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Add end to end integration tests for batches

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Ephrim Stanley 2026-01-12 09:53:54 -05:00
parent 103633c794
commit 935c249c45
16 changed files with 3 additions and 1775 deletions
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15
tests/batches_tests/local-litellm/README.md
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# Local LiteLLM
Local LiteLLM proxy with a mock LLM server for testing.
## Start
```bash
docker compose up --build
```
## Stop
```bash
docker compose down
```

38
tests/batches_tests/local-litellm/docker-compose.dev.yml
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# Docker Compose for local development
# Runs db and mock-server only - proxy runs locally via poetry
services:
db:
image: postgres:16
container_name: litellm_dev_db
restart: always
environment:
POSTGRES_DB: litellm
POSTGRES_USER: llmproxy
POSTGRES_PASSWORD: dbpassword9090
healthcheck:
test: ["CMD-SHELL", "pg_isready -d litellm -U llmproxy"]
interval: 1s
timeout: 5s
retries: 10
volumes:
- postgres_data_dev:/var/lib/postgresql/data
ports:
- "5432:5432"
mock-server:
build:
context: ./mock-server
dockerfile: Dockerfile
ports:
- "8090:8090"
healthcheck:
test: ["CMD-SHELL", "wget --no-verbose --tries=1 http://localhost:8090/health || exit 1"]
interval: 5s
timeout: 5s
retries: 5
start_period: 10s
volumes:
postgres_data_dev:

109
tests/batches_tests/local-litellm/docker-compose.yml
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services:
# Default LiteLLM without patches
litellm:
image: ghcr.io/berriai/litellm:main-latest
profiles: ["default", "unpatched"]
ports:
- "4000:4000"
volumes:
- ./litellm-config.yaml:/app/config.yaml
command:
- "--config=/app/config.yaml"
- "--detailed_debug"
environment:
DATABASE_URL: "postgresql://llmproxy:dbpassword9090@db:5432/litellm"
STORE_MODEL_IN_DB: "True"
LITELLM_MASTER_KEY: "sk-1234"
LITELLM_SALT_KEY: "mock-salt-key-12345"
LITELLM_LOG: "DEBUG"
real_azure_api_key: "${real_azure_api_key:-}"
depends_on:
db:
condition: service_healthy
mock-server:
condition: service_healthy
healthcheck:
test: ["CMD-SHELL", "wget --no-verbose --tries=1 http://localhost:4000/health/liveliness || exit 1"]
interval: 30s
timeout: 10s
retries: 3
start_period: 40s
# LiteLLM with patches enabled
litellm-patched:
image: ghcr.io/berriai/litellm:main-latest
profiles: ["patched"]
ports:
- "4000:4000"
volumes:
- ./litellm-config.yaml:/app/config.yaml
# patch1
- ./patches/managed_files.py:/usr/lib/python3.13/site-packages/litellm_enterprise/proxy/hooks/managed_files.py
- ./patches/managed_files.py:/app/enterprise/litellm_enterprise/proxy/hooks/managed_files.py
# patch2
- ./patches/_types.py:/usr/lib/python3.13/site-packages/litellm/proxy/_types.py
- ./patches/_types.py:/app/litellm/proxy/_types.py
# patch3
- ./patches/batches_endpoints.py:/usr/lib/python3.13/site-packages/litellm/proxy/batches_endpoints/endpoints.py
- ./patches/batches_endpoints.py:/app/litellm/proxy/batches_endpoints/endpoints.py
# patch4
- ./patches/files_endpoints.py:/usr/lib/python3.13/site-packages/litellm/proxy/openai_files_endpoints/files_endpoints.py
- ./patches/files_endpoints.py:/app/litellm/proxy/openai_files_endpoints/files_endpoints.py
command:
- "--config=/app/config.yaml"
- "--detailed_debug"
environment:
DATABASE_URL: "postgresql://llmproxy:dbpassword9090@db:5432/litellm"
STORE_MODEL_IN_DB: "True"
LITELLM_MASTER_KEY: "sk-1234"
LITELLM_SALT_KEY: "mock-salt-key-12345"
LITELLM_LOG: "DEBUG"
real_azure_api_key: "${real_azure_api_key:-}"
depends_on:
db:
condition: service_healthy
mock-server:
condition: service_healthy
healthcheck:
test: ["CMD-SHELL", "wget --no-verbose --tries=1 http://localhost:4000/health/liveliness || exit 1"]
interval: 30s
timeout: 10s
retries: 3
start_period: 40s
db:
image: postgres:16
restart: always
container_name: litellm_local_db
environment:
POSTGRES_DB: litellm
POSTGRES_USER: llmproxy
POSTGRES_PASSWORD: dbpassword9090
ports:
- "5432:5432"
volumes:
- postgres_data:/var/lib/postgresql/data
healthcheck:
test: ["CMD-SHELL", "pg_isready -d litellm -U llmproxy"]
interval: 1s
timeout: 5s
retries: 10
mock-server:
build:
context: ./mock-server
dockerfile: Dockerfile
ports:
- "8090:8090"
environment:
TIME_TO_SLEEP: "0"
healthcheck:
test: ["CMD-SHELL", "curl -f http://localhost:8090/health || exit 1"]
interval: 10s
timeout: 5s
retries: 3
start_period: 5s
volumes:
postgres_data:
name: litellm_local_postgres_data

