 +74,9 @@ def get_ssl_configuration(ssl_verify: Optional[VerifyTypes] = None) -> Union[boo # Get ssl_verify from environment or litellm settings if not provided if ssl_verify is None: ssl_verify = os.getenv("SSL_VERIFY", litellm.ssl_verify) - ssl_verify_bool = str_to_bool(ssl_verify) if isinstance(ssl_verify, str) else ssl_verify + ssl_verify_bool = ( + str_to_bool(ssl_verify) if isinstance(ssl_verify, str) else ssl_verify + ) if ssl_verify_bool is not None: ssl_verify = ssl_verify_bool @@ -89,14 +93,9 @@ def get_ssl_configuration(ssl_verify: Optional[VerifyTypes] = None) -> Union[boo cafile = certifi.where() if ssl_verify is not False: - custom_ssl_context = ssl.create_default_context( - cafile=cafile - ) + custom_ssl_context = ssl.create_default_context(cafile=cafile) # If security level is set, apply it to the SSL context - if ( - ssl_security_level - and isinstance(ssl_security_level, str) - ): + if ssl_security_level and isinstance(ssl_security_level, str): # Create a custom SSL context with reduced security level custom_ssl_context.set_ciphers(ssl_security_level) @@ -260,6 +259,7 @@ class AsyncHTTPHandler: files: Optional[RequestFiles] = None, content: Any = None, ): + start_time = time.time() try: if timeout is None: @@ -586,7 +586,7 @@ class AsyncHTTPHandler: ) -> Dict[str, Any]: """ Helper method to get SSL connector initialization arguments for aiohttp TCPConnector. - + SSL Configuration Priority: 1. If ssl_context is provided -> use the custom SSL context 2. If ssl_verify is False -> disable SSL verification (ssl=False) @@ -597,14 +597,14 @@ class AsyncHTTPHandler: connector_kwargs: Dict[str, Any] = { "local_addr": ("0.0.0.0", 0) if litellm.force_ipv4 else None, } - + if ssl_context is not None: # Priority 1: Use the provided custom SSL context connector_kwargs["ssl"] = ssl_context elif ssl_verify is False: # Priority 2: Explicitly disable SSL verification connector_kwargs["verify_ssl"] = False - + return connector_kwargs @staticmethod diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index 3e7dff91820..c9d70088d04 100644 --- a/litellm/llms/custom_httpx/llm_http_handler.py +++ b/litellm/llms/custom_httpx/llm_http_handler.py @@ -111,6 +111,7 @@ class BaseLLMHTTPHandler: response: Optional[httpx.Response] = None for i in range(max(max_retry_on_unprocessable_entity_error, 1)): try: + response = await async_httpx_client.post( url=api_base, headers=headers, @@ -2712,7 +2713,8 @@ class BaseLLMHTTPHandler: headers = image_generation_provider_config.validate_environment( api_key=litellm_params.get("api_key", None), - headers=image_generation_optional_request_params.get("extra_headers", {}) or {}, + headers=image_generation_optional_request_params.get("extra_headers", {}) + or {}, model=model, messages=[], optional_params=image_generation_optional_request_params, @@ -2763,15 +2765,17 @@ class BaseLLMHTTPHandler: provider_config=image_generation_provider_config, ) - model_response: ImageResponse = image_generation_provider_config.transform_image_generation_response( - model=model, - raw_response=response, - model_response=litellm.ImageResponse(), - logging_obj=logging_obj, - request_data=data, - optional_params=image_generation_optional_request_params, - litellm_params=dict(litellm_params), - encoding=None, + model_response: ImageResponse = ( + image_generation_provider_config.transform_image_generation_response( + model=model, + raw_response=response, + model_response=litellm.ImageResponse(), + logging_obj=logging_obj, + request_data=data, + optional_params=image_generation_optional_request_params, + litellm_params=dict(litellm_params), + encoding=None, + ) ) return model_response @@ -2804,10 +2808,10 @@ class BaseLLMHTTPHandler: else: async_httpx_client = client - headers = image_generation_provider_config.validate_environment( api_key=litellm_params.get("api_key", None), - headers=image_generation_optional_request_params.get("extra_headers", {}) or {}, + headers=image_generation_optional_request_params.get("extra_headers", {}) + or {}, model=model, messages=[], optional_params=image_generation_optional_request_params, @@ -2858,17 +2862,19 @@ class BaseLLMHTTPHandler: provider_config=image_generation_provider_config, ) - model_response: ImageResponse = image_generation_provider_config.transform_image_generation_response( - model=model, - raw_response=response, - model_response=litellm.ImageResponse(), - logging_obj=logging_obj, - request_data=data, - optional_params=image_generation_optional_request_params, - litellm_params=dict(litellm_params), - encoding=None, + model_response: ImageResponse = ( + image_generation_provider_config.transform_image_generation_response( + model=model, + raw_response=response, + model_response=litellm.ImageResponse(), + logging_obj=logging_obj, + request_data=data, + optional_params=image_generation_optional_request_params, + litellm_params=dict(litellm_params), + encoding=None, + ) ) - + return model_response ###### VECTOR STORE HANDLER ###### @@ -2936,7 +2942,9 @@ class BaseLLMHTTPHandler: }, ) - request_data = json.dumps(request_body) if signed_json_body is None else signed_json_body + request_data = ( + json.dumps(request_body) if signed_json_body is None else signed_json_body + ) try: response = await async_httpx_client.post( @@ -3035,7 +3043,9 @@ class BaseLLMHTTPHandler: }, ) - request_data = json.dumps(request_body) if signed_json_body is None else signed_json_body + request_data = ( + json.dumps(request_body) if signed_json_body is None else signed_json_body + ) try: response = sync_httpx_client.post( diff --git a/litellm/llms/datarobot/chat/transformation.py b/litellm/llms/datarobot/chat/transformation.py index e334c94e517..23ce63c25b2 100644 --- a/litellm/llms/datarobot/chat/transformation.py +++ b/litellm/llms/datarobot/chat/transformation.py @@ -6,8 +6,11 @@ Calls done in OpenAI/openai.py as DataRobot is openai-compatible. from typing import Optional, Tuple from litellm.secret_managers.main import get_secret_str +from urllib.parse import urlparse, urlunparse from ...openai_like.chat.transformation import OpenAILikeChatConfig +LLMGW_PATH = "/genai/llmgw/chat/completions" + class DataRobotConfig(OpenAILikeChatConfig): @staticmethod @@ -32,22 +35,28 @@ class DataRobotConfig(OpenAILikeChatConfig): if api_base is None: api_base = "https://app.datarobot.com" - # If the api_base is a deployment URL, we do not append the chat completions path - if "api/v2/deployments" not in api_base: - # If the api_base is not a deployment URL, we need to append the chat completions path - if "api/v2/genai/llmgw/chat/completions" not in api_base: - api_base += "/api/v2/genai/llmgw/chat/completions" + parsed = urlparse(api_base) + path = parsed.path + + if not path or path == "/": # Add full path to LLMGW + path += f"/api/v2/{LLMGW_PATH}" + elif "api/v2/deployments" in path: # Dedicated deployment, leave it + pass + elif ( + "api/v2" in path and LLMGW_PATH not in path + ): # Standard ENDPOINT path, add LLMGW + path += LLMGW_PATH # Ensure the url ends with a trailing slash - if not api_base.endswith("/"): - api_base += "/" + if not path.endswith("/"): + path += "/" + path = path.replace("//", "/") + updated_parsed = parsed._replace(path=path) - return api_base # type: ignore + return urlunparse(updated_parsed) def _get_openai_compatible_provider_info( - self, - api_base: Optional[str], - api_key: Optional[str] + self, api_base: Optional[str], api_key: Optional[str] ) -> Tuple[Optional[str], Optional[str]]: """Attempts to ensure that the API base and key are set, preferring user-provided values, before falling back to secret manager values (``DATAROBOT_ENDPOINT`` and ``DATAROBOT_API_TOKEN`` diff --git a/litellm/llms/deepinfra/rerank/transformation.py b/litellm/llms/deepinfra/rerank/transformation.py new file mode 100644 index 00000000000..8259c6075bb --- /dev/null +++ b/litellm/llms/deepinfra/rerank/transformation.py @@ -0,0 +1,239 @@ +""" +Translate between Cohere's `/rerank` format and Deepinfra's `/rerank` format. +""" + +import uuid +from typing import Any, Dict, List, Optional, Union + +import httpx + +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.base_llm.rerank.transformation import ( + BaseLLMException, + BaseRerankConfig, +) +from litellm.secret_managers.main import get_secret_str +from litellm.types.rerank import ( + OptionalRerankParams, + RerankBilledUnits, + RerankResponse, + RerankResponseMeta, + RerankResponseResult, + RerankTokens, +) + + +class DeepinfraRerankConfig(BaseRerankConfig): + """ + Deepinfra Rerank - Follows the same Spec as Cohere Rerank + """ + + def get_complete_url(self, api_base: Optional[str], model: str) -> str: + """ + Constructs the complete DeepInfra inference endpoint URL for rerank. + + Args: + api_base (Optional[str]): The base URL for the DeepInfra API. + model (str): The model identifier. + + Returns: + str: The complete URL for the DeepInfra rerank inference endpoint. + + Raises: + ValueError: If api_base is None. + """ + if not api_base: + raise ValueError( + "Deepinfra API Base is required. api_base=None. Set in call or via `DEEPINFRA_API_BASE` env var." + ) + + # Remove 'openai' from the base if present + api_base_clean = ( + api_base.replace("openai", "") if "openai" in api_base else api_base + ) + + # Remove any trailing slashes for consistency, then add one + api_base_clean = api_base_clean.rstrip("/") + "/" + + # Compose the full endpoint + return f"{api_base_clean}inference/{model}" + + def validate_environment( + self, + headers: dict, + model: str, + api_key: Optional[str] = None, + ) -> dict: + if api_key is None: + api_key = get_secret_str("DEEPINFRA_API_KEY") + + if api_key is None: + raise ValueError( + "Deepinfra API key is required. Please set 'DEEPINFRA_API_KEY' environment variable" + ) + + default_headers = { + "Authorization": f"Bearer {api_key}", + "accept": "application/json", + "content-type": "application/json", + } + + # If 'Authorization' is provided in headers, it overrides the default. + if "Authorization" in headers: + default_headers["Authorization"] = headers["Authorization"] + + # Merge other headers, overriding any default ones except Authorization + return {**default_headers, **headers} + + def map_cohere_rerank_params( + self, + non_default_params: dict, + model: str, + drop_params: bool, + query: str, + documents: List[Union[str, Dict[str, Any]]], + custom_llm_provider: Optional[str] = None, + top_n: Optional[int] = None, + rank_fields: Optional[List[str]] = None, + return_documents: Optional[bool] = True, + max_chunks_per_doc: Optional[int] = None, + max_tokens_per_doc: Optional[int] = None, + ) -> OptionalRerankParams: + # Start with the basic parameters + optional_rerank_params = {} + if query: + optional_rerank_params["queries"] = [query] * len( + documents + ) # Deepinfra rerank requires queries to be of same length as documents + + if non_default_params is not None: + for k, v in non_default_params.items(): + if k == "queries" and v is not None: + # This should override the query parameter if it is provided + optional_rerank_params["queries"] = v + elif k == "documents" and v is not None: + optional_rerank_params["documents"] = v + elif k == "service_tier" and v is not None: + optional_rerank_params["service_tier"] = v + elif k == "instruction" and v is not None: + optional_rerank_params["instruction"] = v + elif k == "webhook" and v is not None: + optional_rerank_params["webhook"] = v + return OptionalRerankParams(**optional_rerank_params) # type: ignore + + def transform_rerank_request( + self, + model: str, + optional_rerank_params: OptionalRerankParams, + headers: dict, + ) -> dict: + # Convert OptionalRerankParams to dict as expected by parent class + if optional_rerank_params is None: + return {} + return dict(optional_rerank_params) + + def transform_rerank_response( + self, + model: str, + raw_response: httpx.Response, + model_response: RerankResponse, + logging_obj: LiteLLMLoggingObj, + api_key: Optional[str] = None, + request_data: dict = {}, + optional_params: dict = {}, + litellm_params: dict = {}, + ) -> RerankResponse: + try: + response_json = raw_response.json() + logging_obj.post_call(original_response=raw_response.text) + + # Extract the scores from the response + scores = response_json.get("scores", []) + input_tokens = response_json.get("input_tokens", 0) + request_id = response_json.get("request_id") + + # Create inference status information + inference_status = response_json.get("inference_status", {}) + status = inference_status.get("status", "unknown") + runtime_ms = inference_status.get("runtime_ms", 0) + cost = inference_status.get("cost", 0.0) + tokens_generated = inference_status.get("tokens_generated", 0) + tokens_input = inference_status.get("tokens_input", 0) + + # Create RerankResponse + results = [] + for i, score in enumerate(scores): + results.append( + RerankResponseResult(index=i, relevance_score=float(score)) + ) + + # Create metadata for the response + tokens = RerankTokens( + input_tokens=input_tokens, + output_tokens=0, # DeepInfra doesn't provide output tokens for rerank + ) + billed_units = RerankBilledUnits(total_tokens=input_tokens) + meta = RerankResponseMeta(tokens=tokens, billed_units=billed_units) + + rerank_response = RerankResponse( + id=request_id or str(uuid.uuid4()), results=results, meta=meta + ) + + # Store additional information in hidden params + rerank_response._hidden_params = { + "status": status, + "runtime_ms": runtime_ms, + "cost": cost, + "tokens_generated": tokens_generated, + "tokens_input": tokens_input, + "model": model, + } + + return rerank_response + + except Exception: + # If there's an error parsing the response, fall back to the parent implementation + rerank_response = super().transform_rerank_response( + model=model, + raw_response=raw_response, + model_response=model_response, + logging_obj=logging_obj, + api_key=api_key, + request_data=request_data, + optional_params=optional_params, + litellm_params=litellm_params, + ) + + rerank_response._hidden_params["model"] = model + return rerank_response + + def get_supported_cohere_rerank_params(self, model: str) -> list: + return ["query", "documents"] + + def get_error_class( + self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers] + ) -> BaseLLMException: + # Deepinfra errors may come as JSON: {"detail": {"error": "..."}} + import json + + # Try to extract a more specific error message if possible + try: + error_data = error_message + if isinstance(error_message, str): + error_data = json.loads(error_message) + if isinstance(error_data, dict): + # Check for {"detail": {"error": "..."}} + detail = error_data.get("detail") + if isinstance(detail, dict) and "error" in detail: + error_message = detail["error"] + elif isinstance(detail, str): + error_message = detail + except Exception: + # If parsing fails, just use the original error_message + pass + + raise BaseLLMException( + status_code=status_code, + message=error_message, + headers=headers, + ) diff --git a/litellm/llms/gemini/common_utils.py b/litellm/llms/gemini/common_utils.py index 53de6711cad..e53829d3329 100644 --- a/litellm/llms/gemini/common_utils.py +++ b/litellm/llms/gemini/common_utils.py @@ -1,15 +1,16 @@ import base64 import datetime -from typing import Dict, List, Optional, Union +from typing import Any, Dict, List, Optional, Union import httpx import litellm from litellm.constants import DEFAULT_MAX_RECURSE_DEPTH -from litellm.llms.base_llm.base_utils import BaseLLMModelInfo +from litellm.llms.base_llm.base_utils import BaseLLMModelInfo, BaseTokenCounter from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import AllMessageValues +from litellm.types.utils import TokenCountResponse class GeminiError(BaseLLMException): @@ -89,6 +90,16 @@ class GeminiModelInfo(BaseLLMModelInfo): return GeminiError( status_code=status_code, message=error_message, headers=headers ) + + def get_token_counter(self) -> Optional[BaseTokenCounter]: + """ + Factory method to create a token counter for this provider. + + Returns: + Optional TokenCounterInterface implementation for this provider, + or None if token counting is not supported. + """ + return GoogleAIStudioTokenCounter() def encode_unserializable_types( @@ -137,3 +148,46 @@ def encode_unserializable_types( def get_api_key_from_env() -> Optional[str]: return get_secret_str("GOOGLE_API_KEY") or get_secret_str("GEMINI_API_KEY") + + +class GoogleAIStudioTokenCounter(BaseTokenCounter): + """Token counter implementation for Google AI Studio provider.""" + def should_use_token_counting_api( + self, + custom_llm_provider: Optional[str] = None, + ) -> bool: + from litellm.types.utils import LlmProviders + return custom_llm_provider == LlmProviders.GEMINI.value + + async def count_tokens( + self, + model_to_use: str, + messages: Optional[List[Dict[str, Any]]], + contents: Optional[List[Dict[str, Any]]], + deployment: Optional[Dict[str, Any]] = None, + request_model: str = "", + ) -> Optional[TokenCountResponse]: + import copy + + from litellm.llms.gemini.count_tokens.handler import GoogleAIStudioTokenCounter + deployment = deployment or {} + count_tokens_params_request = copy.deepcopy(deployment.get("litellm_params", {})) + count_tokens_params = { + "model": model_to_use, + "contents": contents, + } + count_tokens_params_request.update(count_tokens_params) + result = await GoogleAIStudioTokenCounter().acount_tokens( + **count_tokens_params_request, + ) + + if result is not None: + return TokenCountResponse( + total_tokens=result.get("totalTokens", 0), + request_model=request_model, + model_used=model_to_use, + tokenizer_type=result.get("tokenizer_used", ""), + original_response=result, + ) + + return None \ No newline at end of file diff --git a/litellm/llms/gemini/count_tokens/handler.py b/litellm/llms/gemini/count_tokens/handler.py new file mode 100644 index 00000000000..bcc8ab9553d --- /dev/null +++ b/litellm/llms/gemini/count_tokens/handler.py @@ -0,0 +1,139 @@ +from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union + +import httpx + +import litellm +from litellm.llms.custom_httpx.http_handler import get_async_httpx_client +from litellm.types.utils import LlmProviders + +if TYPE_CHECKING: + from litellm.types.google_genai.main import GenerateContentContentListUnionDict +else: + GenerateContentContentListUnionDict = Any + +class GoogleAIStudioTokenCounter: + + def _construct_url(self, model: str, api_base: Optional[str] = None) -> str: + """ + Construct the URL for the Google Gen AI Studio countTokens endpoint. + """ + base_url = api_base or "https://generativelanguage.googleapis.com" + return f"{base_url}/v1beta/models/{model}:countTokens" + + + async def validate_environment( + self, + api_base: Optional[str] = None, + api_key: Optional[str] = None, + headers: Optional[Dict[str, Any]] = None, + model: str = "", + litellm_params: Optional[Dict[str, Any]] = None, + ) -> Tuple[Dict[str, Any], str]: + """ + Returns a Tuple of headers and url for the Google Gen AI Studio countTokens endpoint. + """ + from litellm.llms.gemini.google_genai.transformation import GoogleGenAIConfig + headers = GoogleGenAIConfig().validate_environment( + api_key=api_key, + headers=headers, + model=model, + litellm_params=litellm_params, + ) + + url = self._construct_url(model=model, api_base=api_base) + return headers, url + + async def acount_tokens( + self, + contents: Any, + model: str, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + **kwargs, + ) -> Dict[str, Any]: + """ + Count tokens using Google Gen AI Studio countTokens endpoint. + + Args: + contents: The content to count tokens for (Google Gen AI format) + Example: [{"parts": [{"text": "Hello world"}]}] + model: The model name (e.g. "gemini-1.5-flash") + api_key: Optional Google API key (will fall back to environment) + api_base: Optional API base URL (defaults to Google Gen AI Studio) + timeout: Optional timeout for the request + **kwargs: Additional parameters + + Returns: + Dict containing token count information from Google Gen AI Studio API. + Example response: + { + "totalTokens": 31, + "totalBillableCharacters": 96, + "promptTokensDetails": [ + { + "modality": "TEXT", + "tokenCount": 31 + } + ] + } + + Raises: + ValueError: If API key is missing + litellm.APIError: If the API call fails + litellm.APIConnectionError: If the connection fails + Exception: For any other unexpected errors + """ + # Set up API base URL + + # Prepare headers + headers, url = await self.validate_environment( + api_key=api_key, + api_base=api_base, + headers={}, + model=model, + litellm_params=kwargs, + ) + + # Prepare request body + request_body = { + "contents": contents + } + + async_httpx_client = get_async_httpx_client( + llm_provider=LlmProviders.GEMINI, + ) + + try: + response = await async_httpx_client.post( + url=url, + headers=headers, + json=request_body + ) + + # Check for HTTP errors + response.raise_for_status() + + # Parse response + result = response.json() + return result + + except httpx.HTTPStatusError as e: + error_msg = f"Google Gen AI Studio API error: {e.response.status_code} - {e.response.text}" + raise litellm.APIError( + message=error_msg, + llm_provider="gemini", + model=model, + status_code=e.response.status_code + ) from e + except httpx.RequestError as e: + error_msg = f"Request to Google Gen AI Studio failed: {str(e)}" + raise litellm.APIConnectionError( + message=error_msg, + llm_provider="gemini", + model=model + ) from e + except Exception as e: + error_msg = f"Unexpected error during token counting: {str(e)}" + raise Exception(error_msg) from e + diff --git a/litellm/llms/gradient_ai/chat/transformation.py b/litellm/llms/gradient_ai/chat/transformation.py new file mode 100644 index 00000000000..d631affdef8 --- /dev/null +++ b/litellm/llms/gradient_ai/chat/transformation.py @@ -0,0 +1,147 @@ +from typing import List, Optional, Tuple, Union, Dict, Literal + +from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import ( + AllMessageValues, +) + +from ...openai_like.chat.transformation import OpenAILikeChatConfig + +# Default GradientAI endpoint +GRADIENT_AI_SERVERLESS_ENDPOINT = "https://inference.do-ai.run" + + +class GradientAIConfig(OpenAILikeChatConfig): + + k: Optional[int] = None + kb_filters: Optional[List[Dict]] = None + filter_kb_content_by_query_metadata: Optional[bool] = None + instruction_override: Optional[str] = None + include_functions_info: Optional[bool] = None + include_retrieval_info: Optional[bool] = None + include_guardrails_info: Optional[bool] = None + provide_citations: Optional[bool] = None + retrieval_method: Optional[Literal["rewrite", "step_back", "sub_queries", "none"]] = None + + def __init__( + self, + frequency_penalty: Optional[float] = None, + max_tokens: Optional[int] = None, + max_completion_tokens: Optional[int] = None, + presence_penalty: Optional[float] = None, + retrieval_method: Optional[str] = None, + stop: Optional[Union[str, List[str]]] = None, + stream: Optional[bool] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + k: Optional[int] = None, + kb_filters: Optional[List[Dict]] = None, + filter_kb_content_by_query_metadata: Optional[bool] = None, + instruction_override: Optional[str] = None, + include_functions_info: Optional[bool] = None, + include_retrieval_info: Optional[bool] = None, + include_guardrails_info: Optional[bool] = None, + provide_citations: Optional[bool] = None, + ) -> None: + locals_ = locals().copy() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return super().get_config() + + def get_supported_openai_params(self, model: str) -> list: + supported_params = [ + "frequency_penalty", + "max_tokens", + "max_completion_tokens", + "presence_penalty", + "stop", + "stream", + "stream_options", + "temperature", + "top_p", + # GradientAI specific parameters + "k", + "kb_filters", + "filter_kb_content_by_query_metadata", + "instruction_override", + "include_functions_info", + "include_retrieval_info", + "include_guardrails_info", + "provide_citations", + "retrieval_method", + ] + return supported_params + + def validate_environment(self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None): + api_key = api_key or get_secret_str("GRADIENT_AI_API_KEY") + if api_key is None: + raise ValueError("GradientAI API key not found") + if headers is None: + headers = {} + headers["Authorization"] = f"Bearer {api_key}" + headers["Content-Type"] = "application/json" + return headers + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + gradient_ai_endpoint = get_secret_str("GRADIENT_AI_AGENT_ENDPOINT") + complete_url = f"{GRADIENT_AI_SERVERLESS_ENDPOINT}/v1/chat/completions" + + if api_base and api_base != GRADIENT_AI_SERVERLESS_ENDPOINT: + complete_url = f"{api_base}/api/v1/chat/completions" + elif gradient_ai_endpoint and gradient_ai_endpoint != GRADIENT_AI_SERVERLESS_ENDPOINT: + complete_url = f"{gradient_ai_endpoint}/api/v1/chat/completions" + + return complete_url + + def _get_openai_compatible_provider_info( + self, api_base: Optional[str], api_key: Optional[str] + ) -> Tuple[Optional[str], Optional[str]]: + gradient_ai_endpoint = get_secret_str("GRADIENT_AI_AGENT_ENDPOINT") + + if not api_base and not gradient_ai_endpoint: + api_base = GRADIENT_AI_SERVERLESS_ENDPOINT + else: + api_base = api_base or gradient_ai_endpoint + + dynamic_api_key = api_key or get_secret_str("GRADIENT_AI_API_KEY") + return api_base, dynamic_api_key + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool = False, + replace_max_completion_tokens_with_max_tokens: bool = False, + ) -> dict: + supported_openai_params = self.get_supported_openai_params(model=model) + for param, value in non_default_params.items(): + if param in supported_openai_params: + optional_params[param] = value + elif not drop_params: + from litellm.utils import UnsupportedParamsError + raise UnsupportedParamsError( + status_code=400, + message=f"GradientAI does not support parameter '{param}'. To drop unsupported params, set `drop_params=True`." + ) + + return optional_params diff --git a/litellm/llms/hosted_vllm/chat/transformation.py b/litellm/llms/hosted_vllm/chat/transformation.py index 529354f80eb..1d21490ea31 100644 --- a/litellm/llms/hosted_vllm/chat/transformation.py +++ b/litellm/llms/hosted_vllm/chat/transformation.py @@ -21,6 +21,11 @@ from ...openai.chat.gpt_transformation import OpenAIGPTConfig class HostedVLLMChatConfig(OpenAIGPTConfig): + def get_supported_openai_params(self, model: str) -> List[str]: + params = super().get_supported_openai_params(model) + params.append("reasoning_effort") + return params + def map_openai_params( self, non_default_params: dict, diff --git a/litellm/llms/litellm_proxy/chat/transformation.py b/litellm/llms/litellm_proxy/chat/transformation.py index ea89c4c3bc7..cf6a6ed7a54 100644 --- a/litellm/llms/litellm_proxy/chat/transformation.py +++ b/litellm/llms/litellm_proxy/chat/transformation.py @@ -4,6 +4,7 @@ Translate from OpenAI's `/v1/chat/completions` to VLLM's `/v1/chat/completions` from typing import TYPE_CHECKING, List, Optional, Tuple +from litellm.constants import OPENAI_CHAT_COMPLETION_PARAMS from litellm.secret_managers.main import get_secret_bool, get_secret_str from litellm.types.router import LiteLLM_Params @@ -16,8 +17,7 @@ if TYPE_CHECKING: class LiteLLMProxyChatConfig(OpenAIGPTConfig): def get_supported_openai_params(self, model: str) -> List: params_list = super().get_supported_openai_params(model) - params_list.append("thinking") - params_list.append("reasoning_effort") + params_list.extend(OPENAI_CHAT_COMPLETION_PARAMS) return params_list def _map_openai_params( diff --git a/litellm/llms/litellm_proxy/image_edit/transformation.py b/litellm/llms/litellm_proxy/image_edit/transformation.py new file mode 100644 index 00000000000..5f5e2bdb24d --- /dev/null +++ b/litellm/llms/litellm_proxy/image_edit/transformation.py @@ -0,0 +1,26 @@ +from typing import Optional + +from litellm.llms.openai.image_edit.transformation import OpenAIImageEditConfig +from litellm.secret_managers.main import get_secret_str + + +class LiteLLMProxyImageEditConfig(OpenAIImageEditConfig): + """Configuration for image edit requests routed through LiteLLM Proxy.""" + + def validate_environment( + self, headers: dict, model: str, api_key: Optional[str] = None + ) -> dict: + api_key = api_key or get_secret_str("LITELLM_PROXY_API_KEY") + headers.update({"Authorization": f"Bearer {api_key}"}) + return headers + + def get_complete_url( + self, model: str, api_base: Optional[str], litellm_params: dict + ) -> str: + api_base = api_base or get_secret_str("LITELLM_PROXY_API_BASE") + if api_base is None: + raise ValueError( + "api_base not set for LiteLLM Proxy route. Set in env via `LITELLM_PROXY_API_BASE`" + ) + api_base = api_base.rstrip("/") + return f"{api_base}/images/edits" diff --git a/litellm/llms/litellm_proxy/image_generation/transformation.py b/litellm/llms/litellm_proxy/image_generation/transformation.py new file mode 100644 index 00000000000..6174424154d --- /dev/null +++ b/litellm/llms/litellm_proxy/image_generation/transformation.py @@ -0,0 +1,40 @@ +from typing import Optional + +from litellm.llms.openai.image_generation.gpt_transformation import ( + GPTImageGenerationConfig, +) +from litellm.secret_managers.main import get_secret_str + + +class LiteLLMProxyImageGenerationConfig(GPTImageGenerationConfig): + """Configuration for image generation requests routed through LiteLLM Proxy.""" + def validate_environment( + self, + headers: dict, + model: str, + messages, + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + api_key = api_key or get_secret_str("LITELLM_PROXY_API_KEY") + headers.update({"Authorization": f"Bearer {api_key}"}) + return headers + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + api_base = api_base or get_secret_str("LITELLM_PROXY_API_BASE") + if api_base is None: + raise ValueError( + "api_base not set for LiteLLM Proxy route. Set in env via `LITELLM_PROXY_API_BASE`" + ) + api_base = api_base.rstrip("/") + return f"{api_base}/images/generations" diff --git a/litellm/llms/mistral/chat/transformation.py b/litellm/llms/mistral/chat/transformation.py index b38a4982471..51fa65244a0 100644 --- a/litellm/llms/mistral/chat/transformation.py +++ b/litellm/llms/mistral/chat/transformation.py @@ -6,7 +6,18 @@ Why separate file? Make it easy to see how transformation works Docs - https://docs.mistral.ai/api/ """ -from typing import Any, Coroutine, List, Literal, Optional, Tuple, Union, cast, overload +from typing import ( + Any, + Coroutine, + List, + Literal, + Optional, + Tuple, + Union, + cast, + get_type_hints, + overload, +) import httpx @@ -17,7 +28,7 @@ from litellm.litellm_core_utils.prompt_templates.common_utils import ( ) from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig from litellm.secret_managers.main import get_secret_str -from litellm.types.llms.mistral import MistralToolCallMessage +from litellm.types.llms.mistral import MistralThinkingBlock, MistralToolCallMessage from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import ModelResponse from litellm.utils import convert_to_model_response_object @@ -145,7 +156,9 @@ class MistralConfig(OpenAIGPTConfig): for param, value in non_default_params.items(): if param == "max_tokens": optional_params["max_tokens"] = value - if param == "max_completion_tokens": # max_completion_tokens should take priority + if ( + param == "max_completion_tokens" + ): # max_completion_tokens should take priority optional_params["max_tokens"] = value if param == "tools": # Clean tools to remove problematic schema fields for Mistral API @@ -159,7 +172,9 @@ class MistralConfig(OpenAIGPTConfig): if param == "stop": optional_params["stop"] = value if param == "tool_choice" and isinstance(value, str): - optional_params["tool_choice"] = self._map_tool_choice(tool_choice=value) + optional_params["tool_choice"] = self._map_tool_choice( + tool_choice=value + ) if param == "seed": optional_params["extra_body"] = {"random_seed": value} if param == "response_format": @@ -185,7 +200,9 @@ class MistralConfig(OpenAIGPTConfig): ) # type: ignore # if api_base does not end with /v1 we add it - if api_base is not None and not api_base.endswith("/v1"): # Mistral always needs a /v1 at the end + if api_base is not None and not api_base.endswith( + "/v1" + ): # Mistral always needs a /v1 at the end api_base = api_base + "/v1" dynamic_api_key = ( api_key @@ -194,10 +211,12 @@ class MistralConfig(OpenAIGPTConfig): ) return api_base, dynamic_api_key + # fmt: off + @overload def _transform_messages( self, messages: List[AllMessageValues], model: str, is_async: Literal[True] - ) -> Coroutine[Any, Any, List[AllMessageValues]]: + ) -> Coroutine[Any, Any, List[AllMessageValues]]: ... @overload @@ -206,8 +225,9 @@ class MistralConfig(OpenAIGPTConfig): messages: List[AllMessageValues], model: str, is_async: Literal[False] = False, - ) -> List[AllMessageValues]: + ) -> List[AllMessageValues]: ... + # fmt: on def _transform_messages( self, messages: List[AllMessageValues], model: str, is_async: bool = False @@ -218,18 +238,20 @@ class MistralConfig(OpenAIGPTConfig): - if image passed in, then just return as is (user-intended) - if `name` is passed, then drop it for mistral API: https://github.com/BerriAI/litellm/issues/6696 - Motivation: mistral api doesn't support content as a list + Motivation: mistral api doesn't support content as a list. + The above statement is not valid now. Need to plan to remove all the #1,2,3 + Mistral API supports content as a list. """ - ## 1. If 'image_url' in content, then return as is + ## 1. If 'image_url' or 'file' in content, then transform with base class and mistral-specific handling for m in messages: _content_block = m.get("content") if _content_block and isinstance(_content_block, list): - for c in _content_block: - if c.get("type") == "image_url": - if is_async: - return super()._transform_messages(messages, model, True) - else: - return super()._transform_messages(messages, model, False) + if any(c.get("type") in ["image_url", "file"] for c in _content_block): + if is_async: + return self._transform_messages_async(messages, model) + else: + messages = self._transform_messages_sync(messages, model) + return messages ## 2. If content is list, then convert to string messages = handle_messages_with_content_list_to_str_conversion(messages) @@ -239,6 +261,8 @@ class MistralConfig(OpenAIGPTConfig): for m in messages: m = MistralConfig._handle_name_in_message(m) m = MistralConfig._handle_tool_call_message(m) + if MistralConfig._is_empty_assistant_message(m): + continue m = strip_none_values_from_message(m) # prevents 'extra_forbidden' error new_messages.append(m) @@ -247,6 +271,51 @@ class MistralConfig(OpenAIGPTConfig): else: return super()._transform_messages(new_messages, model, False) + async def _transform_messages_async(self, + messages: List[AllMessageValues], model: str + ) -> List[AllMessageValues]: + """ + Handle modification of messages for Mistral API in an async context. + """ + # Call parent async method to handle basic transformations + # and then apply Mistral-specific handling for files + messages = await super()._transform_messages(messages, model, True) + messages = self._handle_message_with_file(messages) + return messages + + def _transform_messages_sync(self, + messages: List[AllMessageValues], model: str + ) -> List[AllMessageValues]: + """ Handle modification of messages for Mistral API in a sync context. + """ + # Call parent sync method to handle basic transformations + # and then apply Mistral-specific handling for files + # This is the sync version of the async method above + messages = super()._transform_messages(messages, model, False) + messages = self._handle_message_with_file(messages) + return messages + + def _handle_message_with_file( + self, + messages: List[AllMessageValues]) -> List[AllMessageValues]: + """ + Mistral API supports only 'file_id' in message content with type 'file'. + """ + for m in messages: + _content_block = m.get("content") + if _content_block and