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59
src/llama_stack/core/inspect.py
View file

@ -10,8 +10,14 @@ from pydantic import BaseModel
from llama_stack.core.datatypes import StackRunConfig
from llama_stack.core.external import load_external_apis
from llama_stack.core.server.fastapi_router_registry import (
_ROUTER_FACTORIES,
build_fastapi_router,
get_router_routes,
)
from llama_stack.core.server.routes import get_all_api_routes
from llama_stack_api import (
Api,
HealthInfo,
HealthStatus,
Inspect,
@ -43,6 +49,7 @@ class DistributionInspectImpl(Inspect):
run_config: StackRunConfig = self.config.run_config
# Helper function to determine if a route should be included based on api_filter
# TODO: remove this once we've migrated all APIs to FastAPI routers
def should_include_route(webmethod) -> bool:
if api_filter is None:
# Default: only non-deprecated APIs
@ -54,10 +61,62 @@ class DistributionInspectImpl(Inspect):
# Filter by API level (non-deprecated routes only)
return not webmethod.deprecated and webmethod.level == api_filter
# Helper function to get provider types for an API
def _get_provider_types(api: Api) -> list[str]:
if api.value in ["providers", "inspect"]:
return [] # These APIs don't have "real" providers they're internal to the stack
providers = run_config.providers.get(api.value, [])
return [p.provider_type for p in providers] if providers else []
# Helper function to determine if a router route should be included based on api_filter
def _should_include_router_route(route, router_prefix: str | None) -> bool:
"""Check if a router-based route should be included based on api_filter."""
# Check deprecated status
route_deprecated = getattr(route, "deprecated", False) or False
if api_filter is None:
# Default: only non-deprecated routes
return not route_deprecated
elif api_filter == "deprecated":
# Special filter: show deprecated routes regardless of their actual level
return route_deprecated
else:
# Filter by API level (non-deprecated routes only)
# Extract level from router prefix (e.g., "/v1" -> "v1")
if router_prefix:
prefix_level = router_prefix.lstrip("/")
return not route_deprecated and prefix_level == api_filter
return not route_deprecated
ret = []
external_apis = load_external_apis(run_config)
all_endpoints = get_all_api_routes(external_apis)
# Process routes from APIs with FastAPI routers
for api_name in _ROUTER_FACTORIES.keys():
api = Api(api_name)
router = build_fastapi_router(api, None) # we don't need the impl here, just the routes
if router:
router_routes = get_router_routes(router)
for route in router_routes:
if _should_include_router_route(route, router.prefix):
if route.methods is not None:
available_methods = [m for m in route.methods if m != "HEAD"]
if available_methods:
ret.append(
RouteInfo(
route=route.path,
method=available_methods[0],
provider_types=_get_provider_types(api),
)
)
# Process routes from legacy webmethod-based APIs
for api, endpoints in all_endpoints.items():
# Skip APIs that have routers (already processed above)
if api.value in _ROUTER_FACTORIES:
continue
# Always include provider and inspect APIs, filter others based on run config
if api.value in ["providers", "inspect"]:
ret.extend(

84
src/llama_stack/core/server/fastapi_router_registry.py Normal file
View file

@ -0,0 +1,84 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
"""Router utilities for FastAPI routers.
This module provides utilities to create FastAPI routers from API packages.
APIs with routers are explicitly listed here.
"""
from collections.abc import Callable
from typing import Any, cast
from fastapi import APIRouter
from fastapi.routing import APIRoute
from starlette.routing import Route
# Router factories for APIs that have FastAPI routers
# Add new APIs here as they are migrated to the router system
from llama_stack_api.batches.fastapi_routes import create_router as create_batches_router
from llama_stack_api.datatypes import Api
_ROUTER_FACTORIES: dict[str, Callable[[Any], APIRouter]] = {
"batches": create_batches_router,
}
def build_fastapi_router(api: "Api", impl: Any) -> APIRouter | None:
"""Build a router for an API by combining its router factory with the implementation.
Args:
api: The API enum value
impl: The implementation instance for the API
Returns:
APIRouter if the API has a router factory, None otherwise
"""
router_factory = _ROUTER_FACTORIES.get(api.value)
if router_factory is None:
return None
# cast is safe here: all router factories in API packages are required to return APIRouter.
# If a router factory returns the wrong type, it will fail at runtime when
# app.include_router(router) is called
return cast(APIRouter, router_factory(impl))
def get_router_routes(router: APIRouter) -> list[Route]:
"""Extract routes from a FastAPI router.
Args:
router: The FastAPI router to extract routes from
Returns:
List of Route objects from the router
"""
routes = []
for route in router.routes:
# FastAPI routers use APIRoute objects, which have path and methods attributes
if isinstance(route, APIRoute):
# Combine router prefix with route path
routes.append(
Route(
path=route.path,
methods=route.methods,
name=route.name,
endpoint=route.endpoint,
)
)
elif isinstance(route, Route):
# Fallback for regular Starlette Route objects
routes.append(
Route(
path=route.path,
methods=route.methods,
name=route.name,
endpoint=route.endpoint,
)
)
return routes

