merge

docs/docs_skeleton/docs/modules/agents/agent_types/index.mdx

@@ -12,7 +12,7 @@ Here are the agents available in LangChain.
 
 ### [Zero-shot ReAct](/docs/modules/agents/agent_types/react.html)
 
-This agent uses the [ReAct](https://arxiv.org/pdf/2205.00445.pdf) framework to determine which tool to use
+This agent uses the [ReAct](https://arxiv.org/pdf/2210.03629) framework to determine which tool to use
 based solely on the tool's description. Any number of tools can be provided.
 This agent requires that a description is provided for each tool.
 

libs/langchain/langchain/chains/base.py

@@ -62,9 +62,11 @@ class Chain(Serializable, Runnable[Dict[str, Any], Dict[str, Any]], ABC):
         config: Optional[RunnableConfig] = None,
         **kwargs: Any,
     ) -> Dict[str, Any]:
-        _config: Dict[str, Any] = dict(config) if config else {}
-        _config.pop("_locals", None)
-        return self(input, **_config, **kwargs)
+        config = config or {}
+        config_kwargs: Dict = {
+            k: config.get(k) for k in ("callbacks", "tags", "metadata")
+        }
+        return self(input, **config_kwargs, **kwargs)
 
     async def ainvoke(
         self,
@@ -77,10 +79,11 @@ class Chain(Serializable, Runnable[Dict[str, Any], Dict[str, Any]], ABC):
             return await asyncio.get_running_loop().run_in_executor(
                 None, partial(self.invoke, input, config, **kwargs)
             )
-
-        _config: Dict[str, Any] = dict(config) if config else {}
-        _config.pop("_locals", None)
-        return await self.acall(input, **_config, **kwargs)
+        config = config or {}
+        config_kwargs: Dict = {
+            k: config.get(k) for k in ("callbacks", "tags", "metadata")
+        }
+        return await self.acall(input, **config_kwargs, **kwargs)
 
     memory: Optional[BaseMemory] = None
     """Optional memory object. Defaults to None.

libs/langchain/langchain/chat_models/base.py

@@ -103,14 +103,16 @@ class BaseChatModel(BaseLanguageModel[BaseMessageChunk], ABC):
         stop: Optional[List[str]] = None,
         **kwargs: Any,
     ) -> BaseMessageChunk:
-        _config: Dict[str, Any] = dict(config or {})
-        _config.pop("_locals", None)
+        config = config or {}
+        config_kwargs: Dict = {
+            k: config.get(k) for k in ("callbacks", "tags", "metadata")
+        }
         return cast(
             BaseMessageChunk,
             cast(
                 ChatGeneration,
                 self.generate_prompt(
-                    [self._convert_input(input)], stop=stop, **_config, **kwargs
+                    [self._convert_input(input)], stop=stop, **config_kwargs, **kwargs
                 ).generations[0][0],
             ).message,
         )
@@ -129,10 +131,12 @@ class BaseChatModel(BaseLanguageModel[BaseMessageChunk], ABC):
                 None, partial(self.invoke, input, config, stop=stop, **kwargs)
             )
 
-        _config: Dict[str, Any] = dict(config or {})
-        _config.pop("_locals", None)
+        config = config or {}
+        config_kwargs: Dict = {
+            k: config.get(k) for k in ("callbacks", "tags", "metadata")
+        }
         llm_result = await self.agenerate_prompt(
-            [self._convert_input(input)], stop=stop, **_config, **kwargs
+            [self._convert_input(input)], stop=stop, **config_kwargs, **kwargs
         )
         return cast(
             BaseMessageChunk, cast(ChatGeneration, llm_result.generations[0][0]).message

libs/langchain/langchain/document_loaders/airbyte.py

@@ -1,10 +1,9 @@
 """Loads local airbyte json files."""
 from typing import Any, Callable, Iterator, List, Mapping, Optional
 
