litellm/tests/e2e/transport.py
Sameer Kankute a16d9c6f9e
test(e2e): add live batches suite across providers and routing scenarios (#30958)
* tests: add e2e tests for spend, budgets and llms

* style: make chained comparison of status_code clearer

Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>

* remove e2e_tests folder

* test: add spend tracking tests

* fix: p0 issues, added types and shared functions for each test suite

* style: carry clearer status_code comparison into renamed e2e dir

* refactor: migrate to gateway client

* fix: add new tests, split gateway

* test(e2e): add live batches suite across providers and routing scenarios

* test(batches): cover real cost tracking on completed batch retrieve

* test(e2e): assert managed vs raw file and batch id shapes per routing scenario

* test(e2e): assert full response shape of each batches and files endpoint

* test(e2e): only accept transitional statuses for a freshly created batch

* test(prompt-factory): make test_convert_url deterministic with a data URL

picsum.photos is down (HTTP 522), so test_convert_url failed on every
run. Swap the live external image for an inline data: URL and assert the
round-trip through convert_url_to_base64 genuinely.

A data URL is already inline base64 image data, so convert_url_to_base64
now short-circuits it instead of attempting an impossible HTTP fetch;
add a regression for that branch in the mapped image_handling test

* fix: pass through async image data urls

* fix(image-handling): short-circuit data URLs in async path too

Bugbot flagged that convert_url_to_base64 returns data: base64 URLs
unchanged but async_convert_url_to_base64 still tried to fetch them,
so async OCR flows (Bedrock, Azure) would reject inline images the sync
path accepts. Add the same guard to the async function and a regression
test that asserts the async path returns the data URL without touching
the HTTP client

* Fix: openai batches lifecycle

* Fix: add e2e azure openai tests

* Fix e2e for vertex ai

* Add all models for testing

* test(managed-files): assert idempotent upsert in store_unified_file_id

store_unified_file_id switched from create to upsert to avoid
UniqueViolationError when re-storing the same unified_file_id (e.g.
batch output files stored before metadata is available). Update the
unit test to assert the upsert call and its create payload instead of
the removed create call.

* test(batches): reconcile vertex_ai native batch-id comment with fallback guard

* fix(test-config): keep rust-ocr models in model_list by moving files_settings after it

* fix(test-config): move batch models after OCR block to keep merge with internal_staging clean

* fix(batches): use '24hrs' completion window and allow managed-files listing with provider filter

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* style: ruff format transformation.py and endpoints.py

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* fix(e2e/batches): set Azure raw_model to gpt-4.1-mini-batch to match deployed model

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* fix(vertex-ai/batches): correct completion_window to 24h per Literal type definition

* test(vertex-ai/batches): align completion_window assertion to 24h

* fix: update managed file metadata on upsert

---------

Co-authored-by: mubashir1osmani <mubashir.osmani777@gmail.com>
Co-authored-by: Mateo Wang <277851410+mateo-berri@users.noreply.github.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-07-02 08:05:23 -07:00

