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core[patch]: testing add chat model for unit-tests (#16209)
This PR adds a fake chat model for testing purposes. Used in this PR: https://github.com/langchain-ai/langchain/pull/16172
This commit is contained in:
Eugene Yurtsev
GitHub
parent
27ad65cc68
commit
ecd4f0a7ec
@ -1,15 +1,21 @@
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"""Fake ChatModel for testing purposes."""
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"""Fake Chat Model wrapper for testing purposes."""
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import asyncio
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import re
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import time
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from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, Union
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from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, Union, cast
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from langchain_core.callbacks.manager import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain_core.language_models.chat_models import BaseChatModel, SimpleChatModel
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from langchain_core.messages import AIMessageChunk, BaseMessage
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from langchain_core.messages import (
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AIMessage,
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AIMessageChunk,
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BaseMessage,
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)
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from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
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from langchain_core.runnables import run_in_executor
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class FakeMessagesListChatModel(BaseChatModel):
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@ -114,3 +120,184 @@ class FakeListChatModel(SimpleChatModel):
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@property
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def _identifying_params(self) -> Dict[str, Any]:
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return {"responses": self.responses}
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class FakeChatModel(SimpleChatModel):
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"""Fake Chat Model wrapper for testing purposes."""
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def _call(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> str:
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return "fake response"
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async def _agenerate(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> ChatResult:
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output_str = "fake response"
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message = AIMessage(content=output_str)
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generation = ChatGeneration(message=message)
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return ChatResult(generations=[generation])
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@property
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def _llm_type(self) -> str:
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return "fake-chat-model"
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@property
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def _identifying_params(self) -> Dict[str, Any]:
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return {"key": "fake"}
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class GenericFakeChatModel(BaseChatModel):
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"""A generic fake chat model that can be used to test the chat model interface.
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* Chat model should be usable in both sync and async tests
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* Invokes on_llm_new_token to allow for testing of callback related code for new
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tokens.
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* Includes logic to break messages into message chunk to facilitate testing of
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streaming.
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"""
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messages: Iterator[AIMessage]
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"""Get an iterator over messages.
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This can be expanded to accept other types like Callables / dicts / strings
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to make the interface more generic if needed.
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Note: if you want to pass a list, you can use `iter` to convert it to an iterator.
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Please note that streaming is not implemented yet. We should try to implement it
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in the future by delegating to invoke and then breaking the resulting output
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into message chunks.
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"""
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def _generate(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> ChatResult:
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"""Top Level call"""
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message = next(self.messages)
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generation = ChatGeneration(message=message)
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return ChatResult(generations=[generation])
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def _stream(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Iterator[ChatGenerationChunk]:
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"""Stream the output of the model."""
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chat_result = self._generate(
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messages, stop=stop, run_manager=run_manager, **kwargs
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)
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if not isinstance(chat_result, ChatResult):
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raise ValueError(
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f"Expected generate to return a ChatResult, "
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f"but got {type(chat_result)} instead."
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)
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message = chat_result.generations[0].message
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if not isinstance(message, AIMessage):
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raise ValueError(
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f"Expected invoke to return an AIMessage, "
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f"but got {type(message)} instead."
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)
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content = message.content
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if content:
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# Use a regular expression to split on whitespace with a capture group
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# so that we can preserve the whitespace in the output.