57
tests/batches_tests/local-litellm/litellm-config.yaml
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model_list:
- model_name: openai-fake-gpt-3.5-turbo
litellm_params:
model: openai/openai-fake-gpt-3.5-turbo
api_base: http://localhost:8090/v1
api_key: fake-key
- model_name: openai-fake-gpt-4
litellm_params:
model: openai/openai-fake-gpt-4
api_base: http://localhost:8090/v1
api_key: fake-key
- model_name: openai-fake-gpt-4o
litellm_params:
model: openai/openai-fake-gpt-4o
api_base: http://localhost:8090/v1
api_key: fake-key
- model_name: fake-text-embedding-3-small
litellm_params:
model: openai/fake-text-embedding-3-small
api_base: http://localhost:8090/v1
api_key: fake-key
- model_name: o3-mini-batch-2025-01-31
litellm_params:
model: openai/o3-mini-batch-2025-01-31
api_base: http://localhost:8090/openai/v1
api_key: fake-key
model_info:
mode: batch
- model_name: azure-fake-gpt-5-batch-2025-08-07
litellm_params:
api_base: http://localhost:8090
api_key: fake-key
api_version: 2025-03-01-preview
base_model: azure/gpt-5
model: azure/gpt-5-batch-2025-08-07
custom_llm_provider: azure
model_info:
mode: batch
- model_name: gpt-5-batch-2025-08-07
litellm_params:
api_base: os.environ/OPENAI_API_BASE
api_key: os.environ/OPENAI_API_KEY
api_version: 2025-03-01-preview
base_model: azure/gpt-5
model: azure/gpt-5-batch-2025-08-07
custom_llm_provider: azure
model_info:
mode: batch
general_settings:
master_key: sk-1234
database_url: "postgresql://llmproxy:dbpassword9090@localhost:5432/litellm"
litellm_settings:
drop_params: true
set_verbose: true
json_logs: true

18
tests/batches_tests/local-litellm/mock-server/Dockerfile
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FROM python:3.11-slim
RUN apt-get update && apt-get install -y curl && rm -rf /var/lib/apt/lists/*
WORKDIR /app
COPY pyproject.toml .
COPY main.py .
COPY mock_azure_batch.py .
COPY mock_chat.py .
COPY mock_responses.py .
COPY mock_embeddings.py .
RUN pip install --no-cache-dir $(python -c "import tomllib; print(' '.join(tomllib.load(open('pyproject.toml', 'rb'))['project']['dependencies']))")
EXPOSE 8090
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8090"]

47
tests/batches_tests/local-litellm/mock-server/main.py
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from dotenv import load_dotenv
from fastapi import FastAPI, Request
from fastapi.middleware.cors import CORSMiddleware
from slowapi import Limiter, _rate_limit_exceeded_handler
from slowapi.errors import RateLimitExceeded
from mock_azure_batch import setup_batch_routes
from mock_chat import setup_chat_routes
from mock_embeddings import setup_embeddings_routes
from mock_responses import setup_responses_routes
def get_request_url(request: Request):
return str(request.url)
limiter = Limiter(key_func=get_request_url)
load_dotenv()
app = FastAPI()
app.state.limiter = limiter
app.add_exception_handler(RateLimitExceeded, _rate_limit_exceeded_handler)
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
@app.get("/health")
async def health():
return {"status": "ok"}
setup_chat_routes(app)
setup_responses_routes(app)
setup_embeddings_routes(app)
setup_batch_routes(app)
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8090)

