80 lines
3.0 KiB
Python
80 lines
3.0 KiB
Python
import json
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import os
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from typing import Any, Callable, Dict, Optional, Type, Union
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from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
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from langchain.schema import BaseMessage, HumanMessage, SystemMessage
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from langchain_core.tools import BaseTool
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from langchain_openai import ChatOpenAI
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from pydantic import BaseModel
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from embedchain.config import BaseLlmConfig
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from embedchain.helpers.json_serializable import register_deserializable
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from embedchain.llm.base import BaseLlm
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@register_deserializable
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class OpenAILlm(BaseLlm):
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def __init__(
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self,
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config: Optional[BaseLlmConfig] = None,
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tools: Optional[Union[Dict[str, Any], Type[BaseModel], Callable[..., Any], BaseTool]] = None,
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):
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self.tools = tools
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super().__init__(config=config)
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def get_llm_model_answer(self, prompt) -> str:
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response = self._get_answer(prompt, self.config)
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return response
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def _get_answer(self, prompt: str, config: BaseLlmConfig) -> str:
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messages = []
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if config.system_prompt:
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messages.append(SystemMessage(content=config.system_prompt))
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messages.append(HumanMessage(content=prompt))
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kwargs = {
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"model": config.model or "gpt-3.5-turbo",
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"temperature": config.temperature,
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"max_tokens": config.max_tokens,
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"model_kwargs": config.model_kwargs or {},
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}
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api_key = config.api_key or os.environ["OPENAI_API_KEY"]
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base_url = config.base_url or os.environ.get("OPENAI_API_BASE", None)
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if config.top_p:
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kwargs["model_kwargs"]["top_p"] = config.top_p
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if config.default_headers:
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kwargs["default_headers"] = config.default_headers
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if config.stream:
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callbacks = config.callbacks if config.callbacks else [StreamingStdOutCallbackHandler()]
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chat = ChatOpenAI(
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**kwargs,
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streaming=config.stream,
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callbacks=callbacks,
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api_key=api_key,
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base_url=base_url,
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http_client=config.http_client,
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http_async_client=config.http_async_client,
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)
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else:
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chat = ChatOpenAI(**kwargs, api_key=api_key, base_url=base_url)
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if self.tools:
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return self._query_function_call(chat, self.tools, messages)
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return chat.invoke(messages).content
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def _query_function_call(
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self,
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chat: ChatOpenAI,
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tools: Optional[Union[Dict[str, Any], Type[BaseModel], Callable[..., Any], BaseTool]],
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messages: list[BaseMessage],
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) -> str:
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from langchain.output_parsers.openai_tools import JsonOutputToolsParser
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from langchain_core.utils.function_calling import convert_to_openai_tool
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openai_tools = [convert_to_openai_tool(tools)]
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chat = chat.bind(tools=openai_tools).pipe(JsonOutputToolsParser())
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try:
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return json.dumps(chat.invoke(messages)[0])
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except IndexError:
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return "Input could not be mapped to the function!"
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