mirror of
https://github.com/hwchase17/langchain
synced 2024-11-08 07:10:35 +00:00
127 lines
4.2 KiB
Python
127 lines
4.2 KiB
Python
import json
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import logging
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from typing import (
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Any,
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Dict,
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List,
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Mapping,
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Optional,
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Tuple,
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)
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from langchain.callbacks.manager import CallbackManagerForLLMRun
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from langchain.schema import (
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ChatGeneration,
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ChatResult,
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)
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from langchain_core.language_models import BaseChatModel
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from langchain_core.messages import (
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AIMessage,
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BaseMessage,
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ChatMessage,
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FunctionMessage,
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HumanMessage,
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SystemMessage,
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)
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logger = logging.getLogger(__name__)
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def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
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role = _dict["role"]
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if role == "user":
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return HumanMessage(content=_dict["content"])
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elif role == "assistant":
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# Fix for azure
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# Also OpenAI returns None for tool invocations
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content = _dict.get("content") or ""
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if _dict.get("function_call"):
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_dict["function_call"]["arguments"] = json.dumps(
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_dict["function_call"]["arguments"]
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)
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additional_kwargs = {"function_call": dict(_dict["function_call"])}
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else:
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additional_kwargs = {}
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return AIMessage(content=content, additional_kwargs=additional_kwargs)
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elif role == "system":
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return SystemMessage(content=_dict["content"])
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elif role == "function":
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return FunctionMessage(content=_dict["content"], name=_dict["name"])
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else:
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return ChatMessage(content=_dict["content"], role=role)
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def _convert_message_to_dict(message: BaseMessage) -> dict:
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if isinstance(message, ChatMessage):
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message_dict = {"role": message.role, "content": message.content}
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elif isinstance(message, HumanMessage):
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message_dict = {"role": "user", "content": message.content}
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elif isinstance(message, AIMessage):
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message_dict = {"role": "assistant", "content": message.content}
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if "function_call" in message.additional_kwargs:
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message_dict["function_call"] = message.additional_kwargs["function_call"]
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elif isinstance(message, SystemMessage):
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message_dict = {"role": "system", "content": message.content}
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elif isinstance(message, FunctionMessage):
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message_dict = {
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"role": "function",
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"content": message.content,
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"name": message.name,
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}
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else:
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raise ValueError(f"Got unknown type {message}")
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if "name" in message.additional_kwargs:
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message_dict["name"] = message.additional_kwargs["name"]
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return message_dict
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class ChatLlamaAPI(BaseChatModel):
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"""Chat model using the Llama API."""
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client: Any #: :meta private:
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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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message_dicts, params = self._create_message_dicts(messages, stop)
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_params = {"messages": message_dicts}
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final_params = {**params, **kwargs, **_params}
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response = self.client.run(final_params).json()
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return self._create_chat_result(response)
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def _create_message_dicts(
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self, messages: List[BaseMessage], stop: Optional[List[str]]
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) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
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params = dict(self._client_params)
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if stop is not None:
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if "stop" in params:
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raise ValueError("`stop` found in both the input and default params.")
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params["stop"] = stop
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message_dicts = [_convert_message_to_dict(m) for m in messages]
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return message_dicts, params
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def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
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generations = []
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for res in response["choices"]:
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message = _convert_dict_to_message(res["message"])
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gen = ChatGeneration(
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message=message,
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generation_info=dict(finish_reason=res.get("finish_reason")),
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)
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generations.append(gen)
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return ChatResult(generations=generations)
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@property
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def _client_params(self) -> Mapping[str, Any]:
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"""Get the parameters used for the client."""
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return {}
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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 "llama-api"
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