582
tests/batches_tests/local-litellm/mock-server/mock_azure_batch.py
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import asyncio
import io
import json
import logging
import os
import time
import uuid
from typing import Dict, List, Optional
from fastapi import FastAPI, HTTPException, Query, Request, UploadFile, Depends
from fastapi.responses import StreamingResponse
from fastapi.security import APIKeyHeader
from pydantic import BaseModel
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Azure-like credential validation
# Set MOCK_REQUIRE_CREDENTIALS=true to enforce credential checks (like real Azure)
REQUIRE_CREDENTIALS = os.environ.get("MOCK_REQUIRE_CREDENTIALS", "true").lower() == "true"
VALID_API_KEYS = {"fake-key", "sk-1234", "test-key"} # Accept these API keys
api_key_header = APIKeyHeader(name="api-key", auto_error=False)
auth_header = APIKeyHeader(name="Authorization", auto_error=False)
def validate_credentials(
api_key: Optional[str] = Depends(api_key_header),
authorization: Optional[str] = Depends(auth_header),
):
"""
Validate Azure-style credentials.
Azure accepts either:
- api-key header
- Authorization: Bearer <token> header
"""
if not REQUIRE_CREDENTIALS:
return True
# Check api-key header
if api_key:
if api_key in VALID_API_KEYS:
return True
logger.warning(f"Invalid api-key provided: {api_key[:8]}...")
raise HTTPException(
status_code=401,
detail={
"error": {
"code": "401",
"message": "Access denied due to invalid subscription key or wrong API endpoint. "
"Make sure to provide a valid key for an active subscription and use a "
"correct regional API endpoint for your resource."
}
}
)
# Check Authorization header (Bearer token)
if authorization:
if authorization.startswith("Bearer "):
# Accept any bearer token for mock purposes
return True
logger.warning(f"Invalid Authorization header format")
# No credentials provided
logger.warning("No credentials provided in request")
raise HTTPException(
status_code=401,
detail={
"error": {
"code": "401",
"message": "Missing credentials. Please pass one of `api_key`, `azure_ad_token`, "
"`azure_ad_token_provider`, or the `AZURE_OPENAI_API_KEY` or "
"`AZURE_OPENAI_AD_TOKEN` environment variables."
}
}
)
class FileObject(BaseModel):
id: str
object: str = "file"
bytes: int
created_at: int
filename: str
purpose: str
status: str = "processed"
status_details: Optional[str] = None
expires_at: Optional[int] = None
class BatchObject(BaseModel):
id: str
object: str = "batch"
endpoint: str
errors: Optional[Dict] = None
input_file_id: str
completion_window: str
status: str
output_file_id: Optional[str] = None
error_file_id: Optional[str] = None
created_at: int
in_progress_at: Optional[int] = None
expires_at: Optional[int] = None
finalizing_at: Optional[int] = None
completed_at: Optional[int] = None
failed_at: Optional[int] = None
expired_at: Optional[int] = None
cancelling_at: Optional[int] = None
cancelled_at: Optional[int] = None
request_counts: Optional[Dict[str, int]] = None
metadata: Optional[Dict] = None
class BatchListResponse(BaseModel):
object: str = "list"
data: List[Dict]
first_id: Optional[str] = None
last_id: Optional[str] = None
has_more: bool = False
file_storage: Dict[str, Dict] = {}
batch_storage: Dict[str, BatchObject] = {}
batch_results: Dict[str, List[Dict]] = {}
PROCESSING_DELAY_SECONDS = float(1)
VALIDATING_DELAY_SECONDS = float(3)
async def process_batch(batch_id: str):
logger.info(f"Starting batch processing for {batch_id}")
try:
batch = batch_storage[batch_id]
await asyncio.sleep(VALIDATING_DELAY_SECONDS)
batch.status = "in_progress"
batch.in_progress_at = int(time.time())
logger.info(f"Batch {batch_id} status: in_progress")
await process_batch_requests(batch_id)
await asyncio.sleep(PROCESSING_DELAY_SECONDS)
batch.status = "finalizing"
batch.finalizing_at = int(time.time())
logger.info(f"Batch {batch_id} status: finalizing")
await asyncio.sleep(PROCESSING_DELAY_SECONDS)
await create_output_file(batch_id)
batch.status = "completed"
batch.completed_at = int(time.time())
logger.info(f"Batch {batch_id} status: completed")
except Exception as e:
logger.error(f"Batch {batch_id} failed: {e}")
batch = batch_storage[batch_id]
batch.status = "failed"
batch.failed_at = int(time.time())
batch.errors = {
"object": "list",
"data": [{"code": "processing_error", "message": str(e)}],
}
async def process_batch_requests(batch_id: str):
batch = batch_storage[batch_id]
input_file = file_storage[batch.input_file_id]
requests = []
for line in input_file["content"].split("\n"):
if line.strip():
try:
requests.append(json.loads(line))
except json.JSONDecodeError as e:
logger.warning(f"Invalid JSON line in batch {batch_id}: {e}")
logger.info(f"Batch {batch_id} has {len(requests)} requests")
results = []
failed_count = 0
for req in requests:
result = await process_single_request(req)
if result.get("error"):
failed_count += 1
results.append(result)
batch_results[batch_id] = results
batch.request_counts = {
"total": len(requests),
"completed": len(results) - failed_count,
"failed": failed_count,
}
async def process_single_request(request_data: Dict) -> Dict:
custom_id = request_data.get("custom_id")
url = request_data.get("url", "/v1/chat/completions")
body = request_data.get("body", {})
if "/chat/completions" in url:
response_body = {
