diff --git a/.github/workflows/_pydantic_compatibility.yml b/.github/workflows/_pydantic_compatibility.yml deleted file mode 100644 index e72d83fc..00000000 --- a/.github/workflows/_pydantic_compatibility.yml +++ /dev/null @@ -1,94 +0,0 @@ -name: pydantic v1/v2 compatibility - -on: - workflow_call: - inputs: - working-directory: - required: true - type: string - description: "From which folder this pipeline executes" - -env: - POETRY_VERSION: "1.5.1" - -jobs: - build: - timeout-minutes: 10 - defaults: - run: - working-directory: ${{ inputs.working-directory }} - runs-on: ubuntu-latest - strategy: - matrix: - python-version: - - "3.8" - - "3.9" - - "3.10" - - "3.11" - name: Pydantic v1/v2 compatibility - Python ${{ matrix.python-version }} - steps: - - uses: actions/checkout@v3 - - - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }} - uses: "./.github/actions/poetry_setup" - with: - python-version: ${{ matrix.python-version }} - poetry-version: ${{ env.POETRY_VERSION }} - working-directory: ${{ inputs.working-directory }} - cache-key: pydantic-cross-compat - - - name: Install dependencies - shell: bash - run: poetry install - - - name: Install the opposite major version of pydantic - # If normal tests use pydantic v1, here we'll use v2, and vice versa. - shell: bash - run: | - # Determine the major part of pydantic version - REGULAR_VERSION=$(poetry run python -c "import pydantic; print(pydantic.__version__)" | cut -d. -f1) - - if [[ "$REGULAR_VERSION" == "1" ]]; then - PYDANTIC_DEP=">=2.1,<3" - TEST_WITH_VERSION="2" - elif [[ "$REGULAR_VERSION" == "2" ]]; then - PYDANTIC_DEP="<2" - TEST_WITH_VERSION="1" - else - echo "Unexpected pydantic major version '$REGULAR_VERSION', cannot determine which version to use for cross-compatibility test." - exit 1 - fi - - # Install via `pip` instead of `poetry add` to avoid changing lockfile, - # which would prevent caching from working: the cache would get saved - # to a different key than where it gets loaded from. - poetry run pip install "pydantic${PYDANTIC_DEP}" - - # Ensure that the correct pydantic is installed now. - echo "Checking pydantic version... Expecting ${TEST_WITH_VERSION}" - - # Determine the major part of pydantic version - CURRENT_VERSION=$(poetry run python -c "import pydantic; print(pydantic.__version__)" | cut -d. -f1) - - # Check that the major part of pydantic version is as expected, if not - # raise an error - if [[ "$CURRENT_VERSION" != "$TEST_WITH_VERSION" ]]; then - echo "Error: expected pydantic version ${CURRENT_VERSION} to have been installed, but found: ${TEST_WITH_VERSION}" - exit 1 - fi - echo "Found pydantic version ${CURRENT_VERSION}, as expected" - - name: Run pydantic compatibility tests - shell: bash - run: make test - - - name: Ensure the tests did not create any additional files - shell: bash - run: | - set -eu - - STATUS="$(git status)" - echo "$STATUS" - - # grep will exit non-zero if the target message isn't found, - # and `set -e` above will cause the step to fail. - echo "$STATUS" | grep 'nothing to commit, working tree clean' diff --git a/.github/workflows/langserve_ci.yml b/.github/workflows/langserve_ci.yml index 094c3b2b..1988ddde 100644 --- a/.github/workflows/langserve_ci.yml +++ b/.github/workflows/langserve_ci.yml @@ -39,13 +39,6 @@ jobs: with: working-directory: . secrets: inherit - - pydantic-compatibility: - uses: - ./.github/workflows/_pydantic_compatibility.yml - with: - working-directory: . - secrets: inherit test: timeout-minutes: 10 runs-on: ubuntu-latest diff --git a/langserve/_pydantic.py b/langserve/_pydantic.py new file mode 100644 index 00000000..785598ed --- /dev/null +++ b/langserve/_pydantic.py @@ -0,0 +1,53 @@ +from typing import Any, Dict, Type, cast + +from pydantic import BaseModel, ConfigDict, RootModel +from pydantic.json_schema import ( + DEFAULT_REF_TEMPLATE, + GenerateJsonSchema, + JsonSchemaMode, +) + + +def _create_root_model(name: str, type_: Any) -> Type[RootModel]: + """Create a base class.""" + + def schema( + cls: Type[BaseModel], + by_alias: bool = True, + ref_template: str = DEFAULT_REF_TEMPLATE, + ) -> Dict[str, Any]: + # Complains about schema not being defined in superclass + schema_ = super(cls, cls).schema( # type: ignore[misc] + by_alias=by_alias, ref_template=ref_template + ) + schema_["title"] = name + return schema_ + + def model_json_schema( + cls: Type[BaseModel], + by_alias: bool = True, + ref_template: str = DEFAULT_REF_TEMPLATE, + schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema, + mode: JsonSchemaMode = "validation", + ) -> Dict[str, Any]: + # Complains about model_json_schema not being defined in superclass + schema_ = super(cls, cls).model_json_schema( # type: ignore[misc] + by_alias=by_alias, + ref_template=ref_template, + schema_generator=schema_generator, + mode=mode, + ) + schema_["title"] = name + return schema_ + + base_class_attributes = { + "__annotations__": {"root": type_}, + "model_config": ConfigDict(arbitrary_types_allowed=True), + "schema": classmethod(schema), + "model_json_schema": classmethod(model_json_schema), + # Should replace __module__ with caller based on stack frame. + "__module__": "langserve._pydantic", + } + + custom_root_type = type(name, (RootModel,), base_class_attributes) + return cast(Type[RootModel], custom_root_type) diff --git a/langserve/api_handler.py b/langserve/api_handler.py index ca3da44c..6e70d0b9 100644 --- a/langserve/api_handler.py +++ b/langserve/api_handler.py @@ -41,14 +41,15 @@ from langsmith import client as ls_client from langsmith.schemas import FeedbackIngestToken from langsmith.utils import tracing_is_enabled +from pydantic import BaseModel, Field, RootModel, ValidationError, create_model from starlette.requests import Request from starlette.responses import JSONResponse, Response from typing_extensions import TypedDict +from langserve._pydantic import _create_root_model from langserve.callbacks import AsyncEventAggregatorCallback, CallbackEventDict from langserve.lzstring import LZString from langserve.playground import serve_playground -from langserve.pydantic_v1 import BaseModel, Field, ValidationError, create_model from langserve.schema import ( BatchResponseMetadata, CustomUserType, @@ -256,10 +257,12 @@ def _update_config_with_defaults( } metadata.update(hosted_metadata) - non_overridable_default_config = RunnableConfig( - run_name=run_name, - metadata=metadata, - ) + non_overridable_default_config: RunnableConfig = { + "metadata": metadata, + } + + if run_name: + non_overridable_default_config["run_name"] = run_name # merge_configs is last-writer-wins, so we specifically pass in the # overridable configs first, then the user provided configs, then @@ -280,8 +283,8 @@ def _update_config_with_defaults( def _unpack_input(validated_model: BaseModel) -> Any: """Unpack the decoded input from the validated model.""" - if hasattr(validated_model, "__root__"): - model = validated_model.__root__ + if isinstance(validated_model, RootModel): + model = validated_model.root else: model = validated_model @@ -305,7 +308,7 @@ def _rename_pydantic_model(model: Type[BaseModel], prefix: str) -> Type[BaseMode """Rename the given pydantic model to the given name.""" return create_model( prefix + model.__name__, - __config__=model.__config__, + __config__=model.model_config, **{ fieldname: ( _rename_pydantic_model(field.annotation, prefix) @@ -314,10 +317,10 @@ def _rename_pydantic_model(model: Type[BaseModel], prefix: str) -> Type[BaseMode Field( field.default, title=fieldname, - description=field.field_info.description, + description=field.description, ), ) - for fieldname, field in model.__fields__.items() + for fieldname, field in model.model_fields.items() }, ) @@ -334,7 +337,7 @@ def _resolve_model( if isclass(type_) and issubclass(type_, BaseModel): model = type_ else: - model = create_model(default_name, __root__=(type_, ...)) + model = _create_root_model(default_name, type_) hash_ = model.schema_json() @@ -367,11 +370,7 @@ def _add_namespace_to_model(namespace: str, model: Type[BaseModel]) -> Type[Base A new model with name prepended with the given namespace. """ model_with_unique_name = _rename_pydantic_model(model, namespace) - if "run_id" in model_with_unique_name.