forked from Archives/langchain
Add Anthropic ChatModel to langchain (#2293)
* Adds an Anthropic ChatModel * Factors out common code in our LLMModel and ChatModel * Supports streaming llm-tokens to the callbacks on a delta basis (until a future V2 API does that for us) * Some fixesfix_agent_callbacks
Mike Lambert
1 year ago
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GitHub
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392f1b3218
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from typing import List, Optional
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from pydantic import Extra
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from langchain.chat_models.base import BaseChatModel
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from langchain.llms.anthropic import _AnthropicCommon
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from langchain.schema import (
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AIMessage,
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BaseMessage,
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ChatGeneration,
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ChatMessage,
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ChatResult,
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HumanMessage,
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SystemMessage,
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)
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class ChatAnthropic(BaseChatModel, _AnthropicCommon):
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r"""Wrapper around Anthropic's large language model.
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To use, you should have the ``anthropic`` python package installed, and the
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environment variable ``ANTHROPIC_API_KEY`` set with your API key, or pass
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it as a named parameter to the constructor.
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Example:
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.. code-block:: python
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import anthropic
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from langchain.llms import Anthropic
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model = Anthropic(model="<model_name>", anthropic_api_key="my-api-key")
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# Simplest invocation, automatically wrapped with HUMAN_PROMPT
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# and AI_PROMPT.
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response = model("What are the biggest risks facing humanity?")
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# Or if you want to use the chat mode, build a few-shot-prompt, or
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# put words in the Assistant's mouth, use HUMAN_PROMPT and AI_PROMPT:
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raw_prompt = "What are the biggest risks facing humanity?"
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prompt = f"{anthropic.HUMAN_PROMPT} {prompt}{anthropic.AI_PROMPT}"
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response = model(prompt)
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"""
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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@property
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def _llm_type(self) -> str:
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"""Return type of chat model."""
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return "anthropic-chat"
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def _convert_one_message_to_text(self, message: BaseMessage) -> str:
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if isinstance(message, ChatMessage):
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message_text = f"\n\n{message.role.capitalize()}: {message.content}"
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elif isinstance(message, HumanMessage):
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message_text = f"{self.HUMAN_PROMPT} {message.content}"
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elif isinstance(message, AIMessage):
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message_text = f"{self.AI_PROMPT} {message.content}"
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elif isinstance(message, SystemMessage):
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message_text = f"{self.HUMAN_PROMPT} <admin>{message.content}</admin>"
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else:
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raise ValueError(f"Got unknown type {message}")
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return message_text
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def _convert_messages_to_text(self, messages: List[BaseMessage]) -> str:
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"""Format a list of strings into a single string with necessary newlines.
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Args:
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messages (List[BaseMessage]): List of BaseMessage to combine.
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Returns:
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str: Combined string with necessary newlines.
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"""
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return "".join(
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self._convert_one_message_to_text(message) for message in messages
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)
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def _convert_messages_to_prompt(self, messages: List[BaseMessage]) -> str:
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"""Format a list of messages into a full prompt for the Anthropic model
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Args:
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messages (List[BaseMessage]): List of BaseMessage to combine.
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Returns:
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str: Combined string with necessary HUMAN_PROMPT and AI_PROMPT tags.