12
src/llama_stack/core/server/routes.py
View file

@ -26,6 +26,18 @@ RouteMatch = tuple[EndpointFunc, PathParams, str, WebMethod]
def get_all_api_routes(
external_apis: dict[Api, ExternalApiSpec] | None = None,
) -> dict[Api, list[tuple[Route, WebMethod]]]:
"""Get all API routes from webmethod-based protocols.
This function only returns routes from APIs that use the legacy @webmethod
decorator system. For APIs that have been migrated to FastAPI routers,
use the router registry (fastapi_router_registry.has_router() and fastapi_router_registry.build_fastapi_router()).
Args:
external_apis: Optional dictionary of external API specifications
Returns:
Dictionary mapping API to list of (Route, WebMethod) tuples
"""
apis = {}
protocols = api_protocol_map(external_apis)

28
src/llama_stack/core/server/server.py
View file

@ -44,6 +44,7 @@ from llama_stack.core.request_headers import (
request_provider_data_context,
user_from_scope,
)
from llama_stack.core.server.fastapi_router_registry import build_fastapi_router
from llama_stack.core.server.routes import get_all_api_routes
from llama_stack.core.stack import (
Stack,
@ -84,7 +85,7 @@ def create_sse_event(data: Any) -> str:
async def global_exception_handler(request: Request, exc: Exception):
traceback.print_exception(exc)
traceback.print_exception(type(exc), exc, exc.__traceback__)
http_exc = translate_exception(exc)
return JSONResponse(status_code=http_exc.status_code, content={"error": {"detail": http_exc.detail}})
@ -454,15 +455,22 @@ def create_app() -> StackApp:
apis_to_serve.add("providers")
apis_to_serve.add("prompts")
apis_to_serve.add("conversations")
for api_str in apis_to_serve:
api = Api(api_str)
routes = all_routes[api]
try:
impl = impls[api]
except KeyError as e:
raise ValueError(f"Could not find provider implementation for {api} API") from e
# Try to discover and use a router factory from the API package
impl = impls[api]
router = build_fastapi_router(api, impl)
if router:
app.include_router(router)
logger.debug(f"Registered FastAPIrouter for {api} API")
continue
# Fall back to old webmethod-based route discovery until the migration is complete
impl = impls[api]
routes = all_routes[api]
for route, _ in routes:
if not hasattr(impl, route.name):
# ideally this should be a typing violation already
@ -488,7 +496,15 @@ def create_app() -> StackApp:
logger.debug(f"serving APIs: {apis_to_serve}")
# Register specific exception handlers before the generic Exception handler
# This prevents the re-raising behavior that causes connection resets
app.exception_handler(RequestValidationError)(global_exception_handler)
app.exception_handler(ConflictError)(global_exception_handler)
app.exception_handler(ResourceNotFoundError)(global_exception_handler)
app.exception_handler(AuthenticationRequiredError)(global_exception_handler)
app.exception_handler(AccessDeniedError)(global_exception_handler)
app.exception_handler(BadRequestError)(global_exception_handler)
# Generic Exception handler should be last
app.exception_handler(Exception)(global_exception_handler)
return app