isinstance(_content_block, list): + if any(c.get("type") == "file" for c in _content_block): + # If file content is present, we get file_id from 'file' attribute of content block + # then replace 'file' with 'file_id' and assign the value of 'file_id' attribute to it. + file_contents = [c for c in _content_block if c.get("type") == "file"] + for file_content in file_contents: + file_id = file_content.get("file", {}).get("file_id") + if file_id: + # Replace 'file' with 'file_id' + file_content["file_id"] = file_id # type: ignore + file_content.pop("file", None) + return messages + def _add_reasoning_system_prompt_if_needed( self, messages: List[AllMessageValues], optional_params: dict ) -> List[AllMessageValues]: @@ -269,20 +338,30 @@ class MistralConfig(OpenAIGPTConfig): # Handle both string and list content, preserving original format if isinstance(existing_content, str): # String content - prepend reasoning prompt - new_content: Union[str, list] = f"{reasoning_prompt}\n\n{existing_content}" + new_content: Union[str, list] = ( + f"{reasoning_prompt}\n\n{existing_content}" + ) elif isinstance(existing_content, list): # List content - prepend reasoning prompt as text block - new_content = [{"type": "text", "text": reasoning_prompt + "\n\n"}] + existing_content + new_content = [ + {"type": "text", "text": reasoning_prompt + "\n\n"} + ] + existing_content else: # Fallback for any other type - convert to string new_content = f"{reasoning_prompt}\n\n{str(existing_content)}" - messages[i] = cast(AllMessageValues, {**msg, "content": new_content}) + messages[i] = cast( + AllMessageValues, {**msg, "content": new_content} + ) break else: # Add new system message with reasoning instructions reasoning_message: AllMessageValues = cast( - AllMessageValues, {"role": "system", "content": self._get_mistral_reasoning_system_prompt()} + AllMessageValues, + { + "role": "system", + "content": self._get_mistral_reasoning_system_prompt(), + }, ) messages = [reasoning_message] + messages @@ -294,32 +373,34 @@ class MistralConfig(OpenAIGPTConfig): def _clean_tool_schema_for_mistral(cls, tools: list) -> list: """ Clean tool schemas to remove fields that cause issues with Mistral API. - + Removes: - $id and $schema fields (cause grammar validation errors) - additionalProperties=False (causes OpenAI API schema errors) - strict field (not supported by Mistral) - + Args: tools: List of tool definitions max_depth: Maximum recursion depth for schema cleaning (default: 10) - + Returns: Cleaned tools list """ if not tools: return tools - + import copy from litellm.constants import DEFAULT_MAX_RECURSE_DEPTH from litellm.utils import _remove_json_schema_refs cleaned_tools = copy.deepcopy(tools) - + # Apply all cleaning functions with max_depth protection - cleaned_tools = _remove_json_schema_refs(cleaned_tools, max_depth=DEFAULT_MAX_RECURSE_DEPTH) - + cleaned_tools = _remove_json_schema_refs( + cleaned_tools, max_depth=DEFAULT_MAX_RECURSE_DEPTH + ) + return cleaned_tools @classmethod @@ -360,6 +441,25 @@ class MistralConfig(OpenAIGPTConfig): message["tool_calls"] = mistral_tool_calls # type: ignore return message + @classmethod + def _is_empty_assistant_message(cls, message: AllMessageValues) -> bool: + """ + Mistral API does not support empty string in assistant content. + """ + from litellm.types.llms.openai import ChatCompletionAssistantMessage + + set_keys = get_type_hints(ChatCompletionAssistantMessage).keys() + + all_expected_values_are_empty = True + for key in set_keys: + if key != "role" and message.get(key) is not None: + if key == "content" and message.get(key) == "": + continue + else: + all_expected_values_are_empty = False + break + return all_expected_values_are_empty + @staticmethod def _handle_empty_content_response(response_data: dict) -> dict: """ @@ -380,6 +480,58 @@ class MistralConfig(OpenAIGPTConfig): choice["message"]["content"] = None return response_data + @staticmethod + def _convert_thinking_block_to_reasoning_content( + thinking_blocks: MistralThinkingBlock, + ) -> str: + """ + Convert Mistral thinking blocks to reasoning content. + """ + return "\n".join( + [block.get("text", "") for block in thinking_blocks["thinking"]] + ) + + @staticmethod + def _handle_content_list_to_str_conversion(response_data: dict) -> dict: + """ + Handle Mistral's content list format and extract thinking content. + + Map mistral's content list to string and extract thinking blocks: + - Thinking block -> reasoning_content field + - Text block -> content field + """ + + if response_data.get("choices") and len(response_data["choices"]) > 0: + for choice in response_data["choices"]: + if choice.get("message") and choice["message"].get("content"): + content = choice["message"]["content"] + + # Only process if content is a list + if isinstance(content, list): + thinking_content = "" + text_content = "" + + # Process each content block + for block in content: + if block.get("type") == "thinking": + thinking_blocks = block.get("thinking", []) + thinking_texts = [] + for thinking_block in thinking_blocks: + if thinking_block.get("type") == "text": + thinking_texts.append( + thinking_block.get("text", "") + ) + thinking_content = "\n".join(thinking_texts) + elif block.get("type") == "text": + text_content = block.get("text", "") + + # Set the extracted content + choice["message"]["content"] = text_content + if thinking_content: + choice["message"]["reasoning_content"] = thinking_content + + return response_data + def transform_request( self, model: str, @@ -396,8 +548,12 @@ class MistralConfig(OpenAIGPTConfig): dict: The transformed request. Sent as the body of the API call. """ # Add reasoning system prompt if needed (for magistral models) - if "magistral" in model.lower() and optional_params.get("_add_reasoning_prompt", False): - messages = self._add_reasoning_system_prompt_if_needed(messages, optional_params) + if "magistral" in model.lower() and optional_params.get( + "_add_reasoning_prompt", False + ): + messages = self._add_reasoning_system_prompt_if_needed( + messages, optional_params + ) # Call parent transform_request which handles _transform_messages return super().transform_request( @@ -424,14 +580,16 @@ class MistralConfig(OpenAIGPTConfig): ) -> ModelResponse: """ Transform the raw response from Mistral API. - Handles Mistral-specific behavior like converting empty string content to None. + Handles Mistral-specific behavior like converting empty string content to None + and extracting thinking content from content lists. """ logging_obj.post_call(original_response=raw_response.text) logging_obj.model_call_details["response_headers"] = raw_response.headers - # Handle Mistral-specific empty string content conversion to None + # Handle Mistral-specific response transformations response_data = raw_response.json() response_data = self._handle_empty_content_response(response_data) + response_data = self._handle_content_list_to_str_conversion(response_data) final_response_obj = cast( ModelResponse, diff --git a/litellm/llms/oci/chat/transformation.py b/litellm/llms/oci/chat/transformation.py index 3524817da63..3e05473630d 100644 --- a/litellm/llms/oci/chat/transformation.py +++ b/litellm/llms/oci/chat/transformation.py @@ -2,7 +2,7 @@ import base64 import datetime import hashlib import json -from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union +from typing import TYPE_CHECKING, Any, AsyncIterator, Dict, List, Optional, Tuple, Union from urllib.parse import urlparse import httpx @@ -586,8 +586,15 @@ class OCIChatConfig(BaseConfig): completion_stream = response.aiter_text() + async def split_chunks(completion_stream: AsyncIterator[str]): + async for item in completion_stream: + for chunk in item.split("\n\n"): + if not chunk: + continue + yield chunk.strip() + streaming_response = OCIStreamWrapper( - completion_stream=completion_stream, + completion_stream=split_chunks(completion_stream), model=model, custom_llm_provider=custom_llm_provider, logging_obj=logging_obj, diff --git a/litellm/llms/ollama/completion/transformation.py b/litellm/llms/ollama/completion/transformation.py index aa1da616d89..4f7be507cc2 100644 --- a/litellm/llms/ollama/completion/transformation.py +++ b/litellm/llms/ollama/completion/transformation.py @@ -24,6 +24,8 @@ from litellm.types.utils import ( ModelResponse, ModelResponseStream, ProviderField, + StreamingChoices, + Delta, ) from ..common_utils import OllamaError, _convert_image @@ -260,38 +262,52 @@ class OllamaConfig(BaseConfig): ## RESPONSE OBJECT model_response.choices[0].finish_reason = "stop" if request_data.get("format", "") == "json": - response_content = json.loads(response_json["response"]) - - # Check if this is a function call format with name/arguments structure - if ( - isinstance(response_content, dict) - and "name" in response_content - and "arguments" in response_content - ): - # Handle as function call (original behavior) - function_call = response_content - message = litellm.Message( - content=None, - tool_calls=[ - { - "id": f"call_{str(uuid.uuid4())}", - "function": { - "name": function_call["name"], - "arguments": json.dumps(function_call["arguments"]), - }, - "type": "function", - } - ], - ) - model_response.choices[0].message = message # type: ignore - model_response.choices[0].finish_reason = "tool_calls" - else: - # Handle as regular JSON (new behavior) - message = litellm.Message( - content=json.dumps(response_content), - ) + # Check if response field exists and is not empty before parsing JSON + response_text = response_json.get("response", "") + if not response_text or not response_text.strip(): + # Handle empty response gracefully - set empty content + message = litellm.Message(content="") model_response.choices[0].message = message # type: ignore model_response.choices[0].finish_reason = "stop" + else: + try: + response_content = json.loads(response_text) + + # Check if this is a function call format with name/arguments structure + if ( + isinstance(response_content, dict) + and "name" in response_content + and "arguments" in response_content + ): + # Handle as function call (original behavior) + function_call = response_content + message = litellm.Message( + content=None, + tool_calls=[ + { + "id": f"call_{str(uuid.uuid4())}", + "function": { + "name": function_call["name"], + "arguments": json.dumps(function_call["arguments"]), + }, + "type": "function", + } + ], + ) + model_response.choices[0].message = message # type: ignore + model_response.choices[0].finish_reason = "tool_calls" + else: + # Handle as regular JSON (new behavior) + message = litellm.Message( + content=json.dumps(response_content), + ) + model_response.choices[0].message = message # type: ignore + model_response.choices[0].finish_reason = "stop" + except json.JSONDecodeError: + # If JSON parsing fails, treat as regular text response + message = litellm.Message(content=response_text) + model_response.choices[0].message = message # type: ignore + model_response.choices[0].finish_reason = "stop" else: model_response.choices[0].message.content = response_json["response"] # type: ignore model_response.created = int(time.time()) @@ -423,7 +439,7 @@ class OllamaTextCompletionResponseIterator(BaseModelResponseIterator): ) -> Union[GenericStreamingChunk, ModelResponseStream]: return self.chunk_parser(json.loads(str_line)) - def chunk_parser(self, chunk: dict) -> GenericStreamingChunk: + def chunk_parser(self, chunk: dict) -> Union[GenericStreamingChunk, ModelResponseStream]: try: if "error" in chunk: raise Exception(f"Ollama Error - {chunk}") @@ -459,6 +475,17 @@ class OllamaTextCompletionResponseIterator(BaseModelResponseIterator): finish_reason="stop", usage=None, ) + elif "thinking" in chunk and not chunk["response"]: + # Return reasoning content as ModelResponseStream so UIs can render it + thinking_content = chunk.get("thinking") or "" + return ModelResponseStream( + choices=[ + StreamingChoices( + index=0, + delta=Delta(reasoning_content=thinking_content), + ) + ] + ) else: raise Exception(f"Unable to parse ollama chunk - {chunk}") except Exception as e: diff --git a/litellm/llms/openai/chat/gpt_5_transformation.py b/litellm/llms/openai/chat/gpt_5_transformation.py index 0de2553376d..9a8bb74d447 100644 --- a/litellm/llms/openai/chat/gpt_5_transformation.py +++ b/litellm/llms/openai/chat/gpt_5_transformation.py @@ -15,10 +15,21 @@ class OpenAIGPT5Config(OpenAIGPTConfig): - Mapping ``max_tokens`` -> ``max_completion_tokens``. - Dropping unsupported ``temperature`` values when requested. """ + @classmethod def is_model_gpt_5_model(cls, model: str) -> bool: return "gpt-5" in model + def get_supported_openai_params(self, model: str) -> list: + from litellm.utils import supports_tool_choice + + base_gpt_series_params = super().get_supported_openai_params(model=model) + gpt_5_only_params = ["reasoning_effort"] + base_gpt_series_params.extend(gpt_5_only_params) + if not supports_tool_choice(model=model): + base_gpt_series_params.remove("tool_choice") + return base_gpt_series_params + def map_openai_params( self, non_default_params: dict, @@ -55,4 +66,3 @@ class OpenAIGPT5Config(OpenAIGPTConfig): model=model, drop_params=drop_params, ) - diff --git a/litellm/llms/openai/chat/gpt_transformation.py b/litellm/llms/openai/chat/gpt_transformation.py index 396d59145ff..be0ca3a7086 100644 --- a/litellm/llms/openai/chat/gpt_transformation.py +++ b/litellm/llms/openai/chat/gpt_transformation.py @@ -348,6 +348,7 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): for message in messages: message_content = message.get("content") message_role = message.get("role") + if ( message_role == "user" and message_content @@ -428,6 +429,8 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): if tools is not None and len(tools) > 0: optional_params["tools"] = tools + optional_params.pop("max_retries", None) + return { "model": model, "messages": messages, diff --git a/litellm/llms/openai/image_edit/transformation.py b/litellm/llms/openai/image_edit/transformation.py index c8a1e8f0e1c..be1aeb1b8a4 100644 --- a/litellm/llms/openai/image_edit/transformation.py +++ b/litellm/llms/openai/image_edit/transformation.py @@ -80,24 +80,49 @@ class OpenAIImageEditConfig(BaseImageEditConfig): request_dict = cast(Dict, request) ######################################################### - # Separate images as `files` and send other parameters as `data` + # Separate images and masks as `files` and send other parameters as `data` ######################################################### - _images = request_dict.get("image") or [] - data_without_images = {k: v for k, v in request_dict.items() if k != "image"} + _image = request_dict.get("image") + _mask = request_dict.get("mask") + data_without_files = { + k: v for k, v in request_dict.items() if k not in ["image", "mask"] + } files_list: List[Tuple[str, Any]] = [] - for _image in _images: - image_content_type: str = ImageEditRequestUtils.get_image_content_type( - _image - ) - if isinstance(_image, BufferedReader): - files_list.append( - ("image[]", (_image.name, _image, image_content_type)) + + # Handle image parameter + if _image is not None: + # Handle case where image can be a list (extract first image) + if isinstance(_image, list): + _image = _image[0] if _image else None + + if _image is not None: + image_content_type: str = ImageEditRequestUtils.get_image_content_type( + _image ) - else: - files_list.append( - ("image[]", ("image.png", _image, image_content_type)) + if isinstance(_image, BufferedReader): + files_list.append( + ("image", (_image.name, _image, image_content_type)) + ) + else: + files_list.append( + ("image", ("image.png", _image, image_content_type)) + ) + + # Handle mask parameter if provided + if _mask is not None: + # Handle case where mask can be a list (extract first mask) + if isinstance(_mask, list): + _mask = _mask[0] if _mask else None + + if _mask is not None: + mask_content_type: str = ImageEditRequestUtils.get_image_content_type( + _mask ) - return data_without_images, files_list + if isinstance(_mask, BufferedReader): + files_list.append(("mask", (_mask.name, _mask, mask_content_type))) + else: + files_list.append(("mask", ("mask.png", _mask, mask_content_type))) + return data_without_files, files_list def transform_image_edit_response( self, diff --git a/litellm/llms/openai/realtime/handler.py b/litellm/llms/openai/realtime/handler.py index aca32e1404a..e0c85d18178 100644 --- a/litellm/llms/openai/realtime/handler.py +++ b/litellm/llms/openai/realtime/handler.py @@ -1,5 +1,5 @@ """ -This file contains the calling Azure OpenAI's `/openai/realtime` endpoint. +This file contains the calling OpenAI's `/v1/realtime` endpoint. This requires websockets, and is currently only supported on LiteLLM Proxy. """ @@ -15,7 +15,7 @@ from litellm.types.realtime import RealtimeQueryParams class OpenAIRealtime(OpenAIChatCompletion): def _construct_url(self, api_base: str, query_params: RealtimeQueryParams) -> str: """ - Construct the backend websocket URL with all query parameters (excluding 'model' if present). + Construct the backend websocket URL with all query parameters (including 'model'). """ from httpx import URL @@ -24,10 +24,9 @@ class OpenAIRealtime(OpenAIChatCompletion): url = URL(api_base) # Set the correct path url = url.copy_with(path="/v1/realtime") - # Build query dict excluding 'model' - query_dict = {k: v for k, v in query_params.items() if k != "model"} - if query_dict: - url = url.copy_with(params=query_dict) + # Include all query parameters including 'model' + if query_params: + url = url.copy_with(params=query_params) return str(url) async def async_realtime( @@ -43,11 +42,10 @@ class OpenAIRealtime(OpenAIChatCompletion): ): import websockets from websockets.asyncio.client import ClientConnection - if api_base is None: - raise ValueError("api_base is required for Azure OpenAI calls") + api_base = "https://api.openai.com/" if api_key is None: - raise ValueError("api_key is required for Azure OpenAI calls") + raise ValueError("api_key is