from fastapi import FastAPI, Depends, Request, Response from typing import Any, Dict, List, Generator import asyncio from langchain.llms import OpenAI from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler from langchain.schema import LLMResult, HumanMessage, SystemMessage from ...
from langchain_community.vectorstores import DocArrayInMemorySearch from langchain_core.output_parsers import StrOutputParser from langchain_core.runnables import RunnablePassthrough from langchain_core.prompts import ChatPromptTemplate from langchain_openai import OpenAIEmbeddings from langchain_openai.chat_...
output_parsers import StrOutputParser from langchain_core.runnables import RunnablePassthrough from langchain_core.prompts import ChatPromptTemplate from langchain_openai import OpenAIEmbeddings from langchain_openai.chat_models import ChatOpenAI async def main(): template = """Answer the question based...
I'm helping the LangChain team manage their backlog and am marking this issue as stale. From what I understand, you were looking for a way to stream output for VLLM and Together AI in LangChain, as it is not currently supported according to the documentation. I provided a detailed respo...
# MacBookProM1 FAISS没有包,可能是 faiss-cpu# from langchain_community.vectorstores import FAISSfrom langchain_community.vectorstores import DocArrayInMemorySearchfrom langchain_core.output_parsers import StrOutputParserfrom langchain_core.runnables import RunnablePassthroughfrom langchain_core.prompts imp...
stream(messages))调用它们会发生什么。我怀疑它也会触发错误，并帮助产生一个最小可复现的例子 ...
stream(messages))调用它们会发生什么。我怀疑它也会触发错误，并帮助产生一个最小可复现的例子 ...
自定义langchain的流式接受类BaseCallbackHandler class ChainStreamHandler(StreamingStdOutCallbackHandler): def __init__(self): self.tokens = [] # 记得结束后这里置true self.finish = False def on_llm_new_token(self, token: str, **kwargs): print(token, end="") self.tokens.append(token) de...
importlangchain_core# 假设这是您的NLP库 defprocess_text_stream(text_stream): # 假设text_stream是一个生成器,它产生一系列的文本片段 fortextintext_stream: # 对每个文本片段进行处理 # 例如,分词 tokens = langchain_core.tokenize(text) # 或者情感分析 sentiment = langchain_core.analyze_sentiment(tex...
( memory_key="chat_history", output_key="answer", chat_memory=message_history, return_messages=True, ) # Create a chain that uses the Chroma vector store chain = ConversationalRetrievalChain.from_llm( llm=llm_groq, chain_type="stuff", retriever=docsearch.as_retriever(), memory=memory, ...