langflow_redis

Langflow의 RAG 파이프라인을 위한 Redis 채팅 메모리

저장소는 Redis를 사용하여 RAG 파이프라인에 대한 채팅 메모리를 구현하므로 과거 메시지가 기억됩니다.

이는 사용자 정의 구성 요소 "Redis 메시지 검색 사용자 정의 구성 요소.json" 및 "Redis 메시지 검색 사용자 정의 구성 요소.json"을 생성하여 수행됩니다.

사용자 정의 구성요소에 대한 코드는 Langflow GUI에서 찾을 수 있습니다.

또는 아래:

Redis 메시지 검색 구성 요소

 from langflow . base . memory . model import LCChatMemoryComponent
from langflow . inputs import MessageTextInput , SecretStrInput , DictInput , MultilineInput
from langflow . io import MessageTextInput , Output
from langflow . field_typing import BaseChatMessageHistory
import subprocess
import time
from langchain_community . chat_message_histories import RedisChatMessageHistory
import socket

import logging

# Configure logging
logging . basicConfig ( level = logging . INFO , format = '%(asctime)s - %(levelname)s - %(message)s' )


class RedisChatMemory ( LCChatMemoryComponent ):
    display_name = "Redis Chat Memory"
    description = "Retrieves chat messages from Redis."
    name = "RedisChatMemory"
    icon = "RedisChatMemory"

    inputs = [
        MessageTextInput (
            name = "RedisURL" ,
            display_name = "Redis URL (Optional)" ,
            info = "Redis URL" ,
            required = False ,
        ),
        SecretStrInput (
            name = "password" ,
            display_name = "Password (Optional)" ,
            info = "User password for the database." ,
            required = False ,
        ),
        MessageTextInput (
            name = "redis_session_id" ,
            display_name = "Redis Session ID (Optional)" ,
            info = "Redis Session ID." ,
            advanced = False ,
            required = False
        ),
        DictInput (
            name = "cluster_kwargs" ,
            display_name = "Cluster arguments" ,
            info = "Optional dictionary of additional keyword arguments for the Redis cluster." ,
            advanced = True ,
            is_list = True ,
        ),
    ]
    
    
    def is_port_in_use ( self , port ):
        with socket . socket ( socket . AF_INET , socket . SOCK_STREAM ) as s :
            return s . connect_ex (( 'localhost' , port )) == 0
    
    def start_redis_server ( self , port = 6379 , password = None ):
        if self . is_port_in_use ( port ):
            logging . error ( f"Port { port } is already in use. Please choose a different port." )
            return None
    
        cmd = [ 'redis-server' , '--port' , str ( port )]
        if password :
            cmd . extend ([ '--requirepass' , password ])
    
        try :
            redis_process = subprocess . Popen ( cmd )
            time . sleep ( 2 )  # Wait for Redis server to start
    
            # Check if the process is still running
            if redis_process . poll () is None :
                logging . info ( f"Redis server started successfully on port { port } " )
                return redis_process
            else :
                logging . error ( f"Failed to start Redis server on port { port } " )
                return None
        except Exception as e :
            logging . error ( f"Error starting Redis server: { e } " )
            return None
    

    def build_message_history ( self ) -> BaseChatMessageHistory :
        
        session_id = self . redis_session_id or 'default_redis_user_session'

        if self . RedisURL :
            url = self . RedisURL
        else :
            port = 6379
            password = self . password or 'admin'
            url = f"redis://default: { password } @localhost: { port } "
            self . start_redis_server ( password = password )

        memory = RedisChatMessageHistory ( session_id = session_id , url = url )

        return memory

Redis 메시지 저장소 구성 요소

 from langflow . base . memory . model import LCChatMemoryComponent
from langflow . inputs import MessageTextInput , SecretStrInput , DictInput , MultilineInput
from langflow . io import MessageTextInput , Output
from langflow . field_typing import BaseChatMessageHistory
import subprocess
import time
from langchain_community . chat_message_histories import RedisChatMessageHistory
from langflow . schema . message import Message

class RedisChatMemory ( Component ):
    display_name = "Redis Chat Memory"
    description = "Store chat messages to Redis."
    name = "RedisChatMemory"
    icon = "RedisChatMemory"

    inputs = [
        MessageTextInput (
            name = "RedisURL" ,
            display_name = "Redis URL (Optional)" ,
            info = "Redis URL" ,
            required = False ,
        ),
        SecretStrInput (
            name = "password" ,
            display_name = "Password (Optional)" ,
            info = "User password for the database." ,
            required = False ,
        ),
        MessageTextInput (
            name = "redis_session_id" ,
            display_name = "Redis Session ID (Optional)" ,
            info = "Redis Session ID." ,
            advanced = False ,
            required = False
        ),
        DictInput (
            name = "cluster_kwargs" ,
            display_name = "Cluster arguments" ,
            info = "Optional dictionary of additional keyword arguments for the Redis cluster." ,
            advanced = True ,
            is_list = True ,
        ),
        MultilineInput (
            name = "human_message_input" ,
            display_name = "Human Messages"
            ),
        MessageTextInput (
            name = "ai_message_output" ,
            display_name = "AI Message" ,
            info = "RAG Generated answer" ,
        ),
    ]
    
    outputs = [
    Output ( display_name = "Message" , name = "message" , method = "build_message_history" ),
]

    def build_message_history ( self ) -> Message :
        
 
        session_id = self . redis_session_id or 'default_redis_user_session'

        if self . RedisURL :
            url = self . RedisURL
        else :
            port = 6379
            password = self . password or 'admin'
            url = f"redis://default: { password } @localhost: { port } "

        memory = RedisChatMessageHistory ( session_id = session_id , url = url )        

        memory . add_user_message ( self . human_message_input )
        memory . add_ai_message ( self . ai_message_output )
        
        return Message ( text = self . ai_message_output )

지도 시간

저장소 복제

 git clone https : // github . com / paulomuraroferreira / langflow_redis . git

langflow 및 redis를 설치합니다.

!p ip install langflow
!p ip install redis

터미널에서 다음을 실행하세요.

 langflow run

json 'RAG_pipeline_with_Redis_chat_memory.json'을 업로드하세요.

pdf 문서를 pdf_documents 폴더에 복사하거나 Document Loader 구성 요소에서 경로를 변경합니다.

두 Embeddings 구성 요소 모두에 OpenAI API 키를 입력하세요.

OpenAI 모델 구성요소에서

ChromaDB 구성요소를 실행하여 청킹 파이프라인을 실행합니다.

놀이터에 들어가서 질문하세요:

프롬프트 구성 요소를 검사하여 LLM에 전달된 기록을 확인할 수 있습니다.