__annotations__: - # Help resolve reference by providing namespace references - model_with_unique_name.update_forward_refs(uuid=uuid) - else: - model_with_unique_name.update_forward_refs() + model_with_unique_name.model_rebuild() return model_with_unique_name @@ -404,7 +403,7 @@ def _with_validation_error_translation() -> Generator[None, None, None]: try: yield except ValidationError as e: - raise RequestValidationError(e.errors(), body=e.model) + raise RequestValidationError(e.errors()) def _json_encode_response(model: BaseModel) -> JSONResponse: @@ -424,27 +423,27 @@ def _json_encode_response(model: BaseModel) -> JSONResponse: if isinstance(model, InvokeBaseResponse): # Invoke Response - # Collapse '__root__' from output field if it exists. This is done + # Collapse 'root' from output field if it exists. This is done # automatically by fastapi when annotating request and response with # We need to do this manually since we're using vanilla JSONResponse - if isinstance(obj["output"], dict) and "__root__" in obj["output"]: - obj["output"] = obj["output"]["__root__"] + if isinstance(obj["output"], dict) and "root" in obj["output"]: + obj["output"] = obj["output"]["root"] if "callback_events" in obj: for idx, callback_event in enumerate(obj["callback_events"]): - if isinstance(callback_event, dict) and "__root__" in callback_event: - obj["callback_events"][idx] = callback_event["__root__"] + if isinstance(callback_event, dict) and "root" in callback_event: + obj["callback_events"][idx] = callback_event["root"] elif isinstance(model, BatchBaseResponse): if not isinstance(obj["output"], list): raise AssertionError("Expected output to be a list") - # Collapse '__root__' from output field if it exists. This is done + # Collapse 'root' from output field if it exists. This is done # automatically by fastapi when annotating request and response with # We need to do this manually since we're using vanilla JSONResponse outputs = obj["output"] for idx, output in enumerate(outputs): - if isinstance(output, dict) and "__root__" in output: - outputs[idx] = output["__root__"] + if isinstance(output, dict) and "root" in output: + outputs[idx] = output["root"] if "callback_events" in obj: if not isinstance(obj["callback_events"], list): @@ -452,11 +451,8 @@ def _json_encode_response(model: BaseModel) -> JSONResponse: for callback_events in obj["callback_events"]: for idx, callback_event in enumerate(callback_events): - if ( - isinstance(callback_event, dict) - and "__root__" in callback_event - ): - callback_events[idx] = callback_event["__root__"] + if isinstance(callback_event, dict) and "root" in callback_event: + callback_events[idx] = callback_event["root"] else: raise AssertionError( f"Expected a InvokeBaseResponse or BatchBaseResponse got: {type(model)}" diff --git a/langserve/playground.py b/langserve/playground.py index c1bcfafe..c9b77569 100644 --- a/langserve/playground.py +++ b/langserve/playground.py @@ -6,8 +6,7 @@ from fastapi.responses import Response from langchain_core.runnables import Runnable - -from langserve.pydantic_v1 import BaseModel +from pydantic import BaseModel class PlaygroundTemplate(Template): diff --git a/langserve/pydantic_v1.py b/langserve/pydantic_v1.py deleted file mode 100644 index b31ca823..00000000 --- a/langserve/pydantic_v1.py +++ /dev/null @@ -1,33 +0,0 @@ -from importlib import metadata - -## Create namespaces for pydantic v1 and v2. -# This code must stay at the top of the file before other modules may -# attempt to import pydantic since it adds pydantic_v1 and pydantic_v2 to sys.modules. -# -# This hack is done for the following reasons: -# * Langchain will attempt to remain compatible with both pydantic v1 and v2 since -# both dependencies and dependents may be stuck on either version of v1 or v2. -# * Creating namespaces for pydantic v1 and v2 should allow us to write code that -# unambiguously uses either v1 or v2 API. -# * This change is easier to roll out and roll back. - -try: - # F401: imported but unused - from pydantic.v1 import ( # noqa: F401 - BaseModel, - Field, - ValidationError, - create_model, - ) -except ImportError: - from pydantic import BaseModel, Field, ValidationError, create_model # noqa: F401 - - -# This is not a pydantic v1 thing, but it feels too small to create a new module for. - -PYDANTIC_VERSION = metadata.version("pydantic") - -try: - _PYDANTIC_MAJOR_VERSION: int = int(PYDANTIC_VERSION.split(".")[0]) -except metadata.PackageNotFoundError: - _PYDANTIC_MAJOR_VERSION = -1 diff --git a/langserve/schema.py b/langserve/schema.py index 26392993..8bf4c92f 100644 --- a/langserve/schema.py +++ b/langserve/schema.py @@ -2,10 +2,11 @@ from typing import Dict, List, Optional, Union from uuid import UUID -from pydantic import BaseModel # Floats between v1 and v2 - -from langserve.pydantic_v1 import BaseModel as BaseModelV1 -from langserve.pydantic_v1 import Field +from pydantic import ( + BaseModel, + Field, +) +from pydantic import BaseModel as BaseModelV1 class CustomUserType(BaseModelV1): diff --git a/langserve/serialization.py b/langserve/serialization.py index 186e9ddd..1690c487 100644 --- a/langserve/serialization.py +++ b/langserve/serialization.py @@ -13,7 +13,7 @@ import abc import logging from functools import lru_cache -from typing import Any, Dict, List, Union +from typing import Annotated, Any, Dict, List, Union import orjson from langchain_core.agents import AgentAction, AgentActionMessageLog, AgentFinish @@ -34,12 +34,11 @@ ChatGeneration, ChatGenerationChunk, Generation, - LLMResult, ) from langchain_core.prompt_values import ChatPromptValueConcrete from langchain_core.prompts.base import StringPromptValue +from pydantic import BaseModel, Field, RootModel, ValidationError -from langserve.pydantic_v1 import BaseModel, ValidationError from langserve.validation import CallbackEvent logger = logging.getLogger(__name__) @@ -51,36 +50,35 @@ def _log_error_message_once(error_message: str) -> None: logger.error(error_message) -class WellKnownLCObject(BaseModel): - """A well known LangChain object. - - A pydantic model that defines what constitutes a well known LangChain object. - - All well-known objects are allowed to be serialized and de-serialized. - """ - - __root__: Union[ - Document, - HumanMessage, - SystemMessage, - ChatMessage, - FunctionMessage, - AIMessage, - HumanMessageChunk, - SystemMessageChunk, - ChatMessageChunk, - FunctionMessageChunk, - AIMessageChunk, - StringPromptValue, - ChatPromptValueConcrete, - AgentAction, - AgentFinish, - AgentActionMessageLog, - LLMResult, - ChatGeneration, - Generation, - ChatGenerationChunk, +# A well known LangChain object. +# A pydantic model that defines what constitutes a well known LangChain object. +# All well-known objects are allowed to be serialized and de-serialized. +WellKnownLCObject = RootModel[ + Annotated[ + Union[ + Document, + HumanMessage, + SystemMessage, + ChatMessage, + FunctionMessage, + AIMessage, + HumanMessageChunk, + SystemMessageChunk, + ChatMessageChunk, + FunctionMessageChunk, + AIMessageChunk, + StringPromptValue, + ChatPromptValueConcrete, + AgentAction, + AgentFinish, + AgentActionMessageLog, + ChatGeneration, + Generation, + ChatGenerationChunk, + ], + Field(discriminator="type"), ] +] def default(obj) -> Any: @@ -96,12 +94,12 @@ def _decode_lc_objects(value: Any) -> Any: v = {key: _decode_lc_objects(v) for key, v in value.items()} try: - obj = WellKnownLCObject.parse_obj(v) - parsed = obj.__root__ + obj = WellKnownLCObject.model_validate(v) + parsed = obj.root if set(parsed.dict()) != set(value): raise ValueError("Invalid object") return parsed - except (ValidationError, ValueError): + except (ValidationError, ValueError, TypeError): return v elif isinstance(value, list): return [_decode_lc_objects(item) for item in value] @@ -124,11 +122,11 @@ def _decode_event_data(value: Any) -> Any: if isinstance(value, dict): try: obj = CallbackEvent.parse_obj(value) - return obj.