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assert isinstance(content, str)
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content_chunks = cast(List[str], re.split(r"(\s)", content))
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for token in content_chunks:
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chunk = ChatGenerationChunk(message=AIMessageChunk(content=token))
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yield chunk
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if run_manager:
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run_manager.on_llm_new_token(token, chunk=chunk)
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if message.additional_kwargs:
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for key, value in message.additional_kwargs.items():
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# We should further break down the additional kwargs into chunks
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# Special case for function call
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if key == "function_call":
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for fkey, fvalue in value.items():
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if isinstance(fvalue, str):
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# Break function call by `,`
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fvalue_chunks = cast(List[str], re.split(r"(,)", fvalue))
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for fvalue_chunk in fvalue_chunks:
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chunk = ChatGenerationChunk(
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message=AIMessageChunk(
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content="",
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additional_kwargs={
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"function_call": {fkey: fvalue_chunk}
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},
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)
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)
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yield chunk
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if run_manager:
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run_manager.on_llm_new_token(
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"",
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chunk=chunk, # No token for function call
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)
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else:
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chunk = ChatGenerationChunk(
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message=AIMessageChunk(
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content="",
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additional_kwargs={"function_call": {fkey: fvalue}},
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)
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)
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yield chunk
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if run_manager:
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run_manager.on_llm_new_token(
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"",
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chunk=chunk, # No token for function call
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)
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else:
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chunk = ChatGenerationChunk(
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message=AIMessageChunk(
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content="", additional_kwargs={key: value}
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)
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)
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yield chunk
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if run_manager:
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run_manager.on_llm_new_token(
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"",
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chunk=chunk, # No token for function call
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)
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async def _astream(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> AsyncIterator[ChatGenerationChunk]:
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"""Stream the output of the model."""
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result = await run_in_executor(
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None,
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self._stream,
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messages,
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stop=stop,
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run_manager=run_manager.get_sync() if run_manager else None,
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**kwargs,
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)
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for chunk in result:
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yield chunk
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@property
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def _llm_type(self) -> str:
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return "generic-fake-chat-model"
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184
libs/core/tests/unit_tests/fake/test_fake_chat_model.py
Normal file
184
libs/core/tests/unit_tests/fake/test_fake_chat_model.py
Normal file
@ -0,0 +1,184 @@
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"""Tests for verifying that testing utility code works as expected."""
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from itertools import cycle
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from typing import Any, Dict, List, Optional, Union
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from uuid import UUID
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from langchain_core.callbacks.base import AsyncCallbackHandler
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from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage
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from langchain_core.outputs import ChatGenerationChunk, GenerationChunk
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from tests.unit_tests.fake.chat_model import GenericFakeChatModel
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def test_generic_fake_chat_model_invoke() -> None:
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# Will alternate between responding with hello and goodbye
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infinite_cycle = cycle([AIMessage(content="hello"), AIMessage(content="goodbye")])
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model = GenericFakeChatModel(messages=infinite_cycle)
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response = model.invoke("meow")
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assert response == AIMessage(content="hello")
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response = model.invoke("kitty")
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assert response == AIMessage(content="goodbye")
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response = model.invoke("meow")
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assert response == AIMessage(content="hello")
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async def test_generic_fake_chat_model_ainvoke() -> None:
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# Will alternate between responding with hello and goodbye
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infinite_cycle = cycle([AIMessage(content="hello"), AIMessage(content="goodbye")])
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model = GenericFakeChatModel(messages=infinite_cycle)
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response = await model.ainvoke("meow")
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assert response == AIMessage(content="hello")
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response = await model.ainvoke("kitty")
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assert response == AIMessage(content="goodbye")
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response = await model.ainvoke("meow")
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assert response == AIMessage(content="hello")
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async def test_generic_fake_chat_model_stream() -> None:
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"""Test streaming."""
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infinite_cycle = cycle(
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[
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AIMessage(content="hello goodbye"),
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]
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)
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model = GenericFakeChatModel(messages=infinite_cycle)
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chunks = [chunk async for chunk in model.astream("meow")]
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assert chunks == [
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AIMessageChunk(content="hello"),
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AIMessageChunk(content=" "),
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AIMessageChunk(content="goodbye"),
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]
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chunks = [chunk for chunk in model.stream("meow")]
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assert chunks == [
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AIMessageChunk(content="hello"),
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AIMessageChunk(content=" "),
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AIMessageChunk(content="goodbye"),
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]
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# Test streaming of additional kwargs.