"id": f"chatcmpl-{uuid.uuid4().hex}",
"object": "chat.completion",
"created": int(time.time()),
"model": body.get("model", "gpt-4o"),
"choices": [
{
"index": 0,
"message": {"role": "assistant", "content": "Mock batch response."},
"finish_reason": "stop",
},
],
"usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15},
}
status_code = 200
else:
response_body = {"error": {"message": f"Unsupported endpoint: {url}"}}
status_code = 400
return {
"id": f"batch_req_{uuid.uuid4().hex[:12]}",
"custom_id": custom_id,
"response": {
"status_code": status_code,
"request_id": f"req_{uuid.uuid4().hex[:12]}",
"body": response_body,
},
"error": None,
}
async def create_output_file(batch_id: str):
results = batch_results.get(batch_id, [])
output_lines = [json.dumps(result) for result in results]
output_content = "\n".join(output_lines)
output_file_id = f"file-batch-output-{uuid.uuid4().hex[:12]}"
file_storage[output_file_id] = {
"content": output_content,
"filename": f"batch_output_{batch_id}.jsonl",
"purpose": "batch_output",
"bytes": len(output_content.encode()),
"created_at": int(time.time()),
}
batch = batch_storage[batch_id]
batch.output_file_id = output_file_id
logger.info(f"Created output file {output_file_id} for batch {batch_id}")
def validate_batch_input(content: str) -> tuple[bool, str, List[Dict]]:
requests = []
custom_ids = set()
lines = content.strip().split("\n")
if not lines or all(not line.strip() for line in lines):
return False, "empty_batch", []
for line_num, line in enumerate(lines, 1):
if not line.strip():
continue
try:
req = json.loads(line)
except json.JSONDecodeError:
return False, "invalid_json_line", []
for field in ["custom_id", "method", "url", "body"]:
if field not in req:
return False, "invalid_request", []
if req["custom_id"] in custom_ids:
return False, "duplicate_custom_id", []
custom_ids.add(req["custom_id"])
requests.append(req)
if len(requests) > 100000:
return False, "too_many_tasks", []
return True, "", requests
def setup_batch_routes(app: FastAPI):
# Files endpoints (OpenAI and Azure paths)
# All endpoints require credentials (like real Azure)
@app.post("/openai/v1/files")
@app.post("/openai/files")
@app.post("/v1/files")
@app.post("/files")
async def create_file(request: Request, _=Depends(validate_credentials)):
form = await request.form()
logger.info(f"File upload form fields: {list(form.keys())}")
file: UploadFile = form.get("file")
purpose: str = form.get("purpose", "batch")
if not file:
raise HTTPException(status_code=400, detail="No file provided")
logger.info(f"Uploading file: {file.filename}, purpose: {purpose}")
content = await file.read()
content_str = content.decode("utf-8")
file_id = f"file-{uuid.uuid4().hex[:24]}"
created_at = int(time.time())
expires_at = None
expires_after_seconds = form.get("expires_after[seconds]")
if expires_after_seconds:
try:
seconds = int(expires_after_seconds)
logger.info(f"expires_after[seconds] = {seconds}")
if seconds < 259200 or seconds > 2592000:
raise HTTPException(
status_code=400,
detail={
"error": {
"code": "invalidPayload",
"message": "Value for Seconds must be between 259200 and 2592000.",
},
},
)
expires_at = created_at + seconds
logger.info(f"Calculated expires_at: {expires_at}")
except ValueError as e:
logger.warning(f"Failed to parse expires_after[seconds]: {e}")
file_storage[file_id] = {
"content": content_str,
"filename": file.filename or "batch_input.jsonl",
"purpose": purpose,
"bytes": len(content),
"created_at": created_at,
"expires_at": expires_at,
}
logger.info(f"Created file {file_id}, expires_at={expires_at}")
return FileObject(
id=file_id,
bytes=len(content),
created_at=created_at,
filename=file.filename or "batch_input.jsonl",
purpose=purpose,
expires_at=expires_at,
).model_dump()
@app.get("/openai/v1/files/{file_id}")
@app.get("/openai/files/{file_id}")
@app.get("/v1/files/{file_id}")
@app.get("/files/{file_id}")
async def get_file(file_id: str, _=Depends(validate_credentials)):
logger.info(f"Getting file: {file_id}")
if file_id not in file_storage:
raise HTTPException(status_code=404, detail="File not found")
file_data = file_storage[file_id]
return FileObject(
id=file_id,
bytes=file_data["bytes"],
created_at=file_data["created_at"],
filename=file_data["filename"],
purpose=file_data["purpose"],
expires_at=file_data.get("expires_at"),
).model_dump()
@app.get("/openai/v1/files/{file_id}/content")
@app.get("/openai/files/{file_id}/content")
@app.get("/v1/files/{file_id}/content")
@app.get("/files/{file_id}/content")
async def get_file_content(file_id: str, _=Depends(validate_credentials)):
logger.info(f"Getting file content: {file_id}")
if file_id not in file_storage:
raise HTTPException(status_code=404, detail="File not found")
file_data = file_storage[file_id]
content = file_data["content"]
return StreamingResponse(
io.StringIO(content),
media_type="application/octet-stream",
headers={
"Content-Disposition": f"attachment; filename={file_data['filename']}",
},
)
@app.delete("/openai/v1/files/{file_id}")
@app.delete("/openai/files/{file_id}")
@app.delete("/v1/files/{file_id}")
@app.delete("/files/{file_id}")
async def delete_file(file_id: str, _=Depends(validate_credentials)):
logger.info(f"Deleting file: {file_id}")
if file_id not in file_storage:
raise HTTPException(status_code=404, detail="File not found")
del file_storage[file_id]
return {"id": file_id, "object": "file", "deleted": True}
@app.get("/openai/v1/files")
@app.get("/openai/files")
@app.get("/v1/files")
@app.get("/files")
async def list_files(
purpose: Optional[str] = None,
limit: int = Query(10000, le=10000),
_=Depends(validate_credentials),
):
logger.info(f"Listing files, purpose: {purpose}, limit: {limit}")
files = []
for file_id, file_data in file_storage.items():
if purpose is None or file_data.get("purpose") == purpose:
files.append(
FileObject(
id=file_id,
bytes=file_data["bytes"],
created_at=file_data["created_at"],
filename=file_data["filename"],
purpose=file_data["purpose"],
expires_at=file_data.get("expires_at"),
).model_dump(),
)
return {"object": "list", "data": files[:limit]}
# Batches endpoints (OpenAI and Azure paths)
@app.post("/openai/v1/batches")
@app.post("/openai/batches")
@app.post("/v1/batches")
@app.post("/batches")
async def create_batch(request_data: dict, _=Depends(validate_credentials)):
input_file_id = request_data.get("input_file_id")
endpoint = request_data.get("endpoint", "/v1/chat/completions")
completion_window = request_data.get("completion_window", "24h")
metadata = request_data.get("metadata", {})
output_expires_after = request_data.get("output_expires_after")
logger.info(
f"Creating batch with input_file: {input_file_id}, endpoint: {endpoint}, output_expires_after: {output_expires_after}",
)
if not input_file_id or input_file_id not in file_storage:
raise HTTPException(status_code=400, detail="Input file not found")
input_file = file_storage[input_file_id]
is_valid, error_code, _ = validate_batch_input(input_file["content"])
if not is_valid:
raise HTTPException(
status_code=400,
detail={
"error": {
"code": error_code,
"message": f"Validation failed: {error_code}",
},
},
)
batch_id = f"batch_{uuid.uuid4()}"
created_at = int(time.time())
if output_expires_after:
seconds = (
output_expires_after.get("seconds", 0)
if isinstance(output_expires_after, dict)
else 0
)
expires_at = created_at + seconds
logger.info(
f"Using output_expires_after: {seconds}s, expires_at: {expires_at}",
)
elif completion_window == "24h":
expires_at = created_at + (24 * 60 * 60)
else:
expires_at = created_at + (24 * 60 * 60)
batch = BatchObject(
id=batch_id,
endpoint=endpoint,
input_file_id=input_file_id,
completion_window=completion_window,
status="validating",
created_at=created_at,
expires_at=expires_at,
request_counts={"total": 0, "completed": 0, "failed": 0},
metadata=metadata,
)
batch_storage[batch_id] = batch
logger.info(f"Created batch {batch_id}")
asyncio.create_task(process_batch(batch_id))
return batch.model_dump()
@app.get("/openai/v1/batches/{batch_id}")
@app.get("/openai/batches/{batch_id}")
@app.get("/v1/batches/{batch_id}")
@app.get("/batches/{batch_id}")
async def get_batch(batch_id: str, _=Depends(validate_credentials)):
logger.info(f"Getting batch: {batch_id}")
if batch_id not in batch_storage:
raise HTTPException(status_code=404, detail="Batch not found")
return batch_storage[batch_id].model_dump()
@app.get("/openai/v1/batches")
@app.get("/openai/batches")
@app.get("/v1/batches")
@app.get("/batches")
async def list_batches(
after: Optional[str] = Query(None),
limit: int = Query(20, le=100),
_=Depends(validate_credentials),
):
logger.info(f"Listing batches, after: {after}, limit: {limit}")
batches = list(batch_storage.values())
batches.sort(key=lambda x: x.created_at, reverse=True)
if after:
after_index = next((i for i, b in enumerate(batches) if b.id == after), -1)
if after_index >= 0:
batches = batches[after_index + 1 :]
batches = batches[:limit]
return BatchListResponse(
data=[batch.model_dump() for batch in batches],
first_id=batches[0].id if batches else None,
last_id=batches[-1].id if batches else None,
has_more=len(batches) == limit,
).model_dump()
@app.post("/openai/v1/batches/{batch_id}/cancel")
@app.post("/openai/batches/{batch_id}/cancel")
@app.post("/v1/batches/{batch_id}/cancel")
@app.post("/batches/{batch_id}/cancel")
async def cancel_batch(batch_id: str, _=Depends(validate_credentials)):
logger.info(f"Cancelling batch: {batch_id}")
if batch_id not in batch_storage:
raise HTTPException(status_code=404, detail="Batch not found")
batch = batch_storage[batch_id]
if batch.status in ["completed", "failed", "cancelled", "expired"]:
raise HTTPException(
status_code=400,
detail=f"Cannot cancel batch in {batch.status} status",
)
batch.status = "cancelled"
batch.cancelled_at = int(time.time())
logger.info(f"Batch {batch_id} cancelled")
return batch.model_dump()
# Debug endpoints
@app.get("/debug/batches")
async def debug_list_batches():
return {
"batches": {
batch_id: batch.model_dump()
for batch_id, batch in batch_storage.items()
},
"files": {
file_id: {k: v for k, v in data.items() if k != "content"}
for file_id, data in file_storage.items()
},
}
@app.post("/reset")
@app.post("/debug/clear")
async def reset_all():
file_storage.clear()
batch_storage.clear()
batch_results.clear()
logger.info("All data cleared")
return {"message": "All data cleared"}
@app.get("/debug/status")
async def debug_status():
return {
"files_count": len(file_storage),
"batches_count": len(batch_storage),
"batch_statuses": {bid: b.status for bid, b in batch_storage.items()},
}

