Merge branch 'master' into erick/testutils

libs/misc/testutils/langchain_testutils/fake/chat_model.py

@@ -0,0 +1,325 @@
+"""Fake Chat Model wrapper for testing purposes."""
+
+import asyncio
+import re
+import time
+from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, Union, cast
+
+from langchain_core.callbacks.manager import (
+    AsyncCallbackManagerForLLMRun,
+    CallbackManagerForLLMRun,
+)
+from langchain_core.language_models.chat_models import BaseChatModel, SimpleChatModel
+from langchain_core.messages import (
+    AIMessage,
+    AIMessageChunk,
+    BaseMessage,
+)
+from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
+from langchain_core.runnables import run_in_executor
+
+
+class FakeMessagesListChatModel(BaseChatModel):
+    """Fake ChatModel for testing purposes."""
+
+    responses: List[BaseMessage]
+    sleep: Optional[float] = None
+    i: int = 0
+
+    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
+        generation = ChatGeneration(message=response)
+        return ChatResult(generations=[generation])
+
+    @property
+    def _llm_type(self) -> str:
+        return "fake-messages-list-chat-model"
+
+
+class FakeListChatModel(SimpleChatModel):
+    """Fake ChatModel for testing purposes."""
+
+    responses: List
+    sleep: Optional[float] = None
+    i: int = 0
+    error_on_chunk_number: Optional[int] = None
+
+    @property
+    def _llm_type(self) -> str:
+        return "fake-list-chat-model"
+
+    def _call(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        """First try to lookup in queries, else return 'foo' or 'bar'."""
+        response = self.responses[self.i]
+        if self.i < len(self.responses) - 1:
+            self.i += 1
+        else:
+            self.i = 0
+        return response
+
+    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
+        for i_c, c in enumerate(response):
+            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 Exception("Fake error")
+
+            yield ChatGenerationChunk(message=AIMessageChunk(content=c))
+
+    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
+        for i_c, c in enumerate(response):
+            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 Exception("Fake error")
+            yield ChatGenerationChunk(message=AIMessageChunk(content=c))
+
+    @property
+    def _identifying_params(self) -> Dict[str, Any]:
+        return {"responses": self.responses}
+
+
+class FakeChatModel(SimpleChatModel):
+    """Fake Chat Model wrapper for testing purposes."""
+
+    def _call(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        return "fake response"
+
+    async def _agenerate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        output_str = "fake response"
+        message = AIMessage(content=output_str)
+        generation = ChatGeneration(message=message)
+        return ChatResult(generations=[generation])
+
+    @property
+    def _llm_type(self) -> str:
+        return "fake-chat-model"
+
+    @property
+    def _identifying_params(self) -> Dict[str, Any]:
+        return {"key": "fake"}
+
+
+class GenericFakeChatModel(BaseChatModel):
+    """A generic fake chat model that can be used to test the chat model interface.
+
+    * Chat model should be usable in both sync and async tests
+    * Invokes on_llm_new_token to allow for testing of callback related code for new
+      tokens.
+    * Includes logic to break messages into message chunk to facilitate testing of
+      streaming.
+    """
+
+    messages: Iterator[AIMessage]
+    """Get an iterator over messages.
+
+    This can be expanded to accept other types like Callables / dicts / strings
+    to make the interface more generic if needed.
+
+    Note: if you want to pass a list, you can use `iter` to convert it to an iterator.
+
+    Please note that streaming is not implemented yet. We should try to implement it
+    in the future by delegating to invoke and then breaking the resulting output
+    into message chunks.
+    """
+
+    def _generate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        """Top Level call"""
+        message = next(self.messages)
+        generation = ChatGeneration(message=message)
+        return ChatResult(generations=[generation])
+
+    def _stream(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> Iterator[ChatGenerationChunk]:
+        """Stream the output of the model."""
+        chat_result = self._generate(
+            messages, stop=stop, run_manager=run_manager, **kwargs
+        )
+        if not isinstance(chat_result, ChatResult):
+            raise ValueError(
+                f"Expected generate to return a ChatResult, "
+                f"but got {type(chat_result)} instead."
+            )
+
+        message = chat_result.generations[0].message
+
+        if not isinstance(message, AIMessage):
+            raise ValueError(
+                f"Expected invoke to return an AIMessage, "
+                f"but got {type(message)} instead."
+            )
+
+        content = message.content
+
+        if content:
+            # Use a regular expression to split on whitespace with a capture group
+            # so that we can preserve the whitespace in the output.
+            assert isinstance(content, str)
+            content_chunks = cast(List[str], re.split(r"(\s)", content))
+
+            for token in content_chunks:
+                chunk = ChatGenerationChunk(message=AIMessageChunk(content=token))
+                yield chunk
+                if run_manager:
+                    run_manager.on_llm_new_token(token, chunk=chunk)
+
+        if message.additional_kwargs:
+            for key, value in message.additional_kwargs.items():
+                # We should further break down the additional kwargs into chunks
+                # Special case for function call
+                if key == "function_call":
+                    for fkey, fvalue in value.items():
+                        if isinstance(fvalue, str):
+                            # Break function call by `,`
+                            fvalue_chunks = cast(List[str], re.split(r"(,)", fvalue))
+                            for fvalue_chunk in fvalue_chunks:
+                                chunk = ChatGenerationChunk(
+                                    message=AIMessageChunk(
+                                        content="",
+                                        additional_kwargs={
+                                            "function_call": {fkey: fvalue_chunk}
+                                        },
+                                    )
+                                )
+                                yield chunk
+                                if run_manager:
+                                    run_manager.on_llm_new_token(
+                                        "",
+                                        chunk=chunk,  # No token for function call
+                                    )
+                        else:
+                            chunk = ChatGenerationChunk(
+                                message=AIMessageChunk(
+                                    content="",
+                                    additional_kwargs={"function_call": {fkey: fvalue}},
+                                )
+                            )
+                            yield chunk
+                            if run_manager:
+                                run_manager.on_llm_new_token(
+                                    "",
+                                    chunk=chunk,  # No token for function call
+                                )
+                else:
+                    chunk = ChatGenerationChunk(
+                        message=AIMessageChunk(
+                            content="", additional_kwargs={key: value}
+                        )
+                    )
+                    yield chunk
+                    if run_manager:
+                        run_manager.on_llm_new_token(
+                            "",
+                            chunk=chunk,  # No token for function call
+                        )
+
+    async def _astream(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> AsyncIterator[ChatGenerationChunk]:
+        """Stream the output of the model."""
+        result = await run_in_executor(
+            None,
+            self._stream,
+            messages,
+            stop=stop,
+            run_manager=run_manager.get_sync() if run_manager else None,
+            **kwargs,
+        )
+        for chunk in result:
+            yield chunk
+
+    @property
+    def _llm_type(self) -> str:
+        return "generic-fake-chat-model"
+
+
+class ParrotFakeChatModel(BaseChatModel):
+    """A generic fake chat model that can be used to test the chat model interface.
+
+    * Chat model should be usable in both sync and async tests
+    """
+
+    def _generate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        """Top Level call"""
+        return ChatResult(generations=[ChatGeneration(message=messages[-1])])
+
+    @property
+    def _llm_type(self) -> str:
+        return "parrot-fake-chat-model"