required for OpenAI realtime calls") # Use all query params if provided, else fallback to just model if query_params is None: diff --git a/litellm/llms/openai/responses/transformation.py b/litellm/llms/openai/responses/transformation.py index 12814286f6c..e70cadddaf7 100644 --- a/litellm/llms/openai/responses/transformation.py +++ b/litellm/llms/openai/responses/transformation.py @@ -1,6 +1,7 @@ -from typing import TYPE_CHECKING, Any, Dict, Optional, Union, cast +from typing import TYPE_CHECKING, Any, Dict, Optional, Union, cast, get_type_hints import httpx +from pydantic import BaseModel import litellm from litellm._logging import verbose_logger @@ -12,6 +13,7 @@ from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import * from litellm.types.responses.main import * from litellm.types.router import GenericLiteLLMParams +from litellm.types.utils import LlmProviders from ..common_utils import OpenAIError @@ -24,38 +26,28 @@ else: class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): + @property + def custom_llm_provider(self) -> LlmProviders: + return LlmProviders.OPENAI + def get_supported_openai_params(self, model: str) -> list: """ All OpenAI Responses API params are supported """ - return [ - "input", - "model", - "include", - "instructions", - "max_output_tokens", - "metadata", - "parallel_tool_calls", - "previous_response_id", - "reasoning", - "store", - "background", - "stream", - "prompt", - "temperature", - "text", - "tool_choice", - "tools", - "top_p", - "truncation", - "user", - "service_tier", - "safety_identifier", - "extra_headers", - "extra_query", - "extra_body", - "timeout", - ] + supported_params = get_type_hints(ResponsesAPIRequestParams).keys() + return list( + set( + [ + "input", + "model", + "extra_headers", + "extra_query", + "extra_body", + "timeout", + ] + + list(supported_params) + ) + ) def map_openai_params( self, @@ -75,12 +67,37 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): headers: dict, ) -> Dict: """No transform applied since inputs are in OpenAI spec already""" - return dict( + + input = self._validate_input_param(input) + final_request_params = dict( ResponsesAPIRequestParams( model=model, input=input, **response_api_optional_request_params ) ) + return final_request_params + + def _validate_input_param( + self, input: Union[str, ResponseInputParam] + ) -> Union[str, ResponseInputParam]: + """ + Ensure all input fields if pydantic are converted to dict + + OpenAI API Fails when we try to JSON dumps specific input pydantic fields. + This function ensures all input fields are converted to dict. + """ + if isinstance(input, list): + validated_input = [] + for item in input: + # if it's pydantic, convert to dict + if isinstance(item, BaseModel): + validated_input.append(item.model_dump(exclude_none=True)) + else: + validated_input.append(item) + return validated_input + # Input is expected to be either str or List, no single BaseModel expected + return input + def transform_response_api_response( self, model: str, @@ -90,7 +107,9 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): """No transform applied since outputs are in OpenAI spec already""" try: raw_response_json = raw_response.json() - raw_response_json["created_at"] = _safe_convert_created_field(raw_response_json["created_at"]) + raw_response_json["created_at"] = _safe_convert_created_field( + raw_response_json["created_at"] + ) except Exception: raise OpenAIError( message=raw_response.text, status_code=raw_response.status_code diff --git a/litellm/llms/openai_like/chat/handler.py b/litellm/llms/openai_like/chat/handler.py index 821fc9b7f15..ae0a3cc3551 100644 --- a/litellm/llms/openai_like/chat/handler.py +++ b/litellm/llms/openai_like/chat/handler.py @@ -49,8 +49,15 @@ async def make_call( model_response = ModelResponse(**response.json()) completion_stream = MockResponseIterator(model_response=model_response) else: + # Use aiter_text with explicit UTF-8 encoding to avoid ASCII encoding errors + async def utf8_aiter_lines(): + async for line in response.aiter_text(encoding='utf-8'): + for line_part in line.splitlines(keepends=True): + if line_part.strip(): + yield line_part.rstrip('\r\n') + completion_stream = ModelResponseIterator( - streaming_response=response.aiter_lines(), sync_stream=False + streaming_response=utf8_aiter_lines(), sync_stream=False ) # LOGGING logging_obj.post_call( @@ -93,8 +100,15 @@ def make_sync_call( model_response = ModelResponse(**response.json()) completion_stream = MockResponseIterator(model_response=model_response) else: + # Use iter_text with explicit UTF-8 encoding to avoid ASCII encoding errors + def utf8_iter_lines(): + for line in response.iter_text(encoding='utf-8'): + for line_part in line.splitlines(keepends=True): + if line_part.strip(): + yield line_part.rstrip('\r\n') + completion_stream = ModelResponseIterator( - streaming_response=response.iter_lines(), sync_stream=True + streaming_response=utf8_iter_lines(), sync_stream=True ) # LOGGING diff --git a/litellm/llms/sambanova/common_utils.py b/litellm/llms/sambanova/common_utils.py new file mode 100644 index 00000000000..b622f705845 --- /dev/null +++ b/litellm/llms/sambanova/common_utils.py @@ -0,0 +1,6 @@ +from litellm.llms.base_llm.chat.transformation import BaseLLMException + + +class SambaNovaError(BaseLLMException): + def __init__(self, status_code, message, headers): + super().__init__(status_code=status_code, message=message, headers=headers) diff --git a/litellm/llms/sambanova/embedding/handler.py b/litellm/llms/sambanova/embedding/handler.py new file mode 100644 index 00000000000..c3629e4d75f --- /dev/null +++ b/litellm/llms/sambanova/embedding/handler.py @@ -0,0 +1,5 @@ +""" +SambaNova Embedding - uses `llm_http_handler.py` to make httpx requests + +Request/Response transformation is handled in `transformation.py` +""" diff --git a/litellm/llms/sambanova/embedding/transformation.py b/litellm/llms/sambanova/embedding/transformation.py new file mode 100644 index 00000000000..eca44c7c039 --- /dev/null +++ b/litellm/llms/sambanova/embedding/transformation.py @@ -0,0 +1,139 @@ +""" +This is OpenAI compatible - no transformation is applied + +""" +from typing import List, Optional, Union + +import httpx + +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig +from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues +from litellm.types.utils import EmbeddingResponse, Usage + +from ..common_utils import SambaNovaError + + +class SambaNovaEmbeddingConfig(BaseEmbeddingConfig): + def __init__(self) -> None: + pass + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + if api_base is None: + raise ValueError("api_base is required for SambaNova embeddings") + # Remove trailing slashes and ensure clean base URL + api_base = api_base.rstrip("/") + if not api_base.endswith("/embeddings"): + api_base = f"{api_base}/embeddings" + return api_base + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + if api_key is None: + api_key = get_secret_str("SAMBANOVA_API_KEY") + + default_headers = { + "Authorization": f"Bearer {api_key}", + "accept": "application/json", + "Content-Type": "application/json", + } + + # If 'Authorization' is provided in headers, it overrides the default. + if "Authorization" in headers: + default_headers["Authorization"] = headers["Authorization"] + + # Merge other headers, overriding any default ones except Authorization + return {**default_headers, **headers} + + def get_supported_openai_params(self, model: str): + """ + Non additional params supported, placeholder method for future supported params + https://docs.sambanova.ai/cloud/api-reference/endpoints/embeddings-api + """ + return [] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ): + """ + No transformation is applied - SambaNova is openai compatible + """ + supported_openai_params = self.get_supported_openai_params(model) + for param, value in non_default_params.items(): + if param in supported_openai_params: + optional_params[param] = value + return optional_params + + def transform_embedding_request( + self, + model: str, + input: AllEmbeddingInputValues, + optional_params: dict, + headers: dict, + ) -> dict: + return { + "input": input, + "model": model, + **optional_params, + } + + def transform_embedding_response( + self, + model: str, + raw_response: httpx.Response, + model_response: EmbeddingResponse, + logging_obj: LiteLLMLoggingObj, + api_key: Optional[str], + request_data: dict, + optional_params: dict, + litellm_params: dict, + ) -> EmbeddingResponse: + try: + raw_response_json = raw_response.json() + except Exception: + raise SambaNovaError( + message=raw_response.text, + status_code=raw_response.status_code, + headers=raw_response.headers, + ) + + model_response.model = raw_response_json.get("model") + model_response.data = raw_response_json.get("data") + model_response.object = raw_response_json.get("object") + + usage = Usage( + prompt_tokens=raw_response_json.get("usage", {}).get("prompt_tokens", 0), + total_tokens=raw_response_json.get("usage", {}).get("total_tokens", 0), + ) + + model_response.usage = usage + return model_response + + def get_error_class( + self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers] + ) -> BaseLLMException: + return SambaNovaError( + message=error_message, status_code=status_code, headers=headers + ) diff --git a/litellm/llms/vertex_ai/common_utils.py b/litellm/llms/vertex_ai/common_utils.py index cceac0ea794..4931631d75d 100644 --- a/litellm/llms/vertex_ai/common_utils.py +++ b/litellm/llms/vertex_ai/common_utils.py @@ -7,8 +7,11 @@ import litellm from litellm import supports_response_schema, supports_system_messages, verbose_logger from litellm.constants import DEFAULT_MAX_RECURSE_DEPTH from litellm.litellm_core_utils.prompt_templates.common_utils import unpack_defs +from litellm.llms.base_llm.base_utils import BaseLLMModelInfo, BaseTokenCounter from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.types.llms.openai import AllMessageValues from litellm.types.llms.vertex_ai import PartType, Schema +from litellm.types.utils import TokenCountResponse class VertexAIError(BaseLLMException): @@ -63,7 +66,7 @@ def get_supports_response_schema( from typing import Literal, Optional all_gemini_url_modes = Literal[ - "chat", "embedding", "batch_embedding", "image_generation" + "chat", "embedding", "batch_embedding", "image_generation", "count_tokens" ] @@ -113,6 +116,12 @@ def _get_vertex_url( url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model}:{endpoint}" if model.isdigit(): url = f"https://{vertex_location}-aiplatform.googleapis.com/{vertex_api_version}/projects/{vertex_project}/locations/{vertex_location}/endpoints/{model}:{endpoint}" + elif mode == "count_tokens": + endpoint = "countTokens" + if vertex_location == "global": + url = f"https://aiplatform.googleapis.com/{vertex_api_version}/projects/{vertex_project}/locations/global/publishers/google/models/{model}:{endpoint}" + else: + url = f"https://{vertex_location}-aiplatform.googleapis.com/{vertex_api_version}/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model}:{endpoint}" if not url or not endpoint: raise ValueError(f"Unable to get vertex url/endpoint for mode: {mode}") return url, endpoint @@ -148,10 +157,17 @@ def _get_gemini_url( url = "https://generativelanguage.googleapis.com/v1beta/{}:{}?key={}".format( _gemini_model_name, endpoint, gemini_api_key ) + elif mode == "count_tokens": + endpoint = "countTokens" + url = "https://generativelanguage.googleapis.com/v1beta/{}:{}?key={}".format( + _gemini_model_name, endpoint, gemini_api_key + ) elif mode == "image_generation": raise ValueError( "LiteLLM's `gemini/` route does not support image generation yet. Let us know if you need this feature by opening an issue at https://github.com/BerriAI/litellm/issues" ) + else: + raise ValueError(f"Unsupported mode: {mode}") return url, endpoint @@ -522,3 +538,99 @@ def is_global_only_vertex_model(model: str) -> bool: if supported_regions is None: return False return "global" in supported_regions + +class VertexAIModelInfo(BaseLLMModelInfo): + def get_token_counter(self) -> Optional[BaseTokenCounter]: + """ + Factory method to create a token counter for this provider. + + Returns: + Optional TokenCounterInterface implementation for this provider, + or None if token counting is not supported. + """ + return VertexAITokenCounter() + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + raise NotImplementedError("Vertex AI models are not supported yet") + + def get_models( + self, api_key: Optional[str] = None, api_base: Optional[str] = None + ) -> List[str]: + """ + Returns a list of models supported by this provider. + """ + raise NotImplementedError("Vertex AI models are not supported yet") + + @staticmethod + def get_api_key(api_key: Optional[str] = None) -> Optional[str]: + raise NotImplementedError("Vertex AI models are not supported yet") + + @staticmethod + def get_api_base( + api_base: Optional[str] = None, + ) -> Optional[str]: + raise NotImplementedError("Vertex AI models are not supported yet") + + + + @staticmethod + def get_base_model(model: str) -> Optional[str]: + """ + Returns the base model name from the given model name. + + Some providers like bedrock - can receive model=`invoke/anthropic.claude-3-opus-20240229-v1:0` or `converse/anthropic.claude-3-opus-20240229-v1:0` + This function will return `anthropic.claude-3-opus-20240229-v1:0` + """ + raise NotImplementedError("Vertex AI models are not supported yet") + + +class VertexAITokenCounter(BaseTokenCounter): + """Token counter implementation for Google AI Studio provider.""" + def should_use_token_counting_api( + self, + custom_llm_provider: Optional[str] = None, + ) -> bool: + from litellm.types.utils import LlmProviders + return custom_llm_provider == LlmProviders.VERTEX_AI.value + + async def count_tokens( + self, + model_to_use: str, + messages: Optional[List[Dict[str, Any]]], + contents: Optional[List[Dict[str, Any]]], + deployment: Optional[Dict[str, Any]] = None, + request_model: str = "", + ) -> Optional[TokenCountResponse]: + import copy + + from litellm.llms.vertex_ai.count_tokens.handler import VertexAITokenCounter + deployment = deployment or {} + count_tokens_params_request = copy.deepcopy(deployment.get("litellm_params", {})) + count_tokens_params = { + "model": model_to_use, + "contents": contents, + } + count_tokens_params_request.update(count_tokens_params) + result = await VertexAITokenCounter().acount_tokens( + **count_tokens_params_request, + ) + + if result is not None: + return TokenCountResponse( + total_tokens=result.get("totalTokens", 0), + request_model=request_model, + model_used=model_to_use, + tokenizer_type=result.get("tokenizer_used", ""), + original_response=result, + ) + + return None \ No newline at end of file diff --git a/litellm/llms/vertex_ai/count_tokens/handler.py b/litellm/llms/vertex_ai/count_tokens/handler.py new file mode 100644 index 00000000000..d95c6801e57 --- /dev/null +++ b/litellm/llms/vertex_ai/count_tokens/handler.py @@ -0,0 +1,46 @@ +from typing import Any, Dict, Optional, Tuple + +from litellm.llms.gemini.count_tokens.handler import GoogleAIStudioTokenCounter +from litellm.llms.vertex_ai.vertex_llm_base import VertexBase + + +class VertexAITokenCounter(GoogleAIStudioTokenCounter, VertexBase): + async def validate_environment( + self, + api_base: Optional[str] = None, + api_key: Optional[str] = None, + headers: Optional[Dict[str, Any]] = None, + model: str = "", + litellm_params: Optional[Dict[str, Any]] = None, + ) -> Tuple[Dict[str, Any], str]: + """ + Returns a Tuple of headers and url for the Vertex AI countTokens endpoint. + """ + litellm_params = litellm_params or {} + vertex_credentials = self.get_vertex_ai_credentials(litellm_params=litellm_params) + vertex_project = self.get_vertex_ai_project(litellm_params=litellm_params) + vertex_location = self.get_vertex_ai_location(litellm_params=litellm_params) + should_use_v1beta1_features = self.is_using_v1beta1_features(litellm_params) + _auth_header, vertex_project = await self._ensure_access_token_async( + credentials=vertex_credentials, + project_id=vertex_project, + custom_llm_provider="vertex_ai", + ) + + auth_header, api_base = self._get_token_and_url( + model=model, + gemini_api_key=None, + auth_header=_auth_header, + vertex_project=vertex_project, + vertex_location=vertex_location, + vertex_credentials=vertex_credentials, + stream=False, + custom_llm_provider="vertex_ai", + api_base=None, + should_use_v1beta1_features=should_use_v1beta1_features, + mode="count_tokens", + ) + headers = { + "Authorization": f"Bearer {auth_header}", + } + return headers, api_base \ No newline at end of file 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 37a4ab84dda..c5c4f46c92f 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 @@ -305,9 +305,9 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): return None for tool in value: - openai_function_object: Optional[ - ChatCompletionToolParamFunctionChunk - ] = None + openai_function_object: Optional[ChatCompletionToolParamFunctionChunk] = ( + None + ) if "function" in tool: # tools list _openai_function_object = ChatCompletionToolParamFunctionChunk( # type: ignore **tool["function"] @@ -597,14 +597,14 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): elif param == "seed": optional_params["seed"] = value elif param == "reasoning_effort" and isinstance(value, str): - optional_params[ - "thinkingConfig" - ] = VertexGeminiConfig._map_reasoning_effort_to_thinking_budget(value) + optional_params["thinkingConfig"] = ( + VertexGeminiConfig._map_reasoning_effort_to_thinking_budget(value) + ) elif param == "thinking": - optional_params[ - "thinkingConfig" - ] = VertexGeminiConfig._map_thinking_param( - cast(AnthropicThinkingParam, value) + optional_params["thinkingConfig"] = ( + VertexGeminiConfig._map_thinking_param( + cast(AnthropicThinkingParam, value) + ) ) elif param == "modalities" and isinstance(value, list): response_modalities = self.map_response_modalities(value) @@ -1000,6 +1000,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): GenerateContentResponseBody, BidiGenerateContentServerMessage ], ) -> Usage: + if ( completion_response is not None and "usageMetadata" not in completion_response @@ -1038,6 +1039,16 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): text_tokens = detail.get("tokenCount", 0) if "thoughtsTokenCount" in usage_metadata: reasoning_tokens = usage_metadata["thoughtsTokenCount"] + + ## adjust 'text_tokens' to subtract cached tokens + if ( + (audio_tokens is None or audio_tokens == 0) + and text_tokens is not None + and text_tokens > 0 + and cached_tokens is not None + ): + text_tokens = text_tokens - cached_tokens + prompt_tokens_details = PromptTokensDetailsWrapper( cached_tokens=cached_tokens, audio_tokens=audio_tokens, @@ -1344,28 +1355,28 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): ## ADD METADATA TO RESPONSE ## setattr(model_response, "vertex_ai_grounding_metadata", grounding_metadata) - model_response._hidden_params[ - "vertex_ai_grounding_metadata" - ] = grounding_metadata + model_response._hidden_params["vertex_ai_grounding_metadata"] = ( + grounding_metadata + ) setattr( model_response, "vertex_ai_url_context_metadata", url_context_metadata ) - model_response._hidden_params[ - "vertex_ai_url_context_metadata" - ] = url_context_metadata + model_response._hidden_params["vertex_ai_url_context_metadata"] = ( + url_context_metadata + ) setattr(model_response, "vertex_ai_safety_results", safety_ratings) - model_response._hidden_params[ - "vertex_ai_safety_results" - ] = safety_ratings # older approach - maintaining to prevent regressions + model_response._hidden_params["vertex_ai_safety_results"] = ( + safety_ratings # older approach - maintaining to prevent regressions + ) ## ADD CITATION METADATA ## setattr(model_response, "vertex_ai_citation_metadata", citation_metadata) - model_response._hidden_params[ - "vertex_ai_citation_metadata" - ] = citation_metadata # older approach - maintaining to prevent regressions + model_response._hidden_params["vertex_ai_citation_metadata"] = ( + citation_metadata # older approach - maintaining to prevent regressions + ) except Exception as e: raise VertexAIError( diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/llama3/transformation.