124
tests/batches_tests/local-litellm/mock-server/mock_chat.py
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@ -1,124 +0,0 @@
import json
import time
import uuid
from datetime import datetime
from fastapi import FastAPI, Request
from fastapi.responses import StreamingResponse
def get_request_details(request: Request, body: dict = None) -> str:
details = {
"method": request.method,
"url": str(request.url),
"path": request.url.path,
"headers": dict(request.headers),
"query_params": dict(request.query_params),
}
return json.dumps(details, indent=2)
def data_generator(response_details: str, model: str):
response_id = uuid.uuid4().hex
content = response_details
chunk_size = 50
for i in range(0, len(content), chunk_size):
text_chunk = content[i : i + chunk_size]
chunk = {
"id": f"chatcmpl-{response_id}",
"object": "chat.completion.chunk",
"created": int(time.time()),
"model": model,
"choices": [{"index": 0, "delta": {"content": text_chunk}}],
}
yield f"data: {json.dumps(chunk)}\n\n"
final_chunk = {
"id": f"chatcmpl-{response_id}",
"object": "chat.completion.chunk",
"created": int(time.time()),
"model": model,
"choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}],
}
yield f"data: {json.dumps(final_chunk)}\n\n"
yield "data: [DONE]\n\n"
def setup_chat_routes(app: FastAPI):
@app.post("/chat/completions")
@app.post("/v1/chat/completions")
@app.post("/openai/deployments/{model:path}/chat/completions")
async def completion(request: Request):
data = await request.json()
model = data.get("model", "unknown")
request_details = get_request_details(request, data)
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
response_details = f"Request:{request_details}, Canned Response:{timestamp}"
if data.get("stream"):
return StreamingResponse(
content=data_generator(response_details, model),
media_type="text/event-stream",
)
else:
response_id = uuid.uuid4().hex
response = {
"id": f"chatcmpl-{response_id}",
"object": "chat.completion",
"created": int(time.time()),
"model": model,
"system_fingerprint": "fp_mock_server",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": response_details,
},
"logprobs": None,
"finish_reason": "stop",
},
],
"usage": {
"prompt_tokens": 9,
"completion_tokens": 12,
"total_tokens": 21,
},
}
return response
@app.post("/completions")
@app.post("/v1/completions")
async def text_completion(request: Request):
data = await request.json()
model = data.get("model", "unknown")
request_details = get_request_details(request, data)
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
response_details = f"Request:{request_details}, Canned Response:{timestamp}"
if data.get("stream"):
return StreamingResponse(
content=data_generator(response_details, model),
media_type="text/event-stream",
)
else:
response = {
"id": f"cmpl-{uuid.uuid4().hex}",
"choices": [
{
"finish_reason": "stop",
"index": 0,
"logprobs": None,
"text": response_details,
},
],
"created": int(time.time()),
"model": model,
"object": "text_completion",
"system_fingerprint": None,
"usage": {
"completion_tokens": 16,
"prompt_tokens": 10,
"total_tokens": 26,
},
}
return response