-from libs.langchain.langchain.utils.utils import guard_import
-
 from langchain.docstore.document import Document
 from langchain.document_loaders.base import BaseLoader
+from langchain.utils.utils import guard_import
 
 RecordHandler = Callable[[Any, Optional[str]], Document]
 

libs/langchain/langchain/llms/base.py

@@ -219,10 +219,12 @@ class BaseLLM(BaseLanguageModel[str], ABC):
         stop: Optional[List[str]] = None,
         **kwargs: Any,
     ) -> str:
-        _config: Dict[str, Any] = dict(config or {})
-        _config.pop("_locals", None)
+        config = config or {}
+        config_kwargs: Dict = {
+            k: config.get(k) for k in ("callbacks", "tags", "metadata")
+        }
         result = self.generate_prompt(
-            [self._convert_input(input)], stop=stop, **_config, **kwargs
+            [self._convert_input(input)], stop=stop, **config_kwargs, **kwargs
         )
         return result.generations[0][0].text
 
@@ -240,10 +242,12 @@ class BaseLLM(BaseLanguageModel[str], ABC):
                 None, partial(self.invoke, input, config, stop=stop, **kwargs)
             )
 
-        _config: Dict[str, Any] = dict(config or {})
-        _config.pop("_locals", None)
+        config = config or {}
+        config_kwargs: Dict = {
+            k: config.get(k) for k in ("callbacks", "tags", "metadata")
+        }
         llm_result = await self.agenerate_prompt(
-            [self._convert_input(input)], stop=stop, **_config, **kwargs
+            [self._convert_input(input)], stop=stop, **config_kwargs, **kwargs
         )
         return llm_result.generations[0][0].text
 

libs/langchain/langchain/runnables/openai_functions.py

@@ -0,0 +1,46 @@
+from operator import itemgetter
+from typing import Any, Callable, List, Mapping, Optional, Union
+
+from typing_extensions import TypedDict
+
+from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser
+from langchain.schema.output import ChatGeneration
+from langchain.schema.runnable import RouterRunnable, Runnable, RunnableBinding
+
+
+class OpenAIFunction(TypedDict):
+    """A function description for ChatOpenAI"""
+
+    name: str
+    """The name of the function."""
+    description: str
+    """The description of the function."""
+    parameters: dict
+    """The parameters to the function."""
+
+
+class OpenAIFunctionsRouter(RunnableBinding[ChatGeneration, Any]):
+    """A runnable that routes to the selected function."""
+
+    functions: Optional[List[OpenAIFunction]]
+
+    def __init__(
+        self,
+        runnables: Mapping[
+            str,
+            Union[
+                Runnable[dict, Any],
+                Callable[[dict], Any],
+            ],
+        ],
+        functions: Optional[List[OpenAIFunction]] = None,
+    ):
+        if functions is not None:
+            assert len(functions) == len(runnables)
+            assert all(func["name"] in runnables for func in functions)
+        router = (
+            JsonOutputFunctionsParser(args_only=False)
+            | {"key": itemgetter("name"), "input": itemgetter("arguments")}
+            | RouterRunnable(runnables)
+        )
+        super().__init__(bound=router, kwargs={}, functions=functions)

libs/langchain/langchain/schema/retriever.py

@@ -107,9 +107,11 @@ class BaseRetriever(Serializable, Runnable[str, List[Document]], ABC):
     def invoke(
         self, input: str, config: Optional[RunnableConfig] = None
     ) -> List[Document]:
-        _config: Dict[str, Any] = dict(config or {})
-        _config.pop("_locals", None)
-        return self.get_relevant_documents(input, **_config)
+        config = config or {}
+        config_kwargs: Dict = {
+            k: config.get(k) for k in ("callbacks", "tags", "metadata")
+        }
+        return self.get_relevant_documents(input, **config_kwargs)
 
     async def ainvoke(
         self, input: str, config: Optional[RunnableConfig] = None
@@ -118,9 +120,11 @@ class BaseRetriever(Serializable, Runnable[str, List[Document]], ABC):
             # If the retriever doesn't implement async, use default implementation
             return await super().ainvoke(input, config)
 
