Release 0.30dev2 (#758)

Fix serialization issue when there are defaults

.github/workflows/_pydantic_compatibility.yml

@@ -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'

.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

README.md

@@ -380,7 +380,7 @@ prompt = ChatPromptTemplate.from_messages(
     ]
 )
 
-chain = prompt | ChatAnthropic(model="claude-2")
+chain = prompt | ChatAnthropic(model="claude-2.1")
 
 
 class InputChat(BaseModel):
@@ -772,7 +772,7 @@ prompt = ChatPromptTemplate.from_messages(
     ]
 )
 
-chain = prompt | ChatAnthropic(model="claude-2")
+chain = prompt | ChatAnthropic(model="claude-2.1")
 
 
 class MessageListInput(BaseModel):

examples/agent/server.py

@@ -23,12 +23,12 @@ from fastapi import FastAPI
 from langchain.agents import AgentExecutor
 from langchain.agents.format_scratchpad import format_to_openai_functions
 from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
-from langchain.pydantic_v1 import BaseModel
 from langchain_community.vectorstores import FAISS
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 from langchain_core.tools import tool
 from langchain_core.utils.function_calling import format_tool_to_openai_function
 from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from pydantic import BaseModel
 
 from langserve import add_routes
 

examples/agent_custom_streaming/server.py

@@ -57,9 +57,9 @@ from langchain_core.runnables import RunnableLambda
 from langchain_core.tools import tool
 from langchain_core.utils.function_calling import format_tool_to_openai_tool
 from langchain_openai import ChatOpenAI
+from pydantic import BaseModel
 
 from langserve import add_routes
-from langserve.pydantic_v1 import BaseModel
 
 prompt = ChatPromptTemplate.from_messages(
     [

examples/agent_with_history/server.py

@@ -36,9 +36,9 @@ from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 from langchain_core.tools import tool
 from langchain_core.utils.function_calling import format_tool_to_openai_tool
 from langchain_openai import ChatOpenAI
+from pydantic import BaseModel, Field
 
 from langserve import add_routes
-from langserve.pydantic_v1 import BaseModel, Field
 
 prompt = ChatPromptTemplate.from_messages(
     [

examples/auth/api_handler/server.py

@@ -44,10 +44,10 @@ from langchain_core.runnables import (
 )
 from langchain_core.vectorstores import VectorStore
 from langchain_openai import OpenAIEmbeddings
+from pydantic import BaseModel, ConfigDict
 from typing_extensions import Annotated
 
 from langserve import APIHandler
-from langserve.pydantic_v1 import BaseModel
 
 
 class User(BaseModel):
@@ -150,10 +150,9 @@ class PerUserVectorstore(RunnableSerializable):
     user_id: Optional[str]
     vectorstore: VectorStore
 
-    class Config:
-        # Allow arbitrary types since VectorStore is an abstract interface
-        # and not a pydantic model
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+    )
 
     def _invoke(
         self, input: str, config: Optional[RunnableConfig] = None, **kwargs: Any

examples/auth/per_req_config_modifier/server.py

@@ -45,10 +45,10 @@ from langchain_core.runnables import (
 )
 from langchain_core.vectorstores import VectorStore
 from langchain_openai import OpenAIEmbeddings
+from pydantic import BaseModel, ConfigDict
 from typing_extensions import Annotated
 
 from langserve import add_routes
-from langserve.pydantic_v1 import BaseModel
 
 
 class User(BaseModel):
@@ -147,10 +147,9 @@ class PerUserVectorstore(RunnableSerializable):
     user_id: Optional[str]
     vectorstore: VectorStore
 
-    class Config:
-        # Allow arbitrary types since VectorStore is an abstract interface
-        # and not a pydantic model
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+    )
 
     def _invoke(
         self, input: str, config: Optional[RunnableConfig] = None, **kwargs: Any

examples/chat_playground/legacy_input/server.py

@@ -8,9 +8,9 @@ from fastapi import FastAPI
 from langchain_anthropic import ChatAnthropic
 from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from pydantic import BaseModel, Field
 
 from langserve import add_routes
-from langserve.pydantic_v1 import BaseModel, Field
 
 app = FastAPI(
     title="LangChain Server",
@@ -28,7 +28,7 @@ prompt = ChatPromptTemplate.from_messages(
     ]
 )
 
-chain = prompt | ChatAnthropic(model="claude-2")
+chain = prompt | ChatAnthropic(model="claude-2.1")
 
 
 class InputChat(BaseModel):

examples/chat_playground/server.py

@@ -8,9 +8,9 @@ from fastapi import FastAPI
 from langchain_anthropic.chat_models import ChatAnthropic
 from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from pydantic import BaseModel, Field
 
 from langserve import add_routes
-from langserve.pydantic_v1 import BaseModel, Field
 
 app = FastAPI(
     title="LangChain Server",

examples/chat_with_persistence/server.py

@@ -18,9 +18,9 @@ from langchain_community.chat_message_histories import FileChatMessageHistory
 from langchain_core.chat_history import BaseChatMessageHistory
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 from langchain_core.runnables.history import RunnableWithMessageHistory
+from pydantic import BaseModel, Field
 
 from langserve import add_routes
-from langserve.pydantic_v1 import BaseModel, Field
 
 
 def _is_valid_identifier(value: str) -> bool:
@@ -76,7 +76,7 @@ prompt = ChatPromptTemplate.from_messages(
     ]
 )
 
