import gradio as gr import os from langchain import OpenAI, ConversationChain from langchain.prompts import PromptTemplate from langchain.embeddings.openai import OpenAIEmbeddings from langchain.text_splitter import CharacterTextSplitter from langchain.vectorstores.faiss import FAISS from langchain.docstore.document import Document from langchain.agents import Tool from langchain.chains.conversation.memory import ConversationBufferMemory from langchain.utilities import GoogleSearchAPIWrapper from langchain.agents import initialize_agent from langchain.chains.conversation.memory import ConversationEntityMemory from langchain.chains.conversation.prompt import ENTITY_MEMORY_CONVERSATION_TEMPLATE from langchain.agents import ZeroShotAgent, Tool, AgentExecutor from langchain import SerpAPIWrapper, LLMChain # ツールの準備 search = GoogleSearchAPIWrapper() tools = [ Tool( name = "Current Search", func=search.run, description="Use this allways", ), ] # メモリの準備 memory = ConversationBufferMemory(memory_key="chat_history") # エージェントの準備 llm=OpenAI(model_name = "text-davinci-003",temperature=0) agent_chain = initialize_agent( tools, llm, agent="zero-shot-react-description", verbose=True, memory=memory ) def chat(message, site,history): history = history or [] #siteの//以前を削除 site = site.replace("https://","") response = "" try: response = agent_chain.run(input=message+" site:"+site) except KeyError: if not response: response = "not found in the site" history.append((message, response)) return history, history with gr.Blocks() as demo: gr.Markdown("