__root__ + return obj.root except ValidationError: try: obj = WellKnownLCObject.parse_obj(value) - return obj.__root__ + return obj.root except ValidationError: return {key: _decode_event_data(v) for key, v in value.items()} elif isinstance(value, list): @@ -217,7 +215,7 @@ def load_events(events: Any) -> List[Dict[str, Any]]: _log_error_message_once(msg) continue - decoded_event_data = _project_top_level(full_event.__root__) + decoded_event_data = _project_top_level(full_event.root) if decoded_event_data["type"].endswith("_error"): # Data is validated by this point, so we can assume that the shape diff --git a/langserve/server.py b/langserve/server.py index 22757b7d..99a02039 100644 --- a/langserve/server.py +++ b/langserve/server.py @@ -16,6 +16,7 @@ ) from langchain_core.runnables import Runnable +from pydantic import BaseModel from typing_extensions import Annotated from langserve.api_handler import ( @@ -24,11 +25,6 @@ TokenFeedbackConfig, _is_hosted, ) -from langserve.pydantic_v1 import ( - _PYDANTIC_MAJOR_VERSION, - PYDANTIC_VERSION, - BaseModel, -) try: from fastapi import APIRouter, Depends, FastAPI, Request, Response @@ -237,20 +233,6 @@ def orange(text: str) -> str: print(f'{green("LANGSERVE:")}') print(f'{green("LANGSERVE:")} See all available routes at {app.docs_url}/') - if _PYDANTIC_MAJOR_VERSION == 2: - print() - print(f'{orange("LANGSERVE:")} ', end="") - print( - f"⚠️ Using pydantic {PYDANTIC_VERSION}. " - f"OpenAPI docs for invoke, batch, stream, stream_log " - f"endpoints will not be generated. API endpoints and playground " - f"should work as expected. " - f"If you need to see the docs, you can downgrade to pydantic 1. " - "For example, `pip install pydantic==1.10.13`. " - f"See https://github.com/tiangolo/fastapi/issues/10360 for details." - ) - print() - # PUBLIC API @@ -473,35 +455,9 @@ def _route_name_with_config(name: str) -> str: if hasattr(app, "openapi_tags") and (path or (app not in _APP_SEEN)): if not path: _APP_SEEN.add(app) - - if _PYDANTIC_MAJOR_VERSION == 1: - # Documentation for the default endpoints - default_endpoint_tags = { - "name": route_tags[0] if route_tags else "default", - } - elif _PYDANTIC_MAJOR_VERSION == 2: - # When using pydantic v2, we cannot generate openapi docs for - # the invoke/batch/stream/stream_log endpoints since the underlying - # models are from the pydantic.v1 namespace and cannot be supported - # by FastAPI's. - # https://github.com/tiangolo/fastapi/issues/10360 - default_endpoint_tags = { - "name": route_tags[0] if route_tags else "default", - "description": ( - f"⚠️ Using pydantic {PYDANTIC_VERSION}. " - f"OpenAPI docs for `invoke`, `batch`, `stream`, `stream_log` " - f"endpoints will not be generated. API endpoints and playground " - f"should work as expected. " - f"If you need to see the docs, you can downgrade to pydantic 1. " - "For example, `pip install pydantic==1.10.13`" - f"See https://github.com/tiangolo/fastapi/issues/10360 for details." - ), - } - else: - raise AssertionError( - f"Expected pydantic major version 1 or 2, got {_PYDANTIC_MAJOR_VERSION}" - ) - + default_endpoint_tags = { + "name": route_tags[0] if route_tags else "default", + } if endpoint_configuration.is_config_hash_enabled: app.openapi_tags = [ *(getattr(app, "openapi_tags", []) or []), @@ -778,329 +734,326 @@ async def config_schema_with_config( # Documentation variants of end points. ####################################### # At the moment, we only support pydantic 1.x for documentation - if _PYDANTIC_MAJOR_VERSION == 1: - InvokeRequest = api_handler.InvokeRequest - InvokeResponse = api_handler.InvokeResponse - BatchRequest = api_handler.BatchRequest - BatchResponse = api_handler.BatchResponse - StreamRequest = api_handler.StreamRequest - StreamLogRequest = api_handler.StreamLogRequest - StreamEventsRequest = api_handler.StreamEventsRequest - - if endpoint_configuration.is_invoke_enabled: - - async def _invoke_docs( - invoke_request: Annotated[InvokeRequest, InvokeRequest], - config_hash: str = "", - ) -> InvokeResponse: - """Invoke the runnable with the given input and config.""" - raise AssertionError("This endpoint should not be reachable.") - - invoke_docs = app.post( - f"{namespace}/invoke", + InvokeRequest = api_handler.InvokeRequest + InvokeResponse = api_handler.InvokeResponse + BatchRequest = api_handler.BatchRequest + BatchResponse = api_handler.BatchResponse + StreamRequest = api_handler.StreamRequest + StreamLogRequest = api_handler.StreamLogRequest + StreamEventsRequest = api_handler.StreamEventsRequest + + if endpoint_configuration.is_invoke_enabled: + + async def _invoke_docs( + invoke_request: Annotated[InvokeRequest, InvokeRequest], + config_hash: str = "", + ) -> InvokeResponse: + """Invoke the runnable with the given input and config.""" + raise AssertionError("This endpoint should not be reachable.") + + invoke_docs = app.post( + f"{namespace}/invoke", + response_model=api_handler.InvokeResponse, + tags=route_tags, + name=_route_name("invoke"), + dependencies=dependencies, + )(_invoke_docs) + + if endpoint_configuration.is_config_hash_enabled: + app.post( + namespace + "/c/{config_hash}/invoke", response_model=api_handler.InvokeResponse, - tags=route_tags, - name=_route_name("invoke"), + tags=route_tags_with_config, + name=_route_name_with_config("invoke"), dependencies=dependencies, - )(_invoke_docs) - - if endpoint_configuration.is_config_hash_enabled: - app.post( - namespace + "/c/{config_hash}/invoke", - response_model=api_handler.InvokeResponse, - tags=route_tags_with_config, - name=_route_name_with_config("invoke"), - dependencies=dependencies, - description=( - "This endpoint is to be used with share links generated by the " - "LangServe playground. " - "The hash is an LZString compressed JSON string. " - "For regular use cases, use the /invoke endpoint without " - "the `c/{config_hash}` path parameter." - ), - )(invoke_docs) + description=( + "This endpoint is to be used with share links generated by the " + "LangServe playground. " + "The hash is an LZString compressed JSON string. " + "For regular use cases, use the /invoke endpoint without " + "the `c/{config_hash}` path parameter." + ), + )(invoke_docs) - if endpoint_configuration.is_batch_enabled: + if endpoint_configuration.is_batch_enabled: - async def _batch_docs( - batch_request: Annotated[BatchRequest, BatchRequest], - config_hash: str = "", - ) -> BatchResponse: - """Batch invoke the runnable with the given inputs and config.""" - raise AssertionError("This endpoint should not be reachable.") + async def _batch_docs( + batch_request: Annotated[BatchRequest, BatchRequest], + config_hash: str = "", + ) -> BatchResponse: + """Batch invoke the runnable with the given inputs and config.""" + raise AssertionError("This endpoint should not be reachable.") + + batch_docs = app.post( + f"{namespace}/batch", + response_model=BatchResponse, + tags=route_tags, + name=_route_name("batch"), + dependencies=dependencies, + )(_batch_docs) - batch_docs = app.post( - f"{namespace}/batch", + if endpoint_configuration.is_config_hash_enabled: + app.post( + namespace + "/c/{config_hash}/batch", response_model=BatchResponse, - tags=route_tags, - name=_route_name("batch"), + tags=route_tags_with_config, + name=_route_name_with_config("batch"), dependencies=dependencies, - )(_batch_docs) - - if endpoint_configuration.is_config_hash_enabled: - app.post( - namespace + "/c/{config_hash}/batch", - response_model=BatchResponse, - tags=route_tags_with_config, - name=_route_name_with_config("batch"), - dependencies=dependencies, - description=( - "This endpoint is to be used with share links generated by the " - "LangServe playground. " - "The hash is an LZString compressed JSON string. " - "For regular use cases, use the /batch endpoint without " - "the `c/{config_hash}` path