-        _config: Dict[str, Any] = dict(config or {})
-        _config.pop("_locals", None)
-        return await self.aget_relevant_documents(input, **_config)
+        config = config or {}
+        config_kwargs: Dict = {
+            k: config.get(k) for k in ("callbacks", "tags", "metadata")
+        }
+        return await self.aget_relevant_documents(input, **config_kwargs)
 
     @abstractmethod
     def _get_relevant_documents(

libs/langchain/langchain/schema/runnable.py

@@ -1229,7 +1229,7 @@ class RunnablePassthrough(Serializable, Runnable[Input, Input]):
 
 class RunnableBinding(Serializable, Runnable[Input, Output]):
     """
-    A runnable that binds a runnable to a set of kwargs.
+    A runnable that delegates calls to another runnable with a set of kwargs.
     """
 
     bound: Runnable[Input, Output]
@@ -1314,8 +1314,15 @@ class RouterRunnable(
 
     runnables: Mapping[str, Runnable[Input, Output]]
 
-    def __init__(self, runnables: Mapping[str, Runnable[Input, Output]]) -> None:
-        super().__init__(runnables=runnables)
+    def __init__(
+        self,
+        runnables: Mapping[
+            str, Union[Runnable[Input, Output], Callable[[Input], Output]]
+        ],
+    ) -> None:
+        super().__init__(
+            runnables={key: _coerce_to_runnable(r) for key, r in runnables.items()}
+        )
 
     class Config:
         arbitrary_types_allowed = True

libs/langchain/langchain/smith/evaluation/runner_utils.py

@@ -502,6 +502,18 @@ def _construct_run_evaluator(
     return run_evaluator
 
 
+def _get_keys(
+    config: RunEvalConfig,
+    run_inputs: Optional[List[str]],
+    run_outputs: Optional[List[str]],
+    example_outputs: Optional[List[str]],
+) -> Tuple[Optional[str], Optional[str], Optional[str]]:
+    input_key = _determine_input_key(config, run_inputs)
+    prediction_key = _determine_prediction_key(config, run_outputs)
+    reference_key = _determine_reference_key(config, example_outputs)
+    return input_key, prediction_key, reference_key
+
+
 def _load_run_evaluators(
     config: RunEvalConfig,
     run_type: str,
@@ -521,9 +533,13 @@ def _load_run_evaluators(
     """
     eval_llm = config.eval_llm or ChatOpenAI(model="gpt-4", temperature=0.0)
     run_evaluators = []
-    input_key = _determine_input_key(config, run_inputs)
-    prediction_key = _determine_prediction_key(config, run_outputs)
-    reference_key = _determine_reference_key(config, example_outputs)
+    input_key, prediction_key, reference_key = None, None, None
+    if config.evaluators or any(
+        [isinstance(e, EvaluatorType) for e in config.evaluators]
+    ):
+        input_key, prediction_key, reference_key = _get_keys(
+            config, run_inputs, run_outputs, example_outputs
+        )
     for eval_config in config.evaluators:
         run_evaluator = _construct_run_evaluator(
             eval_config,
@@ -1078,15 +1094,15 @@ def _run_on_examples(
         A dictionary mapping example ids to the model outputs.
     """
     results: Dict[str, Any] = {}
-    llm_or_chain_factory = _wrap_in_chain_factory(llm_or_chain_factory)
-    project_name = _get_project_name(project_name, llm_or_chain_factory)
+    wrapped_model = _wrap_in_chain_factory(llm_or_chain_factory)
+    project_name = _get_project_name(project_name, wrapped_model)
     tracer = LangChainTracer(
         project_name=project_name, client=client, use_threading=False
     )
     run_evaluators, examples = _setup_evaluation(
-        llm_or_chain_factory, examples, evaluation, data_type
+        wrapped_model, examples, evaluation, data_type
     )
-    examples = _validate_example_inputs(examples, llm_or_chain_factory, input_mapper)
+    examples = _validate_example_inputs(examples, wrapped_model, input_mapper)
     evalution_handler = EvaluatorCallbackHandler(
         evaluators=run_evaluators or [],
         client=client,
@@ -1095,7 +1111,7 @@ def _run_on_examples(
     for i, example in enumerate(examples):
         result = _run_llm_or_chain(
             example,
-            llm_or_chain_factory,
+            wrapped_model,
             num_repetitions,
             tags=tags,
             callbacks=callbacks,
@@ -1118,8 +1134,8 @@ def _prepare_eval_run(
     llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
     project_name: Optional[str],
 ) -> Tuple[MCF, str, Dataset, Iterator[Example]]:
-    llm_or_chain_factory = _wrap_in_chain_factory(llm_or_chain_factory, dataset_name)
-    project_name = _get_project_name(project_name, llm_or_chain_factory)
+    wrapped_model = _wrap_in_chain_factory(llm_or_chain_factory, dataset_name)
+    project_name = _get_project_name(project_name, wrapped_model)
     try:
         project = client.create_project(project_name)
     except ValueError as e:
@@ -1134,7 +1150,7 @@ def _prepare_eval_run(
     )
     dataset = client.read_dataset(dataset_name=dataset_name)
     examples = client.list_examples(dataset_id=str(dataset.id))
-    return llm_or_chain_factory, project_name, dataset, examples
+    return wrapped_model, project_name, dataset, examples
 