81
src/llama_stack/providers/inline/batches/reference/batches.py
View file

@ -11,7 +11,7 @@ import json
import time
import uuid
from io import BytesIO
from typing import Any, Literal
from typing import Any
from openai.types.batch import BatchError, Errors
from pydantic import BaseModel
@ -38,6 +38,12 @@ from llama_stack_api import (
OpenAIUserMessageParam,
ResourceNotFoundError,
)
from llama_stack_api.batches.models import (
CancelBatchRequest,
CreateBatchRequest,
ListBatchesRequest,
RetrieveBatchRequest,
)
from .config import ReferenceBatchesImplConfig
@ -140,11 +146,7 @@ class ReferenceBatchesImpl(Batches):
# TODO (SECURITY): this currently works w/ configured api keys, not with x-llamastack-provider-data or with user policy restrictions
async def create_batch(
self,
input_file_id: str,
endpoint: str,
completion_window: Literal["24h"],
metadata: dict[str, str] | None = None,
idempotency_key: str | None = None,
request: CreateBatchRequest,
) -> BatchObject:
"""
Create a new batch for processing multiple API requests.
@ -185,14 +187,14 @@ class ReferenceBatchesImpl(Batches):
# TODO: set expiration time for garbage collection
if endpoint not in ["/v1/chat/completions", "/v1/completions", "/v1/embeddings"]:
if request.endpoint not in ["/v1/chat/completions", "/v1/completions", "/v1/embeddings"]:
raise ValueError(
f"Invalid endpoint: {endpoint}. Supported values: /v1/chat/completions, /v1/completions, /v1/embeddings. Code: invalid_value. Param: endpoint",
f"Invalid endpoint: {request.endpoint}. Supported values: /v1/chat/completions, /v1/completions, /v1/embeddings. Code: invalid_value. Param: endpoint",
)
if completion_window != "24h":
if request.completion_window != "24h":
raise ValueError(
f"Invalid completion_window: {completion_window}. Supported values are: 24h. Code: invalid_value. Param: completion_window",
f"Invalid completion_window: {request.completion_window}. Supported values are: 24h. Code: invalid_value. Param: completion_window",
)
batch_id = f"batch_{uuid.uuid4().hex[:16]}"
@ -200,22 +202,22 @@ class ReferenceBatchesImpl(Batches):
# For idempotent requests, use the idempotency key for the batch ID
# This ensures the same key always maps to the same batch ID,
# allowing us to detect parameter conflicts
if idempotency_key is not None:
hash_input = idempotency_key.encode("utf-8")
if request.idempotency_key is not None:
hash_input = request.idempotency_key.encode("utf-8")
hash_digest = hashlib.sha256(hash_input).hexdigest()[:24]
batch_id = f"batch_{hash_digest}"
try:
existing_batch = await self.retrieve_batch(batch_id)
existing_batch = await self.retrieve_batch(RetrieveBatchRequest(batch_id=batch_id))
if (
existing_batch.input_file_id != input_file_id
or existing_batch.endpoint != endpoint
or existing_batch.completion_window != completion_window
or existing_batch.metadata != metadata
existing_batch.input_file_id != request.input_file_id
or existing_batch.endpoint != request.endpoint
or existing_batch.completion_window != request.completion_window
or existing_batch.metadata != request.metadata
):
raise ConflictError(
f"Idempotency key '{idempotency_key}' was previously used with different parameters. "
f"Idempotency key '{request.idempotency_key}' was previously used with different parameters. "
"Either use a new idempotency key or ensure all parameters match the original request."
)
@ -230,12 +232,12 @@ class ReferenceBatchesImpl(Batches):
batch = BatchObject(
id=batch_id,
object="batch",
endpoint=endpoint,
input_file_id=input_file_id,
completion_window=completion_window,
endpoint=request.endpoint,
input_file_id=request.input_file_id,
