Update app.py

app.py

@@ -3,63 +3,41 @@ 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.vectorstores import Chroma
 from langchain.docstore.document import Document
-from langchain.agents import Tool
+from langchain.embeddings import HuggingFaceInstructEmbeddings
 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
+from langchain import 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
-)
+persist_directory="db"
+llm=OpenAI(model_name = "text-davinci-003", temperature=0)
+vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding)
+model_name = "hkunlp/instructor-large"
+embed_instruction = "Represent the text from the BMW website for retrieval"
+query_instruction = "Query the most relevant text from the BMW website"
+embeddings = HuggingFaceInstructEmbeddings(model_name=model_name, embed_instruction=embed_instruction, query_instruction=query_instruction)
+chain = RetrievalQAWithSourcesChain.from_chain_type(llm, chain_type="stuff", retriever=db.as_retriever(), 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"
+        response = chain.run(input=message)
     history.append((message, response))
 
     return history, history
 
 
 with gr.Blocks() as demo:
-    gr.Markdown("<h3><center>WebSiteChatBotAI</center></h3>")
-    gr.Markdown("<p><center>paste web site URL and input question and push Run</center></p>")
-    site = gr.Textbox(placeholder="paste URL",label="WebSite")
+    gr.Markdown("<h3><center>BMW Chat Bot</center></h3>")
+    gr.Markdown("<p><center>Ask questions about BMW</center></p>")
     chatbot = gr.Chatbot()
     with gr.Row():
         inp = gr.Textbox(placeholder="Question",label =None)