-chain = prompt | ChatAnthropic(model="claude-2")
+chain = prompt | ChatAnthropic(model="claude-2.1")
 
 
 class InputChat(BaseModel):

examples/configurable_agent_executor/server.py

@@ -20,7 +20,6 @@ from fastapi import FastAPI
 from langchain.agents import AgentExecutor
 from langchain.agents.format_scratchpad import format_to_openai_functions
 from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
-from langchain.pydantic_v1 import BaseModel
 from langchain_community.vectorstores import FAISS
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 from langchain_core.runnables import (
@@ -33,6 +32,7 @@ from langchain_core.runnables.utils import Input, Output
 from langchain_core.tools import tool
 from langchain_core.utils.function_calling import format_tool_to_openai_function
 from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from pydantic import BaseModel
 
 from langserve import add_routes
 

examples/configurable_retrieval/server.py

@@ -15,9 +15,9 @@ from langchain_core.runnables import (
 )
 from langchain_core.vectorstores import VectorStore
 from langchain_openai import OpenAIEmbeddings
+from pydantic import BaseModel, Field
 
 from langserve import add_routes
-from langserve.pydantic_v1 import BaseModel, Field
 
 vectorstore1 = FAISS.from_texts(
     ["cats like fish", "dogs like sticks"], embedding=OpenAIEmbeddings()

examples/conversational_retrieval_chain/server.py

@@ -18,9 +18,9 @@ from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import ChatPromptTemplate, PromptTemplate, format_document
 from langchain_core.runnables import RunnableMap, RunnablePassthrough
 from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from pydantic import BaseModel, Field
 
 from langserve import add_routes
-from langserve.pydantic_v1 import BaseModel, Field
 
 _TEMPLATE = """Given the following conversation and a follow up question, rephrase the 
 follow up question to be a standalone question, in its original language.

examples/file_processing/server.py

@@ -15,10 +15,10 @@ allowing one to upload a binary file using the langserve playground UI.
 import base64
 
 from fastapi import FastAPI
-from langchain.pydantic_v1 import Field
 from langchain_community.document_loaders.parsers.pdf import PDFMinerParser
 from langchain_core.document_loaders import Blob
 from langchain_core.runnables import RunnableLambda
+from pydantic import Field
 
 from langserve import CustomUserType, add_routes
 

examples/widgets/chat/message_list/server.py

@@ -10,9 +10,9 @@ from langchain_anthropic import ChatAnthropic
 from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from pydantic import BaseModel, Field
 
 from langserve import add_routes
-from langserve.pydantic_v1 import BaseModel, Field
 
 app = FastAPI(
     title="LangChain Server",
@@ -40,7 +40,7 @@ prompt = ChatPromptTemplate.from_messages(
     ]
 )
 
-chain = prompt | ChatAnthropic(model="claude-2") | StrOutputParser()
+chain = prompt | ChatAnthropic(model="claude-2.1") | StrOutputParser()
 
 
 class InputChat(BaseModel):

examples/widgets/chat/tuples/server.py

@@ -6,7 +6,6 @@ from typing import Any, Dict, List, Tuple
 
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
-from langchain.pydantic_v1 import BaseModel, Field
 from langchain_community.document_loaders.parsers.pdf import PDFMinerParser
 from langchain_core.document_loaders import Blob
 from langchain_core.messages import (
@@ -17,6 +16,7 @@ from langchain_core.messages import (
 )
 from langchain_core.runnables import RunnableLambda, RunnableParallel
 from langchain_openai import ChatOpenAI
+from pydantic import BaseModel, Field
 
 from langserve import CustomUserType
 from langserve.server import add_routes

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)

langserve/api_handler.py

@@ -29,6 +29,7 @@ from fastapi.encoders import jsonable_encoder
 from fastapi.exceptions import RequestValidationError
 from langchain_core._api.beta_decorator import warn_beta
 from langchain_core.callbacks.base import AsyncCallbackHandler
+from langchain_core.callbacks.manager import BaseCallbackManager
 from langchain_core.load.serializable import Serializable
 from langchain_core.runnables import Runnable, RunnableConfig
 from langchain_core.runnables.config import (
@@ -40,14 +41,15 @@ from langchain_core.tracers import RunLogPatch
 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,
@@ -184,11 +186,11 @@ async def _unpack_request_config(
     config_dicts = []
     for config in client_sent_configs:
         if isinstance(config, str):
-            config_dicts.append(model(**_config_from_hash(config)).dict())
+            config_dicts.append(model(**_config_from_hash(config)).model_dump())
         elif isinstance(config, BaseModel):
-            config_dicts.append(config.dict())
+            config_dicts.append(config.model_dump())
         elif isinstance(config, Mapping):
-            config_dicts.append(model(**config).dict())
+            config_dicts.append(model(**config).model_dump())
         else:
             raise TypeError(f"Expected a string, dict or BaseModel got {type(config)}")
     config = merge_configs(*config_dicts)
@@ -255,10 +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
@@ -279,8 +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
 