 
 async def arun_on_dataset(
@@ -1260,13 +1276,13 @@ async def arun_on_dataset(
             evaluation=evaluation_config,
         )
     """  # noqa: E501
-    llm_or_chain_factory, project_name, dataset, examples = _prepare_eval_run(
+    wrapped_model, project_name, dataset, examples = _prepare_eval_run(
         client, dataset_name, llm_or_chain_factory, project_name
     )
     results = await _arun_on_examples(
         client,
         examples,
-        llm_or_chain_factory,
+        wrapped_model,
         concurrency_level=concurrency_level,
         num_repetitions=num_repetitions,
         project_name=project_name,
@@ -1427,14 +1443,14 @@ def run_on_dataset(
             evaluation=evaluation_config,
         )
     """  # noqa: E501
-    llm_or_chain_factory, project_name, dataset, examples = _prepare_eval_run(
+    wrapped_model, project_name, dataset, examples = _prepare_eval_run(
         client, dataset_name, llm_or_chain_factory, project_name
     )
     if concurrency_level in (0, 1):
         results = _run_on_examples(
             client,
             examples,
-            llm_or_chain_factory,
+            wrapped_model,
             num_repetitions=num_repetitions,
             project_name=project_name,
             verbose=verbose,
@@ -1448,7 +1464,7 @@ def run_on_dataset(
         coro = _arun_on_examples(
             client,
             examples,
-            llm_or_chain_factory,
+            wrapped_model,
             concurrency_level=concurrency_level,
             num_repetitions=num_repetitions,
             project_name=project_name,

libs/langchain/langchain/tools/base.py

@@ -203,7 +203,13 @@ class BaseTool(BaseModel, Runnable[Union[str, Dict], Any], metaclass=ToolMetacla
         **kwargs: Any,
     ) -> Any:
         config = config or {}
-        return self.run(input, **config, **kwargs)
+        return self.run(
+            input,
+            callbacks=config.get("callbacks"),
+            tags=config.get("tags"),
+            metadata=config.get("metadata"),
+            **kwargs,
+        )
 
     async def ainvoke(
         self,
@@ -216,7 +222,13 @@ class BaseTool(BaseModel, Runnable[Union[str, Dict], Any], metaclass=ToolMetacla
             return super().ainvoke(input, config, **kwargs)
 
         config = config or {}
-        return await self.arun(input, **config, **kwargs)
+        return await self.arun(
+            input,
+            callbacks=config.get("callbacks"),
+            tags=config.get("tags"),
+            metadata=config.get("metadata"),
+            **kwargs,
+        )
 
