File size: 2,032 Bytes
b8eea15 0b0c1d7 b8eea15 44d0963 b8eea15 44d0963 b8eea15 3cdf8fd b8eea15 7868931 b8eea15 b3a8661 b8eea15 7868931 0b0265a 7868931 b8eea15 0b0c1d7 7868931 0b0265a 44d0963 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 |
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS, Chroma, Pinecone
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI
from gradio import gradio as gr
from langchain.chat_models import ChatOpenAI
from langchain.schema import AIMessage, HumanMessage
from langchain.cache import InMemoryCache
import langchain
import time
import os
OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
USER=os.getenv('USER')
PASS=os.getenv('PASS')
import pinecone
# 初始化 pinecone
pinecone.init(
api_key=os.getenv('pinecone_api_key'),
environment="gcp-starter"
)
index_name="text-index"
embeddings = OpenAIEmbeddings()
llm = ChatOpenAI(temperature=0,model_name="gpt-3.5-turbo", verbose=True)
# 加载数据
docsearch = Pinecone.from_existing_index(index_name, embeddings)
chain = load_qa_chain(llm, chain_type="stuff")
def predict(message, history):
history_langchain_format = []
for human, ai in history:
history_langchain_format.append(HumanMessage(content=human))
history_langchain_format.append(AIMessage(content=ai))
history_langchain_format.append(HumanMessage(content=message))
docs = docsearch.similarity_search(message)
response = chain.run(input_documents=docs, question=message)
partial_message = ""
for chunk in response:
if len(chunk[0]) != 0:
time.sleep(0.1)
partial_message = partial_message + chunk[0]
yield partial_message
langchain.llm_cache = InMemoryCache()
gr.ChatInterface(predict, theme=gr.themes.Default(),
textbox=gr.Textbox(placeholder="请输入您的问题...", container=False, scale=7),
title="欢迎使用智造云AI助手",
examples=["老师反馈文件传输慢怎么处理?", "用户作业同步状态速度为0应该联系谁?"]).queue().launch(debug=True,auth=(USER, PASS))
|