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/llama3/transformation.py index 7e965313a0b..748a5f5fb40 100644 --- a/litellm/llms/vertex_ai/vertex_ai_partner_models/llama3/transformation.py +++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/llama3/transformation.py @@ -113,10 +113,10 @@ class VertexAILlama3Config(OpenAIGPTConfig): status_code=raw_response.status_code, headers=response_headers, ) - model_response.model = completion_response["model"] - model_response.id = completion_response["id"] - model_response.created = completion_response["created"] - setattr(model_response, "usage", Usage(**completion_response["usage"])) + model_response.model = completion_response.get("model", model) + model_response.id = completion_response.get("id", "") + model_response.created = completion_response.get("created", 0) + setattr(model_response, "usage", Usage(**completion_response.get("usage", {}))) model_response.choices = self._transform_choices( # type: ignore choices=completion_response["choices"], diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py index 7303ab0786c..f281cab3b58 100644 --- a/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py +++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py @@ -48,9 +48,21 @@ class VertexAIPartnerModels(VertexBase): or model.startswith("codestral") or model.startswith("jamba") or model.startswith("claude") + or model.startswith("qwen") ): return True return False + + @staticmethod + def should_use_openai_handler(model: str): + OPENAI_LIKE_VERTEX_PROVIDERS = [ + "llama", + "deepseek-ai", + "qwen", + ] + if any(provider in model for provider in OPENAI_LIKE_VERTEX_PROVIDERS): + return True + return False def completion( self, @@ -115,7 +127,7 @@ class VertexAIPartnerModels(VertexBase): optional_params["stream"] = stream - if "llama" in model or "deepseek-ai" in model: + if self.should_use_openai_handler(model): partner = VertexPartnerProvider.llama elif "mistral" in model or "codestral" in model: partner = VertexPartnerProvider.mistralai @@ -191,7 +203,7 @@ class VertexAIPartnerModels(VertexBase): client=client, custom_llm_provider=LlmProviders.VERTEX_AI.value, ) - elif "llama" in model: + elif self.should_use_openai_handler(model): return base_llm_http_handler.completion( model=model, stream=stream, diff --git a/litellm/llms/volcengine.py b/litellm/llms/volcengine.py index 58d2371af53..c878aaf933c 100644 --- a/litellm/llms/volcengine.py +++ b/litellm/llms/volcengine.py @@ -81,8 +81,18 @@ class VolcEngineConfig(OpenAILikeChatConfig): ) if "thinking" in optional_params: - optional_params.setdefault("extra_body", {})["thinking"] = ( - optional_params.pop("thinking") - ) + thinking_value = optional_params.pop("thinking") + + # Handle disabled thinking case - don't add to extra_body if disabled + if ( + thinking_value is not None + and isinstance(thinking_value, dict) + and thinking_value.get("type") == "disabled" + ): + # Skip adding thinking parameter when it's disabled + pass + else: + # Add thinking parameter to extra_body for all other cases + optional_params.setdefault("extra_body", {})["thinking"] = thinking_value return optional_params diff --git a/litellm/llms/voyage/embedding/transformation_contextual.py b/litellm/llms/voyage/embedding/transformation_contextual.py new file mode 100644 index 00000000000..4df2fa4ba31 --- /dev/null +++ b/litellm/llms/voyage/embedding/transformation_contextual.py @@ -0,0 +1,153 @@ +""" +This module is used to transform the request and response for the Voyage contextualized embeddings API. +This would be used for all the contextualized embeddings models in Voyage. +""" +from typing import List, Optional, Union + +import httpx + +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig +from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues +from litellm.types.utils import EmbeddingResponse, Usage + + +class VoyageError(BaseLLMException): + def __init__( + self, + status_code: int, + message: str, + headers: Union[dict, httpx.Headers] = {}, + ): + self.status_code = status_code + self.message = message + self.request = httpx.Request( + method="POST", url="https://api.voyageai.com/v1/contextualizedembeddings" + ) + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + status_code=status_code, + message=message, + headers=headers, + ) + + +class VoyageContextualEmbeddingConfig(BaseEmbeddingConfig): + """ + Reference: https://docs.voyageai.com/reference/embeddings-api + """ + + def __init__(self) -> None: + pass + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + if api_base: + if not api_base.endswith("/contextualizedembeddings"): + api_base = f"{api_base}/contextualizedembeddings" + return api_base + return "https://api.voyageai.com/v1/contextualizedembeddings" + + def get_supported_openai_params(self, model: str) -> list: + return ["encoding_format", "dimensions"] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + """ + Map OpenAI params to Voyage params + + Reference: https://docs.voyageai.com/reference/contextualized-embeddings-api + """ + if "encoding_format" in non_default_params: + optional_params["encoding_format"] = non_default_params["encoding_format"] + if "dimensions" in non_default_params: + optional_params["output_dimension"] = non_default_params["dimensions"] + return optional_params + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + if api_key is None: + api_key = ( + get_secret_str("VOYAGE_API_KEY") + or get_secret_str("VOYAGE_AI_API_KEY") + or get_secret_str("VOYAGE_AI_TOKEN") + ) + return { + "Authorization": f"Bearer {api_key}", + } + + def transform_embedding_request( + self, + model: str, + input: Union[AllEmbeddingInputValues, List[List[str]]], + optional_params: dict, + headers: dict, + ) -> dict: + return { + "inputs": input, + "model": model, + **optional_params, + } + + def transform_embedding_response( + self, + model: str, + raw_response: httpx.Response, + model_response: EmbeddingResponse, + logging_obj: LiteLLMLoggingObj, + api_key: Optional[str] = None, + request_data: dict = {}, + optional_params: dict = {}, + litellm_params: dict = {}, + ) -> EmbeddingResponse: + try: + raw_response_json = raw_response.json() + except Exception: + raise VoyageError( + message=raw_response.text, status_code=raw_response.status_code + ) + + # model_response.usage + model_response.model = raw_response_json.get("model") + model_response.data = raw_response_json.get("data") + model_response.object = raw_response_json.get("object") + + usage = Usage( + prompt_tokens=raw_response_json.get("usage", {}).get("total_tokens", 0), + total_tokens=raw_response_json.get("usage", {}).get("total_tokens", 0), + ) + model_response.usage = usage + return model_response + + def get_error_class( + self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers] + ) -> BaseLLMException: + return VoyageError( + message=error_message, status_code=status_code, headers=headers + ) + + @staticmethod + def is_contextualized_embeddings(model: str) -> bool: + return "context" in model.lower() diff --git a/litellm/main.py b/litellm/main.py index 6bedf8f7ea5..c8442d483e9 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -77,7 +77,7 @@ from litellm.llms.base_llm import BaseConfig, BaseImageGenerationConfig from litellm.llms.bedrock.common_utils import BedrockModelInfo from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler from litellm.realtime_api.main import _realtime_health_check -from litellm.secret_managers.main import get_secret_str +from litellm.secret_managers.main import get_secret_bool, get_secret_str from litellm.types.router import GenericLiteLLMParams from litellm.types.utils import RawRequestTypedDict from litellm.utils import ( @@ -130,7 +130,6 @@ from .litellm_core_utils.prompt_templates.factory import ( stringify_json_tool_call_content, ) from .litellm_core_utils.streaming_chunk_builder_utils import ChunkProcessor -from .llms import baseten from .llms.anthropic.chat import AnthropicChatCompletion from .llms.azure.audio_transcriptions import AzureAudioTranscription from .llms.azure.azure import AzureChatCompletion, _check_dynamic_azure_params @@ -354,7 +353,7 @@ async def acompletion( logprobs: Optional[bool] = None, top_logprobs: Optional[int] = None, deployment_id=None, - reasoning_effort: Optional[Literal["low", "medium", "high"]] = None, + reasoning_effort: Optional[Literal["minimal", "low", "medium", "high"]] = None, # set api_base, api_version, api_key base_url: Optional[str] = None, api_version: Optional[str] = None, @@ -893,7 +892,7 @@ def completion( # type: ignore # noqa: PLR0915 logit_bias: Optional[dict] = None, user: Optional[str] = None, # openai v1.0+ new params - reasoning_effort: Optional[Literal["low", "medium", "high"]] = None, + reasoning_effort: Optional[Literal["minimal", "low", "medium", "high"]] = None, response_format: Optional[Union[dict, Type[BaseModel]]] = None, seed: Optional[int] = None, tools: Optional[List] = None, @@ -1562,6 +1561,7 @@ def completion( # type: ignore # noqa: PLR0915 ) elif custom_llm_provider == "deepseek": ## COMPLETION CALL + try: response = base_llm_http_handler.completion( model=model, @@ -1592,18 +1592,11 @@ def completion( # type: ignore # noqa: PLR0915 raise e elif custom_llm_provider == "azure_ai": - api_base = ( - api_base # for deepinfra/perplexity/anyscale/groq/friendliai we check in get_llm_provider and pass in the api base from there - or litellm.api_base - or get_secret("AZURE_AI_API_BASE") - ) + from litellm.llms.azure_ai.common_utils import AzureFoundryModelInfo + + api_base = AzureFoundryModelInfo.get_api_base(api_base) # set API KEY - api_key = ( - api_key - or litellm.api_key # for deepinfra/perplexity/anyscale/friendliai we check in get_llm_provider and pass in the api key from there - or litellm.openai_key - or get_secret("AZURE_AI_API_KEY") - ) + api_key = AzureFoundryModelInfo.get_api_key(api_key) headers = headers or litellm.headers @@ -1883,6 +1876,45 @@ def completion( # type: ignore # noqa: PLR0915 encoding=encoding, stream=stream, ) + elif custom_llm_provider == "cometapi": + api_key = ( + api_key + or litellm.cometapi_key + or get_secret_str("COMETAPI_KEY") + or litellm.api_key + ) + + api_base = ( + api_base + or litellm.api_base + or get_secret_str("COMETAPI_API_BASE") + or "https://api.cometapi.com/v1" + ) + + ## COMPLETION CALL + response = base_llm_http_handler.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + timeout=timeout, + client=client, + custom_llm_provider=custom_llm_provider, + encoding=encoding, + stream=stream, + provider_config=provider_config, + ) + + ## LOGGING + logging.post_call( + input=messages, api_key=api_key, original_response=response + ) elif ( model in litellm.open_ai_chat_completion_models or custom_llm_provider == "custom_openai" @@ -1890,6 +1922,7 @@ def completion( # type: ignore # noqa: PLR0915 or custom_llm_provider == "perplexity" or custom_llm_provider == "nvidia_nim" or custom_llm_provider == "cerebras" + or custom_llm_provider == "baseten" or custom_llm_provider == "sambanova" or custom_llm_provider == "volcengine" or custom_llm_provider == "anyscale" @@ -1942,26 +1975,51 @@ def completion( # type: ignore # noqa: PLR0915 optional_params[k] = v ## COMPLETION CALL + use_base_llm_http_handler = get_secret_bool( + "EXPERIMENTAL_OPENAI_BASE_LLM_HTTP_HANDLER" + ) + try: - response = openai_chat_completions.completion( - model=model, - messages=messages, - headers=headers, - model_response=model_response, - print_verbose=print_verbose, - api_key=api_key, - api_base=api_base, - acompletion=acompletion, - logging_obj=logging, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - timeout=timeout, # type: ignore - custom_prompt_dict=custom_prompt_dict, - client=client, # pass AsyncOpenAI, OpenAI client - organization=organization, - custom_llm_provider=custom_llm_provider, - ) + if use_base_llm_http_handler: + + response = base_llm_http_handler.completion( + model=model, + messages=messages, + api_base=api_base, + custom_llm_provider=custom_llm_provider, + model_response=model_response, + encoding=encoding, + logging_obj=logging, + optional_params=optional_params, + timeout=timeout, + litellm_params=litellm_params, + acompletion=acompletion, + stream=stream, + api_key=api_key, + headers=headers, + client=client, + provider_config=provider_config, + ) + else: + response = openai_chat_completions.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + print_verbose=print_verbose, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + timeout=timeout, # type: ignore + custom_prompt_dict=custom_prompt_dict, + client=client, # pass AsyncOpenAI, OpenAI client + organization=organization, + custom_llm_provider=custom_llm_provider, + ) except Exception as e: ## LOGGING - log the original exception returned logging.post_call( @@ -2942,7 +3000,7 @@ def completion( # type: ignore # noqa: PLR0915 logger_fn=logger_fn, encoding=encoding, logging_obj=logging, - extra_headers=extra_headers, + extra_headers=headers, # Use merged headers instead of original extra_headers timeout=timeout, acompletion=acompletion, client=client, @@ -3206,42 +3264,7 @@ def completion( # type: ignore # noqa: PLR0915 api_key=api_key, logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements ) - elif ( - custom_llm_provider == "baseten" - or litellm.api_base == "https://app.baseten.co" - ): - custom_llm_provider = "baseten" - baseten_key = ( - api_key - or litellm.baseten_key - or os.environ.get("BASETEN_API_KEY") - or litellm.api_key - ) - model_response = baseten.completion( - model=model, - messages=messages, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - encoding=encoding, - api_key=baseten_key, - logging_obj=logging, - ) - if inspect.isgenerator(model_response) or ( - "stream" in optional_params and optional_params["stream"] is True - ): - # don't try to access stream object, - response = CustomStreamWrapper( - model_response, - model, - custom_llm_provider="baseten", - logging_obj=logging, - ) - return response - response = model_response elif custom_llm_provider == "petals" or model in litellm.petals_models: api_base = api_base or litellm.api_base @@ -3303,6 +3326,25 @@ def completion( # type: ignore # noqa: PLR0915 additional_args={"headers": headers}, ) raise e + elif custom_llm_provider == "gradient_ai": + + api_base = litellm.api_base or api_base + response = base_llm_http_handler.completion( + model=model, + stream=stream, + messages=messages, + acompletion=acompletion, + api_base=api_base, + model_response=model_response, + optional_params=optional_params, + litellm_params=litellm_params, + custom_llm_provider="gradient_ai", + timeout=timeout, + headers=headers, + encoding=encoding, + api_key=api_key, + logging_obj=logging, + ) elif custom_llm_provider == "bytez": api_key = ( @@ -4221,6 +4263,28 @@ def embedding( # noqa: PLR0915 client=client, aembedding=aembedding, ) + elif custom_llm_provider == "sambanova": + api_key = api_key or litellm.api_key or get_secret_str("SAMBANOVA_API_KEY") + api_base = ( + api_base + or litellm.api_base + or get_secret_str("SAMBANOVA_API_BASE") + or "https://api.sambanova.ai/v1" + ) + response = base_llm_http_handler.embedding( + model=model, + input=input, + custom_llm_provider=custom_llm_provider, + api_base=api_base, + api_key=api_key, + logging_obj=logging, + timeout=timeout, + model_response=EmbeddingResponse(), + optional_params=optional_params, + client=client, + aembedding=aembedding, + litellm_params={}, + ) elif custom_llm_provider == "voyage": response = base_llm_http_handler.embedding( model=model, @@ -5514,9 +5578,9 @@ async def