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# Relying on insertion order of the additional kwargs dict
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message = AIMessage(content="", additional_kwargs={"foo": 42, "bar": 24})
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model = GenericFakeChatModel(messages=cycle([message]))
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chunks = [chunk async for chunk in model.astream("meow")]
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assert chunks == [
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AIMessageChunk(content="", additional_kwargs={"foo": 42}),
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AIMessageChunk(content="", additional_kwargs={"bar": 24}),
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]
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message = AIMessage(
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content="",
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additional_kwargs={
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"function_call": {
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"name": "move_file",
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"arguments": '{\n "source_path": "foo",\n "'
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'destination_path": "bar"\n}',
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}
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},
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)
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model = GenericFakeChatModel(messages=cycle([message]))
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chunks = [chunk async for chunk in model.astream("meow")]
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assert chunks == [
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AIMessageChunk(
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content="", additional_kwargs={"function_call": {"name": "move_file"}}
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),
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AIMessageChunk(
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content="",
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additional_kwargs={
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"function_call": {"arguments": '{\n "source_path": "foo"'}
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},
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),
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AIMessageChunk(
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content="", additional_kwargs={"function_call": {"arguments": ","}}
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),
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AIMessageChunk(
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content="",
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additional_kwargs={
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"function_call": {"arguments": '\n "destination_path": "bar"\n}'}
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},
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),
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]
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accumulate_chunks = None
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for chunk in chunks:
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if accumulate_chunks is None:
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accumulate_chunks = chunk
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else:
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accumulate_chunks += chunk
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assert accumulate_chunks == AIMessageChunk(
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content="",
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additional_kwargs={
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"function_call": {
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"name": "move_file",
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"arguments": '{\n "source_path": "foo",\n "'
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'destination_path": "bar"\n}',
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}
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},
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)
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async def test_generic_fake_chat_model_astream_log() -> None:
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"""Test streaming."""
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infinite_cycle = cycle([AIMessage(content="hello goodbye")])
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model = GenericFakeChatModel(messages=infinite_cycle)
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log_patches = [
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log_patch async for log_patch in model.astream_log("meow", diff=False)
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]
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final = log_patches[-1]
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assert final.state["streamed_output"] == [
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AIMessageChunk(content="hello"),
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AIMessageChunk(content=" "),
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AIMessageChunk(content="goodbye"),
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]
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async def test_callback_handlers() -> None:
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"""Verify that model is implemented correctly with handlers working."""
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class MyCustomAsyncHandler(AsyncCallbackHandler):
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def __init__(self, store: List[str]) -> None:
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self.store = store
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async def on_chat_model_start(
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self,
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serialized: Dict[str, Any],
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messages: List[List[BaseMessage]],
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*,
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run_id: UUID,
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parent_run_id: Optional[UUID] = None,
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tags: Optional[List[str]] = None,
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metadata: Optional[Dict[str, Any]] = None,
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**kwargs: Any,
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) -> Any:
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# Do nothing
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# Required to implement since this is an abstract method
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pass
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async def on_llm_new_token(
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self,
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token: str,
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*,
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chunk: Optional[Union[GenerationChunk, ChatGenerationChunk]] = None,
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run_id: UUID,
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parent_run_id: Optional[UUID] = None,
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tags: Optional[List[str]] = None,
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**kwargs: Any,
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) -> None:
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self.store.append(token)
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infinite_cycle = cycle(
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[
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AIMessage(content="hello goodbye"),
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]
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)
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model = GenericFakeChatModel(messages=infinite_cycle)
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tokens: List[str] = []
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# New model
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results = list(model.stream("meow", {"callbacks": [MyCustomAsyncHandler(tokens)]}))
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assert results == [
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AIMessageChunk(content="hello"),
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AIMessageChunk(content=" "),
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AIMessageChunk(content="goodbye"),
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]
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assert tokens == ["hello", " ", "goodbye"]
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