@@ -294,7 +298,7 @@ def _unpack_input(validated_model: BaseModel) -> Any:
         # This logic should be applied recursively to nested models.
         return {
             fieldname: _unpack_input(getattr(model, fieldname))
-            for fieldname in model.__fields__.keys()
+            for fieldname in model.model_fields.keys()
         }
 
     return model
@@ -304,7 +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)
@@ -313,10 +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()
         },
     )
 
@@ -326,6 +330,11 @@ def _replace_non_alphanumeric_with_underscores(s: str) -> str:
     return re.sub(r"[^a-zA-Z0-9]", "_", s)
 
 
+def _schema_json(model: Type[BaseModel]) -> str:
+    """Return the JSON representation of the model schema."""
+    return json.dumps(model.model_json_schema(), sort_keys=True, indent=False)
+
+
 def _resolve_model(
     type_: Union[Type, BaseModel], default_name: str, namespace: str
 ) -> Type[BaseModel]:
@@ -333,15 +342,15 @@ 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()
+    hash_ = _schema_json(model)
 
     if model.__name__ in _SEEN_NAMES and hash_ not in _MODEL_REGISTRY:
         # If the model name has been seen before, but the model itself is different
         # generate a new name for the model.
         model_to_use = _rename_pydantic_model(model, namespace)
-        hash_ = model_to_use.schema_json()
+        hash_ = _schema_json(model_to_use)
     else:
         model_to_use = model
 
@@ -366,11 +375,7 @@ 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
 
 
@@ -403,7 +408,7 @@ 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:
@@ -423,27 +428,27 @@ 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):
@@ -451,11 +456,8 @@ 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)}"
@@ -470,7 +472,12 @@ def _add_callbacks(
     """Add the callback aggregator to the config."""
     if "callbacks" not in config:
         config["callbacks"] = []
-    config["callbacks"].extend(callbacks)
+    if "callbacks" in config:
+        if isinstance(config["callbacks"], list):
+            config["callbacks"].extend(callbacks)
+        elif isinstance(config["callbacks"], BaseCallbackManager):
+            for callback in callbacks:
+                config["callbacks"].add_handler(callback, inherit=True)
 
 
 _MODEL_REGISTRY = {}
@@ -753,7 +760,7 @@ class APIHandler:
         except json.JSONDecodeError:
             raise RequestValidationError(errors=["Invalid JSON body"])
         try:
-            body = InvokeRequestShallowValidator.validate(body)
+            body = InvokeRequestShallowValidator.model_validate(body)
 
             # Merge the config from the path with the config from the body.
             user_provided_config = await _unpack_request_config(
@@ -775,7 +782,7 @@ class APIHandler:
             # This takes into account changes in the input type when
             # using configuration.
             schema = self._runnable.with_config(config).input_schema
-            input_ = schema.validate(body.input)
+            input_ = schema.model_validate(body.input)
             return config, _unpack_input(input_)
         except ValidationError as e:
             raise RequestValidationError(e.errors(), body=body)
@@ -885,7 +892,7 @@ class APIHandler:
             raise RequestValidationError(errors=["Invalid JSON body"])
 
         with _with_validation_error_translation():
-            body = BatchRequestShallowValidator.validate(body)
+            body = BatchRequestShallowValidator.model_validate(body)
             config = body.config
 
             # First unpack the config
@@ -936,7 +943,7 @@ class APIHandler:
 
         inputs = [
             _unpack_input(
-                self._runnable.with_config(config_).input_schema.validate(input_)
+                self._runnable.with_config(config_).input_schema.model_validate(input_)
             )
             for config_, input_ in zip(configs_, inputs_)
         ]
@@ -1405,7 +1412,7 @@ class APIHandler:
                 self._run_name, user_provided_config, request
             )
 
-        return self._runnable.get_input_schema(config).schema()
+        return self._runnable.get_input_schema(config).model_json_schema()
 
     async def output_schema(
         self,
@@ -1432,7 +1439,7 @@ class APIHandler:
             config = _update_config_with_defaults(
                 self._run_name, user_provided_config, request
             )
-        return self._runnable.get_output_schema(config).schema()
+        return self._runnable.get_output_schema(config).model_json_schema()
 
     async def config_schema(
         self,
@@ -1462,7 +1469,7 @@ class APIHandler:
         return (
             self._runnable.with_config(config)
             .config_schema(include=self._config_keys)
-            .schema()
+            .model_json_schema()
         )
 
     async def playground(

langserve/chat_playground/dist/index.html

@@ -5,7 +5,7 @@
     <link rel="icon" href="/____LANGSERVE_BASE_URL/favicon.ico" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>Chat Playground</title>
-    <script type="module" crossorigin src="/____LANGSERVE_BASE_URL/assets/index-86d4d9c0.js"></script>
+    <script type="module" crossorigin src="/____LANGSERVE_BASE_URL/assets/index-53ad47d4.js"></script>
     <link rel="stylesheet" href="/____LANGSERVE_BASE_URL/assets/index-434ff580.css">
   </head>
   <body>