parameter." - ), - )(batch_docs) + description=( + "This endpoint is to be used with share links generated by the " + "LangServe playground. " + "The hash is an LZString compressed JSON string. " + "For regular use cases, use the /batch endpoint without " + "the `c/{config_hash}` path parameter." + ), + )(batch_docs) - if endpoint_configuration.is_stream_enabled: + if endpoint_configuration.is_stream_enabled: - async def _stream_docs( - stream_request: Annotated[StreamRequest, StreamRequest], - config_hash: str = "", - ) -> EventSourceResponse: - """Invoke the runnable stream the output. + async def _stream_docs( + stream_request: Annotated[StreamRequest, StreamRequest], + config_hash: str = "", + ) -> EventSourceResponse: + """Invoke the runnable stream the output. - This endpoint allows to stream the output of the runnable. + This endpoint allows to stream the output of the runnable. - The endpoint uses a server sent event stream to stream the output. + The endpoint uses a server sent event stream to stream the output. - https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events + https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events - Important: Set the "text/event-stream" media type for request headers if - not using an existing SDK. + Important: Set the "text/event-stream" media type for request headers if + not using an existing SDK. - The events that the endpoint uses are the following: - * "data" -- used for streaming the output of the runnale - * "error" -- signaling an error while streaming and ends the stream. - * "end" -- used for signaling the end of the stream - * "metadata" -- used for sending metadata about the run; e.g., run id. + The events that the endpoint uses are the following: + * "data" -- used for streaming the output of the runnale + * "error" -- signaling an error while streaming and ends the stream. + * "end" -- used for signaling the end of the stream + * "metadata" -- used for sending metadata about the run; e.g., run id. - The event type is in the "event" field of the event. - The payload associated with the event is in the "data" field - of the event, and it is JSON encoded. + The event type is in the "event" field of the event. + The payload associated with the event is in the "data" field + of the event, and it is JSON encoded. - Here are some examples of events that the endpoint can send: + Here are some examples of events that the endpoint can send: - Regular streaming event: - { - "event": "data", - "data": { - ... - } + Regular streaming event: + { + "event": "data", + "data": { + ... } + } - Internal server error: - { - "event": "error", - "data": { - "status_code": 500, - "message": "Internal Server Error" - } + Internal server error: + { + "event": "error", + "data": { + "status_code": 500, + "message": "Internal Server Error" } + } - Streaming ended so client should stop listening for events: - { - "event": "end", - } - """ - raise AssertionError("This endpoint should not be reachable.") + Streaming ended so client should stop listening for events: + { + "event": "end", + } + """ + raise AssertionError("This endpoint should not be reachable.") + + stream_docs = app.post( + f"{namespace}/stream", + include_in_schema=True, + tags=route_tags, + name=_route_name("stream"), + dependencies=dependencies, + description=( + "This endpoint allows to stream the output of the runnable. " + "The endpoint uses a server sent event stream to stream the " + "output. " + "https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events" + ), + )(_stream_docs) - stream_docs = app.post( - f"{namespace}/stream", + if endpoint_configuration.is_config_hash_enabled: + app.post( + namespace + "/c/{config_hash}/stream", include_in_schema=True, - tags=route_tags, - name=_route_name("stream"), + tags=route_tags_with_config, + name=_route_name_with_config("stream"), dependencies=dependencies, description=( - "This endpoint allows to stream the output of the runnable. " - "The endpoint uses a server sent event stream to stream the " - "output. " - "https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events" + "This endpoint is to be used with share links generated by the " + "LangServe playground. " + "The hash is an LZString compressed JSON string. " + "For regular use cases, use the /stream endpoint without " + "the `c/{config_hash}` path parameter." ), - )(_stream_docs) - - if endpoint_configuration.is_config_hash_enabled: - app.post( - namespace + "/c/{config_hash}/stream", - include_in_schema=True, - tags=route_tags_with_config, - name=_route_name_with_config("stream"), - dependencies=dependencies, - description=( - "This endpoint is to be used with share links generated by the " - "LangServe playground. " - "The hash is an LZString compressed JSON string. " - "For regular use cases, use the /stream endpoint without " - "the `c/{config_hash}` path parameter." - ), - )(stream_docs) - - if endpoint_configuration.is_stream_log_enabled: + )(stream_docs) - async def _stream_log_docs( - stream_log_request: Annotated[StreamLogRequest, StreamLogRequest], - config_hash: str = "", - ) -> EventSourceResponse: - """Invoke the runnable stream_log the output. - - This endpoint allows to stream the output of the runnable, including - the output of all intermediate steps. + if endpoint_configuration.is_stream_log_enabled: - The endpoint uses a server sent event stream to stream the output. + async def _stream_log_docs( + stream_log_request: Annotated[StreamLogRequest, StreamLogRequest], + config_hash: str = "", + ) -> EventSourceResponse: + """Invoke the runnable stream_log the output. - https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events + This endpoint allows to stream the output of the runnable, including + the output of all intermediate steps. - Important: Set the "text/event-stream" media type for request headers if - not using an existing SDK. + The endpoint uses a server sent event stream to stream the output. - This endpoint uses two different types of events: + https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events - * data - for streaming the output of the runnable + Important: Set the "text/event-stream" media type for request headers if + not using an existing SDK. - { - "event": "data", - "data": { - ... - } - } + This endpoint uses two different types of events: - * error - for signaling an error in the stream, also ends the stream. + * data - for streaming the output of the runnable { - "event": "error", + "event": "data", "data": { - "status_code": 500, - "message": "Internal Server Error" + ... } } - * end - for signaling the end of the stream. + * error - for signaling an error in the stream, also ends the stream. - This helps the client to know when to stop listening for events and - know that the streaming has ended successfully. + { + "event": "error", + "data": { + "status_code": 500, + "message": "Internal Server Error" + } + } - { - "event": "end", - } - """ - raise AssertionError("This endpoint should not be reachable.") + * end - for signaling the end of the stream. + + This helps the client to know when to stop listening for events and + know that the streaming has ended successfully. + + { + "event": "end", + } + """ + raise AssertionError("This endpoint should not be reachable.") + + app.post( + f"{namespace}/stream_log", + include_in_schema=True, + tags=route_tags, + name=_route_name("stream_log"), + dependencies=dependencies, + )(_stream_log_docs) + if endpoint_configuration.is_config_hash_enabled: app.post( - f"{namespace}/stream_log", + namespace + "/c/{config_hash}/stream_log", include_in_schema=True, - tags=route_tags, - name=_route_name("stream_log"), + tags=route_tags_with_config, + name=_route_name_with_config("stream_log"), + description=( + "This endpoint is to be used with share links generated by the " + "LangServe playground. " + "The hash is an LZString compressed JSON string. " + "For regular use cases, use the /stream_log endpoint without " + "the `c/{config_hash}` path parameter." + ), dependencies=dependencies, )(_stream_log_docs) - if endpoint_configuration.is_config_hash_enabled: - app.post( - namespace + "/c/{config_hash}/stream_log", - include_in_schema=True, - tags=route_tags_with_config, - name=_route_name_with_config("stream_log"), - description=( - "This endpoint is to be used with share links generated by the " - "LangServe playground. " - "The hash is an LZString compressed JSON string. " - "For regular use cases, use the /stream_log endpoint without " - "the `c/{config_hash}` path parameter." - ), - dependencies=dependencies, - )(_stream_log_docs) - - if has_astream_events and endpoint_configuration.is_stream_events_enabled: - - async def _stream_events_docs( - stream_events_request: Annotated[ - StreamEventsRequest, StreamEventsRequest - ], - config_hash: str = "", - ) -> EventSourceResponse: - """Stream events from the given runnable. - - This endpoint allows to stream events from the runnable, including - events from all intermediate steps. + if has_astream_events and endpoint_configuration.is_stream_events_enabled: - **Attention** + async def _stream_events_docs( + stream_events_request: Annotated[StreamEventsRequest, StreamEventsRequest], + config_hash: str = "", + ) -> EventSourceResponse: + """Stream events from the given runnable. - This is a new endpoint that only works for langchain-core >= 0.1.14. + This endpoint allows to stream events from the runnable, including + events from all intermediate steps. - It belongs to a Beta API that may change in the future. + **Attention** - **Important** - Specify filters to the events you want to receive by setting - the appropriate filters in the request body. + This is a new endpoint that only works for langchain-core >= 0.1.14. - This will help avoid sending too much data over the network. + It belongs to a Beta API that may change in the future. - It will also prevent serialization issues with - any unsupported types since it won't need to serialize events - that aren't transmitted. + **Important** + Specify filters to the events you want to receive by setting + the appropriate filters in the request body. - The endpoint uses a server sent event stream to stream the output. + This will help avoid sending too much data over the network. - https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events + It will also prevent serialization issues with + any unsupported types since it won't need to serialize events + that aren't transmitted. - The encoding of events follows the following format: + The endpoint uses a server sent event stream to stream the output. - * data - for streaming the output of the runnable + https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events - { - "event": "data", - "data": { - ... - } - } + The encoding of events follows the following format: - * error - for signaling an error in the stream, also ends the stream. + * data - for streaming the output of the runnable { - "event": "error", + "event": "data", "data": { - "status_code": 500, - "message": "Internal Server Error" + ... } } - * end - for signaling the end of the stream. + * error - for signaling an error in the stream, also ends the stream. - This helps the client to know when to stop listening for events and - know that the streaming has ended successfully. + { + "event": "error", + "data": { + "status_code": 500, + "message": "Internal Server Error" + } + } - { - "event": "end", - } + * end - for signaling the end of the stream. + + This helps the client to know when to stop listening for events and + know that the streaming has ended successfully. - `data` for the `data` event is a JSON object that corresponds - to a serialized representation of a StreamEvent. + { + "event": "end", + } - See LangChain documentation for more information about astream_events. - """ - raise AssertionError("This endpoint should not be reachable.") + `data` for the `data` event is a JSON object that corresponds + to a serialized representation of a StreamEvent. + See LangChain documentation for more information about astream_events. + """ + raise AssertionError("This endpoint should not be reachable.") + + app.post( + f"{namespace}/stream_events", + include_in_schema=True, + tags=route_tags, + name=_route_name("stream_events"), + dependencies=dependencies, + )(_stream_events_docs) + + if endpoint_configuration.is_config_hash_enabled: app.post( - f"{namespace}/stream_events", + namespace + "/c/{config_hash}/stream_events", include_in_schema=True, - tags=route_tags, - name=_route_name("stream_events"), + tags=route_tags_with_config, + name=_route_name_with_config("stream_events"), + description=( + "This endpoint is to be used with share links generated by the " + "LangServe playground. " + "The hash is an LZString compressed JSON string. " + "For regular use cases, use the /stream_events endpoint " + "without the `c/{config_hash}` path parameter." + ), dependencies=dependencies, )(_stream_events_docs) - - if endpoint_configuration.is_config_hash_enabled: - app.post( - namespace + "/c/{config_hash}/stream_events", - include_in_schema=True, - tags=route_tags_with_config, - name=_route_name_with_config("stream_events"), - description=( - "This endpoint is to be used with share links generated by the " - "LangServe playground. " - "The hash is an LZString compressed JSON string. " - "For regular use cases, use the /stream_events endpoint " - "without the `c/{config_hash}` path parameter." - ), - dependencies=dependencies, - )(_stream_events_docs) diff --git a/langserve/validation.py b/langserve/validation.py index dacba609..2ec56cc4 100644 --- a/langserve/validation.py +++ b/langserve/validation.py @@ -22,16 +22,11 @@ from langchain_core.documents import Document from langchain_core.messages import BaseMessage from langchain_core.outputs import ChatGeneration, Generation, RunInfo +from pydantic import BaseModel, Field, RootModel, create_model from typing_extensions import Type from langserve.schema import BatchResponseMetadata, InvokeResponseMetadata -try: - from pydantic.v1 import BaseModel, Field, create_model -except ImportError: - from pydantic import BaseModel, Field, create_model - - # Type that is either a python annotation or a pydantic model that can be # used to validate the input or output of a runnable. Validator = Union[Type[BaseModel], type] @@ -66,7 +61,7 @@ def create_invoke_request_model( ), ), ) - invoke_request_type.update_forward_refs() + invoke_request_type.model_rebuild() return invoke_request_type @@ -97,7 +92,7 @@ def create_stream_request_model( ), ), ) - stream_request_model.update_forward_refs() + stream_request_model.model_rebuild() return stream_request_model @@ -129,7 +124,7 @@ def create_batch_request_model( ), ), ) - batch_request_type.update_forward_refs() + batch_request_type.model_rebuild() return batch_request_type @@ -187,7 +182,7 @@ def create_stream_log_request_model( ), kwargs=(dict, Field(default_factory=dict)), ) - stream_log_request.update_forward_refs() + stream_log_request.model_rebuild() return stream_log_request @@ -245,7 +240,7 @@ def create_stream_events_request_model( ), kwargs=(dict, Field(default_factory=dict)), ) - stream_events_request.update_forward_refs() + stream_events_request.model_rebuild() return stream_events_request @@ -297,7 +292,7 @@ def create_invoke_response_model( __base__=InvokeBaseResponse, **fields, ) - invoke_response_type.update_forward_refs() + invoke_response_type.model_rebuild() return invoke_response_type @@ -347,7 +342,7 @@ def create_batch_response_model( __base__=BatchBaseResponse, **fields, ) - batch_response_type.update_forward_refs() + batch_response_type.model_rebuild() return batch_response_type @@ -566,8 +561,8 @@ class OnRetrieverEnd(BaseModel): type: Literal["on_retriever_end"] = "on_retriever_end" -class CallbackEvent(BaseModel): - __root__: Union[ +CallbackEvent = RootModel[ + Union[ OnChainStart, OnChainEnd, OnChainError, @@ -581,3 +576,4 @@ class