     # --- Tool ---
 

libs/langchain/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langchain"
-version = "0.0.259"
+version = "0.0.260"
 description = "Building applications with LLMs through composability"
 authors = []
 license = "MIT"

libs/langchain/tests/unit_tests/document_loaders/test_airbyte.py

@@ -0,0 +1,9 @@
+"""Test the airbyte document loader.
+
+Light test to ensure that the airbyte document loader can be imported.
+"""
+
+
+def test_airbyte_import() -> None:
+    """Test that the airbyte document loader can be imported."""
+    from langchain.document_loaders import airbyte  # noqa

libs/langchain/tests/unit_tests/runnables/__snapshots__/test_openai_functions.ambr

@@ -0,0 +1,31 @@
+# serializer version: 1
+# name: test_openai_functions_router
+  list([
+    dict({
+      'description': 'Sends the draft for revision.',
+      'name': 'revise',
+      'parameters': dict({
+        'properties': dict({
+          'notes': dict({
+            'description': "The editor's notes to guide the revision.",
+            'type': 'string',
+          }),
+        }),
+        'type': 'object',
+      }),
+    }),
+    dict({
+      'description': 'Accepts the draft.',
+      'name': 'accept',
+      'parameters': dict({
+        'properties': dict({
+          'draft': dict({
+            'description': 'The draft to accept.',
+            'type': 'string',
+          }),
+        }),
+        'type': 'object',
+      }),
+    }),
+  ])
+# ---

libs/langchain/tests/unit_tests/runnables/test_openai_functions.py

@@ -0,0 +1,95 @@
+from typing import Any, List, Optional
+
+from pytest_mock import MockerFixture
+from syrupy import SnapshotAssertion
+
+from langchain.callbacks.manager import CallbackManagerForLLMRun
+from langchain.chat_models.base import BaseChatModel
+from langchain.runnables.openai_functions import OpenAIFunctionsRouter
+from langchain.schema import ChatResult
+from langchain.schema.messages import AIMessage, BaseMessage
+from langchain.schema.output import ChatGeneration
+
+
+class FakeChatOpenAI(BaseChatModel):
+    @property
+    def _llm_type(self) -> str:
+        return "fake-openai-chat-model"
+
+    def _generate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        return ChatResult(
+            generations=[
+                ChatGeneration(
+                    message=AIMessage(
+                        content="",
+                        additional_kwargs={
+                            "function_call": {
+                                "name": "accept",
+                                "arguments": '{\n  "draft": "turtles"\n}',
+                            }
+                        },
+                    )
+                )
+            ]
+        )
+
+
+def test_openai_functions_router(
+    snapshot: SnapshotAssertion, mocker: MockerFixture
+) -> None:
+    revise = mocker.Mock(
+        side_effect=lambda kw: f'Revised draft: no more {kw["notes"]}!'
+    )
+    accept = mocker.Mock(side_effect=lambda kw: f'Accepted draft: {kw["draft"]}!')
+
+    router = OpenAIFunctionsRouter(
+        {
+            "revise": revise,
+            "accept": accept,
+        },
+        functions=[
+            {
+                "name": "revise",
+                "description": "Sends the draft for revision.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "notes": {
+                            "type": "string",
+                            "description": "The editor's notes to guide the revision.",
+                        },
+                    },
+                },
+            },
+            {
+                "name": "accept",
+                "description": "Accepts the draft.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "draft": {
+                            "type": "string",
+                            "description": "The draft to accept.",
+                        },
+                    },
+                },
+            },
+        ],
+    )
+
+    model = FakeChatOpenAI()
+
+    chain = model.bind(functions=router.functions) | router
+
+    assert router.functions == snapshot
+
+    assert chain.invoke("Something about turtles?") == "Accepted draft: turtles!"
+
+    revise.assert_not_called()
+    accept.assert_called_once_with({"draft": "turtles"})