ahealth_check( messages=[], stream=False, call_type="acompletion", - litellm_call_id="1234", + litellm_call_id=str(uuid.uuid4()), start_time=datetime.datetime.now(), - function_id="1234", + function_id=str(uuid.uuid4()), log_raw_request_response=True, ) model_params["litellm_logging_obj"] = litellm_logging_obj diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 5b9d96bd6dc..a29d03f2c6c 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -709,14 +709,12 @@ "supports_reasoning": true }, "gpt-5-chat": { - "max_tokens": 32768, - "max_input_tokens": 1047576, - "max_output_tokens": 32768, - "input_cost_per_token": 5e-06, - "output_cost_per_token": 2e-05, - "input_cost_per_token_batches": 2.5e-06, - "output_cost_per_token_batches": 1e-05, - "cache_read_input_token_cost": 1.25e-06, + "max_tokens": 128000, + "max_input_tokens": 400000, + "max_output_tokens": 128000, + "input_cost_per_token": 1.25e-06, + "output_cost_per_token": 1e-05, + "cache_read_input_token_cost": 1.25e-07, "litellm_provider": "openai", "mode": "chat", "supported_endpoints": [ @@ -724,7 +722,7 @@ "/v1/batch", "/v1/responses" ], - "supported_modalities": [ + "supported_modalities": [ "text", "image" ], @@ -732,14 +730,15 @@ "text" ], "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, + "supports_function_calling": false, + "supports_parallel_function_calling": false, "supports_response_schema": true, "supports_vision": true, "supports_prompt_caching": true, "supports_system_messages": true, - "supports_tool_choice": true, - "supports_native_streaming": true + "supports_tool_choice": false, + "supports_native_streaming": true, + "supports_reasoning": true }, "gpt-5-chat-latest": { "max_tokens": 128000, @@ -763,13 +762,13 @@ "text" ], "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, + "supports_function_calling": false, + "supports_parallel_function_calling": false, "supports_response_schema": true, "supports_vision": true, "supports_prompt_caching": true, "supports_system_messages": true, - "supports_tool_choice": true, + "supports_tool_choice": false, "supports_native_streaming": true, "supports_reasoning": true }, @@ -2266,7 +2265,7 @@ }, "azure/gpt-5": { "max_tokens": 128000, - "max_input_tokens": 400000, + "max_input_tokens": 272000, "max_output_tokens": 128000, "input_cost_per_token": 1.25e-06, "output_cost_per_token": 1e-05, @@ -2298,7 +2297,7 @@ }, "azure/gpt-5-2025-08-07": { "max_tokens": 128000, - "max_input_tokens": 400000, + "max_input_tokens": 272000, "max_output_tokens": 128000, "input_cost_per_token": 1.25e-06, "output_cost_per_token": 1e-05, @@ -2330,7 +2329,7 @@ }, "azure/gpt-5-mini": { "max_tokens": 128000, - "max_input_tokens": 400000, + "max_input_tokens": 272000, "max_output_tokens": 128000, "input_cost_per_token": 2.5e-07, "output_cost_per_token": 2e-06, @@ -2362,7 +2361,7 @@ }, "azure/gpt-5-mini-2025-08-07": { "max_tokens": 128000, - "max_input_tokens": 400000, + "max_input_tokens": 272000, "max_output_tokens": 128000, "input_cost_per_token": 2.5e-07, "output_cost_per_token": 2e-06, @@ -2394,7 +2393,7 @@ }, "azure/gpt-5-nano-2025-08-07": { "max_tokens": 128000, - "max_input_tokens": 400000, + "max_input_tokens": 272000, "max_output_tokens": 128000, "input_cost_per_token": 5e-08, "output_cost_per_token": 4e-07, @@ -2426,7 +2425,7 @@ }, "azure/gpt-5-nano": { "max_tokens": 128000, - "max_input_tokens": 400000, + "max_input_tokens": 272000, "max_output_tokens": 128000, "input_cost_per_token": 5e-08, "output_cost_per_token": 4e-07, @@ -2457,41 +2456,8 @@ "supports_reasoning": true }, "azure/gpt-5-chat": { - "max_tokens": 32768, - "max_input_tokens": 1047576, - "max_output_tokens": 32768, - "input_cost_per_token": 5e-06, - "output_cost_per_token": 2e-05, - "input_cost_per_token_batches": 2.5e-06, - "output_cost_per_token_batches": 1e-05, - "cache_read_input_token_cost": 1.25e-06, - "litellm_provider": "azure", - "mode": "chat", - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/batch", - "/v1/responses" - ], - "supported_modalities": [ - "text", - "image" - ], - "supported_output_modalities": [ - "text" - ], - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_native_streaming": true - }, - "azure/gpt-5-chat-latest": { "max_tokens": 128000, - "max_input_tokens": 400000, + "max_input_tokens": 272000, "max_output_tokens": 128000, "input_cost_per_token": 1.25e-06, "output_cost_per_token": 1e-05, @@ -2517,7 +2483,40 @@ "supports_vision": true, "supports_prompt_caching": true, "supports_system_messages": true, - "supports_tool_choice": true, + "supports_tool_choice": false, + "supports_native_streaming": true, + "supports_reasoning": true, + "source": "https://azure.microsoft.com/en-us/blog/gpt-5-in-azure-ai-foundry-the-future-of-ai-apps-and-agents-starts-here/" + }, + "azure/gpt-5-chat-latest": { + "max_tokens": 128000, + "max_input_tokens": 272000, + "max_output_tokens": 128000, + "input_cost_per_token": 1.25e-06, + "output_cost_per_token": 1e-05, + "cache_read_input_token_cost": 1.25e-07, + "litellm_provider": "azure", + "mode": "chat", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_pdf_input": true, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_response_schema": true, + "supports_vision": true, + "supports_prompt_caching": true, + "supports_system_messages": true, + "supports_tool_choice": false, "supports_native_streaming": true, "supports_reasoning": true }, @@ -4791,6 +4790,24 @@ ], "source": "https://azuremarketplace.microsoft.com/pt-br/marketplace/apps/cohere.cohere-embed-4-offer?tab=PlansAndPrice" }, + "azure_ai/FLUX-1.1-pro": { + "output_cost_per_image": 0.04, + "litellm_provider": "azure_ai", + "mode": "image_generation", + "supported_endpoints": [ + "/v1/images/generations" + ], + "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/black-forest-labs-flux-1-kontext-pro-and-flux1-1-pro-now-available-in-azure-ai-f/4434659" + }, + "azure_ai/FLUX.1-Kontext-pro": { + "output_cost_per_image": 0.04, + "litellm_provider": "azure_ai", + "mode": "image_generation", + "supported_endpoints": [ + "/v1/images/generations" + ], + "source": "https://azuremarketplace.microsoft.com/pt-br/marketplace/apps/cohere.cohere-embed-4-offer?tab=PlansAndPrice" + }, "babbage-002": { "max_tokens": 16384, "max_input_tokens": 16384, @@ -6075,6 +6092,36 @@ "supports_tool_choice": true, "source": "https://inference-docs.cerebras.ai/support/pricing" }, + "cerebras/openai/gpt-oss-20b": { + "max_tokens": 32768, + "max_input_tokens": 131072, + "max_output_tokens": 32768, + "input_cost_per_token": 7e-08, + "output_cost_per_token": 3e-07, + "litellm_provider": "cerebras", + "mode": "chat", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_response_schema": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "source": "https://inference-docs.cerebras.ai/support/pricing" + }, + "cerebras/openai/gpt-oss-120b": { + "max_tokens": 32768, + "max_input_tokens": 131072, + "max_output_tokens": 32768, + "input_cost_per_token": 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"image_generation", "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing" }, + "gemini/imagen-4.0-fast-generate-001": { + "output_cost_per_image": 0.02, + "litellm_provider": "gemini", + "mode": "image_generation", + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing" + }, "gemini/imagen-4.0-fast-generate-preview-06-06": { "output_cost_per_image": 0.02, "litellm_provider": "gemini", @@ -11110,6 +11236,21 @@ "supports_tool_choice": true, "supports_prompt_caching": true }, + "openrouter/deepseek/deepseek-chat-v3.1": { + "max_tokens": 8192, + "max_input_tokens": 163840, + "max_output_tokens": 163840, + "input_cost_per_token": 2e-07, + "input_cost_per_token_cache_hit": 2e-08, + "output_cost_per_token": 8e-07, + "litellm_provider": "openrouter", + "mode": "chat", + "supports_function_calling": true, + "supports_assistant_prefill": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_prompt_caching": true + }, "openrouter/x-ai/grok-4": { "max_tokens": 256000, "max_input_tokens": 256000, @@ -11161,6 +11302,17 @@ "mode": "chat", "supports_tool_choice": true }, + "openrouter/deepseek/deepseek-chat-v3-0324": { + "max_tokens": 8192, + "max_input_tokens": 65536, + "max_output_tokens": 8192, + "input_cost_per_token": 1.4e-07, + "output_cost_per_token": 2.8e-07, + "litellm_provider": "openrouter", + "supports_prompt_caching": true, + "mode": "chat", + "supports_tool_choice": true + }, "openrouter/deepseek/deepseek-coder": { "max_tokens": 8192, "max_input_tokens": 66000, @@ -11403,9 +11555,9 @@ }, "openrouter/anthropic/claude-sonnet-4": { "supports_computer_use": true, - "max_tokens": 8192, + "max_tokens": 64000, "max_input_tokens": 200000, - "max_output_tokens": 8192, + "max_output_tokens": 64000, "input_cost_per_token": 3e-06, "output_cost_per_token": 1.5e-05, "input_cost_per_image": 0.0048, @@ -11659,6 +11811,36 @@ "mode": "chat", "supports_tool_choice": true }, + "openrouter/openai/gpt-oss-20b": 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{ - "max_tokens": 128000, + "max_tokens": 96000, "max_input_tokens": 128000, "max_output_tokens": 96000, "input_cost_per_token": 2.2e-07, @@ -16447,6 +17626,14 @@ "litellm_provider": "voyage", "mode": "embedding" }, + "voyage/voyage-context-3": { + "max_tokens": 120000, + "max_input_tokens": 120000, + "input_cost_per_token": 1.8e-07, + "output_cost_per_token": 0.0, + "litellm_provider": "voyage", + "mode": "embedding" + }, "voyage/rerank-2": { "max_tokens": 16000, "max_input_tokens": 16000, @@ -16504,22 +17691,6 @@ }, "supports_tool_choice": true }, - "databricks/databricks-meta-llama-3-1-70b-instruct": { - "max_tokens": 128000, - "max_input_tokens": 128000, - "max_output_tokens": 128000, - "input_cost_per_token": 1.00002e-06, - "input_dbu_cost_per_token": 1.4286e-05, - "output_cost_per_token": 2.99999e-06, - "output_dbu_cost_per_token": 4.2857e-05, - "litellm_provider": "databricks", - "mode": "chat", - "source": "https://www.databricks.com/product/pricing/foundation-model-serving", - "metadata": { - "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." - }, - "supports_tool_choice": true - }, "databricks/databricks-meta-llama-3-3-70b-instruct": { "max_tokens": 128000, "max_input_tokens": 128000, @@ -16552,22 +17723,6 @@ }, "supports_tool_choice": true }, - "databricks/databricks-dbrx-instruct": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 32768, - "input_cost_per_token": 7.4998e-07, - "input_dbu_cost_per_token": 1.0714e-05, - "output_cost_per_token": 2.24901e-06, - "output_dbu_cost_per_token": 3.2143e-05, - "litellm_provider": "databricks", - "mode": "chat", - "source": "https://www.databricks.com/product/pricing/foundation-model-serving", - "metadata": { - "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." - }, - "supports_tool_choice": true - }, "databricks/databricks-meta-llama-3-70b-instruct": { "max_tokens": 128000, "max_input_tokens": 128000, @@ -17039,6 +18194,130 @@ "litellm_provider": "snowflake", "mode": "chat" }, + "gradient_ai/anthropic-claude-3.7-sonnet": { + "input_cost_per_token": 3e-06, + "output_cost_per_token": 15e-06, + "litellm_provider": "gradient_ai", + "mode": "chat", + "max_tokens": 1024, + "supported_endpoints": ["/v1/chat/completions"], + "supported_modalities": ["text"], + "supports_tool_choice": false + }, + "gradient_ai/anthropic-claude-3.5-sonnet": { + "input_cost_per_token": 3e-06, + "output_cost_per_token": 15e-06, + "litellm_provider": "gradient_ai", + "mode": "chat", + "max_tokens": 1024, + "supported_endpoints": ["/v1/chat/completions"], + "supported_modalities": ["text"], + "supports_tool_choice": false + }, + "gradient_ai/anthropic-claude-3.5-haiku": { + "input_cost_per_token": 8e-07, + "output_cost_per_token": 4e-06, + "litellm_provider": "gradient_ai", + "mode": "chat", + "max_tokens": 1024, + "supported_endpoints": ["/v1/chat/completions"], + "supported_modalities": ["text"], + "supports_tool_choice": false + }, + "gradient_ai/anthropic-claude-3-opus": { + "input_cost_per_token": 15e-06, + "output_cost_per_token": 75e-06, + "litellm_provider": "gradient_ai", + "mode": "chat", + "max_tokens": 1024, + "supported_endpoints": ["/v1/chat/completions"], + "supported_modalities": ["text"], + "supports_tool_choice": false + }, + "gradient_ai/deepseek-r1-distill-llama-70b": { + "input_cost_per_token": 99e-08, + "output_cost_per_token": 99e-08, + "litellm_provider": "gradient_ai", + "mode": "chat", + "max_tokens": 8000, + "supported_endpoints": ["/v1/chat/completions"], + "supported_modalities": ["text"], + "supports_tool_choice": false + }, + "gradient_ai/llama3.3-70b-instruct": { + "input_cost_per_token": 65e-08, + "output_cost_per_token": 65e-08, + "litellm_provider": "gradient_ai", + "mode": "chat", + "max_tokens": 2048, + "supported_endpoints": ["/v1/chat/completions"], + "supported_modalities": ["text"], + "supports_tool_choice": false + }, + "gradient_ai/llama3-8b-instruct": { + "input_cost_per_token": 2e-07, + "output_cost_per_token": 2e-07, + "litellm_provider": "gradient_ai", + "mode": "chat", + "max_tokens": 512, + "supported_endpoints": ["/v1/chat/completions"], + "supported_modalities": ["text"], + "supports_tool_choice": false + }, + "gradient_ai/mistral-nemo-instruct-2407": { + "input_cost_per_token": 3e-07, + "output_cost_per_token": 3e-07, + "litellm_provider": "gradient_ai", + "mode": "chat", + "max_tokens": 512, + "supported_endpoints": ["/v1/chat/completions"], + "supported_modalities": ["text"], + "supports_tool_choice": false + }, + "gradient_ai/openai-o3": { + "input_cost_per_token": 2e-06, + "output_cost_per_token": 8e-06, + "litellm_provider": "gradient_ai", + "mode": "chat", + "max_tokens": 100000, + "supported_endpoints": ["/v1/chat/completions"], + "supported_modalities": ["text"], + "supports_tool_choice": false + }, + "gradient_ai/openai-o3-mini": { + "input_cost_per_token": 11e-07, + "output_cost_per_token": 44e-07, + "litellm_provider": "gradient_ai", + "mode": "chat", + "max_tokens": 100000, + "supported_endpoints": ["/v1/chat/completions"], + "supported_modalities": ["text"], + "supports_tool_choice": false + }, + "gradient_ai/openai-gpt-4o": { + "litellm_provider": "gradient_ai", + "mode": "chat", + "max_tokens": 16384, + "supported_endpoints": ["/v1/chat/completions"], + "supported_modalities": ["text"], + "supports_tool_choice": false + }, + "gradient_ai/openai-gpt-4o-mini": { + "litellm_provider": "gradient_ai", + "mode": "chat", + "max_tokens": 16384, + "supported_endpoints": ["/v1/chat/completions"], + "supported_modalities": ["text"], + "supports_tool_choice": false + }, + "gradient_ai/alibaba-qwen3-32b": { + "litellm_provider": "gradient_ai", + "mode": "chat", + "max_tokens": 2048, + "supported_endpoints": ["/v1/chat/completions"], + "supported_modalities": ["text"], + "supports_tool_choice": false + }, "nscale/meta-llama/Llama-4-Scout-17B-16E-Instruct": { "input_cost_per_token": 9e-08, "output_cost_per_token": 2.9e-07, @@ -18354,5 +19633,118 @@ "supports_function_calling": true, "supports_response_schema": false, "source": "https://www.oracle.com/artificial-intelligence/generative-ai/generative-ai-service/pricing" + }, + "aiml/flux/kontext-pro/text-to-image":{ + "output_cost_per_image": 0.042, + "litellm_provider": "aiml", + "mode": "image_generation", + "supported_endpoints": [ + "/v1/images/generations" + ], + "source": "https://docs.aimlapi.com/", + "metadata": { + "notes": "Flux Pro v1.1 - Enhanced version with improved capabilities and 6x faster inference speed" + } + + }, + "aiml/flux/kontext-max/text-to-image": { + "output_cost_per_image": 0.084, + "litellm_provider": "aiml", + "mode": "image_generation", + "supported_endpoints": [ + "/v1/images/generations" + ], + "source": "https://docs.aimlapi.com/", + "metadata": { + "notes": "Flux Pro v1.1 - Enhanced version with improved capabilities and 6x faster inference speed" + } + }, + "aiml/flux-pro/v1.1-ultra": { + "output_cost_per_image": 0.063, + "litellm_provider": "aiml", + "mode": "image_generation", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "aiml/flux-pro/v1.1": { + "output_cost_per_image": 0.042, + "litellm_provider": "aiml", + "mode": "image_generation", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "aiml/flux-realism": { + "output_cost_per_image": 0.037, + "litellm_provider": "aiml", + "mode": "image_generation", + "supported_endpoints": [ + "/v1/images/generations" + ], + "source": "https://docs.aimlapi.com/", + "metadata": { + "notes": "Flux Pro - Professional-grade image generation model" + } + }, + "aiml/flux/schnell": { + "output_cost_per_image": 0.003, + "litellm_provider": "aiml", + "mode": "image_generation", + "supported_endpoints": [ + "/v1/images/generations" + ], + "source": "https://docs.aimlapi.com/", + "metadata": { + "notes": "Flux Schnell - Fast generation model optimized for speed" + } + }, + "aiml/flux/dev": { + "output_cost_per_image": 0.026, + "litellm_provider": "aiml", + "mode": "image_generation", + "supported_endpoints": [ + "/v1/images/generations" + ], + "source": "https://docs.aimlapi.com/", + "metadata": { + "notes": "Flux Dev - Development version optimized for experimentation" + } + }, + "aiml/flux-pro": { + "output_cost_per_image": 0.053, + "litellm_provider": "aiml", + "mode": "image_generation", + "supported_endpoints": [ + "/v1/images/generations" + ], + "source": "https://docs.aimlapi.com/", + "metadata": { + "notes": "Flux Dev - Development version optimized for experimentation" + } + }, + "aiml/dall-e-3": { + "output_cost_per_image": 0.042, + "litellm_provider": "aiml", + "mode": "image_generation", + "supported_endpoints": [ + "/v1/images/generations" + ], + "source": "https://docs.aimlapi.com/", + "metadata": { + "notes": "DALL-E 3 via AI/ML API - High-quality text-to-image generation" + } + }, + "aiml/dall-e-2": { + "output_cost_per_image": 0.021, + "litellm_provider": "aiml", + "mode": "image_generation", + "supported_endpoints": [ + "/v1/images/generations" + ], + "source": "https://docs.aimlapi.com/", + "metadata": { + "notes": "DALL-E 2 via AI/ML API - Reliable text-to-image generation" + } } } diff --git a/litellm/passthrough/main.py b/litellm/passthrough/main.py index 59fab1b3369..f4dc1ef6c84 100644 --- a/litellm/passthrough/main.py +++ b/litellm/passthrough/main.py @@ -24,6 +24,7 @@ import litellm from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler +from litellm.passthrough.utils import CommonUtils from litellm.utils import client base_llm_http_handler = BaseLLMHTTPHandler() @@ -241,6 +242,12 @@ def llm_passthrough_route( request_query_params=request_query_params, litellm_params=litellm_params_dict, ) + + # need to encode the id of application-inference-profile for bedrock + if custom_llm_provider == "bedrock" and "application-inference-profile" in endpoint: + encoded_url_str = CommonUtils.encode_bedrock_runtime_modelid_arn(str(updated_url)) + updated_url = httpx.URL(encoded_url_str) + # Add or update query parameters provider_api_key = provider_config.get_api_key(api_key) diff --git a/litellm/passthrough/utils.py b/litellm/passthrough/utils.py index c52d0e3688d..4bf66d49881 100644 --- a/litellm/passthrough/utils.py +++ b/litellm/passthrough/utils.py @@ -37,3 +37,56 @@ class BasePassthroughUtils: # Combine request headers with custom headers headers = {**request_headers, **headers} return headers + +class CommonUtils: + @staticmethod + def encode_bedrock_runtime_modelid_arn(endpoint: str) -> str: + """ + Encodes any "/" found in the modelId of an AWS Bedrock Runtime Endpoint when arns are passed in. + - modelID value can be an ARN which contains slashes that SHOULD NOT be treated as path separators. + e.g endpoint: /model//invoke + containing arns with slashes need to be encoded from + arn:aws:bedrock:ap-southeast-1:123456789012:application-inference-profile/abdefg12334 => + arn:aws:bedrock:ap-southeast-1:123456789012:application-inference-profile%2Fabdefg12334 + so that it is treated as one part of the path. + Otherwise, the encoded endpoint will return 500 error when passed to Bedrock endpoint. + + See the apis in https://docs.aws.amazon.com/bedrock/latest/APIReference/API_Operations_Amazon_Bedrock_Runtime.html + for more details on the regex patterns of modelId which we use in the regex logic below. + + Args: + endpoint (str): The original endpoint string which may contain ARNs that contain slashes. + + Returns: + str: The endpoint with properly encoded ARN slashes + """ + import re + + # Early exit: if no ARN detected, return unchanged + if 'arn:aws:' not in endpoint: + return endpoint + + # Handle all patterns in one go - more efficient and cleaner + patterns = [ + # Custom model with 2 slashes (order matters - do this first) + (r'(custom-model)/([a-z0-9.