CallbackEvent(BaseModel): OnRetrieverEnd, OnRetrieverError, ] +] diff --git a/poetry.lock b/poetry.lock index bd605723..8997ded1 100644 --- a/poetry.lock +++ b/poetry.lock @@ -13,98 +13,113 @@ files = [ [[package]] name = "aiohappyeyeballs" -version = "2.3.6" +version = "2.4.0" description = "Happy Eyeballs for asyncio" optional = false 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"977dc6680008cf9a04eb19176e58c00fb61436e90a957846963b787cb1f3e1ea" +python-versions = "^3.9" +content-hash = "5aa787997045c361c7b6d1367b4bd1f4b00353946de9904d9475f7a22404babf" diff --git a/pyproject.toml b/pyproject.toml index ab0e66a6..f31745bb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,12 +10,11 @@ exclude = ["langserve/playground,langserve/chat_playground"] include = ["langserve/playground/dist/**/*", "langserve/chat_playground/dist/**/*"] [tool.poetry.dependencies] -python = "^3.8.1" +python = "^3.9" httpx = ">=0.23.0" # May be able to decrease this version fastapi = {version = ">=0.90.1,<1", optional = true} sse-starlette = {version = "^1.3.0", optional = true} -pydantic = ">=1" -langchain-core = ">=0.1,<0.3" +langchain-core = "0.3.0dev4" orjson = ">=2" pyproject-toml = "^0.0.10" diff --git a/tests/unit_tests/test_serialization.py b/tests/unit_tests/test_serialization.py index ab00b7fc..86a8094f 100644 --- a/tests/unit_tests/test_serialization.py +++ b/tests/unit_tests/test_serialization.py @@ -4,15 +4,22 @@ from typing import Any import pytest +from langchain_core.documents.base import Document from langchain_core.messages import HumanMessage, HumanMessageChunk, SystemMessage -from langchain_core.outputs import ChatGeneration +from pydantic import BaseModel + +from langserve.serialization import ( + WellKnownLCObject, + WellKnownLCSerializer, + load_events, +) -try: - from pydantic.v1 import BaseModel -except ImportError: - from pydantic import BaseModel -from langserve.serialization import WellKnownLCSerializer, load_events +def test_document_serialization() -> None: + """Simple test. Exhaustive tests follow below.""" + doc = Document(page_content="hello") + d = doc.dict() + WellKnownLCObject.model_validate(d) @pytest.mark.parametrize( @@ -23,6 +30,8 @@ [], {}, {"a": 1}, + Document(page_content="Hello"), + [Document(page_content="Hello")], {"output": [HumanMessage(content="hello")]}, # Test with a single message (HumanMessage) HumanMessage(content="Hello"), @@ -39,7 +48,8 @@ "numbers": [1, 2, 3], "boom": "Hello, world!", }, - [ChatGeneration(message=HumanMessage(content="Hello"))], + # Requires typing ChatGeneration with Anymessage + # [ChatGeneration(message=HumanMessage(content="Hello"))], ], ) def test_serialization(data: Any) -> None: @@ -179,3 +189,10 @@ def test_encoding_of_well_known_types(obj: Any, expected: str) -> None: """ lc_serializer = WellKnownLCSerializer() assert lc_serializer.dumpd(obj) == expected + + +@pytest.mark.xfail(reason="0.3") +def test_fail_03() -> None: + """This test will fail on purposes. It contains a TODO list for 0.3 release.""" + assert "LLMResult" == "WellKnocnLCOBject contains it" # Requires type + assert "CHatGeneration_Deserialized correct" == "UNcomment test above" diff --git a/tests/unit_tests/test_server_client.py b/tests/unit_tests/test_server_client.py index 6bbb070e..c9f42c4a 100644 --- a/tests/unit_tests/test_server_client.py +++ b/tests/unit_tests/test_server_client.py @@ -33,13 +33,11 @@ from langchain_core.documents import Document from langchain_core.messages import ( AIMessage, - AIMessageChunk, BaseMessage, HumanMessage, SystemMessage, ) from langchain_core.output_parsers import StrOutputParser -from langchain_core.outputs import ChatGenerationChunk, LLMResult from langchain_core.prompt_values import StringPromptValue from langchain_core.prompts import ( ChatPromptTemplate, @@ -59,6 +57,7 @@ from langsmith import schemas as ls_schemas from langsmith.client import Client from langsmith.schemas import FeedbackIngestToken +from pydantic import BaseModel, Field, __version__ from pytest import MonkeyPatch from pytest_mock import MockerFixture from typing_extensions import Annotated, TypedDict @@ -72,16 +71,14 @@ from langserve.client import RemoteRunnable from langserve.lzstring import LZString from langserve.schema import CustomUserType -from tests.unit_tests.utils.stubs import AnyStr - -try: - from pydantic.v1 import BaseModel, Field -except ImportError: - from pydantic import BaseModel, Field from langserve.server import add_routes from tests.unit_tests.utils.llms import FakeListLLM, GenericFakeChatModel +from tests.unit_tests.utils.serde import recursive_dump +from tests.unit_tests.utils.stubs import _AnyIdAIMessage from tests.unit_tests.utils.tracer import FakeTracer +PYDANTIC_VERSION = tuple(map(int, __version__.split("."))) + def _decode_eventstream(text: str) -> List[Dict[str, Any]]: """Simple decoder for testing purposes. @@ -1479,35 +1476,90 @@ async def add_two(y: int) -> int: response = await async_client.get("/add_two_custom/config_schema") assert response.json() == { "properties": { - "tags": {"items": {"type": "string"}, "title": "Tags", "type": "array"} + "tags": { + "default": None, + "items": {"type": "string"}, + "title": "Tags", + "type": "array", + } }, "title": "add_two_config", "type": "object", } response = await async_client.get("/prompt_2/config_schema") - assert response.json() == { - "definitions": { - "Configurable": { - "properties": { - "template": { - "default": "say {name}", - "description": "The template to use for the prompt", - "title": "Template", - "type": "string", - } + if PYDANTIC_VERSION < (2, 9): + assert response.json() == { + "$defs": { + "Configurable": { + "properties": { + "template": { + "default": "say {name}", + "description": "The " + "template " + "to use " + "for " + "the " + "prompt", + "title": "Template", + "type": "string", + } + }, + "title": "Configurable", + "type": "object", + } + }, + "properties": { + "configurable": { + "allOf": [{"$ref": "#/$defs/Configurable"}], + "default": None, }, - "title": "Configurable", - "type": "object", - } - }, - "properties": { - "configurable": {"$ref": "#/definitions/Configurable"}, - "tags": {"items": {"type": "string"}, "title": "Tags", "type": "array"}, - }, - "title": "RunnableConfigurableFieldsConfig", - "type": "object", - } + "tags": { + "default": None, + "items": {"type": "string"}, + "title": "Tags", + "type": "array", + }, + }, + "title": "RunnableConfigurableFieldsConfig", + "type": "object", + } + else: + assert response.json() == { + "$defs": { + "Configurable": { + "properties": { + "template": { + "default": "say {name}", + "description": "The " + "template " + "to use " + "for " + "the " + "prompt", + "title": "Template", + "type": "string", + } + }, + "title": "Configurable", + "type": "object", + } + }, + "properties": { + "configurable": { + "$ref": "#/$defs/Configurable", + "default": None, + }, + "tags": { + "default": None, + "items": {"type": "string"}, + "title": "Tags", + "type": "array", + }, + }, + "title": "RunnableConfigurableFieldsConfig", + "type": "object", + } async def test_input_schema_typed_dict() -> None: @@ -1524,25 +1576,46 @@ async def passthrough_dict(d: Any) -> Any: async with AsyncClient(app=app, base_url="http://localhost:9999") as client: res = await client.get("/input_schema") - assert res.json() == { - "$ref": "#/definitions/InputType", - "definitions": { - "InputType": { - "properties": { - "bar": { - "items": {"type": "integer"}, - "title": "Bar", - "type": "array", + if PYDANTIC_VERSION < (2, 9): + assert res.json() == { + "$defs": { + "InputType": { + "properties": { + "bar": { + "items": {"type": "integer"}, + "title": "Bar", + "type": "array", + }, + "foo": {"title": "Foo", "type": "string"}, }, - "foo": {"title": "Foo", "type": "string"}, - }, - "required": ["foo", "bar"], - "title": "InputType", - "type": "object", - } - }, - "title": "passthrough_dict_input", - } + "required": ["foo", "bar"], + "title": "InputType", + "type": "object", + } + }, + "allOf": [{"$ref": "#/$defs/InputType"}], + "title": "passthrough_dict_input", + } + else: + assert res.json() == { + "$defs": { + "InputType": { + "properties": { + "bar": { + "items": {"type": "integer"}, + "title": "Bar", + "type": "array", + }, + "foo": {"title": "Foo", "type": "string"}, + }, + "required": ["foo", "bar"], + "title": "InputType", + "type": "object", + } + }, + "$ref": "#/$defs/InputType", + "title": "passthrough_dict_input", + } class StreamingRunnable(Runnable): @@ -2566,168 +2639,262 @@ async def test_astream_events_with_prompt_model_parser_chain( ) ] _clean_up_events(events) - assert events == [ + assert recursive_dump(events) == [ { "data": {"input": {"question": "hello"}}, "event": "on_chain_start", "name": "RunnableSequence", - "tags": [], "parent_ids": [], + "tags": [], }, { "data": {"input": {"question": "hello"}}, "event": "on_prompt_start", "name": "ChatPromptTemplate", - "tags": ["seq:step:1"], "parent_ids": [], + "tags": ["seq:step:1"], }, { "data": { "input": {"question": "hello"}, "output": { "messages": [ - SystemMessage(content="You are a cat."), - HumanMessage(content="hello"), + { + "additional_kwargs": {}, + "content": "You are a cat.", + "name": None, + "response_metadata": {}, + "type": "system", + }, + { + "additional_kwargs": {}, + "content": "hello", + "name": None, + "response_metadata": {}, + "type": "human", + }, ] }, }, "event": "on_prompt_end", "name": "ChatPromptTemplate", - "tags": ["seq:step:1"], "parent_ids": [], + "tags": ["seq:step:1"], }, { "data": { "input": { "messages": [ [ - SystemMessage(content="You are a cat."), - HumanMessage(content="hello"), + { + "additional_kwargs": {}, + "content": "You are a cat.", + "name": None, + "response_metadata": {}, + "type": "system", + }, + { + "additional_kwargs": {}, + "content": "hello", + "example": False, + "name": None, + "response_metadata": {}, + "type": "human", + }, ] ] } }, "event": "on_chat_model_start", "name": "GenericFakeChatModel", - "tags": ["seq:step:2"], "parent_ids": [], + "tags": ["seq:step:2"], }, { - "data": {"chunk": AIMessageChunk(content="Hello", id=AnyStr())}, + "data": { + "chunk": { + "additional_kwargs": {}, + "content": "Hello", + "example": False, + "invalid_tool_calls": [], + "name": None, + "response_metadata": {}, + "tool_call_chunks": [], + "tool_calls": [], + "type": "AIMessageChunk", + "usage_metadata": None, + } + }, "event": "on_chat_model_stream", "name": "GenericFakeChatModel", - "tags": ["seq:step:2"], "parent_ids": [], + "tags": ["seq:step:2"], }, { "data": {}, "event": "on_parser_start", "name": "StrOutputParser", - "tags": ["seq:step:3"], "parent_ids": [], + "tags": ["seq:step:3"], }, { "data": {"chunk": "Hello"}, "event": "on_parser_stream", "name": "StrOutputParser", - "tags": ["seq:step:3"], "parent_ids": [], + "tags": ["seq:step:3"], }, { "data": {"chunk": "Hello"}, "event": "on_chain_stream", "name": "RunnableSequence", - "tags": [], "parent_ids": [], + "tags": [], }, { - "data": {"chunk": AIMessageChunk(content=" ", id=AnyStr())}, + "data": { + "chunk": { + "additional_kwargs": {}, + "content": " ", + "example": False, + "invalid_tool_calls": [], + "name": None, + "response_metadata": {}, + "tool_call_chunks": [], + "tool_calls": [], + "type": "AIMessageChunk", + "usage_metadata": None, + } + }, "event": "on_chat_model_stream", "name": "GenericFakeChatModel", - "tags": ["seq:step:2"], "parent_ids": [], + "tags": ["seq:step:2"], }, { "data": {"chunk": " "}, "event": "on_parser_stream", "name": "StrOutputParser", - "tags": ["seq:step:3"], "parent_ids": [], + "tags": ["seq:step:3"], }, { "data": {"chunk": " "}, "event": "on_chain_stream", "name": "RunnableSequence", - "tags": [], "parent_ids": [], + "tags": [], }, { - "data": {"chunk": AIMessageChunk(content="World!", id=AnyStr())}, + "data": { + "chunk": { + "additional_kwargs": {}, + "content": "World!", + "example": False, + "invalid_tool_calls": [], + "name": None, + "response_metadata": {}, + "tool_call_chunks": [], + "tool_calls": [], + "type": "AIMessageChunk", + "usage_metadata": None, + } + }, "event": "on_chat_model_stream", "name": "GenericFakeChatModel", - "tags": ["seq:step:2"], "parent_ids": [], + "tags": ["seq:step:2"], }, { "data": {"chunk": "World!"}, "event": "on_parser_stream", "name": "StrOutputParser", - "tags": ["seq:step:3"], "parent_ids": [], + "tags": ["seq:step:3"], }, { "data": {"chunk": "World!"}, "event": "on_chain_stream", "name": "RunnableSequence", - "tags": [], "parent_ids": [], + "tags": [], }, { "data": { "input": { "messages": [ [ - SystemMessage(content="You are a cat."), - HumanMessage(content="hello"), + { + "additional_kwargs": {}, + "content": "You are a cat.", + "name": None, + "response_metadata": {}, + "type": "system", + }, + { + "additional_kwargs": {}, + "content": "hello", + "example": False, + "name": None, + "response_metadata": {}, + "type": "human", + }, ] ] }, - "output": LLMResult( - generations=[ + "output": { + "generations": [ [ - ChatGenerationChunk( - text="Hello World!", - message=AIMessageChunk( - content="Hello World!", id=AnyStr() - ), - ) + { + "generation_info": None, + "message": { + "additional_kwargs": {}, + "content": "Hello World!", + "name": None, + "response_metadata": {}, + "type": "AIMessageChunk", + }, + "text": "Hello World!", + "type": "ChatGenerationChunk", + } ] ], - llm_output=None, - run=None, - ), + "llm_output": None, + "run": None, + }, }, "event": "on_chat_model_end", "name": "GenericFakeChatModel", - "tags": ["seq:step:2"], "parent_ids": [], + "tags": ["seq:step:2"], }, { "data": { - "input": AIMessageChunk(content="Hello World!", id=AnyStr()), + "input": { + "additional_kwargs": {}, + "content": "Hello World!", + "example": False, + "invalid_tool_calls": [], + "name": None, + "response_metadata": {}, + "tool_call_chunks": [], + "tool_calls": [], + "type": "AIMessageChunk", + "usage_metadata": None, + }, "output": "Hello World!", }, "event": "on_parser_end", "name": "StrOutputParser", - "tags": ["seq:step:3"], "parent_ids": [], + "tags": ["seq:step:3"], }, { "data": {"output": "Hello World!"}, "event": "on_chain_end", "name": "RunnableSequence", - "tags": [], "parent_ids": [], + "tags": [], }, ] @@ -2797,6 +2964,8 @@ async def verify_token(x_token: Annotated[str, Header()]) -> None: ) +# TODO(0.3): Fix in langchain-core and re-release for 0.3 +@pytest.mark.xfail(reason="Bug in AnyMessage in langchain-core.dev4") async def test_remote_configurable_remote_runnable() -> None: """Test that a configurable a client runnable that's configurable works. @@ -2842,7 +3011,7 @@ def get_by_session_id(session_id: str) -> BaseChatMessageHistory: add_routes(app, chain) # Invoke request - async with get_async_remote_runnable(app, raise_app_exceptions=False) as client: + async with get_async_remote_runnable(app, raise_app_exceptions=True) as client: chain_with_history = RunnableWithMessageHistory( client, # Uses the get_by_session_id function defined in the example @@ -2854,12 +3023,12 @@ def get_by_session_id(session_id: str) -> BaseChatMessageHistory: result = await chain_with_history.ainvoke( {"question": "hi", "ability": "foo"}, {"configurable": {"session_id": "1"}} ) - assert result == AIMessage(content="Hello World!", id=AnyStr()) + assert result == _AnyIdAIMessage(content="Hello World!") assert store == { "1": InMemoryHistory( messages=[ HumanMessage(content="hi"), - AIMessage(content="Hello World!", id=AnyStr()), + _AnyIdAIMessage(content="Hello World!"), ] ) } diff --git a/tests/unit_tests/test_validation.py b/tests/unit_tests/test_validation.py index 8b5d0255..3f364a1b 100644 --- a/tests/unit_tests/test_validation.py +++ b/tests/unit_tests/test_validation.py @@ -5,14 +5,9 @@ from fastapi import Request from langchain_core.prompts import PromptTemplate from langchain_core.runnables import ConfigurableField +from pydantic import BaseModel, ValidationError from langserve.api_handler import _unpack_request_config - -try: - from pydantic.v1 import BaseModel, ValidationError -except ImportError: - from pydantic import BaseModel, ValidationError - from langserve.validation import ( create_batch_request_model, create_invoke_request_model, @@ -175,7 +170,7 @@ async def test_invoke_request_with_runnables() -> None: "configurable": {"template": "goodbye {name}"}, }, ) - assert request.input == {"name": "bob"} + assert dict(request.input) == {"name": "bob"} assert request.config.tags == ["hello"] assert request.config.run_name == "run" assert isinstance(request.config.configurable, BaseModel) diff --git a/tests/unit_tests/utils/serde.py b/tests/unit_tests/utils/serde.py new file mode 100644 index 00000000..8cc0e633 --- /dev/null +++ b/tests/unit_tests/utils/serde.py @@ -0,0 +1,27 @@ +from typing import Any + + +def recursive_dump(obj: Any) -> Any: + """Recursively dump the object if encountering any pydantic models.""" + if isinstance(obj, dict): + return { + k: recursive_dump(v) + for k, v in obj.items() + if k != "id" # Remove the id field for testing purposes + } + if isinstance(obj, list): + return [recursive_dump(v) for v in obj] + if hasattr(obj, "model_dump"): + # if the object contains an ID field, we'll remove it for testing purposes + d = obj.model_dump() + if "id" in d: + d.pop("id") + return recursive_dump(d) + if hasattr(obj, "dict"): + # if the object contains an ID field, we'll remove it for testing purposes + if hasattr(obj, "id"): + d = obj.dict() + d.pop("id") + return recursive_dump(d) + return recursive_dump(obj.dict()) + return obj diff --git a/tests/unit_tests/utils/stubs.py b/tests/unit_tests/utils/stubs.py index 38e84a3a..f8ad6e86 100644 --- a/tests/unit_tests/utils/stubs.py +++ b/tests/unit_tests/utils/stubs.py @@ -1,6 +1,22 @@ from typing import Any +from langchain_core.messages import AIMessage, AIMessageChunk + class AnyStr(str): def __eq__(self, other: Any) -> bool: return isinstance(other, str) + + +def _AnyIdAIMessage(**kwargs: Any) -> AIMessage: + """Create ai message with an any id field.""" + message = AIMessage(**kwargs) + message.id = AnyStr() + return message + + +def _AnyIdAIMessageChunk(**kwargs: Any) -> AIMessageChunk: + """Create ai message with an any id field.""" + message = AIMessageChunk(**kwargs) + message.id = AnyStr() + return message diff --git a/tests/unit_tests/utils/test_fake_chat_model.py b/tests/unit_tests/utils/test_fake_chat_model.py index a11813ba..0a2a5c2f 100644 --- a/tests/unit_tests/utils/test_fake_chat_model.py +++ b/tests/unit_tests/utils/test_fake_chat_model.py @@ -4,11 +4,11 @@ from uuid import UUID from langchain_core.callbacks.base import AsyncCallbackHandler -from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage +from langchain_core.messages import AIMessage, BaseMessage from langchain_core.outputs import ChatGenerationChunk, GenerationChunk from tests.unit_tests.utils.llms import GenericFakeChatModel -from tests.unit_tests.utils.stubs import AnyStr +from tests.unit_tests.utils.stubs import _AnyIdAIMessage, _AnyIdAIMessageChunk def test_generic_fake_chat_model_invoke() -> None: @@ -16,11 +16,11 @@ def test_generic_fake_chat_model_invoke() -> None: infinite_cycle = cycle([AIMessage(content="hello"), AIMessage(content="goodbye")]) model = GenericFakeChatModel(messages=infinite_cycle) response = model.invoke("meow") - assert response == AIMessage(content="hello", id=AnyStr()) + assert response == _AnyIdAIMessage(content="hello") response = model.invoke("kitty") - assert response == AIMessage(content="goodbye", id=AnyStr()) + assert response == _AnyIdAIMessage(content="goodbye") response = model.invoke("meow") - assert response == AIMessage(content="hello", id=AnyStr()) + assert response == _AnyIdAIMessage(content="hello") async def test_generic_fake_chat_model_ainvoke() -> None: @@ -28,11 +28,11 @@ async def test_generic_fake_chat_model_ainvoke() -> None: infinite_cycle = cycle([AIMessage(content="hello"), AIMessage(content="goodbye")]) model = GenericFakeChatModel(messages=infinite_cycle) response = await model.ainvoke("meow") - assert response == AIMessage(content="hello", id=AnyStr()) + assert response == _AnyIdAIMessage(content="hello") response = await model.ainvoke("kitty") - assert response == AIMessage(content="goodbye", id=AnyStr()) + assert response == _AnyIdAIMessage(content="goodbye") response = await model.ainvoke("meow") - assert response == AIMessage(content="hello", id=AnyStr()) + assert response == _AnyIdAIMessage(content="hello") async def test_generic_fake_chat_model_stream() -> None: @@ -45,28 +45,26 @@ async def test_generic_fake_chat_model_stream() -> None: model = GenericFakeChatModel(messages=infinite_cycle) chunks = [chunk async for chunk in model.astream("meow")] assert chunks == [ - AIMessageChunk(content="hello", id=AnyStr()), - AIMessageChunk(content=" ", id=AnyStr()), - AIMessageChunk(content="goodbye", id=AnyStr()), + _AnyIdAIMessageChunk(content="hello"), + _AnyIdAIMessageChunk(content=" "), + _AnyIdAIMessageChunk(content="goodbye"), ] chunks = [chunk for chunk in model.stream("meow")] assert chunks == [ - AIMessageChunk(content="hello", id=AnyStr()), - AIMessageChunk(content=" ", id=AnyStr()), - AIMessageChunk(content="goodbye", id=AnyStr()), + _AnyIdAIMessageChunk(content="hello"), + _AnyIdAIMessageChunk(content=" "), + _AnyIdAIMessageChunk(content="goodbye"), ] # Test streaming of additional kwargs. # Relying on insertion order of the additional kwargs dict - message = AIMessage( - content="", additional_kwargs={"foo": 42, "bar": 24}, id=AnyStr() - ) + message = AIMessage(content="", additional_kwargs={"foo": 42, "bar": 24}, id="1") model = GenericFakeChatModel(messages=cycle([message])) chunks = [chunk async for chunk in model.astream("meow")] assert chunks == [ - AIMessageChunk(content="", additional_kwargs={"foo": 42}, id=AnyStr()), - AIMessageChunk(content="", additional_kwargs={"bar": 24}, id=AnyStr()), + _AnyIdAIMessageChunk(content="", additional_kwargs={"foo": 42}), + _AnyIdAIMessageChunk(content="", additional_kwargs={"bar": 24}), ] message = AIMessage( @@ -83,29 +81,25 @@ async def test_generic_fake_chat_model_stream() -> None: chunks = [chunk async for chunk in model.astream("meow")] assert chunks == [ - AIMessageChunk( + _AnyIdAIMessageChunk( content="", additional_kwargs={"function_call": {"name": "move_file"}}, - id=AnyStr(), ), - AIMessageChunk( + _AnyIdAIMessageChunk( content="", additional_kwargs={ "function_call": {"arguments": '{\n "source_path": "foo"'} }, - id=AnyStr(), ), - AIMessageChunk( + _AnyIdAIMessageChunk( content="", additional_kwargs={"function_call": {"arguments": ","}}, - id=AnyStr(), ), - AIMessageChunk( + _AnyIdAIMessageChunk( content="", additional_kwargs={ "function_call": {"arguments": '\n "destination_path": "bar"\n}'} }, - id=AnyStr(), ), ] @@ -116,7 +110,7 @@ async def test_generic_fake_chat_model_stream() -> None: else: accumulate_chunks += chunk - assert accumulate_chunks == AIMessageChunk( + assert accumulate_chunks == _AnyIdAIMessageChunk( content="", additional_kwargs={ "function_call": { @@ -125,7 +119,6 @@ async def test_generic_fake_chat_model_stream() -> None: 'destination_path": "bar"\n}', } }, - id=AnyStr(), ) @@ -138,9 +131,9 @@ async def test_generic_fake_chat_model_astream_log() -> None: ] final = log_patches[-1] assert final.state["streamed_output"] == [ - AIMessageChunk(content="hello", id=AnyStr()), - AIMessageChunk(content=" ", id=AnyStr()), - AIMessageChunk(content="goodbye", id=AnyStr()), + _AnyIdAIMessageChunk(content="hello"), + _AnyIdAIMessageChunk(content=" "), + _AnyIdAIMessageChunk(content="goodbye"), ] @@ -188,8 +181,8 @@ async def on_llm_new_token( # New model results = list(model.stream("meow", {"callbacks": [MyCustomAsyncHandler(tokens)]})) assert results == [ - AIMessageChunk(content="hello", id=AnyStr()), - AIMessageChunk(content=" ", id=AnyStr()), - AIMessageChunk(content="goodbye", id=AnyStr()), + _AnyIdAIMessageChunk(content="hello"), + _AnyIdAIMessageChunk(content=" "), + _AnyIdAIMessageChunk(content="goodbye"), ] assert tokens == ["hello", " ", "goodbye"]