langserve/chat_playground/src/App.tsx

@@ -30,6 +30,7 @@ export function App() {
   );
   const outputSchemaSupported = (
     outputDataSchema?.anyOf?.find((option) => option.properties?.type?.enum?.includes("ai")) ||
+    outputDataSchema?.oneOf?.find((option) => option.properties?.type?.enum?.includes("ai")) ||
     outputDataSchema?.type === "string"
   );
   const isSupported = isLoading || (inputSchemaSupported && outputSchemaSupported);

langserve/client.py

@@ -431,11 +431,15 @@ class RemoteRunnable(Runnable[Input, Output]):
         self,
         inputs: List[Input],
         config: Optional[RunnableConfig] = None,
+        *,
+        return_exceptions: bool = False,
         **kwargs: Any,
     ) -> List[Output]:
         if kwargs:
-            raise NotImplementedError("kwargs not implemented yet.")
-        return self._batch_with_config(self._batch, inputs, config)
+            raise NotImplementedError(f"kwargs not implemented yet. Got {kwargs}")
+        return self._batch_with_config(
+            self._batch, inputs, config, return_exceptions=return_exceptions
+        )
 
     async def _abatch(
         self,

langserve/playground.py

@@ -6,8 +6,7 @@ from typing import Literal, Sequence, Type
 
 from fastapi.responses import Response
 from langchain_core.runnables import Runnable
-
-from langserve.pydantic_v1 import BaseModel
+from pydantic import BaseModel
 
 
 class PlaygroundTemplate(Template):
@@ -90,10 +89,12 @@ async def serve_playground(
                     if base_url.startswith("/")
                     else base_url,
                     LANGSERVE_CONFIG_SCHEMA=json.dumps(
-                        runnable.config_schema(include=config_keys).schema()
+                        runnable.config_schema(include=config_keys).model_json_schema()
+                    ),
+                    LANGSERVE_INPUT_SCHEMA=json.dumps(input_schema.model_json_schema()),
+                    LANGSERVE_OUTPUT_SCHEMA=json.dumps(
+                        output_schema.model_json_schema()
                     ),
-                    LANGSERVE_INPUT_SCHEMA=json.dumps(input_schema.schema()),
-                    LANGSERVE_OUTPUT_SCHEMA=json.dumps(output_schema.schema()),
                     LANGSERVE_FEEDBACK_ENABLED=json.dumps(
                         "true" if feedback_enabled else "false"
                     ),

langserve/playground/dist/index.html

@@ -5,7 +5,7 @@
     <link rel="icon" href="/____LANGSERVE_BASE_URL/favicon.ico" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>Playground</title>
-    <script type="module" crossorigin src="/____LANGSERVE_BASE_URL/assets/index-dbc96538.js"></script>
+    <script type="module" crossorigin src="/____LANGSERVE_BASE_URL/assets/index-400979f0.js"></script>
     <link rel="stylesheet" href="/____LANGSERVE_BASE_URL/assets/index-52e8ab2f.css">
   </head>
   <body>

langserve/playground/src/components/ChatMessagesControlRenderer.tsx

@@ -6,12 +6,30 @@ import {
   schemaMatches,
   Paths,
   isControl,
+  JsonSchema,
 } from "@jsonforms/core";
 import { useStreamCallback } from "../useStreamCallback";
 import { isJsonSchemaExtra } from "../utils/schema";
 import { MessageFields, ChatMessageInput } from "./ChatMessageInput";
 import { useEffect } from "react";
 
+function checkItemSchema(schema: JsonSchema) {
+  const isObjectMessage =
+    schema.type === "object" &&
+    (schema.title?.endsWith("Message") ||
+      schema.title?.endsWith("MessageChunk"));
+
+  const isTupleMessage =
+    schema.type === "array" &&
+    schema.minItems === 2 &&
+    schema.maxItems === 2 &&
+    Array.isArray(schema.items) &&
+    schema.items.length === 2 &&
+    schema.items.every((schema) => schema.type === "string");
+
+  return isObjectMessage || isTupleMessage;
+}
+
 export const chatMessagesTester = rankWith(
   12,
   and(
@@ -34,22 +52,11 @@ export const chatMessagesTester = rankWith(
       }
 
       if ("anyOf" in schema.items && schema.items.anyOf != null) {
-        return schema.items.anyOf.every((schema) => {
-          const isObjectMessage =
-            schema.type === "object" &&
-            (schema.title?.endsWith("Message") ||
-              schema.title?.endsWith("MessageChunk"));
+        return schema.items.anyOf.every(checkItemSchema);
+      }
 