-]+)/([a-z0-9]+)', r'\1%2F\2%2F\3'), + + # All other resource types with 1 slash + (r'(:application-inference-profile)/', r'\1%2F'), + (r'(:inference-profile)/', r'\1%2F'), + (r'(:foundation-model)/', r'\1%2F'), + (r'(:imported-model)/', r'\1%2F'), + (r'(:provisioned-model)/', r'\1%2F'), + (r'(:prompt)/', r'\1%2F'), + (r'(:endpoint)/', r'\1%2F'), + (r'(:prompt-router)/', r'\1%2F'), + (r'(:default-prompt-router)/', r'\1%2F'), + ] + + for pattern, replacement in patterns: + # Check if pattern exists before applying regex (early exit optimization) + if re.search(pattern, endpoint): + endpoint = re.sub(pattern, replacement, endpoint) + break # Exit after first match since each ARN has only one resource type + + return endpoint \ No newline at end of file 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 7469848e2f2..a075de13fb1 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 @@ -236,12 +236,17 @@ class MCPRequestHandler: ) ######################################################### - # If team has mcp_servers, then key must have a subset of the team's mcp_servers + # If team has mcp_servers, handle inheritance and intersection logic ######################################################### if len(allowed_mcp_servers_for_team) > 0: - for _mcp_server in allowed_mcp_servers_for_key: - if _mcp_server in allowed_mcp_servers_for_team: - allowed_mcp_servers.append(_mcp_server) + if len(allowed_mcp_servers_for_key) > 0: + # Key has its own MCP permissions - use intersection with team permissions + for _mcp_server in allowed_mcp_servers_for_key: + if _mcp_server in allowed_mcp_servers_for_team: + allowed_mcp_servers.append(_mcp_server) + else: + # Key has no MCP permissions - inherit from team + allowed_mcp_servers = allowed_mcp_servers_for_team else: allowed_mcp_servers = allowed_mcp_servers_for_key diff --git a/litellm/proxy/_experimental/mcp_server/db.py b/litellm/proxy/_experimental/mcp_server/db.py index 3d90c99eee2..d5d9f978908 100644 --- a/litellm/proxy/_experimental/mcp_server/db.py +++ b/litellm/proxy/_experimental/mcp_server/db.py @@ -1,6 +1,7 @@ import uuid from typing import Any, Dict, Iterable, List, Optional, Set, Union +from litellm._logging import verbose_proxy_logger from litellm.proxy._types import ( LiteLLM_MCPServerTable, LiteLLM_ObjectPermissionTable, @@ -53,11 +54,20 @@ async def get_all_mcp_servers( """ Returns all of the mcp servers from the db """ - mcp_servers = await prisma_client.db.litellm_mcpservertable.find_many() + try: + mcp_servers = await prisma_client.db.litellm_mcpservertable.find_many() - return [ - LiteLLM_MCPServerTable(**mcp_server.model_dump()) for mcp_server in mcp_servers - ] + return [ + LiteLLM_MCPServerTable(**mcp_server.model_dump()) + for mcp_server in mcp_servers + ] + except Exception as e: + verbose_proxy_logger.debug( + "litellm.proxy._experimental.mcp_server.db.py::get_all_mcp_servers - {}".format( + str(e) + ) + ) + return [] async def get_mcp_server( @@ -91,9 +101,7 @@ async def get_mcp_servers( ) final_mcp_servers: List[LiteLLM_MCPServerTable] = [] for _mcp_server in _mcp_servers: - final_mcp_servers.append( - LiteLLM_MCPServerTable(**_mcp_server.model_dump()) - ) + final_mcp_servers.append(LiteLLM_MCPServerTable(**_mcp_server.model_dump())) return final_mcp_servers diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index 15891b53f4e..34a0d604f39 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -295,6 +295,7 @@ class MCPServerManager: command=getattr(mcp_server, "command", None), args=getattr(mcp_server, "args", None) or [], env=env_dict, + access_groups=getattr(mcp_server, "mcp_access_groups", None), ) self.registry[mcp_server.server_id] = new_server verbose_logger.debug(f"Added MCP Server: {name_for_prefix}") @@ -1050,7 +1051,9 @@ class MCPServerManager: auth_type=_server_config.auth_type, created_at=datetime.datetime.now(), updated_at=datetime.datetime.now(), + description=_server_config.mcp_info.get("description") if _server_config.mcp_info else None, mcp_info=_server_config.mcp_info, + mcp_access_groups=_server_config.access_groups or [], # Stdio-specific fields command=getattr(_server_config, "command", None), args=getattr(_server_config, "args", None) or [], diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py index dd4ac9b007d..38619112ccc 100644 --- a/litellm/proxy/_experimental/mcp_server/server.py +++ b/litellm/proxy/_experimental/mcp_server/server.py @@ -40,6 +40,7 @@ except ImportError as e: # Global variables to track initialization _SESSION_MANAGERS_INITIALIZED = False +_INITIALIZATION_LOCK = asyncio.Lock() if MCP_AVAILABLE: from mcp.server import Server @@ -64,7 +65,7 @@ if MCP_AVAILABLE: global_mcp_tool_registry, ) from litellm.proxy._experimental.mcp_server.utils import ( - get_server_name_prefix_tool_mcp, + get_server_name_prefix_tool_mcp, ) ###################################################### @@ -113,23 +114,23 @@ if MCP_AVAILABLE: """Initialize the session managers. Can be called from main app lifespan.""" global _SESSION_MANAGERS_INITIALIZED, _session_manager_cm, _sse_session_manager_cm - if _SESSION_MANAGERS_INITIALIZED: - return + # Use async lock to prevent concurrent initialization + async with _INITIALIZATION_LOCK: + if _SESSION_MANAGERS_INITIALIZED: + return - verbose_logger.info("Initializing MCP session managers...") + verbose_logger.info("Initializing MCP session managers...") - # Start the session managers with context managers - _session_manager_cm = session_manager.run() - _sse_session_manager_cm = sse_session_manager.run() + # Start the session managers with context managers + _session_manager_cm = session_manager.run() + _sse_session_manager_cm = sse_session_manager.run() - # Enter the context managers - await _session_manager_cm.__aenter__() - await _sse_session_manager_cm.__aenter__() + # Enter the context managers + await _session_manager_cm.__aenter__() + await _sse_session_manager_cm.__aenter__() - _SESSION_MANAGERS_INITIALIZED = True - verbose_logger.info( - "MCP Server started with StreamableHTTP and SSE session managers!" - ) + _SESSION_MANAGERS_INITIALIZED = True + verbose_logger.info("MCP Server started with StreamableHTTP and SSE session managers!") async def shutdown_session_managers(): """Shutdown the session managers.""" @@ -170,13 +171,11 @@ if MCP_AVAILABLE: """ try: # Get user authentication from context variable - user_api_key_auth, mcp_auth_header, mcp_servers, mcp_server_auth_headers, mcp_protocol_version = get_auth_context() - verbose_logger.debug( - f"MCP list_tools - User API Key Auth from context: {user_api_key_auth}" - ) - verbose_logger.debug( - f"MCP list_tools - MCP servers from context: {mcp_servers}" + user_api_key_auth, mcp_auth_header, mcp_servers, mcp_server_auth_headers, mcp_protocol_version = ( + get_auth_context() ) + verbose_logger.debug(f"MCP list_tools - User API Key Auth from context: {user_api_key_auth}") + verbose_logger.debug(f"MCP list_tools - MCP servers from context: {mcp_servers}") verbose_logger.debug( f"MCP list_tools - MCP server auth headers: {list(mcp_server_auth_headers.keys()) if mcp_server_auth_headers else None}" ) @@ -223,9 +222,7 @@ if MCP_AVAILABLE: # Validate arguments user_api_key_auth, mcp_auth_header, _, mcp_server_auth_headers, mcp_protocol_version = get_auth_context() - verbose_logger.debug( - f"MCP mcp_server_tool_call - User API Key Auth from context: {user_api_key_auth}" - ) + verbose_logger.debug(f"MCP mcp_server_tool_call - User API Key Auth from context: {user_api_key_auth}") try: # Create a body date for logging body_data = {"name": name, "arguments": arguments} @@ -258,31 +255,19 @@ if MCP_AVAILABLE: except BlockedPiiEntityError as e: verbose_logger.error(f"BlockedPiiEntityError in MCP tool call: {str(e)}") # Return error as text content for MCP protocol - return [TextContent( - text=f"Error: Blocked PII entity detected - {str(e)}", - type="text" - )] + return [TextContent(text=f"Error: Blocked PII entity detected - {str(e)}", type="text")] except GuardrailRaisedException as e: verbose_logger.error(f"GuardrailRaisedException in MCP tool call: {str(e)}") # Return error as text content for MCP protocol - return [TextContent( - text=f"Error: Guardrail violation - {str(e)}", - type="text" - )] + return [TextContent(text=f"Error: Guardrail violation - {str(e)}", type="text")] except HTTPException as e: verbose_logger.error(f"HTTPException in MCP tool call: {str(e)}") # Return error as text content for MCP protocol - return [TextContent( - text=f"Error: {str(e.detail)}", - type="text" - )] + return [TextContent(text=f"Error: {str(e.detail)}", type="text")] except Exception as e: verbose_logger.exception(f"MCP mcp_server_tool_call - error: {e}") # Return error as text content for MCP protocol - return [TextContent( - text=f"Error: {str(e)}", - type="text" - )] + return [TextContent(text=f"Error: {str(e)}", type="text")] return response @@ -317,29 +302,40 @@ if MCP_AVAILABLE: return [] # Get allowed MCP servers based on user permissions - allowed_mcp_servers = await global_mcp_server_manager.get_allowed_mcp_servers( - user_api_key_auth - ) + allowed_mcp_servers = await global_mcp_server_manager.get_allowed_mcp_servers(user_api_key_auth) + + filtered_server_ids = set() # Filter servers based on mcp_servers parameter if provided if mcp_servers is not None: - # Convert to lowercase for case-insensitive comparison - mcp_servers_lower = [s.lower() for s in mcp_servers] - allowed_mcp_servers = [ - server_id - for server_id in allowed_mcp_servers - if any( - server_alias.lower() in mcp_servers_lower - for server in [global_mcp_server_manager.get_mcp_server_by_id(server_id)] - if server is not None - for server_alias in [ - server.alias, - server.server_name, - server_id, - ] - if server_alias is not None - ) - ] + for server_or_group in mcp_servers: + server_name_matched = False + + for server_id in allowed_mcp_servers: + server = global_mcp_server_manager.get_mcp_server_by_id(server_id) + + if server: + match_list = [s.lower() for s in [server.alias, server.server_name, server_id] if s is not None] + + if server_or_group.lower() in match_list: + filtered_server_ids.add(server_id) + server_name_matched = True + break + + if not server_name_matched: + try: + access_group_server_ids = await MCPRequestHandler._get_mcp_servers_from_access_groups( + [server_or_group] + ) + # Only include servers that the user has access to + for server_id in access_group_server_ids: + if server_id in allowed_mcp_servers: + filtered_server_ids.add(server_id) + except Exception as e: + verbose_logger.debug(f"Could not resolve '{server_or_group}' as access group: {e}") + + if filtered_server_ids: + allowed_mcp_servers = list(filtered_server_ids) # Get tools from each allowed server all_tools = [] @@ -354,7 +350,7 @@ if MCP_AVAILABLE: server_auth_header = mcp_server_auth_headers.get(server.alias) elif mcp_server_auth_headers and server.server_name is not None: server_auth_header = mcp_server_auth_headers.get(server.server_name) - + # Fall back to deprecated mcp_auth_header if no server-specific header found if server_auth_header is None: server_auth_header = mcp_auth_header @@ -368,9 +364,7 @@ if MCP_AVAILABLE: all_tools.extend(tools) verbose_logger.debug(f"Successfully fetched {len(tools)} tools from server {server.name}") except Exception as e: - verbose_logger.exception( - f"Error getting tools from server {server.name}: {str(e)}" - ) + verbose_logger.exception(f"Error getting tools from server {server.name}: {str(e)}") # Continue with other servers instead of failing completely verbose_logger.info(f"Successfully fetched {len(all_tools)} tools total from all MCP servers") @@ -416,15 +410,11 @@ if MCP_AVAILABLE: local_tools = [] try: local_tools_raw = global_mcp_tool_registry.list_tools() - + # Convert local tools to MCPTool format for tool in local_tools_raw: # Convert from litellm.types.mcp_server.tool_registry.MCPTool to mcp.types.Tool - mcp_tool = MCPTool( - name=tool.name, - description=tool.description, - inputSchema=tool.input_schema - ) + mcp_tool = MCPTool(name=tool.name, description=tool.description, inputSchema=tool.input_schema) local_tools.append(mcp_tool) except Exception as e: verbose_logger.exception(f"Error getting tools from local registry: {str(e)}") @@ -437,54 +427,42 @@ if MCP_AVAILABLE: @client async def call_mcp_tool( - name: str, - arguments: Optional[Dict[str, Any]] = None, - user_api_key_auth: Optional[UserAPIKeyAuth] = None, - mcp_auth_header: Optional[str] = None, - mcp_server_auth_headers: Optional[Dict[str, str]] = None, - mcp_protocol_version: Optional[str] = None, - **kwargs: Any + name: str, + arguments: Optional[Dict[str, Any]] = None, + user_api_key_auth: Optional[UserAPIKeyAuth] = None, + mcp_auth_header: Optional[str] = None, + mcp_server_auth_headers: Optional[Dict[str, str]] = None, + mcp_protocol_version: Optional[str] = None, + **kwargs: Any, ) -> List[Union[TextContent, ImageContent, EmbeddedResource]]: """ Call a specific tool with the provided arguments (handles prefixed tool names) """ start_time = datetime.now() if arguments is None: - raise HTTPException( - status_code=400, detail="Request arguments are required" - ) + raise HTTPException(status_code=400, detail="Request arguments are required") # Remove prefix from tool name for logging and processing - original_tool_name, server_name_from_prefix = get_server_name_prefix_tool_mcp( - name - ) + original_tool_name, server_name_from_prefix = get_server_name_prefix_tool_mcp(name) - standard_logging_mcp_tool_call: StandardLoggingMCPToolCall = ( - _get_standard_logging_mcp_tool_call( - name=original_tool_name, # Use original name for logging - arguments=arguments, - server_name=server_name_from_prefix, - ) - ) - litellm_logging_obj: Optional[LiteLLMLoggingObj] = kwargs.get( - "litellm_logging_obj", None + standard_logging_mcp_tool_call: StandardLoggingMCPToolCall = _get_standard_logging_mcp_tool_call( + name=original_tool_name, # Use original name for logging + arguments=arguments, + server_name=server_name_from_prefix, ) + litellm_logging_obj: Optional[LiteLLMLoggingObj] = kwargs.get("litellm_logging_obj", None) if litellm_logging_obj: - litellm_logging_obj.model_call_details["mcp_tool_call_metadata"] = ( - standard_logging_mcp_tool_call - ) + litellm_logging_obj.model_call_details["mcp_tool_call_metadata"] = standard_logging_mcp_tool_call litellm_logging_obj.model = f"MCP: {name}" # Try managed server tool first (pass the full prefixed name) # Primary and recommended way to use MCP servers ######################################################### - mcp_server: Optional[MCPServer] = ( - global_mcp_server_manager._get_mcp_server_from_tool_name(name) - ) + mcp_server: Optional[MCPServer] = global_mcp_server_manager._get_mcp_server_from_tool_name(name) if mcp_server: - standard_logging_mcp_tool_call["mcp_server_cost_info"] = ( - mcp_server.mcp_info or {} - ).get("mcp_server_cost_info") - response = await _handle_managed_mcp_tool( + standard_logging_mcp_tool_call["mcp_server_cost_info"] = (mcp_server.mcp_info or {}).get( + "mcp_server_cost_info" + ) + response = await _handle_managed_mcp_tool( name=name, # Pass the full name (potentially prefixed) arguments=arguments, user_api_key_auth=user_api_key_auth, @@ -500,7 +478,7 @@ if MCP_AVAILABLE: ######################################################### else: response = await _handle_local_mcp_tool(original_tool_name, arguments) - + ######################################################### # Post MCP Tool Call Hook # Allow modifying the MCP tool call response before it is returned to the user @@ -549,7 +527,7 @@ if MCP_AVAILABLE: """Handle tool execution for managed server tools""" # Import here to avoid circular import from litellm.proxy.proxy_server import proxy_logging_obj - + call_tool_result = await global_mcp_server_manager.call_tool( name=name, arguments=arguments, @@ -584,6 +562,7 @@ if MCP_AVAILABLE: Returns: (user_api_key_auth, mcp_auth_header, mcp_servers, mcp_server_auth_headers) """ import re + mcp_servers_from_path = None mcp_path_match = re.match(r"^/mcp/([^/]+)(/.