File size: 2,367 Bytes
197f59d dd38e98 197f59d f0b0422 dd38e98 eb3b23d 84e13f4 197f59d 417067d 197f59d 417067d 2f94971 a651eac c39601f 8ac8ab1 dd38e98 64e8704 dd38e98 98f2449 882d372 8ac8ab1 64e8704 197f59d f0b0422 417067d f0b0422 417067d 8ac8ab1 417067d 379e870 98f2449 |
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 56 57 58 59 60 61 62 |
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
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 import PromptTemplate, LLMChain
from langchain.llms import TextGen
from langchain.cache import InMemoryCache
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
AIMessagePromptTemplate,
HumanMessagePromptTemplate,
)
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage
)
import time
import langchain
import os
OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
# 嵌入模型
#embeddings = OpenAIEmbeddings()
embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-large-en")
# 加载数据
#docsearch = FAISS.from_texts(texts, embeddings)
docsearch = FAISS.load_local("./faiss_index", embeddings)
template="您是回答ANSYS软件使用查询的得力助手,所有回复必需用中文"
chain = load_qa_chain(OpenAI(temperature=0,model_name="gpt-3.5-turbo"), chain_type="stuff",verbose=True)
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 + template)
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,
textbox=gr.Textbox(placeholder="请提问关于ANSYS软件的问题", container=False, scale=7),
title="欢迎使用ANSYS软件AI机器人",
examples=["你是谁?", "请介绍一下Fluent 软件", "create-bounding-box","ANSYS Fluent Architecture","ANSYS Fluent 的软件架构是怎么样的"],
description="请避免输入有违公序良俗的问题，模型可能无法回答不合适的问题",).queue().launch(debug=True) |