-          const isTupleMessage =
-            schema.type === "array" &&
-            schema.minItems === 2 &&
-            schema.maxItems === 2 &&
-            Array.isArray(schema.items) &&
-            schema.items.length === 2 &&
-            schema.items.every((schema) => schema.type === "string");
-
-          return isObjectMessage || isTupleMessage;
-        });
+      if ("oneOf" in schema.items && schema.items.oneOf != null) {
+        return schema.items.oneOf.every(checkItemSchema);
       }
 
       return false;
@@ -64,10 +71,14 @@ export const ChatMessagesControlRenderer = withJsonFormsControlProps(
     useEffect(() => {
       if (!isJsonSchemaExtra(props.schema)) return;
       if (props.schema.extra.widget.type !== "chat") return;
-      setTimeout(() => props.handleChange(props.path, [
-        ...data,
-        { content: "", type: "human" },
-      ]), 10);
+      setTimeout(
+        () =>
+          props.handleChange(props.path, [
+            ...data,
+            { content: "", type: "human" },
+          ]),
+        10
+      );
     }, []);
 
     useStreamCallback("onStart", () => {
@@ -81,7 +92,10 @@ export const ChatMessagesControlRenderer = withJsonFormsControlProps(
       if (props.schema.extra.widget.type !== "chat") return;
       if (aggregatedState?.final_output !== undefined) {
         const msgPath = Paths.compose(props.path, `${data.length - 1}`);
-        if ((aggregatedState.final_output as MessageFields)?.type === "AIMessageChunk") {
+        if (
+          (aggregatedState.final_output as MessageFields)?.type ===
+          "AIMessageChunk"
+        ) {
           props.handleChange(
             Paths.compose(msgPath, "content"),
             (aggregatedState.final_output as MessageFields)?.content
@@ -140,7 +154,7 @@ export const ChatMessagesControlRenderer = withJsonFormsControlProps(
                 props.path,
                 data.filter((_, i) => i !== index)
               );
-            }
+            };
             return (
               <ChatMessageInput
                 message={message}
@@ -148,7 +162,7 @@ export const ChatMessagesControlRenderer = withJsonFormsControlProps(
                 handleRemoval={handleChatMessageRemoval}
                 path={props.path}
                 key={index}
-                ></ChatMessageInput>
+              ></ChatMessageInput>
             );
           })}
         </div>

langserve/pydantic_v1.py

@@ -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

langserve/schema.py

@@ -2,10 +2,11 @@ from datetime import datetime
 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):

langserve/serialization.py

@@ -13,7 +13,7 @@ sensitive information from the server to the client.
 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
@@ -29,6 +29,8 @@ from langchain_core.messages import (
     HumanMessageChunk,
     SystemMessage,
     SystemMessageChunk,
+    ToolMessage,
+    ToolMessageChunk,
 )
 from langchain_core.outputs import (
     ChatGeneration,
@@ -38,8 +40,8 @@ from langchain_core.outputs import (
 )
 from langchain_core.prompt_values import ChatPromptValueConcrete
 from langchain_core.prompts.base import StringPromptValue
+from pydantic import BaseModel, Discriminator, Field, RootModel, Tag, ValidationError
 
-from langserve.pydantic_v1 import BaseModel, ValidationError
 from langserve.validation import CallbackEvent
 
 logger = logging.getLogger(__name__)
@@ -51,42 +53,58 @@ def _log_error_message_once(error_message: str) -> None:
     logger.error(error_message)
 
 
-class WellKnownLCObject(BaseModel):
-    """A well known LangChain object.
+def _get_type(v: Any) -> str:
+    """Get the type associated with the object for serialization purposes."""
+    if isinstance(v, dict) and "type" in v:
+        return v["type"]
+    elif hasattr(v, "type"):
+        return v.type
+    else:
+        raise TypeError(
+            f"Expected either a dictionary with a 'type' key or an object "
+            f"with a 'type' attribute. Instead got type {type(v)}."
+        )
 
-    A pydantic model that defines what constitutes a well known LangChain object.
 
-    All well-known objects are allowed to be serialized and de-serialized.
-    """
+# 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,
+WellKnownLCObject = RootModel[
+    Annotated[
+        Union[
+            Annotated[AIMessage, Tag(tag="ai")],
+            Annotated[HumanMessage, Tag(tag="human")],
+            Annotated[ChatMessage, Tag(tag="chat")],
+            Annotated[SystemMessage, Tag(tag="system")],
+            Annotated[FunctionMessage, Tag(tag="function")],
+            Annotated[ToolMessage, Tag(tag="tool")],
+            Annotated[AIMessageChunk, Tag(tag="AIMessageChunk")],
+            Annotated[HumanMessageChunk, Tag(tag="HumanMessageChunk")],
+            Annotated[ChatMessageChunk, Tag(tag="ChatMessageChunk")],
+            Annotated[SystemMessageChunk, Tag(tag="SystemMessageChunk")],
+            Annotated[FunctionMessageChunk, Tag(tag="FunctionMessageChunk")],
+            Annotated[ToolMessageChunk, Tag(tag="ToolMessageChunk")],
+            Annotated[Document, Tag(tag="Document")],
+            Annotated[StringPromptValue, Tag(tag="StringPromptValue")],
+            Annotated[ChatPromptValueConcrete, Tag(tag="ChatPromptValueConcrete")],
+            Annotated[AgentAction, Tag(tag="AgentAction")],
+            Annotated[AgentFinish, Tag(tag="AgentFinish")],
+            Annotated[AgentActionMessageLog, Tag(tag="AgentActionMessageLog")],
+            Annotated[ChatGeneration, Tag(tag="ChatGeneration")],
+            Annotated[Generation, Tag(tag="Generation")],
+            Annotated[ChatGenerationChunk, Tag(tag="ChatGenerationChunk")],
+            Annotated[LLMResult, Tag(tag="LLMResult")],
+        ],
+        Field(discriminator=Discriminator(_get_type)),
     ]
+]
 
 
 def default(obj) -> Any:
     """Default serialization for well known objects."""
     if isinstance(obj, BaseModel):
-        return obj.dict()
+        return obj.model_dump()
     return super().default(obj)
 