completion_window=request.completion_window,
status="validating",
created_at=current_time,
metadata=metadata,
metadata=request.metadata,
)
await self.kvstore.set(f"batch:{batch_id}", batch.to_json())
@ -247,28 +249,27 @@ class ReferenceBatchesImpl(Batches):
return batch
async def cancel_batch(self, batch_id: str) -> BatchObject:
async def cancel_batch(self, request: CancelBatchRequest) -> BatchObject:
"""Cancel a batch that is in progress."""
batch = await self.retrieve_batch(batch_id)
batch = await self.retrieve_batch(RetrieveBatchRequest(batch_id=request.batch_id))
if batch.status in ["cancelled", "cancelling"]:
return batch
if batch.status in ["completed", "failed", "expired"]:
raise ConflictError(f"Cannot cancel batch '{batch_id}' with status '{batch.status}'")
raise ConflictError(f"Cannot cancel batch '{request.batch_id}' with status '{batch.status}'")
await self._update_batch(batch_id, status="cancelling", cancelling_at=int(time.time()))
await self._update_batch(request.batch_id, status="cancelling", cancelling_at=int(time.time()))
if batch_id in self._processing_tasks:
self._processing_tasks[batch_id].cancel()
if request.batch_id in self._processing_tasks:
self._processing_tasks[request.batch_id].cancel()
# note: task removal and status="cancelled" handled in finally block of _process_batch
return await self.retrieve_batch(batch_id)
return await self.retrieve_batch(RetrieveBatchRequest(batch_id=request.batch_id))
async def list_batches(
self,
after: str | None = None,
limit: int = 20,
request: ListBatchesRequest,
) -> ListBatchesResponse:
"""
List all batches, eventually only for the current user.
@ -285,14 +286,14 @@ class ReferenceBatchesImpl(Batches):
batches.sort(key=lambda b: b.created_at, reverse=True)
start_idx = 0
if after:
if request.after:
for i, batch in enumerate(batches):
if batch.id == after:
if batch.id == request.after:
start_idx = i + 1
break
page_batches = batches[start_idx : start_idx + limit]
has_more = (start_idx + limit) < len(batches)
page_batches = batches[start_idx : start_idx + request.limit]
has_more = (start_idx + request.limit) < len(batches)
first_id = page_batches[0].id if page_batches else None
last_id = page_batches[-1].id if page_batches else None
@ -304,11 +305,11 @@ class ReferenceBatchesImpl(Batches):
has_more=has_more,
)
async def retrieve_batch(self, batch_id: str) -> BatchObject:
async def retrieve_batch(self, request: RetrieveBatchRequest) -> BatchObject:
"""Retrieve information about a specific batch."""
batch_data = await self.kvstore.get(f"batch:{batch_id}")
batch_data = await self.kvstore.get(f"batch:{request.batch_id}")
if not batch_data:
raise ResourceNotFoundError(batch_id, "Batch", "batches.list()")
raise ResourceNotFoundError(request.batch_id, "Batch", "batches.list()")
return BatchObject.model_validate_json(batch_data)
@ -316,7 +317,7 @@ class ReferenceBatchesImpl(Batches):
"""Update batch fields in kvstore."""
async with self._update_batch_lock:
try:
batch = await self.retrieve_batch(batch_id)
batch = await self.retrieve_batch(RetrieveBatchRequest(batch_id=batch_id))
# batch processing is async. once cancelling, only allow "cancelled" status updates
if batch.status == "cancelling" and updates.get("status") != "cancelled":
@ -536,7 +537,7 @@ class ReferenceBatchesImpl(Batches):
async def _process_batch_impl(self, batch_id: str) -> None:
"""Implementation of batch processing logic."""
errors: list[BatchError] = []
batch = await self.retrieve_batch(batch_id)
batch = await self.retrieve_batch(RetrieveBatchRequest(batch_id=batch_id))
errors, requests = await self._validate_input(batch)
if errors:

14
src/llama_stack_api/__init__.py
View file

@ -26,7 +26,15 @@ from . import common # noqa: F401
# Import all public API symbols
from .agents import Agents, ResponseGuardrail, ResponseGuardrailSpec
from .batches import Batches, BatchObject, ListBatchesResponse
from .batches import (
Batches,
BatchObject,
CancelBatchRequest,
CreateBatchRequest,
ListBatchesRequest,
ListBatchesResponse,
RetrieveBatchRequest,
)
from .benchmarks import (
Benchmark,
BenchmarkInput,
@ -462,6 +470,9 @@ __all__ = [
"BasicScoringFnParams",
"Batches",
"BatchObject",
"CancelBatchRequest",
"CreateBatchRequest",
"ListBatchesRequest",
"Benchmark",
"BenchmarkConfig",
"BenchmarkInput",
@ -555,6 +566,7 @@ __all__ = [
"LLMAsJudgeScoringFnParams",
"LLMRAGQueryGeneratorConfig",
"ListBatchesResponse",
"RetrieveBatchRequest",
"ListBenchmarksResponse",
"ListDatasetsResponse",
"ListModelsResponse",

96
src/llama_stack_api/batches.py
View file

@ -1,96 +0,0 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import Literal, Protocol, runtime_checkable
from pydantic import BaseModel, Field
from llama_stack_api.schema_utils import json_schema_type, webmethod
from llama_stack_api.version import LLAMA_STACK_API_V1
try:
from openai.types import Batch as BatchObject
except ImportError as e:
raise ImportError("OpenAI package is required for batches API. Please install it with: pip install openai") from e
@json_schema_type
class ListBatchesResponse(BaseModel):
"""Response containing a list of batch objects."""
object: Literal["list"] = "list"
data: list[BatchObject] = Field(..., description="List of batch objects")
first_id: str | None = Field(default=None, description="ID of the first batch in the list")
last_id: str | None = Field(default=None, description="ID of the last batch in the list")
has_more: bool = Field(default=False, description="Whether there are more batches available")
@runtime_checkable
class Batches(Protocol):
"""
The Batches API enables efficient processing of multiple requests in a single operation,
particularly useful for processing large datasets, batch evaluation workflows, and
cost-effective inference at scale.
The API is designed to allow use of openai client libraries for seamless integration.
This API provides the following extensions:
- idempotent batch creation
Note: This API is currently under active development and may undergo changes.
"""
@webmethod(route="/batches", method="POST", level=LLAMA_STACK_API_V1)
async def create_batch(
self,
input_file_id: str,
endpoint: str,
completion_window: Literal["24h"],
metadata: dict[str, str] | None = None,
idempotency_key: str | None = None,
) -> BatchObject:
"""Create a new batch for processing multiple API requests.
:param input_file_id: The ID of an uploaded file containing requests for the batch.
:param endpoint: The endpoint to be used for all requests in the batch.
:param completion_window: The time window within which the batch should be processed.
:param metadata: Optional metadata for the batch.
:param idempotency_key: Optional idempotency key. When provided, enables idempotent behavior.
:returns: The created batch object.
"""
...
@webmethod(route="/batches/{batch_id}", method="GET", level=LLAMA_STACK_API_V1)
async def retrieve_batch(self, batch_id: str) -> BatchObject:
"""Retrieve information about a specific batch.
:param batch_id: The ID of the batch to retrieve.
:returns: The batch object.
"""
...
@webmethod(route="/batches/{batch_id}/cancel", method="POST", level=LLAMA_STACK_API_V1)
async def cancel_batch(self, batch_id: str) -> BatchObject:
"""Cancel a batch that is in progress.
:param batch_id: The ID of the batch to cancel.
:returns: The updated batch object.
"""
...
@webmethod(route="/batches", method="GET", level=LLAMA_STACK_API_V1)
async def list_batches(
self,
after: str | None = None,
limit: int = 20,
) -> ListBatchesResponse:
"""List all batches for the current user.
:param after: A cursor for pagination; returns batches after this batch ID.
:param limit: Number of batches to return (default 20, max 100).
:returns: A list of batch objects.
"""
...

39
src/llama_stack_api/batches/__init__.py Normal file
View file

@ -0,0 +1,39 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
"""Batches API protocol and models.
This module contains the Batches protocol definition.
Pydantic models are defined in llama_stack_api.batches.models.
The FastAPI router is defined in llama_stack_api.batches.fastapi_routes.
"""
try:
from openai.types import Batch as BatchObject
except ImportError as e:
raise ImportError("OpenAI package is required for batches API. Please install it with: pip install openai") from e
# Import protocol for re-export
from llama_stack_api.batches.api import Batches
# Import models for re-export
from llama_stack_api.batches.models import (
CancelBatchRequest,
CreateBatchRequest,
ListBatchesRequest,
ListBatchesResponse,
RetrieveBatchRequest,
)
__all__ = [
"Batches",
"BatchObject",
"CreateBatchRequest",
"ListBatchesRequest",
"RetrieveBatchRequest",
"CancelBatchRequest",
"ListBatchesResponse",
]