26
tests/batches_tests/local-litellm/mock-server/mock_embeddings.py
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@ -1,26 +0,0 @@
from fastapi import FastAPI, Request
def setup_embeddings_routes(app: FastAPI):
@app.post("/embeddings")
@app.post("/v1/embeddings")
@app.post("/openai/deployments/{model:path}/embeddings")
async def embeddings(request: Request):
data = await request.json()
model = data.get("model", "unknown")
_small_embedding = [
-0.006929283495992422,
-0.005336422007530928,
-4.547132266452536e-05,
-0.024047505110502243,
]
big_embedding = _small_embedding * 100
return {
"object": "list",
"data": [{"object": "embedding", "index": 0, "embedding": big_embedding}],
"model": model,
"usage": {"prompt_tokens": 5, "total_tokens": 5},
}

92
tests/batches_tests/local-litellm/mock-server/mock_responses.py
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@ -1,92 +0,0 @@
import json
import time
import uuid
from datetime import datetime
from fastapi import FastAPI, Request
def get_request_details(request: Request, body: dict = None) -> str:
details = {
"method": request.method,
"url": str(request.url),
"path": request.url.path,
"headers": dict(request.headers),
"query_params": dict(request.query_params),
}
return json.dumps(details, indent=2)
def setup_responses_routes(app: FastAPI):
@app.post("/responses")
@app.post("/v1/responses")
@app.post("/openai/responses")
async def responses_api(request: Request):
data = await request.json()
model = data.get("model", "unknown")
request_details = get_request_details(request, data)
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
response_details = f"Request:{request_details}, Canned Response:{timestamp}"
response_id = uuid.uuid4().hex
message_id = f"msg_{uuid.uuid4().hex[:34]}"
return {
"id": f"resp_{response_id}",
"created_at": int(time.time()),
"error": None,
"incomplete_details": None,
"instructions": None,
"metadata": {},
"model": model,
"object": "response",
"output": [
{
"id": message_id,
"content": [
{
"annotations": [],
"text": response_details,
"type": "output_text",
"logprobs": [],
},
],
"role": "assistant",
"status": "completed",
"type": "message",
},
],
"parallel_tool_calls": True,
"temperature": data.get("temperature", 1.0),
"tool_choice": data.get("tool_choice", "auto"),
"tools": data.get("tools", []),
"top_p": data.get("top_p", 1.0),
"max_output_tokens": data.get("max_output_tokens"),
"previous_response_id": None,
"reasoning": {"effort": None, "summary": None},
"status": "completed",
"text": {"format": {"type": "text"}, "verbosity": "medium"},
"truncation": "disabled",
"usage": {
"input_tokens": 11,
"input_tokens_details": {
"audio_tokens": None,
"cached_tokens": 0,
"text_tokens": None,
},
"output_tokens": 19,
"output_tokens_details": {"reasoning_tokens": 0, "text_tokens": None},
"total_tokens": 30,
"cost": None,
},
"user": None,
"store": True,
"background": False,
"content_filters": None,
"max_tool_calls": None,
"prompt_cache_key": None,
"safety_identifier": None,
"service_tier": "default",
"top_logprobs": 0,
}