 
@@ -96,12 +114,10 @@ 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__
-            if set(parsed.dict()) != set(value):
-                raise ValueError("Invalid object")
+            obj = WellKnownLCObject.model_validate(v)
+            parsed = obj.root
             return parsed
-        except (ValidationError, ValueError):
+        except (ValidationError, ValueError, TypeError):
             return v
     elif isinstance(value, list):
         return [_decode_lc_objects(item) for item in value]
@@ -123,12 +139,12 @@ def _decode_event_data(value: Any) -> Any:
     """Decode the event data from a JSON object representation."""
     if isinstance(value, dict):
         try:
-            obj = CallbackEvent.parse_obj(value)
-            return obj.__root__
+            obj = CallbackEvent.model_validate(value)
+            return obj.root
         except ValidationError:
             try:
-                obj = WellKnownLCObject.parse_obj(value)
-                return obj.__root__
+                obj = WellKnownLCObject.model_validate(value)
+                return obj.root
             except ValidationError:
                 return {key: _decode_event_data(v) for key, v in value.items()}
     elif isinstance(value, list):
@@ -178,7 +194,7 @@ class WellKnownLCSerializer(Serializer):
 
 def _project_top_level(model: BaseModel) -> Dict[str, Any]:
     """Project the top level of the model as dict."""
-    return {key: getattr(model, key) for key in model.__fields__}
+    return {key: getattr(model, key) for key in model.model_fields}
 
 
 def load_events(events: Any) -> List[Dict[str, Any]]:
@@ -209,7 +225,7 @@ def load_events(events: Any) -> List[Dict[str, Any]]:
 
         # Then validate the event
         try:
-            full_event = CallbackEvent.parse_obj(decoded_event_data)
+            full_event = CallbackEvent.model_validate(decoded_event_data)
         except ValidationError as e:
             msg = f"Encountered an invalid event: {e}"
             if "type" in decoded_event_data:
@@ -217,7 +233,7 @@ 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

langserve/server.py

@@ -5,6 +5,7 @@ This code contains integration for langchain runnables with FastAPI.
 The main entry point is the `add_routes` function which adds the routes to an existing
 FastAPI app or APIRouter.
 """
+import warnings
 import weakref
 from typing import (
     Any,
@@ -16,6 +17,7 @@ from typing import (
 )
 
 from langchain_core.runnables import Runnable
+from pydantic import BaseModel
 from typing_extensions import Annotated
 
 from langserve.api_handler import (
@@ -24,11 +26,6 @@ from langserve.api_handler import (
     TokenFeedbackConfig,
     _is_hosted,
 )
-from langserve.pydantic_v1 import (
-    _PYDANTIC_MAJOR_VERSION,
-    PYDANTIC_VERSION,
-    BaseModel,
-)
 
 try:
     from fastapi import APIRouter, Depends, FastAPI, Request, Response
@@ -205,51 +202,43 @@ def _register_path_for_app(
 def _setup_global_app_handlers(
     app: Union[FastAPI, APIRouter], endpoint_configuration: _EndpointConfiguration
 ) -> None:
-    @app.on_event("startup")
-    async def startup_event():
-        LANGSERVE = r"""
- __          ___      .__   __.   _______      _______. _______ .______     ____    ____  _______
-|  |        /   \     |  \ |  |  /  _____|    /       ||   ____||   _  \    \   \  /   / |   ____|
-|  |       /  ^  \    |   \|  | |  |  __     |   (----`|  |__   |  |_)  |    \   \/   /  |  |__
-|  |      /  /_\  \   |  . `  | |  | |_ |     \   \    |   __|  |      /      \      /   |   __|
-|  `----./  _____  \  |  |\   | |  |__| | .----)   |   |  |____ |  |\  \----.  \    /    |  |____
-|_______/__/     \__\ |__| \__|  \______| |_______/    |_______|| _| `._____|   \__/     |_______|
-"""  # noqa: E501
+    with warnings.catch_warnings():
+        # We are using deprecated functionality here.
+        # We should re-write to use lifetime events at some point, and yielding
+        # an APIRouter instance to the caller.
+        warnings.filterwarnings(
+            "ignore",
+            "[\\s.]*on_event is deprecated[\\s.]*",
+            category=DeprecationWarning,
+        )
 
