mirror of https://github.com/hwchase17/langchain
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/16172pull/16216/head
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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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