mirror of
https://github.com/hwchase17/langchain
synced 2024-11-02 09:40:22 +00:00
235 lines
8.0 KiB
Python
235 lines
8.0 KiB
Python
from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, cast
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from langchain_core._api.deprecation import deprecated
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from langchain_core.callbacks 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 (
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BaseChatModel,
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agenerate_from_stream,
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generate_from_stream,
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)
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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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ChatMessage,
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HumanMessage,
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SystemMessage,
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)
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from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
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from langchain_core.prompt_values import PromptValue
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from langchain_community.llms.anthropic import _AnthropicCommon
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def _convert_one_message_to_text(
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message: BaseMessage,
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human_prompt: str,
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ai_prompt: str,
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) -> str:
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content = cast(str, message.content)
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if isinstance(message, ChatMessage):
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message_text = f"\n\n{message.role.capitalize()}: {content}"
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elif isinstance(message, HumanMessage):
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message_text = f"{human_prompt} {content}"
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elif isinstance(message, AIMessage):
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message_text = f"{ai_prompt} {content}"
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elif isinstance(message, SystemMessage):
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message_text = content
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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_prompt_anthropic(
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messages: List[BaseMessage],
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*,
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human_prompt: str = "\n\nHuman:",
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ai_prompt: str = "\n\nAssistant:",
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) -> 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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human_prompt (str, optional): Human prompt tag. Defaults to "\n\nHuman:".
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ai_prompt (str, optional): AI prompt tag. Defaults to "\n\nAssistant:".
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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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messages = messages.copy() # don't mutate the original list
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if not isinstance(messages[-1], AIMessage):
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messages.append(AIMessage(content=""))
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text = "".join(
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_convert_one_message_to_text(message, human_prompt, ai_prompt)
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for message in messages
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)
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# trim off the trailing ' ' that might come from the "Assistant: "
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return text.rstrip()
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@deprecated(
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since="0.0.28",
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removal="0.2",
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alternative_import="langchain_anthropic.ChatAnthropic",
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)
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class ChatAnthropic(BaseChatModel, _AnthropicCommon):
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"""`Anthropic` chat large language models.
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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_community.chat_models import ChatAnthropic
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model = ChatAnthropic(model="<model_name>", anthropic_api_key="my-api-key")
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"""
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class Config:
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"""Configuration for this pydantic object."""
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allow_population_by_field_name = True
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arbitrary_types_allowed = True
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@property
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def lc_secrets(self) -> Dict[str, str]:
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return {"anthropic_api_key": "ANTHROPIC_API_KEY"}
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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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@classmethod
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def is_lc_serializable(cls) -> bool:
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"""Return whether this model can be serialized by Langchain."""
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return True
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@classmethod
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def get_lc_namespace(cls) -> List[str]:
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"""Get the namespace of the langchain object."""
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return ["langchain", "chat_models", "anthropic"]
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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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prompt_params = {}
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if self.HUMAN_PROMPT:
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prompt_params["human_prompt"] = self.HUMAN_PROMPT
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if self.AI_PROMPT:
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prompt_params["ai_prompt"] = self.AI_PROMPT
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return convert_messages_to_prompt_anthropic(messages=messages, **prompt_params)
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def convert_prompt(self, prompt: PromptValue) -> str:
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return self._convert_messages_to_prompt(prompt.to_messages())
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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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prompt = self._convert_messages_to_prompt(messages)
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params: Dict[str, Any] = {"prompt": prompt, **self._default_params, **kwargs}
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if stop:
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params["stop_sequences"] = stop
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stream_resp = self.client.completions.create(**params, stream=True)
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for data in stream_resp:
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delta = data.completion
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chunk = ChatGenerationChunk(message=AIMessageChunk(content=delta))
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if run_manager:
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run_manager.on_llm_new_token(delta, chunk=chunk)
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yield chunk
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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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prompt = self._convert_messages_to_prompt(messages)
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params: Dict[str, Any] = {"prompt": prompt, **self._default_params, **kwargs}
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if stop:
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params["stop_sequences"] = stop
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stream_resp = await self.async_client.completions.create(**params, stream=True)
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async for data in stream_resp:
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delta = data.completion
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chunk = ChatGenerationChunk(message=AIMessageChunk(content=delta))
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if run_manager:
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await run_manager.on_llm_new_token(delta, chunk=chunk)
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yield chunk
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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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if self.streaming:
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stream_iter = self._stream(
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messages, stop=stop, run_manager=run_manager, **kwargs
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)
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return generate_from_stream(stream_iter)
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prompt = self._convert_messages_to_prompt(
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messages,
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)
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params: Dict[str, Any] = {
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"prompt": prompt,
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**self._default_params,
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**kwargs,
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}
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if stop:
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params["stop_sequences"] = stop
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response = self.client.completions.create(**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,
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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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if self.streaming:
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stream_iter = self._astream(
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messages, stop=stop, run_manager=run_manager, **kwargs
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)
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return await agenerate_from_stream(stream_iter)
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prompt = self._convert_messages_to_prompt(
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messages,
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)
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params: Dict[str, Any] = {
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"prompt": prompt,
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**self._default_params,
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**kwargs,
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}
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if stop:
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params["stop_sequences"] = stop
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response = await self.async_client.completions.create(**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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def get_num_tokens(self, text: str) -> int:
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"""Calculate number of tokens."""
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if not self.count_tokens:
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raise NameError("Please ensure the anthropic package is loaded")
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return self.count_tokens(text)
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