-        def green(text: str) -> str:
-            """Return the given text in green."""
-            return "\x1b[1;32;40m" + text + "\x1b[0m"
+        @app.on_event("startup")
+        async def startup_event():
+            LANGSERVE = r"""
+     __          ___      .__   __.   _______      _______. _______ .______     ____    ____  _______
+    |  |        /   \     |  \ |  |  /  _____|    /       ||   ____||   _  \    \   \  /   / |   ____|
+    |  |       /  ^  \    |   \|  | |  |  __     |   (----`|  |__   |  |_)  |    \   \/   /  |  |__
+    |  |      /  /_\  \   |  . `  | |  | |_ |     \   \    |   __|  |      /      \      /   |   __|
+    |  `----./  _____  \  |  |\   | |  |__| | .----)   |   |  |____ |  |\  \----.  \    /    |  |____
+    |_______/__/     \__\ |__| \__|  \______| |_______/    |_______|| _| `._____|   \__/     |_______|
+    """  # noqa: E501
 
-        def orange(text: str) -> str:
-            """Return the given text in orange."""
-            return "\x1b[1;31;40m" + text + "\x1b[0m"
+            def green(text: str) -> str:
+                """Return the given text in green."""
+                return "\x1b[1;32;40m" + text + "\x1b[0m"
 
-        paths = _APP_TO_PATHS[app]
-        print(LANGSERVE)
-        for path in paths:
-            if endpoint_configuration.is_playground_enabled:
-                print(
-                    f'{green("LANGSERVE:")} Playground for chain "{path or ""}/" is '
-                    f"live at:"
-                )
-                print(f'{green("LANGSERVE:")}  │')
-                print(f'{green("LANGSERVE:")}  └──> {path}/playground/')
-                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()
+            paths = _APP_TO_PATHS[app]
+            print(LANGSERVE)
+            for path in paths:
+                if endpoint_configuration.is_playground_enabled:
+                    print(
+                        f'{green("LANGSERVE:")} Playground for chain "{path or ""}/" '
+                        f'is live at:'
+                    )
+                    print(f'{green("LANGSERVE:")}  │')
+                    print(f'{green("LANGSERVE:")}  └──> {path}/playground/')
+                    print(f'{green("LANGSERVE:")}')
+            print(f'{green("LANGSERVE:")} See all available routes at {app.docs_url}/')
 
 
 # PUBLIC API
@@ -473,35 +462,9 @@ def add_routes(
     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 +741,326 @@ def add_routes(
     # 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
+    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:
+    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.")
+        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",
+        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)
+                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_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)
+    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)
+                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_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)
+    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": {
+                    ...
+                }
+            }
+
+            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.")
+
+        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)
+
+        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:
+
+        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.
+
+            The endpoint uses a server sent event stream to stream the output.
+
+            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.
+
+            This endpoint uses two different types of events:
+
+            * data - for streaming the output of the runnable
 
-                Regular streaming event:
                 {
                     "event": "data",
                     "data": {
-                        ...
+                    ...
                     }
                 }
 
-                Internal server error:
-                {
-                    "event": "error",
-                    "data": {
-                        "status_code": 500,
-                        "message": "Internal Server Error"
-                    }
-                }
+            * error - for signaling an error in the stream, also ends the stream.
+
+            {
+                "event": "error",
+                "data": {
+                    "status_code": 500,
+                    "message": "Internal Server Error"
+                }
+            }
+
+            * 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.
 
-                Streaming ended so client should stop listening for events:
                 {
                     "event": "end",
                 }
-                """
-                raise AssertionError("This endpoint should not be reachable.")
+            """
+            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)
-
-            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:
-
-            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.
-
-                The endpoint uses a server sent event stream to stream the output.
-
-                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.
-
-                This endpoint uses two different types of events:
-
-                * data - for streaming the output of the runnable
-
-                    {
-                        "event": "data",
-                        "data": {
-                        ...
-                        }
-                    }
-
-                * error - for signaling an error in the stream, also ends the stream.
-
-                {
-                    "event": "error",
-                    "data": {
-                        "status_code": 500,
-                        "message": "Internal Server Error"
-                    }
-                }
-
-                * 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:
 
-        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.
 
-            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.
 
-                This endpoint allows to stream events from the runnable, including
-                events from all intermediate steps.
+            **Attention**
 
-                **Attention**
+                This is a new endpoint that only works for langchain-core >= 0.1.14.
 
-                    This is a new endpoint that only works for langchain-core >= 0.1.14.
+                It belongs to a Beta API that may change in the future.
 
-                    It belongs to a Beta API that may change in the future.
+            **Important**
+                Specify filters to the events you want to receive by setting
+                the appropriate filters in the request body.
 
-                **Important**
-                    Specify filters to the events you want to receive by setting
-                    the appropriate filters in the request body.
+                This will help avoid sending too much data over the network.
 
-                    This will help avoid sending too much data over the network.
+                It will also prevent serialization issues with
+                any unsupported types since it won't need to serialize events
+                that aren't transmitted.
 
-                    It will also prevent serialization issues with
-                    any unsupported types since it won't need to serialize events
-                    that aren't transmitted.
+            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
+            The encoding of events follows the following format:
 
-                The encoding of events follows the following format:
-
-                * data - for streaming the output of the runnable
-
-                    {
-                        "event": "data",
-                        "data": {
-                        ...
-                        }
-                    }
-
-                * 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.
 