*)?$", path) if mcp_path_match: @@ -592,25 +571,39 @@ if MCP_AVAILABLE: mcp_servers_from_path = [s.strip() for s in mcp_servers_str.split(",") if s.strip()] if mcp_servers_from_path is not None: - user_api_key_auth, mcp_auth_header, _, mcp_server_auth_headers, mcp_protocol_version = ( - await MCPRequestHandler.process_mcp_request(scope) - ) + ( + user_api_key_auth, + mcp_auth_header, + _, + mcp_server_auth_headers, + mcp_protocol_version, + ) = await MCPRequestHandler.process_mcp_request(scope) mcp_servers = mcp_servers_from_path else: - user_api_key_auth, mcp_auth_header, mcp_servers, mcp_server_auth_headers, mcp_protocol_version = ( - await MCPRequestHandler.process_mcp_request(scope) - ) + ( + user_api_key_auth, + mcp_auth_header, + mcp_servers, + mcp_server_auth_headers, + mcp_protocol_version, + ) = await MCPRequestHandler.process_mcp_request(scope) return user_api_key_auth, mcp_auth_header, mcp_servers, mcp_server_auth_headers, mcp_protocol_version - async def handle_streamable_http_mcp( - scope: Scope, receive: Receive, send: Send - ) -> None: + async def handle_streamable_http_mcp(scope: Scope, receive: Receive, send: Send) -> None: """Handle MCP requests through StreamableHTTP.""" try: path = scope.get("path", "") - user_api_key_auth, mcp_auth_header, mcp_servers, mcp_server_auth_headers, mcp_protocol_version = await extract_mcp_auth_context(scope, path) + ( + user_api_key_auth, + mcp_auth_header, + mcp_servers, + mcp_server_auth_headers, + mcp_protocol_version, + ) = await extract_mcp_auth_context(scope, path) verbose_logger.debug(f"MCP request mcp_servers (header/path): {mcp_servers}") - verbose_logger.debug(f"MCP server auth headers: {list(mcp_server_auth_headers.keys()) if mcp_server_auth_headers else None}") + verbose_logger.debug( + f"MCP server auth headers: {list(mcp_server_auth_headers.keys()) if mcp_server_auth_headers else None}" + ) verbose_logger.debug(f"MCP protocol version: {mcp_protocol_version}") # Set the auth context variable for easy access in MCP functions set_auth_context( @@ -635,10 +628,10 @@ if MCP_AVAILABLE: # Send a proper HTTP error response instead of letting the exception bubble up from starlette.responses import JSONResponse from starlette.status import HTTP_500_INTERNAL_SERVER_ERROR - + error_response = JSONResponse( status_code=HTTP_500_INTERNAL_SERVER_ERROR, - content={"error": "MCP request failed", "details": str(e)} + content={"error": "MCP request failed", "details": str(e)}, ) await error_response(scope, receive, send) except Exception as response_error: @@ -650,9 +643,17 @@ if MCP_AVAILABLE: """Handle MCP requests through SSE.""" try: path = scope.get("path", "") - user_api_key_auth, mcp_auth_header, mcp_servers, mcp_server_auth_headers, mcp_protocol_version = await extract_mcp_auth_context(scope, path) + ( + user_api_key_auth, + mcp_auth_header, + mcp_servers, + mcp_server_auth_headers, + mcp_protocol_version, + ) = await extract_mcp_auth_context(scope, path) verbose_logger.debug(f"MCP request mcp_servers (header/path): {mcp_servers}") - verbose_logger.debug(f"MCP server auth headers: {list(mcp_server_auth_headers.keys()) if mcp_server_auth_headers else None}") + verbose_logger.debug( + f"MCP server auth headers: {list(mcp_server_auth_headers.keys()) if mcp_server_auth_headers else None}" + ) verbose_logger.debug(f"MCP protocol version: {mcp_protocol_version}") set_auth_context( user_api_key_auth=user_api_key_auth, @@ -674,10 +675,10 @@ if MCP_AVAILABLE: # Send a proper HTTP error response instead of letting the exception bubble up from starlette.responses import JSONResponse from starlette.status import HTTP_500_INTERNAL_SERVER_ERROR - + error_response = JSONResponse( status_code=HTTP_500_INTERNAL_SERVER_ERROR, - content={"error": "MCP request failed", "details": str(e)} + content={"error": "MCP request failed", "details": str(e)}, ) await error_response(scope, receive, send) except Exception as response_error: @@ -737,14 +738,14 @@ if MCP_AVAILABLE: ) auth_context_var.set(auth_user) - def get_auth_context() -> ( - Tuple[Optional[UserAPIKeyAuth], Optional[str], Optional[List[str]], Optional[Dict[str, str]], Optional[str]] - ): + def get_auth_context() -> Tuple[ + Optional[UserAPIKeyAuth], Optional[str], Optional[List[str]], Optional[Dict[str, str]], Optional[str] + ]: """ Get the UserAPIKeyAuth from the auth context variable. Returns: - Tuple[Optional[UserAPIKeyAuth], Optional[str], Optional[List[str]], Optional[Dict[str, str]]]: + Tuple[Optional[UserAPIKeyAuth], Optional[str], Optional[List[str]], Optional[Dict[str, str]]]: UserAPIKeyAuth object, MCP auth header (deprecated), MCP servers (can include access groups), and server-specific auth headers """ auth_user = auth_context_var.get() diff --git a/litellm/proxy/_experimental/out/_next/static/poVnZDt3J0aYERGVwpJKO/_buildManifest.js b/litellm/proxy/_experimental/out/_next/static/0rzQKhrKTur56aOgaxVrq/_buildManifest.js similarity index 100% rename from litellm/proxy/_experimental/out/_next/static/poVnZDt3J0aYERGVwpJKO/_buildManifest.js rename to litellm/proxy/_experimental/out/_next/static/0rzQKhrKTur56aOgaxVrq/_buildManifest.js diff --git a/litellm/proxy/_experimental/out/_next/static/poVnZDt3J0aYERGVwpJKO/_ssgManifest.js b/litellm/proxy/_experimental/out/_next/static/0rzQKhrKTur56aOgaxVrq/_ssgManifest.js similarity index 100% rename from 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fetch("".concat(r),{method:"GET",headers:{[g]:"Bearer ".concat(e),"Content-Type":"application/json"}});if(!n.ok)throw await n.text(),Error("Network response was not ok");let c=await n.json();return console.log(c),c}catch(e){throw console.error("Failed to create key:",e),e}},e_=async e=>{try{let t=l?"".concat(l,"/global/spend/all_tag_names"):"/global/spend/all_tag_names";console.log("in global/spend/all_tag_names call",t);let o=await fetch("".concat(t),{method:"GET",headers:{[g]:"Bearer ".concat(e),"Content-Type":"application/json"}});if(!o.ok)throw await o.text(),Error("Network response was not ok");let a=await o.json();return console.log(a),a}catch(e){throw console.error("Failed to create key:",e),e}},eT=async e=>{try{let t=l?"".concat(l,"/global/all_end_users"):"/global/all_end_users";console.log("in global/all_end_users call",t);let o=await fetch("".concat(t),{method:"GET",headers:{[g]:"Bearer ".concat(e),"Content-Type":"application/json"}});if(!o.ok)throw await o.text(),Error("Network response was not ok");let a=await o.json();return console.log(a),a}catch(e){throw console.error("Failed to create key:",e),e}},eE=async(e,t)=>{try{let o=l?"".concat(l,"/user/filter/ui"):"/user/filter/ui";t.get("user_email")&&(o+="?user_email=".concat(t.get("user_email"))),t.get("user_id")&&(o+="?user_id=".concat(t.get("user_id")));let a=await fetch(o,{method:"GET",headers:{[g]:"Bearer ".concat(e),"Content-Type":"application/json"}});if(!a.ok){let e=await a.text();throw w(e),Error("Network response was not ok")}return await a.json()}catch(e){throw console.error("Failed to create key:",e),e}},ej=async(e,t,o,a,r,n)=>{try{console.log("user role in spend logs call: ".concat(o));let t=l?"".concat(l,"/spend/logs"):"/spend/logs";t="App Owner"==o?"".concat(t,"?user_id=").concat(a,"&start_date=").concat(r,"&end_date=").concat(n):"".concat(t,"?start_date=").concat(r,"&end_date=").concat(n);let c=await fetch(t,{method:"GET",headers:{[g]:"Bearer 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c={method:"POST",headers:{[g]:"Bearer ".concat(e),"Content-Type":"application/json"},body:n},s=await fetch(r,c);if(!s.ok){let e=await s.text();throw w(e),Error("Network response was not ok")}let i=await s.json();return console.log(i),i}catch(e){throw console.error("Failed to create key:",e),e}},eF=async(e,t,o,a)=>{try{let r=l?"".concat(l,"/global/spend/provider"):"/global/spend/provider";o&&a&&(r+="?start_date=".concat(o,"&end_date=").concat(a)),t&&(r+="&api_key=".concat(t));let n={method:"GET",headers:{[g]:"Bearer ".concat(e)}},c=await fetch(r,n);if(!c.ok){let e=await c.text();throw w(e),Error("Network response was not ok")}let s=await c.json();return console.log(s),s}catch(e){throw console.error("Failed to fetch spend data:",e),e}},ex=async(e,t,o)=>{try{let a=l?"".concat(l,"/global/activity"):"/global/activity";t&&o&&(a+="?start_date=".concat(t,"&end_date=").concat(o));let r={method:"GET",headers:{[g]:"Bearer ".concat(e)}},n=await fetch(a,r);if(!n.ok)throw await n.text(),Error("Network response was not ok");let c=await n.json();return console.log(c),c}catch(e){throw console.error("Failed to fetch spend data:",e),e}},eP=async(e,t,o)=>{try{let a=l?"".concat(l,"/global/activity/cache_hits"):"/global/activity/cache_hits";t&&o&&(a+="?start_date=".concat(t,"&end_date=").concat(o));let r={method:"GET",headers:{[g]:"Bearer ".concat(e)}},n=await fetch(a,r);if(!n.ok)throw await n.text(),Error("Network response was not ok");let c=await n.json();return console.log(c),c}catch(e){throw console.error("Failed to fetch spend data:",e),e}},eO=async(e,t,o)=>{try{let a=l?"".concat(l,"/global/activity/model"):"/global/activity/model";t&&o&&(a+="?start_date=".concat(t,"&end_date=").concat(o));let r={method:"GET",headers:{[g]:"Bearer ".concat(e)}},n=await fetch(a,r);if(!n.ok)throw await n.text(),Error("Network response was not ok");let c=await n.json();return console.log(c),c}catch(e){throw console.error("Failed to fetch spend data:",e),e}},eB=async(e,t,o,a)=>{try{let r=l?"".concat(l,"/global/activity/exceptions"):"/global/activity/exceptions";t&&o&&(r+="?start_date=".concat(t,"&end_date=").concat(o)),a&&(r+="&model_group=".concat(a));let n={method:"GET",headers:{[g]:"Bearer ".concat(e)}},c=await fetch(r,n);if(!c.ok)throw await c.text(),Error("Network response was not ok");let s=await c.json();return console.log(s),s}catch(e){throw console.error("Failed to fetch spend data:",e),e}},eG=async(e,t,o,a)=>{try{let r=l?"".concat(l,"/global/activity/exceptions/deployment"):"/global/activity/exceptions/deployment";t&&o&&(r+="?start_date=".concat(t,"&end_date=").concat(o)),a&&(r+="&model_group=".concat(a));let n={method:"GET",headers:{[g]:"Bearer ".concat(e)}},c=await fetch(r,n);if(!c.ok)throw await c.text(),Error("Network response was not ok");let s=await c.json();return console.log(s),s}catch(e){throw console.error("Failed to fetch spend data:",e),e}},eJ=async e=>{try{let t=l?"".concat(l,"/global/spend/models?limit=5"):"/global/spend/models?limit=5",o=await fetch(t,{method:"GET",headers:{[g]:"Bearer ".concat(e),"Content-Type":"application/json"}});if(!o.ok){let e=await o.text();throw w(e),Error("Network response was not ok")}let a=await o.json();return console.log(a),a}catch(e){throw console.error("Failed to create key:",e),e}},eU=async(e,t)=>{try{let o=l?"".concat(l,"/v2/key/info"):"/v2/key/info",a=await fetch(o,{method:"POST",headers:{[g]:"Bearer ".concat(e),"Content-Type":"application/json"},body:JSON.stringify({keys:t})});if(!a.ok){let e=await a.text();if(e.includes("Invalid proxy server token passed"))throw Error("Invalid proxy server token passed");throw w(e),Error("Network response was not ok")}let r=await a.json();return console.log(r),r}catch(e){throw console.error("Failed to create key:",e),e}},eA=async(e,t,o)=>{try{console.log("Sending model connection test request:",JSON.stringify(t));let r=l?"".concat(l,"/health/test_connection"):"/health/test_connection",n=await fetch(r,{method:"POST",headers:{"Content-Type":"application/json",[g]:"Bearer ".concat(e)},body:JSON.stringify({litellm_params:t,mode:o})}),c=n.headers.get("content-type");if(!c||!c.includes("application/json")){let e=await n.text();throw console.error("Received non-JSON response:",e),Error("Received non-JSON response (".concat(n.status,": ").concat(n.statusText,"). 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or .pdf\n# response_with_file = client.chat.completions.create(\n# model="').concat(v,'",\n# messages=[\n# {\n# "role": "user",\n# "content": [\n# {\n# "type": "text",\n# "text": "').concat(f,'"\n# },\n# {\n# "type": "image_url",\n# "image_url": {\n# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file}\n# }\n# }\n# ]\n# }\n# ]').concat(n,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(b).length>0,n="";if(e){let e=JSON.stringify({metadata:b},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n extra_body=".concat(e)}let a=_.length>0?_:[{role:"user",content:h}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n model="'.concat(v,'",\n input=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.responses.create(\n# model="').concat(v,'",\n# input=[\n# {\n# "role": "user",\n# "content": [\n# {"type": "input_text", "text": "').concat(f,'"},\n# {\n# "type": "input_image",\n# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file}\n# },\n# ],\n# }\n# ]').concat(n,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:t="azure"===u?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n model=\"".concat(v,'",\n prompt="').concat(i,'",\n n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n # Retrieve the generated image\n if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n image_url = json_response["data"][0]["url"]\n generated_image = requests.get(image_url).content\n with open(image_path, "wb") as image_file:\n image_file.write(generated_image)\n\n print(f"Image saved to {image_path}")\n # Display the image\n image = Image.open(image_path)\n image.show()\n else:\n print("Could not find image URL in response.")\n print("Full response:", json_response)\nexcept Exception as e:\n print(f"An error occurred: {e}")\n print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(f,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(v,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n');break;case a.KP.IMAGE_EDITS:t="azure"===u?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(f,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(v,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(f,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(v,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n');break;default:t="\n# Code generation for this endpoint is not implemented yet."}return"".concat(j,"\n").concat(t)}},49817:function(e,t,n){var a,s,r,i;n.d(t,{KP:function(){return s},vf:function(){return l}}),(r=a||(a={})).IMAGE_GENERATION="image_generation",r.CHAT="chat",r.RESPONSES="responses",r.IMAGE_EDITS="image_edits",r.ANTHROPIC_MESSAGES="anthropic_messages",(i=s||(s={})).IMAGE="image",i.CHAT="chat",i.RESPONSES="responses",i.IMAGE_EDITS="image_edits",i.ANTHROPIC_MESSAGES="anthropic_messages";let o={image_generation:"image",chat:"chat",responses:"responses",image_edits:"image_edits",anthropic_messages:"anthropic_messages"},l=e=>{if(console.log("getEndpointType:",e),Object.values(a).includes(e)){let t=o[e];return console.log("endpointType:",t),t}return"chat"}},29488:function(e,t,n){n.d(t,{Hc:function(){return 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t;let{apiKeySource:n,accessToken:a,apiKey:r,inputMessage:i,chatHistory:o,selectedTags:l,selectedVectorStores:c,selectedGuardrails:d,endpointType:m,selectedModel:p,selectedSdk:u}=e,g="session"===n?a:r,x=window.location.origin,h=(i||"Your prompt here").replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),f=o.filter(e=>!e.isImage).map(e=>{let{role:t,content:n}=e;return{role:t,content:n}}),_={};l.length>0&&(_.tags=l),c.length>0&&(_.vector_stores=c),d.length>0&&(_.guardrails=d);let b=p||"your-model-name",v="azure"===u?'import openai\n\nclient = openai.AzureOpenAI(\n api_key="'.concat(g||"YOUR_LITELLM_API_KEY",'",\n azure_endpoint="').concat(x,'",\n api_version="2024-02-01"\n)'):'import openai\n\nclient = openai.OpenAI(\n api_key="'.concat(g||"YOUR_LITELLM_API_KEY",'",\n base_url="').concat(x,'"\n)');switch(m){case s.KP.CHAT:{let e=Object.keys(_).length>0,n="";if(e){let e=JSON.stringify({metadata:_},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n extra_body=".concat(e)}t='\n# request sent to model set on litellm proxy, `litellm --model`\nresponse = client.chat.completions.create(\n model="'.concat(b,'",\n messages = ').concat(JSON.stringify(f,null,4)).concat(n,"\n)\n\nprint(response)\n");break}case s.KP.RESPONSES:{let e=Object.keys(_).length>0,n="";if(e){let e=JSON.stringify({metadata:_},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n extra_body=".concat(e)}t='\n# request sent to model set on litellm proxy, `litellm --model`\nresponse = client.responses.create(\n model="'.concat(b,'",\n messages = ').concat(JSON.stringify(f,null,4)).concat(n,"\n)\n\nprint(response)\n");break}case s.KP.IMAGE:t="azure"===u?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n model=\"".concat(b,'",\n prompt="').concat(i,'",\n n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n # Retrieve the generated image\n if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n image_url = json_response["data"][0]["url"]\n generated_image = requests.get(image_url).content\n with open(image_path, "wb") as image_file:\n image_file.write(generated_image)\n\n print(f"Image saved to {image_path}")\n # Display the image\n image = Image.open(image_path)\n image.show()\n else:\n print("Could not find image URL in response.")\n print("Full response:", json_response)\nexcept Exception as e:\n print(f"An error occurred: {e}")\n print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(h,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(b,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n 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