56
src/llama_stack_api/batches/api.py Normal file
View file

@ -0,0 +1,56 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import Protocol, runtime_checkable
try:
from openai.types import Batch as BatchObject
except ImportError as e:
raise ImportError("OpenAI package is required for batches API. Please install it with: pip install openai") from e
from llama_stack_api.batches.models import (
CancelBatchRequest,
CreateBatchRequest,
ListBatchesRequest,
ListBatchesResponse,
RetrieveBatchRequest,
)
@runtime_checkable
class Batches(Protocol):
"""
The Batches API enables efficient processing of multiple requests in a single operation,
particularly useful for processing large datasets, batch evaluation workflows, and
cost-effective inference at scale.
The API is designed to allow use of openai client libraries for seamless integration.
This API provides the following extensions:
- idempotent batch creation
Note: This API is currently under active development and may undergo changes.
"""
async def create_batch(
self,
request: CreateBatchRequest,
) -> BatchObject: ...
async def retrieve_batch(
self,
request: RetrieveBatchRequest,
) -> BatchObject: ...
async def cancel_batch(
self,
request: CancelBatchRequest,
) -> BatchObject: ...
async def list_batches(
self,
request: ListBatchesRequest,
) -> ListBatchesResponse: ...

111
src/llama_stack_api/batches/fastapi_routes.py Normal file
View file

@ -0,0 +1,111 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
"""FastAPI router for the Batches API.
This module defines the FastAPI router for the Batches API using standard
FastAPI route decorators. The router is defined in the API package to keep
all API-related code together.
"""
from typing import Annotated
from fastapi import APIRouter, Body, Depends
from llama_stack_api.batches import Batches, BatchObject, ListBatchesResponse
from llama_stack_api.batches.models import (
CancelBatchRequest,
CreateBatchRequest,
ListBatchesRequest,
RetrieveBatchRequest,
)
from llama_stack_api.router_utils import create_path_dependency, create_query_dependency, standard_responses
from llama_stack_api.version import LLAMA_STACK_API_V1
# Automatically generate dependency functions from Pydantic models
# This ensures the models are the single source of truth for descriptions
get_retrieve_batch_request = create_path_dependency(RetrieveBatchRequest)
get_cancel_batch_request = create_path_dependency(CancelBatchRequest)
# Automatically generate dependency function from Pydantic model
# This ensures the model is the single source of truth for descriptions and defaults
get_list_batches_request = create_query_dependency(ListBatchesRequest)
def create_router(impl: Batches) -> APIRouter:
"""Create a FastAPI router for the Batches API.
Args:
impl: The Batches implementation instance
Returns:
APIRouter configured for the Batches API
"""
router = APIRouter(
prefix=f"/{LLAMA_STACK_API_V1}",
tags=["Batches"],
responses=standard_responses,
)
@router.post(
"/batches",
response_model=BatchObject,
summary="Create a new batch for processing multiple API requests.",
description="Create a new batch for processing multiple API requests.",
responses={
200: {"description": "The created batch object."},
409: {"description": "Conflict: The idempotency key was previously used with different parameters."},
},
)
async def create_batch(
request: Annotated[CreateBatchRequest, Body(...)],
) -> BatchObject:
return await impl.create_batch(request)
@router.get(
"/batches/{batch_id}",
response_model=BatchObject,
summary="Retrieve information about a specific batch.",
description="Retrieve information about a specific batch.",
responses={
200: {"description": "The batch object."},
},
)
async def retrieve_batch(
request: Annotated[RetrieveBatchRequest, Depends(get_retrieve_batch_request)],
) -> BatchObject:
return await impl.retrieve_batch(request)
@router.post(
"/batches/{batch_id}/cancel",
response_model=BatchObject,
summary="Cancel a batch that is in progress.",
description="Cancel a batch that is in progress.",
responses={
200: {"description": "The updated batch object."},
},
)
async def cancel_batch(
request: Annotated[CancelBatchRequest, Depends(get_cancel_batch_request)],
) -> BatchObject:
return await impl.cancel_batch(request)
@router.get(
"/batches",
response_model=ListBatchesResponse,
summary="List all batches for the current user.",
description="List all batches for the current user.",
responses={
200: {"description": "A list of batch objects."},
},
)
async def list_batches(
request: Annotated[ListBatchesRequest, Depends(get_list_batches_request)],
) -> ListBatchesResponse:
return await impl.list_batches(request)
return router