-                `data` for the `data` event is a JSON object that corresponds
-                to a serialized representation of a StreamEvent.
+                This helps the client to know when to stop listening for events and
+                know that the streaming has ended successfully.
 
-                See LangChain documentation for more information about astream_events.
-                """
-                raise AssertionError("This endpoint should not be reachable.")
+                {
+                    "event": "end",
+                }
 
+            `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)

langserve/validation.py

@@ -22,16 +22,11 @@ from uuid import UUID
 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,
     ]
+]

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langserve"
-version = "0.2.3"
+version = "0.3.0dev2"
 description = ""
 readme = "README.md"
 authors = ["LangChain"]
@@ -10,14 +10,14 @@ 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 = "0.3.0dev5"
 orjson = ">=2"
 pyproject-toml = "^0.0.10"
+pydantic = "^2.7"
 
 [tool.poetry.group.dev.dependencies]
 jupyterlab = "^3.6.1"
@@ -95,3 +95,7 @@ addopts = "--strict-markers --strict-config --durations=5 -vv"
 # take more than 5 seconds
 timeout = 5
 asyncio_mode = "auto"
+filterwarnings =  [
+    "ignore::langchain_core._api.beta_decorator.LangChainBetaWarning",
+]
+

tests/unit_tests/test_api_playground.py

@@ -1,5 +1,6 @@
 """Test the playground API."""
 
+import httpx
 from fastapi import APIRouter, FastAPI
 from httpx import AsyncClient
 from langchain_core.runnables import RunnableLambda
@@ -15,7 +16,9 @@ async def test_serve_playground() -> None:
         RunnableLambda(lambda foo: "hello"),
     )
 
-    async with AsyncClient(app=app, base_url="http://localhost:9999") as client:
+    async with AsyncClient(
+        base_url="http://localhost:9999", transport=httpx.ASGITransport(app=app)
+    ) as client:
         response = await client.get("/playground/index.html")
         assert response.status_code == 200
         # Test that we can't access files that do not exist.
@@ -42,7 +45,9 @@ async def test_serve_playground_with_api_router() -> None:
 
     app.include_router(router)
 
-    async with AsyncClient(app=app, base_url="http://localhost:9999") as client:
+    async with AsyncClient(
+        base_url="http://localhost:9999", transport=httpx.ASGITransport(app=app)
+    ) as client:
         response = await client.get("/langserve_runnables/chat/playground/index.html")
         assert response.status_code == 200
 
@@ -64,7 +69,9 @@ async def test_serve_playground_with_api_router_in_api_router() -> None:
     # Now add parent router to the app
     app.include_router(parent_router)
 
-    async with AsyncClient(app=app, base_url="http://localhost:9999") as client:
+    async with AsyncClient(
+        base_url="http://localhost:9999", transport=httpx.ASGITransport(app=app)
+    ) as client:
         response = await client.get("/parent/bar/foo/playground/index.html")
         assert response.status_code == 200
 
@@ -88,7 +95,9 @@ async def test_root_path_on_playground() -> None:
         )
         app.include_router(router)
 
-        async_client = AsyncClient(app=app, base_url="http://localhost:9999")
+        async_client = AsyncClient(
+            base_url="http://localhost:9999", transport=httpx.ASGITransport(app=app)
+        )
 
         response = await async_client.get("/chat/playground/index.html")
         assert response.status_code == 200

tests/unit_tests/test_serialization.py

@@ -4,15 +4,23 @@ from enum import Enum
 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
 
-try:
-    from pydantic.v1 import BaseModel
-except ImportError:
-    from pydantic import BaseModel
+from langserve.serialization import (
+    WellKnownLCObject,
+    WellKnownLCSerializer,
+    load_events,
+)
 
-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.model_dump()
+    WellKnownLCObject.model_validate(d)
 
 
 @pytest.mark.parametrize(
@@ -23,6 +31,8 @@ from langserve.serialization import WellKnownLCSerializer, load_events
         [],
         {},
         {"a": 1},
+        Document(page_content="Hello"),
+        [Document(page_content="Hello")],
         {"output": [HumanMessage(content="hello")]},
         # Test with a single message (HumanMessage)
         HumanMessage(content="Hello"),
@@ -77,7 +87,7 @@ def _get_full_representation(data: Any) -> Any:
     elif isinstance(data, list):
         return [_get_full_representation(value) for value in data]
     elif isinstance(data, BaseModel):
-        return data.schema()
+        return data.model_json_schema()
     else:
         return data
 
@@ -179,3 +189,9 @@ 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 "CHatGeneration_Deserialized correct" == "UNcomment test above"

tests/unit_tests/test_validation.py

@@ -5,14 +5,9 @@ import pytest
 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,
@@ -175,11 +170,11 @@ 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)
-    assert request.config.configurable.dict() == {
+    assert request.config.configurable.model_dump() == {
         "template": "goodbye {name}",
     }
 

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

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

tests/unit_tests/utils/test_fake_chat_model.py

@@ -4,11 +4,11 @@ from typing import Any, Dict, List, Optional, Union
 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 test_callback_handlers() -> None:
     # 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"]