61 lines
2.3 KiB
Python
61 lines
2.3 KiB
Python
import json
|
|
import os
|
|
from typing import Any, Dict, Optional
|
|
|
|
from langchain.chat_models import ChatOpenAI
|
|
from langchain.schema import AIMessage, HumanMessage, SystemMessage
|
|
|
|
from embedchain.config import BaseLlmConfig
|
|
from embedchain.helpers.json_serializable import register_deserializable
|
|
from embedchain.llm.base import BaseLlm
|
|
|
|
|
|
@register_deserializable
|
|
class OpenAILlm(BaseLlm):
|
|
def __init__(self, config: Optional[BaseLlmConfig] = None, functions: Optional[Dict[str, Any]] = None):
|
|
self.functions = functions
|
|
super().__init__(config=config)
|
|
|
|
def get_llm_model_answer(self, prompt) -> str:
|
|
response = self._get_answer(prompt, self.config)
|
|
return response
|
|
|
|
def _get_answer(self, prompt: str, config: BaseLlmConfig) -> str:
|
|
messages = []
|
|
if config.system_prompt:
|
|
messages.append(SystemMessage(content=config.system_prompt))
|
|
messages.append(HumanMessage(content=prompt))
|
|
kwargs = {
|
|
"model": config.model or "gpt-3.5-turbo",
|
|
"temperature": config.temperature,
|
|
"max_tokens": config.max_tokens,
|
|
"model_kwargs": {},
|
|
}
|
|
api_key = config.api_key or os.environ["OPENAI_API_KEY"]
|
|
if config.top_p:
|
|
kwargs["model_kwargs"]["top_p"] = config.top_p
|
|
if config.stream:
|
|
from langchain.callbacks.streaming_stdout import \
|
|
StreamingStdOutCallbackHandler
|
|
|
|
callbacks = config.callbacks if config.callbacks else [StreamingStdOutCallbackHandler()]
|
|
chat = ChatOpenAI(**kwargs, streaming=config.stream, callbacks=callbacks, api_key=api_key)
|
|
else:
|
|
chat = ChatOpenAI(**kwargs, api_key=api_key)
|
|
|
|
if self.functions is not None:
|
|
from langchain.chains.openai_functions import \
|
|
create_openai_fn_runnable
|
|
from langchain.prompts import ChatPromptTemplate
|
|
|
|
structured_prompt = ChatPromptTemplate.from_messages(messages)
|
|
runnable = create_openai_fn_runnable(functions=self.functions, prompt=structured_prompt, llm=chat)
|
|
fn_res = runnable.invoke(
|
|
{
|
|
"input": prompt,
|
|
}
|
|
)
|
|
messages.append(AIMessage(content=json.dumps(fn_res)))
|
|
|
|
return chat(messages).content
|