82
src/llama_stack_api/batches/models.py Normal file
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@ -0,0 +1,82 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
"""Pydantic models for Batches API requests and responses.
This module defines the request and response models for the Batches API
using Pydantic with Field descriptions for OpenAPI schema generation.
"""
from typing import Literal
from pydantic import BaseModel, Field
from llama_stack_api.schema_utils import json_schema_type
try:
from openai.types import Batch as BatchObject
except ImportError as e:
raise ImportError("OpenAI package is required for batches API. Please install it with: pip install openai") from e
@json_schema_type
class CreateBatchRequest(BaseModel):
"""Request model for creating a batch."""
input_file_id: str = Field(..., description="The ID of an uploaded file containing requests for the batch.")
endpoint: str = Field(..., description="The endpoint to be used for all requests in the batch.")
completion_window: Literal["24h"] = Field(
..., description="The time window within which the batch should be processed."
)
metadata: dict[str, str] | None = Field(default=None, description="Optional metadata for the batch.")
idempotency_key: str | None = Field(
default=None, description="Optional idempotency key. When provided, enables idempotent behavior."
)
@json_schema_type
class ListBatchesRequest(BaseModel):
"""Request model for listing batches."""
after: str | None = Field(
default=None, description="Optional cursor for pagination. Returns batches after this ID."
)
limit: int = Field(default=20, description="Maximum number of batches to return. Defaults to 20.")
@json_schema_type
class RetrieveBatchRequest(BaseModel):
"""Request model for retrieving a batch."""
batch_id: str = Field(..., description="The ID of the batch to retrieve.")
@json_schema_type
class CancelBatchRequest(BaseModel):
"""Request model for canceling a batch."""
batch_id: str = Field(..., description="The ID of the batch to cancel.")
@json_schema_type
class ListBatchesResponse(BaseModel):
"""Response containing a list of batch objects."""
object: Literal["list"] = "list"
data: list[BatchObject] = Field(..., description="List of batch objects")
first_id: str | None = Field(default=None, description="ID of the first batch in the list")
last_id: str | None = Field(default=None, description="ID of the last batch in the list")
has_more: bool = Field(default=False, description="Whether there are more batches available")
__all__ = [
"CreateBatchRequest",
"ListBatchesRequest",
"RetrieveBatchRequest",
"CancelBatchRequest",
"ListBatchesResponse",
"BatchObject",
]

1
src/llama_stack_api/pyproject.toml
View file

@ -24,6 +24,7 @@ classifiers = [
"Topic :: Scientific/Engineering :: Information Analysis",
]
dependencies = [
"fastapi>=0.115.0,<1.0",
"pydantic>=2.11.9",
"jsonschema",
"opentelemetry-sdk>=1.30.0",