316 lines
8.9 KiB
Python

"""Transport: the typed request primitives clients use, behind a Protocol.
`Transport` is what each client depends on (composition + DI); `HttpTransport` is
the concrete frozen-slots dataclass that fulfils it via the e2e_http wrapper. No
client touches requests.* or builds raw dicts; they pass pydantic models here.
"""
from __future__ import annotations
from dataclasses import dataclass
from typing import Protocol
from pydantic import BaseModel
import e2e_http
from e2e_http import (
URL,
AuthHeaders,
FileUploadForm,
ProbeResult,
Result,
StreamingResponse,
)
class Transport(Protocol):
def post[R: BaseModel](
self, path: str, *, headers: BaseModel, json: BaseModel, response_type: type[R]
) -> Result[R]: ...
def stream(
self, path: str, *, headers: BaseModel, json: BaseModel
) -> StreamingResponse: ...
def send(
self,
path: str,
*,
headers: BaseModel,
json: BaseModel,
params: BaseModel | None = None,
stream: bool = False,
) -> StreamingResponse: ...
def get[R: BaseModel](
self,
path: str,
*,
headers: BaseModel,
params: BaseModel,
response_type: type[R],
) -> Result[R]: ...
def delete[R: BaseModel](
self, path: str, *, headers: BaseModel, json: BaseModel, response_type: type[R]
) -> Result[R]: ...
def probe(self, path: str, *, params: BaseModel) -> ProbeResult: ...
def upload[R: BaseModel](
self,
path: str,
*,
headers: BaseModel,
form: FileUploadForm,
filename: str,
content: bytes,
params: BaseModel | None = None,
response_type: type[R],
) -> Result[R]: ...
def download(self, path: str, *, headers: BaseModel) -> StreamingResponse: ...
def bearer(self, key: str) -> AuthHeaders: ...
@property
def master(self) -> AuthHeaders: ...
@dataclass(frozen=True, slots=True)
class HttpTransport:
base_url: str
master_key: str
request_timeout: float = 60.0
def _url(self, path: str) -> URL:
return URL(f"{self.base_url.rstrip('/')}{path}")
def bearer(self, key: str) -> AuthHeaders:
return AuthHeaders(authorization=f"Bearer {key}")
@property
def master(self) -> AuthHeaders:
return self.bearer(self.master_key)
def post[R: BaseModel](
self, path: str, *, headers: BaseModel, json: BaseModel, response_type: type[R]
) -> Result[R]:
return e2e_http.post(
self._url(path),
headers=headers,
json=json,
response_type=response_type,
timeout=self.request_timeout,
)
def get[R: BaseModel](
self,
path: str,
*,
headers: BaseModel,
params: BaseModel,
response_type: type[R],
) -> Result[R]:
return e2e_http.get(
self._url(path),
headers=headers,
params=params,
response_type=response_type,
timeout=self.request_timeout,
)
def delete[R: BaseModel](
self, path: str, *, headers: BaseModel, json: BaseModel, response_type: type[R]
) -> Result[R]:
return e2e_http.delete(
self._url(path),
headers=headers,
json=json,
response_type=response_type,
timeout=self.request_timeout,
)
def stream(
self, path: str, *, headers: BaseModel, json: BaseModel
) -> StreamingResponse:
return e2e_http.stream(
self._url(path), headers=headers, json=json, timeout=self.request_timeout
)
def send(
self,
path: str,
*,
headers: BaseModel,
json: BaseModel,
params: BaseModel | None = None,
stream: bool = False,
) -> StreamingResponse:
return e2e_http.send(
self._url(path),
headers=headers,
json=json,
params=params,
stream=stream,
timeout=self.request_timeout,
)
def probe(self, path: str, *, params: BaseModel) -> ProbeResult:
return e2e_http.probe(
self._url(path),
headers=self.master,
params=params,
timeout=self.request_timeout,
)
def upload[R: BaseModel](
self,
path: str,
*,
headers: BaseModel,
form: FileUploadForm,
filename: str,
content: bytes,
params: BaseModel | None = None,
response_type: type[R],
) -> Result[R]:
return e2e_http.upload(
self._url(path),
headers=headers,
form=form,
filename=filename,
content=content,
params=params,
response_type=response_type,
timeout=self.request_timeout,
)
def download(self, path: str, *, headers: BaseModel) -> StreamingResponse:
return e2e_http.download(
self._url(path), headers=headers, timeout=self.request_timeout
)
# Top-level management/admin route groups. In a split deployment these are served
# by the control plane (a different service from the LLM data plane). LLM routes
# (/chat, /embeddings, and native passthrough like /gemini, /anthropic) are NOT
# here and fall through to the data plane. Matched as path prefixes.
CONTROL_PLANE_PREFIXES: tuple[str, ...] = (
"/key",
"/user",
"/team",
"/organization",
"/customer",
"/tag",
"/budget",
"/model/info",
"/spend",
"/global",
"/openapi.json",
)
def is_control_plane_path(path: str) -> bool:
"""True if `path` is a management/admin route (served by the control plane in a
split deployment), false for LLM data-plane routes."""
return path.startswith(CONTROL_PLANE_PREFIXES)
@dataclass(frozen=True, slots=True)
class SplitTransport:
"""A Transport that dispatches each call by path to one of two backends: the
management/admin control plane or the LLM data plane.
Litellm can run as a split control-plane/data-plane deployment where the two
surfaces live on different services. Clients here stay plane-agnostic — they
keep calling ``transport.post("/budget/new", ...)`` or
``transport.send("/chat/completions", ...)`` — and routing happens in one place
by path (see ``CONTROL_PLANE_PREFIXES``). When ``control`` and ``data`` share a
base URL (the monolithic default), routing is a no-op. ``bearer``/``master``
are plane-agnostic (same master key both planes), so they come from ``data``.
"""
data: HttpTransport
control: HttpTransport
def _route(self, path: str) -> HttpTransport:
return self.control if is_control_plane_path(path) else self.data
def bearer(self, key: str) -> AuthHeaders:
return self.data.bearer(key)
@property
def master(self) -> AuthHeaders:
return self.data.master
def post[R: BaseModel](
self, path: str, *, headers: BaseModel, json: BaseModel, response_type: type[R]
) -> Result[R]:
return self._route(path).post(
path, headers=headers, json=json, response_type=response_type
)
def get[R: BaseModel](
self,
path: str,
*,
headers: BaseModel,
params: BaseModel,
response_type: type[R],
) -> Result[R]:
return self._route(path).get(
path, headers=headers, params=params, response_type=response_type
)
def delete[R: BaseModel](
self, path: str, *, headers: BaseModel, json: BaseModel, response_type: type[R]
) -> Result[R]:
return self._route(path).delete(
path, headers=headers, json=json, response_type=response_type
)
def stream(
self, path: str, *, headers: BaseModel, json: BaseModel
) -> StreamingResponse:
return self._route(path).stream(path, headers=headers, json=json)
def send(
self,
path: str,
*,
headers: BaseModel,
json: BaseModel,
params: BaseModel | None = None,
stream: bool = False,
) -> StreamingResponse:
return self._route(path).send(
path, headers=headers, json=json, params=params, stream=stream
)
def probe(self, path: str, *, params: BaseModel) -> ProbeResult:
return self._route(path).probe(path, params=params)
def upload[R: BaseModel](
self,
path: str,
*,
headers: BaseModel,
form: FileUploadForm,
filename: str,
content: bytes,
params: BaseModel | None = None,
response_type: type[R],
) -> Result[R]:
return self._route(path).upload(
path,
headers=headers,
form=form,
filename=filename,
content=content,
params=params,
response_type=response_type,
)
def download(self, path: str, *, headers: BaseModel) -> StreamingResponse:
return self._route(path).download(path, headers=headers)