Archivia e recupera la cronologia e il contesto delle conversazioni con la BedrockSessionSaver LangGraph libreria

Invece di utilizzare direttamente la gestione delle sessioni di Amazon Bedrock APIs, puoi archiviare e recuperare la cronologia e il contesto delle conversazioni all' LangGraph interno della BedrockSessionSaver libreria. Si tratta di un'implementazione personalizzata di. LangGraph CheckpointSaver Utilizza Amazon Bedrock APIs con un'interfaccia LangGraph basata. Per ulteriori informazioni, consulta langgraph-checkpoint-awsil LangChain GitHub repository.

Il seguente esempio di codice mostra come usare la BedrockSessionSaver LangGraph libreria per tenere traccia dello stato mentre un utente interagisce con Claude. Per utilizzare questo esempio di codice:

• Installa le dipendenze richieste:

  • boto3

  • langgraph

  • langgraph-checkpoint-aws

  • langchain-core

• Assicurati di avere accesso al modello Claude 3.5 Sonnet v2 nel tuo account. Oppure puoi modificare il codice per utilizzare un modello diverso.

• Sostituisci REGION con la tua regione:

  • Questa regione per il tuo runtime client e la BedrockSessionSaver devono corrispondere.

  • Deve supportare Claude 3.5 Sonnet v2 (o il modello che state utilizzando).

import boto3
from typing import Dict, TypedDict, Annotated, Sequence, Union
from langgraph.graph import StateGraph, END
from langgraph_checkpoint_aws.saver import BedrockSessionSaver
from langchain_core.messages import HumanMessage, AIMessage
import json


# Define state structure
class State(TypedDict):
    messages: Sequence[Union[HumanMessage, AIMessage]]
    current_question: str


# Function to get response from Claude
def get_response(messages):
    bedrock = boto3.client('bedrock-runtime', region_name="us-west-2")
    prompt = "\n".join([f"{'Human' if isinstance(m, HumanMessage) else 'Assistant'}: {m.content}"
                        for m in messages])

    response = bedrock.invoke_model(
        modelId="anthropic.claude-3-5-sonnet-20241022-v2:0",
        body=json.dumps({
            "anthropic_version": "bedrock-2023-05-31",
            "max_tokens": 1000,
            "messages": [
                {
                    "role": "user",
                    "content": [
                        {
                            "type": "text",
                            "text": prompt
                        }
                    ]
                }
            ],
            "temperature": 0.7
        })
    )

    response_body = json.loads(response['body'].read())
    return response_body['content'][0]['text']


# Node function to process user question
def process_question(state: State) -> Dict:
    messages = list(state["messages"])
    messages.append(HumanMessage(content=state["current_question"]))

    # Get response from Claude
    response = get_response(messages)
    messages.append(AIMessage(content=response))

    # Print assistant's response
    print("\nAssistant:", response)

    # Get next user input
    next_question = input("\nYou: ").strip()

    return {
        "messages": messages,
        "current_question": next_question
    }


# Node function to check if conversation should continue
def should_continue(state: State) -> bool:
    # Check if the last message was from the user and contains 'quit'
    if state["current_question"].lower() == 'quit':
        return False
    return True


# Create the graph
def create_graph(session_saver):
    # Initialize state graph
    workflow = StateGraph(State)

    # Add nodes
    workflow.add_node("process_question", process_question)

    # Add conditional edges
    workflow.add_conditional_edges(
        "process_question",
        should_continue,
        {
            True: "process_question",
            False: END
        }
    )

    # Set entry point
    workflow.set_entry_point("process_question")

    return workflow.compile(session_saver)


def main():
    # Create a runtime client
    agent_run_time_client = boto3.client("bedrock-agent-runtime",
                                         region_name="REGION")
            
    # Initialize Bedrock session saver. The region must match the region used for the agent_run_time_client.
    session_saver = BedrockSessionSaver(region_name="REGION")

    # Create graph
    graph = create_graph(session_saver)

    # Create session
    session_id = agent_run_time_client.create_session()["sessionId"]
    print("Session started. Type 'quit' to end.")

    # Configure graph
    config = {"configurable": {"thread_id": session_id}}

    # Initial state
    state = {
        "messages": [],
        "current_question": "Hello! How can I help you today? (Type 'quit' to end)"
    }

    # Print initial greeting
    print(f"\nAssistant: {state['current_question']}")

    state["current_question"] = input("\nYou: ").strip()

    # Process the question through the graph
    graph.invoke(state, config)
    print("\nSession contents:")
    for i in graph.get_state_history(config, limit=3):
        print(i)


if __name__ == "__main__":
    main()