155
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@ -0,0 +1,155 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
"""Utilities for creating FastAPI routers with standard error responses.
This module provides standard error response definitions for FastAPI routers.
These responses use OpenAPI $ref references to component responses defined
in the OpenAPI specification.
"""
import inspect
from collections.abc import Callable
from typing import Annotated, Any, TypeVar
from fastapi import Path, Query
from pydantic import BaseModel
standard_responses: dict[int | str, dict[str, Any]] = {
400: {"$ref": "#/components/responses/BadRequest400"},
429: {"$ref": "#/components/responses/TooManyRequests429"},
500: {"$ref": "#/components/responses/InternalServerError500"},
"default": {"$ref": "#/components/responses/DefaultError"},
}
T = TypeVar("T", bound=BaseModel)
def create_query_dependency[T: BaseModel](model_class: type[T]) -> Callable[..., T]:
"""Create a FastAPI dependency function from a Pydantic model for query parameters.
FastAPI does not natively support using Pydantic models as query parameters
without a dependency function. Using a dependency function typically leads to
duplication: field types, default values, and descriptions must be repeated in
`Query(...)` annotations even though they already exist in the Pydantic model.
This function automatically generates a dependency function that extracts query parameters
from the request and constructs an instance of the Pydantic model. The descriptions and
defaults are automatically extracted from the model's Field definitions, making the model
the single source of truth.
Args:
model_class: The Pydantic model class to create a dependency for
Returns:
A dependency function that can be used with FastAPI's Depends()
```
"""
# Build function signature dynamically from model fields
annotations: dict[str, Any] = {}
defaults: dict[str, Any] = {}
for field_name, field_info in model_class.model_fields.items():
# Extract description from Field
description = field_info.description
# Create Query annotation with description from model
query_annotation = Query(description=description) if description else Query()
# Create Annotated type with Query
field_type = field_info.annotation
annotations[field_name] = Annotated[field_type, query_annotation]
# Set default value from model
if field_info.default is not inspect.Parameter.empty:
defaults[field_name] = field_info.default
# Create the dependency function dynamically
def dependency_func(**kwargs: Any) -> T:
return model_class(**kwargs)
# Set function signature
sig_params = []
for field_name, field_type in annotations.items():
default = defaults.get(field_name, inspect.Parameter.empty)
param = inspect.Parameter(
field_name,
inspect.Parameter.POSITIONAL_OR_KEYWORD,
default=default,
annotation=field_type,
)
sig_params.append(param)
# These attributes are set dynamically at runtime. While mypy can't verify them statically,
# they are standard Python function attributes that exist on all callable objects at runtime.
# Setting them allows FastAPI to properly introspect the function signature for dependency injection.
dependency_func.__signature__ = inspect.Signature(sig_params) # type: ignore[attr-defined]
dependency_func.__annotations__ = annotations # type: ignore[attr-defined]
dependency_func.__name__ = f"get_{model_class.__name__.lower()}_request" # type: ignore[attr-defined]
return dependency_func
def create_path_dependency[T: BaseModel](model_class: type[T]) -> Callable[..., T]:
"""Create a FastAPI dependency function from a Pydantic model for path parameters.
FastAPI requires path parameters to be explicitly annotated with `Path()`. When using
a Pydantic model that contains path parameters, you typically need a dependency function
that extracts the path parameter and constructs the model. This leads to duplication:
the parameter name, type, and description must be repeated in `Path(...)` annotations
even though they already exist in the Pydantic model.
This function automatically generates a dependency function that extracts path parameters
from the request and constructs an instance of the Pydantic model. The descriptions are
automatically extracted from the model's Field definitions, making the model the single
source of truth.
Args:
model_class: The Pydantic model class to create a dependency for. The model should
have exactly one field that represents the path parameter.
Returns:
A dependency function that can be used with FastAPI's Depends()
```
"""
# Get the single field from the model (path parameter models typically have one field)
if len(model_class.model_fields) != 1:
raise ValueError(
f"Path parameter model {model_class.__name__} must have exactly one field, "
f"but has {len(model_class.model_fields)} fields"
)
field_name, field_info = next(iter(model_class.model_fields.items()))
# Extract description from Field
description = field_info.description
# Create Path annotation with description from model
path_annotation = Path(description=description) if description else Path()
# Create Annotated type with Path
field_type = field_info.annotation
annotations: dict[str, Any] = {field_name: Annotated[field_type, path_annotation]}
# Create the dependency function dynamically
def dependency_func(**kwargs: Any) -> T:
return model_class(**kwargs)
# Set function signature
param = inspect.Parameter(
field_name,
inspect.Parameter.POSITIONAL_OR_KEYWORD,
annotation=annotations[field_name],
)
# These attributes are set dynamically at runtime. While mypy can't verify them statically,
# they are standard Python function attributes that exist on all callable objects at runtime.
# Setting them allows FastAPI to properly introspect the function signature for dependency injection.
dependency_func.__signature__ = inspect.Signature([param]) # type: ignore[attr-defined]
dependency_func.__annotations__ = annotations # type: ignore[attr-defined]
dependency_func.__name__ = f"get_{model_class.__name__.lower()}_request" # type: ignore[attr-defined]
return dependency_func