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from langchain.embeddings.openai import OpenAIEmbeddings |
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from langchain.embeddings import HuggingFaceEmbeddings |
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from langchain.text_splitter import CharacterTextSplitter |
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from langchain.vectorstores import FAISS |
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from langchain.chains.question_answering import load_qa_chain |
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from langchain.llms import OpenAI |
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from gradio import gradio as gr |
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from langchain.chat_models import ChatOpenAI |
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from langchain.schema import AIMessage, HumanMessage |
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from langchain import PromptTemplate, LLMChain |
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from langchain.llms import TextGen |
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import os |
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OPENAI_API_KEY=os.getenv('OPENAI_API_KEY') |
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embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-large-en") |
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docsearch = FAISS.load_local("./bge-large-en_faiss_index/faiss_index", embeddings) |
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chain = load_qa_chain(OpenAI(), chain_type="stuff",verbose=True) |
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prompt = "您是回答所有ANSYS软件使用查询的得力助手,如果所问的内容不在范围内，请回答“您提的问题不在本知识库内，请重新提问”，所有问题必需用中文回答" |
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def predict(message, history): |
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history_openai_format = [] |
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for human, assistant in history: |
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history_openai_format.append({"role": "system", "content": prompt }) |
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history_openai_format.append({"role": "user", "content": human }) |
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history_openai_format.append({"role": "assistant", "content":assistant}) |
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history_openai_format.append({"role": "user", "content": message}) |
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response = openai.ChatCompletion.create( |
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model='gpt-3.5-turbo', |
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messages= history_openai_format, |
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temperature=1.0, |
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stream=True |
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) |
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partial_message = "" |
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for chunk in response: |
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if len(chunk['choices'][0]['delta']) != 0: |
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partial_message = partial_message + chunk['choices'][0]['delta']['content'] |
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yield partial_message |
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gr.ChatInterface(predict, |
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textbox=gr.Textbox(placeholder="请输入您的问题", container=False, scale=7), |
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title="欢迎使用ANSYS软件AI机器人", |
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examples=["你是谁?", "请介绍一下Fluent 软件的用户界面说明", "请用关于春天写一首100字的诗","数学题：小红有3元钱，小红买了2斤香蕉，香蕉的价格是每斤1元。问小红一共花了多少钱?","请用表格做一份学生课程表"], |
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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() |
