feat(core): Adding a fake LLM with tool calling capabilities for easy integration with LangGraph

libs/core/langchain_core/language_models/fake_chat_models.py

@@ -3,8 +3,8 @@
 import asyncio
 import re
 import time
-from collections.abc import AsyncIterator, Iterator
-from typing import Any, Optional, Union, cast
+from collections.abc import AsyncIterator, Iterator, Sequence
+from typing import Any, Callable, Dict, Optional, Union, cast
 
 from typing_extensions import override
 
@@ -17,6 +17,12 @@
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
 from langchain_core.runnables import RunnableConfig
 
+from langchain_core.language_models.base import LanguageModelInput
+
+from langchain_core.runnables import Runnable, RunnableConfig
+from langchain_core.runnables.config import run_in_executor
+from langchain_core.tools import BaseTool
+
 
 class FakeMessagesListChatModel(BaseChatModel):
     """Fake ChatModel for testing purposes."""
@@ -363,3 +369,191 @@ def _generate(
     @property
     def _llm_type(self) -> str:
         return "parrot-fake-chat-model"
+
+
+class FakeToolCallingListChatModel(BaseChatModel):
+    """Fake Tool calling ChatModel for testing purposes."""
+
+    responses: list[Union[dict,str,BaseMessage]]
+    """List of responses to **cycle** through in order."""
+    sleep: Optional[float] = None
+    i: int = 0
+    """List of responses to **cycle** through in order."""
+    error_on_chunk_number: Optional[int] = None
+    """Internally incremented after every model invocation."""
+
+    @property
+    @override
+    def _llm_type(self) -> str:
+        return "fake-list-chat-model"
+
+    @override
+    def _generate(
+        self,
+        messages: list[BaseMessage],
+        stop: Optional[list[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        response = self.responses[self.i]
+        if self.i < len(self.responses) - 1:
+            self.i += 1
+        else:
+            self.i = 0
+
+        if isinstance(response,BaseMessage):
+            message = response
+        elif isinstance(response,str):
+            message = AIMessage(content=response)
+        elif isinstance(response,dict):
+            message = AIMessage(content=response["content"], tool_calls = response["tool_calls"])
+        else:
+            raise ValueError("Incorrect response type")
+
+        generation = ChatGeneration(message=message)
+        return ChatResult(generations=[generation])
+
+    @override
+    async def _agenerate(
+        self,
+        messages: list[BaseMessage],
+        stop: Optional[list[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        return await run_in_executor(
+            None,
+            self._generate,
+            messages,
+            stop=stop,
+            run_manager=run_manager.get_sync() if run_manager else None,
+            **kwargs,
+        )
+
+    @override
+    def _stream(
+        self,
+        messages: list[BaseMessage],
+        stop: Union[list[str], None] = None,
+        run_manager: Union[CallbackManagerForLLMRun, None] = None,
+        **kwargs: Any,
+    ) -> Iterator[ChatGenerationChunk]:
+        response = self.responses[self.i]
+        if self.i < len(self.responses) - 1:
+            self.i += 1
+        else:
+            self.i = 0
+
+        tool_calls = []
+        if isinstance(response,BaseMessage):
+            output = response.content
+        elif isinstance(response,str):
+            output = response
+        elif isinstance(response,dict):
+            output = response["content"]
+            tool_calls = response["tool_calls"]
+        else:
+            raise ValueError("Incorrect response type")
+
+        for i_c, c in enumerate(output):
+            if self.sleep is not None:
+                time.sleep(self.sleep)
+            if (
+                self.error_on_chunk_number is not None
+                and i_c == self.error_on_chunk_number
+            ):
+                raise FakeListChatModelError
+
+            yield ChatGenerationChunk(message=AIMessageChunk(content=c, tool_calls=tool_calls))
+
+    @override
+    async def _astream(
+        self,
+        messages: list[BaseMessage],
+        stop: Union[list[str], None] = None,
+        run_manager: Union[AsyncCallbackManagerForLLMRun, None] = None,
+        **kwargs: Any,
+    ) -> AsyncIterator[ChatGenerationChunk]:
+        response = self.responses[self.i]
+        if self.i < len(self.responses) - 1:
+            self.i += 1
+        else:
+            self.i = 0
+
+        tool_calls = []
+        if isinstance(response,BaseMessage):
+            output = response.content
+        elif isinstance(response,str):
+            output = response
+        elif isinstance(response,dict):
+            output = response["content"]
+            tool_calls = response["tool_calls"]
+        else:
+            raise ValueError("Incorrect response type")
+
+        for i_c, c in enumerate(output):
+            if self.sleep is not None:
+                await asyncio.sleep(self.sleep)
+            if (
+                self.error_on_chunk_number is not None
+                and i_c == self.error_on_chunk_number
+            ):
+                raise FakeListChatModelError
+            yield ChatGenerationChunk(message=AIMessageChunk(content=c, tool_calls=tool_calls))
+
+    @property
+    @override
+    def _identifying_params(self) -> dict[str, Any]:
+        return {"responses": self.responses}
+
+    @override
+    # manually override batch to preserve batch ordering with no concurrency
+    def batch(
+        self,
+        inputs: list[Any],
+        config: Optional[Union[RunnableConfig, list[RunnableConfig]]] = None,
+        *,
+        return_exceptions: bool = False,
+        **kwargs: Any,
+    ) -> list[BaseMessage]:
+        if isinstance(config, list):
+            return [self.invoke(m, c, **kwargs) for m, c in zip(inputs, config)]
+        return [self.invoke(m, config, **kwargs) for m in inputs]
+
+    @override
+    async def abatch(
+        self,
+        inputs: list[Any],
+        config: Optional[Union[RunnableConfig, list[RunnableConfig]]] = None,
+        *,
+        return_exceptions: bool = False,
+        **kwargs: Any,
+    ) -> list[BaseMessage]:
+        if isinstance(config, list):
+            # do Not use an async iterator here because need explicit ordering
+            return [await self.ainvoke(m, c, **kwargs) for m, c in zip(inputs, config)]
+        # do Not use an async iterator here because need explicit ordering
+        return [await self.ainvoke(m, config, **kwargs) for m in inputs]
+
+    @override
+    def bind_tools(
+        self,
+        tools: Sequence[
+            Union[Dict[str, Any], type, Callable, BaseTool]  # noqa: UP006
+        ],
+        *,
+        tool_choice: Optional[Union[str]] = None,
+        **kwargs: Any,
+    ) -> Runnable[LanguageModelInput, BaseMessage]:
+        """Bind tools to the model.
+
+        Args:
+            tools: Sequence of tools to bind to the model.
+            tool_choice: The tool to use. If "any" then any tool can be used.
+
+        Returns:
+            A Runnable that returns a message.
+        """
+        kwargs["tool_choice"] = tool_choice
+        raise super().bind(tools=tools, **kwargs)
+