libs/misc/testutils/poetry.lock

@@ -0,0 +1,477 @@
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+
+[[package]]
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+version = "0.6.0"
+description = "Reusable constraint types to use with typing.Annotated"
+optional = false
+python-versions = ">=3.8"
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+    {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"},
+]
+
+[[package]]
+name = "anyio"
+version = "4.3.0"
+description = "High level compatibility layer for multiple asynchronous event loop implementations"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "anyio-4.3.0-py3-none-any.whl", hash = "sha256:048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8"},
+    {file = "anyio-4.3.0.tar.gz", hash = "sha256:f75253795a87df48568485fd18cdd2a3fa5c4f7c5be8e5e36637733fce06fed6"},
+]
+
+[package.dependencies]
+idna = ">=2.8"
+sniffio = ">=1.1"
+
+[package.extras]
+doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
+trio = ["trio (>=0.23)"]
+
+[[package]]
+name = "certifi"
+version = "2024.2.2"
+description = "Python package for providing Mozilla's CA Bundle."
+optional = false
+python-versions = ">=3.6"
+files = [
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+]
+
+[[package]]
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+optional = false
+python-versions = ">=3.7.0"
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libs/misc/testutils/pyproject.toml

@@ -0,0 +1,15 @@
+[tool.poetry]
+name = "langchain-testutils"
+version = "0.1.0"
+description = ""
+authors = ["Erick Friis <erick@langchain.dev>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.11"
+langchain-core = "^0.1.25"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"