import os os.environ["LANGCHAIN_PROJECT"] = f"RAG_workflow_2" os.environ['LANGCHAIN_TRACING_V2'] = 'true' os.environ['LANGCHAIN_ENDPOINT'] = 'https://api.smith.langchain.com' os.environ['LANGCHAIN_API_KEY'] = "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" with open("openai.txt","r") as f: key=f.read() with open("google.txt","r") as f1: key2=f1.read() os.environ['OPENAI_API_KEY'] = "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" from langchain_community.document_loaders import WebBaseLoader import os import gradio as gr from langchain_groq import ChatGroq from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain_community.vectorstores import Chroma from langchain_core.output_parsers import StrOutputParser from langchain_core.runnables import RunnablePassthrough from langchain.schema import AIMessage, HumanMessage, SystemMessage from langchain import hub from langchain_community.document_loaders import PyPDFLoader from langchain_openai import OpenAIEmbeddings # Importing OpenAI Embeddings # Set the google key os.environ["GOOGLE_API_KEY"]="AIzaSyCHgWZbKzPsgA4DjWUPB8FJHqXLglhSwmI" from langchain_google_genai import ChatGoogleGenerativeAI # Initialize the google LLM llm = ChatGoogleGenerativeAI( model="gemini-1.5-pro", temperature=0, max_tokens=None, timeout=None, max_retries=2 ) # Function to process the content from a URL and get a response from LLM def process_url(url, user_query): # Load the content from the URL loader = WebBaseLoader(web_paths=[url]) docs = loader.load() # Split the text text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) splits = text_splitter.split_documents(docs) # Embed the splits embeddings = OpenAIEmbeddings() # Initialize OpenAI Embeddings vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings) retriever = vectorstore.as_retriever(search_kwargs={"k": 5}) # Prompt prompt = hub.pull("rlm/rag-prompt") # Chain rag_chain = ( {"context": retriever | (lambda docs: "\n\n".join(doc.page_content for doc in docs)), "question": RunnablePassthrough()} | prompt | llm | StrOutputParser() ) # Get the response from the LLM response = rag_chain.invoke(user_query) return response # Gradio interface def gradio_interface(url, user_query): response = process_url(url, user_query) return response # Create Gradio app demo_interface = gr.Interface( fn=gradio_interface, inputs=[ gr.Textbox(label="输入 URL"), gr.Textbox(label="询问关于网站的问题") ], outputs="text", title="RAG连接问答", description="输入一个URL然后询问关于URL的相关的问题." ) # Launch the app demo_interface.launch(share=True, debug=True)