Stockage et extraction de l’historique et du contexte des conversations avec la bibliothèque LangGraph BedrockSessionSaver

Au lieu d’utiliser directement les API de gestion de session Amazon Bedrock, vous pouvez stocker et extraire l’historique et le contexte des conversations dans LangGraph avec la bibliothèque BedrockSessionSaver. Il s’agit d’une implémentation personnalisée de LangGraph CheckpointSaver. Elle utilise les API Amazon Bedrock avec une interface basée sur LangGraph. Pour plus d’informations, consultez langgraph-checkpoint-aws dans le référentiel GitHub LangChain.

L’exemple de code suivant montre comment utiliser la bibliothèque LangGraph BedrockSessionSaver pour suivre l’état lorsqu’un utilisateur interagit avec Claude. Pour utiliser cet exemple de code :

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()