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"""
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if not self.AI_PROMPT:
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raise NameError("Please ensure the anthropic package is loaded")
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if not isinstance(messages[-1], AIMessage):
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messages.append(AIMessage(content=""))
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text = self._convert_messages_to_text(messages)
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return (
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text.rstrip()
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) # trim off the trailing ' ' that might come from the "Assistant: "
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def _generate(
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self, messages: List[BaseMessage], stop: Optional[List[str]] = None
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) -> ChatResult:
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prompt = self._convert_messages_to_prompt(messages)
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params = {"prompt": prompt, "stop_sequences": stop, **self._default_params}
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if self.streaming:
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completion = ""
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stream_resp = self.client.completion_stream(**params)
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for data in stream_resp:
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delta = data["completion"][len(completion) :]
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completion = data["completion"]
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self.callback_manager.on_llm_new_token(
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delta,
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verbose=self.verbose,
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)
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else:
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response = self.client.completion(**params)
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completion = response["completion"]
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message = AIMessage(content=completion)
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return ChatResult(generations=[ChatGeneration(message=message)])
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async def _agenerate(
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self, messages: List[BaseMessage], stop: Optional[List[str]] = None
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) -> ChatResult:
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prompt = self._convert_messages_to_prompt(messages)
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params = {"prompt": prompt, "stop_sequences": stop, **self._default_params}
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if self.streaming:
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completion = ""
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stream_resp = await self.client.acompletion_stream(**params)
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async for data in stream_resp:
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delta = data["completion"][len(completion) :]
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completion = data["completion"]
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if self.callback_manager.is_async:
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await self.callback_manager.on_llm_new_token(
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delta,
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verbose=self.verbose,
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)
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else:
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self.callback_manager.on_llm_new_token(
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delta,
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verbose=self.verbose,
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)
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else:
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response = await self.client.acompletion(**params)
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completion = response["completion"]
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message = AIMessage(content=completion)
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return ChatResult(generations=[ChatGeneration(message=message)])
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@ -0,0 +1,81 @@
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"""Test Anthropic API wrapper."""
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from typing import List
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import pytest
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from langchain.callbacks.base import CallbackManager
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from langchain.chat_models.anthropic import ChatAnthropic
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from langchain.schema import (
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AIMessage,
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BaseMessage,
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ChatGeneration,
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HumanMessage,
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LLMResult,
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)
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from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
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def test_anthropic_call() -> None:
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"""Test valid call to anthropic."""
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chat = ChatAnthropic(model="bare-nano-0")
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message = HumanMessage(content="Hello")
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response = chat([message])
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assert isinstance(response, AIMessage)
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assert isinstance(response.content, str)
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def test_anthropic_streaming() -> None:
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"""Test streaming tokens from anthropic."""
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chat = ChatAnthropic(model="bare-nano-0", streaming=True)
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message = HumanMessage(content="Hello")
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response = chat([message])
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assert isinstance(response, AIMessage)
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assert isinstance(response.content, str)
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def test_anthropic_streaming_callback() -> None:
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"""Test that streaming correctly invokes on_llm_new_token callback."""
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callback_handler = FakeCallbackHandler()
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callback_manager = CallbackManager([callback_handler])
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chat = ChatAnthropic(
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streaming=True,
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callback_manager=callback_manager,
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verbose=True,
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)
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message = HumanMessage(content="Write me a sentence with 100 words.")
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chat([message])
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assert callback_handler.llm_streams > 1
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@pytest.mark.asyncio
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async def test_anthropic_async_streaming_callback() -> None:
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"""Test that streaming correctly invokes on_llm_new_token callback."""
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callback_handler = FakeCallbackHandler()
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callback_manager = CallbackManager([callback_handler])
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chat = ChatAnthropic(
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streaming=True,
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callback_manager=callback_manager,
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verbose=True,
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)
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chat_messages: List[BaseMessage] = [
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HumanMessage(content="How many toes do dogs have?")
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]
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result: LLMResult = await chat.agenerate([chat_messages])
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assert callback_handler.llm_streams > 1
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assert isinstance(result, LLMResult)
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for response in result.generations[0]:
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assert isinstance(response, ChatGeneration)
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assert isinstance(response.text, str)
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assert response.text == response.message.content
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def test_formatting() -> None:
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chat = ChatAnthropic()
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chat_messages: List[BaseMessage] = [HumanMessage(content="Hello")]
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result = chat._convert_messages_to_prompt(chat_messages)
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assert result == "\n\nHuman: Hello\n\nAssistant:"
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chat_messages = [HumanMessage(content="Hello"), AIMessage(content="Answer:")]
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result = chat._convert_messages_to_prompt(chat_messages)
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assert result == "\n\nHuman: Hello\n\nAssistant: Answer:"
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