20
tests/batches_tests/local-litellm/mock-server/pyproject.toml
View file

@ -1,20 +0,0 @@
[project]
name = "mock-server"
version = "0.1.0"
description = "Mock LLM server for testing LiteLLM"
requires-python = ">=3.11"
dependencies = [
"fastapi",
"uvicorn",
"slowapi",
"python-dotenv>=0.2.0",
"python-multipart",
"pydantic",
]
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[tool.hatch.build.targets.wheel]
packages = ["."]

416
tests/batches_tests/local-litellm/mock-server/uv.lock generated
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@ -1,416 +0,0 @@
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tests/batches_tests/local-litellm/patches/README.md
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@ -1,231 +0,0 @@
# LiteLLM Patches
Patches for LiteLLM `main-latest` (as of 2025-12-19).
---
## 1. File Deletion Fails for Batch Output Files
### Broken Feature
`DELETE /files/{file_id}` - Deleting batch output files fails with a Pydantic validation error.
### Error Message
```
openai.InternalServerError: Error code: 500 - {
'error': {
'message': '1 validation error for LiteLLM_ManagedFileTable\nfile_object\n Input should be a valid dictionary or instance of OpenAIFileObject [type=model_type, input_value=None, input_type=NoneType]'
}
}
```
### Root Cause
When LiteLLM stores batch output files in `LiteLLM_ManagedFileTable`, it sets `file_object=None`. However, the Pydantic model requires this field to be a valid `OpenAIFileObject`.
### Code Change (`_types.py`)
```python
# Before
class LiteLLM_ManagedFileTable(LiteLLMPydanticObjectBase):
file_object: OpenAIFileObject
# After
class LiteLLM_ManagedFileTable(LiteLLMPydanticObjectBase):
file_object: Optional[OpenAIFileObject] = None # PATCHED
```
---
## 2. File Deletion Returns Wrong Response
### Broken Feature
`DELETE /files/{file_id}` - Even after fixing patch #1, the method returns `None` instead of the delete confirmation.
### Error Message
```
Exception: LiteLLM Managed File object with id=... not found
```
### Root Cause
`afile_delete` in `managed_files.py` calls `llm_router.afile_delete` (which deletes the file at the provider) but discards the response.
### Code Change (`managed_files.py`)
```python
# Before
async def afile_delete(self, file_id, ...):
for model_id, model_file_id in mapping.items():
await llm_router.afile_delete(model=model_id, file_id=model_file_id, **data)
# Returns None
# After
async def afile_delete(self, file_id, ...):
delete_response = None
for model_id, model_file_id in mapping.items():
delete_response = await llm_router.afile_delete(...) # PATCHED: Capture response
if delete_response:
delete_response.id = file_id # PATCHED: Replace with unified ID
return delete_response
```
---
## 3. Batch Listing Fails with Duplicate Argument
### Broken Feature
`GET /batches?target_model_names=...` - Listing batches fails when using `target_model_names` query parameter.
### Error Message
```
openai.InternalServerError: Error code: 500 - {
'error': {
'message': "alist_batches() got multiple values for keyword argument 'model'"
}
}
```
### Root Cause
The code passes `model` explicitly AND includes it in `**data`:
```python
model = target_model_names.split(",")[0]
response = await llm_router.alist_batches(
model=model, # Passed explicitly
**data, # Also contains 'model' key
)
```
### Code Change (`batches_endpoints.py`)
```python
# Before
model = target_model_names.split(",")[0]
response = await llm_router.alist_batches(model=model, **data)
# After
model = target_model_names.split(",")[0]
data.pop("model", None) # PATCHED: Remove duplicate
data.pop("target_model_names", None) # PATCHED: Remove to avoid passing to downstream
response = await llm_router.alist_batches(model=model, **data)
```
---
## 4. File Retrieve Returns None for Batch Output Files
### Broken Feature
`GET /files/{file_id}` - Retrieving batch output file metadata returns `None`.
### Error Message
```
AttributeError: 'NoneType' object has no attribute 'id'
```
### Root Cause
`afile_retrieve` returns `stored_file_object.file_object` which is `None` for batch output files. It should fetch from the provider.
### Code Change (`managed_files.py` + `files_endpoints.py`)
```python
# managed_files.py - After
async def afile_retrieve(self, file_id, litellm_parent_otel_span, llm_router=None):
stored = await self.get_unified_file_id(file_id, ...)
if stored.file_object:
return stored.file_object
# PATCHED: Fetch from provider when file_object is None
for model_id, model_file_id in stored.model_mappings.items():
response = await llm_router.afile_retrieve(model=model_id, file_id=model_file_id)
response.id = file_id
return response
```
```python
# files_endpoints.py - After
response = await managed_files_obj.afile_retrieve(
file_id=file_id,
litellm_parent_otel_span=user_api_key_dict.parent_otel_span,
llm_router=llm_router, # PATCHED: Pass router
)
```
---
## Known Issues (Not Bugs)
### Azure Batch Creation Response Missing `endpoint`
**Behavior:** Azure's batch creation response returns `endpoint=''` (empty string).
**Expected:** When you call `batches.retrieve()` or `batches.list()`, Azure returns `endpoint='/v1/chat/completions'` correctly.
**Workaround:** If you need the endpoint immediately after creation, retrieve the batch to get the correct value.
### Azure Batch Listing Returns Raw IDs
**Behavior:** `batches.list()` returns raw Azure batch IDs (e.g., `batch_abc123`) instead of LiteLLM unified IDs.
**Root Cause:** LiteLLM routes `batches.list()` directly to Azure instead of querying its internal managed batches database.
**Workaround:** Use `batches.retrieve(unified_batch_id)` instead of relying on list.
### Config Fix: Azure Batch Listing 404
**Behavior:** `batches.list()` returns empty results because Azure returns 404.
**Root Cause:** LiteLLM defaults to OpenAI handler instead of Azure handler for batch operations.
**Fix (config):** Add `custom_llm_provider: azure` to your model's `litellm_params`:
```yaml
litellm_params:
model: azure/gpt-5-batch
custom_llm_provider: azure # Required for Azure batch operations
```
---
## Patch Files
| Patch File | Container Path |
|------------|----------------|
| `_types.py` | `/usr/lib/python3.13/site-packages/litellm/proxy/_types.py` |
| `managed_files.py` | `/usr/lib/python3.13/site-packages/litellm_enterprise/proxy/hooks/managed_files.py` |
| `batches_endpoints.py` | `/usr/lib/python3.13/site-packages/litellm/proxy/batches_endpoints/endpoints.py` |
| `files_endpoints.py` | `/usr/lib/python3.13/site-packages/litellm/proxy/openai_files_endpoints/files_endpoints.py` |
---
## Usage
### With Patches
```bash
./start-patched.sh
```
### Without Patches
```bash
./start-unpatched.sh
```
### Docker Compose Volumes
```yaml
volumes:
- ./patches/_types.py:/usr/lib/python3.13/site-packages/litellm/proxy/_types.py
- ./patches/managed_files.py:/usr/lib/python3.13/site-packages/litellm_enterprise/proxy/hooks/managed_files.py
- ./patches/batches_endpoints.py:/usr/lib/python3.13/site-packages/litellm/proxy/batches_endpoints/endpoints.py
- ./patches/files_endpoints.py:/usr/lib/python3.13/site-packages/litellm/proxy/openai_files_endpoints/files_endpoints.py
```

1
tests/batches_tests/test_managed_files_base.py
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@ -218,3 +218,4 @@ class ManagedFilesBase:

1
tests/batches_tests/test_managed_files_endtoend.py
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@ -202,3 +202,4 @@ class TestManagedFilesAPI(ManagedFilesBase):

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tests/batches_tests/test_managed_files_permissions.py
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@ -434,3 +434,4 @@ class TestManagedFilesPermissions(ManagedFilesBase):