使用Streamlit、LangChain和Bedrock构建一个交互式聊天机器人

import streamlit 
import chatbot_backend 
from langchain.chains import ConversationChain
 from langchain.memory import ConversationSummaryBufferMemory
 import boto3
 from langchain_aws import ChatBedrock
import pandas as pd
 
 # 2 Set Title for Chatbot - streamlit.title("Hi, This is your Chatbott")  
 
 # 3 LangChain memory to the session cache - Session State - 
if 'memory' not in streamlit.session_state:
    streamlit.session_state.memory = demo.demo_memory()  

 # 4 Add the UI chat history to the session cache - Session State 
 if 'chat_history' not in streamlit.session_state:  
    streamlit.session_state.chat_history = []  

# 5 Re-render the chat history 
 for message in streamlit.session_state.chat_history:
     with streamlit.chat_message(message["role"]):
         streamlit.markdown(message["text"])
 
# 6 Enter the details for chatbot input box
input_text = streamlit.chat_input("Powered by Bedrock")  

 if input_text:
    with streamlit.chat_message("user"):
        streamlit.markdown(input_text)
 
     streamlit.session_state.chat_history.append({"role": "user", "text": input_text})
 
     chat_response = demo.demo_conversation(input_text=input_text,
                                            memory=streamlit.session_state.memory)  
    with streamlit.chat_message("assistant"):
         streamlit.markdown(chat_response)
 
     streamlit.session_state.chat_history.append({"role": "assistant", "text": chat_response})
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from langchain.chains import ConversationChain
 from langchain.memory import ConversationSummaryBufferMemory
 import boto3
 from langchain_aws import ChatBedrock
 
 # 2a Write a function for invoking model- client connection with Bedrock with profile, model_id
 def demo_chatbot():
     boto3_session = boto3.Session(
              # Your aws_access_key_id, 
              # Your aws_secret_access_key,
         region_name='us-east-1'
     )
     llm = ChatBedrock(
         model_id="anthropic.claude-3-sonnet-20240229-v1:0",
         client=boto3_session.client('bedrock-runtime'),
         model_kwargs={
             "anthropic_version": "bedrock-2023-05-31",
             "max_tokens": 20000,
             "temperature": .3,
             "top_p": 0.3,
             "stop_sequences": ["\n\nHuman:"]
         }
     )
     return llm
 
 # 3 Create a Function for  ConversationSummaryBufferMemory  (llm and max token limit)
 def demo_memory():
     llm_data = demo_chatbot()
    memory = ConversationSummaryBufferMemory(llm=llm_data, max_token_limit=20000)
     return memory
 
 # 4 Create a Function for Conversation Chain - Input text + Memory
 def demo_conversation(input_text, memory):
     llm_chain_data = demo_chatbot()
 
     # Initialize ConversationChain with proper llm and memory
     llm_conversation = ConversationChain(llm=llm_chain_data, memory=memory, verbose=True)
 
     # Call the invoke method
    full_input = f" \nHuman: {input_text}"
     llm_start_time = time.time()
     chat_reply = llm_conversation.invoke({"input": full_input})
     llm_end_time = time.time()
     llm_elapsed_time = llm_end_time - llm_start_time
     memory.save_context({"input": input_text}, {"output": chat_reply.get('response', 'No Response')})
     return chat_reply.get('response', 'No Response')
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