feat: Implement WebSocket support for chat functionality and refactor chat service
- Added WebSocket endpoint for real-time chat interactions. - Refactored ChatPage component to utilize WebSocket for sending and receiving messages. - Updated chat service to handle streaming responses from the LLM agent. - Introduced error handling for WebSocket connections and message processing. - Modified Editor_Test to include AppShellWithMenu for better layout. - Adjusted file path in generar_tree.py for correct directory structure. - Created llm_chat_endpoint_v1.py and llm_chat_srvc.py for handling chat requests and responses. - Established logging for WebSocket interactions and errors.
This commit is contained in:
@@ -0,0 +1,44 @@
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# backend/domains/llm/agent_endpoints.py
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from fastapi import APIRouter, HTTPException
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from fastapi.responses import StreamingResponse
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from pydantic import BaseModel
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from fastapi.concurrency import run_in_threadpool
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from backend.domains.llms.llm_chat_srvc import construir_agente_llm, responder, responder_stream
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from src.Logger.logger_db import LoggerDB, logger
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from entrypoint.init_db import db_credencial
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LoggerDB(db_credencial, "logger_llm", created_by="sistema")
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router = APIRouter()
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agente = construir_agente_llm() # inicializa el agente una vez
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# 📥 Schema para entrada de prompt
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class ChatInput(BaseModel):
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prompt: str
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# ✅ Endpoint de respuesta simple
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@router.post("/chat", summary="Enviar prompt y obtener respuesta completa del agente")
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async def chat_endpoint(data: ChatInput):
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try:
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return await responder(data.prompt, agente)
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except ValueError as e:
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raise HTTPException(status_code=400, detail=str(e))
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except Exception as e:
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logger.exception("[ERROR] Fallo durante respuesta del agente:")
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raise HTTPException(status_code=500, detail="Error interno al procesar la solicitud.")
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# 🔁 Endpoint de streaming
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@router.post("/chat-stream", summary="Enviar prompt y recibir respuesta del agente en streaming")
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async def chat_stream_endpoint(data: ChatInput):
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try:
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return StreamingResponse(
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responder_stream(data.prompt, agente),
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media_type="text/plain"
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)
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except ValueError as e:
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raise HTTPException(status_code=400, detail=str(e))
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except Exception as e:
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logger.exception("[ERROR] Fallo durante respuesta en streaming:")
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raise HTTPException(status_code=500, detail="Error interno en el agente.")
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@@ -0,0 +1,84 @@
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# src/services/agent_service.py
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from src.ApiKeys.openai_apikey_mmr import OpenAICredencialRepo
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from src.ConexionSql.Postgres_conexion import PostgresConexion
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from src.ConexionApis.OpenAi_conexion import OpenAICliente
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from src.Llms.Modelos.Openai_model import ModeloOpenAI
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from src.Llms.Agente import AgenteAI
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from src.Llms.Memory.postgres_MemoryConv import MemoryConvPostgres
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from src.Llms.MCPs.McpClient import MCPClient
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from src.Llms.MCPs.McpClient_Registry import ClientRegistry
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from entrypoint.init_db import db_credencial
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from src.Logger.logger_db import LoggerDB, logger
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LoggerDB(db_credencial, "logger_llm", created_by="sistema")
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from typing import AsyncGenerator
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# 🔧 Inicialización única del agente
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def construir_agente_llm() -> AgenteAI:
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logger.info("[INICIO] Inicializando agente LLM...")
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conexion = PostgresConexion(db_credencial)
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# Paso 1: Obtener credencial
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repo = OpenAICredencialRepo(conexion)
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credencial = repo.get_by_id("OPAK20250513-61b29978b7604031014")
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if not credencial:
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raise ValueError("No se encontró la credencial OpenAI")
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logger.debug(f"[OK] Credencial OpenAI cargada: {credencial.titulo}")
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# Paso 2: Crear cliente
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cliente = OpenAICliente(credencial)
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# Paso 3: Instanciar modelo
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modelo = ModeloOpenAI(
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cliente=cliente,
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model="gpt-4o",
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temperature=1
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)
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# Paso 4: Memoria en PostgreSQL
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memoria = MemoryConvPostgres(
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credencial=db_credencial,
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nombre_tabla="memoria_conversacion_pruebas",
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k=10
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)
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# Paso 5: Herramientas MCP (ej. archivos)
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archivos = MCPClient.from_http(
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name="files",
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url="http://127.0.0.1:4201/fs"
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)
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registry = ClientRegistry()
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registry.add("files", archivos)
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# Paso 6: Agente
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agente = AgenteAI(
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modelo=modelo,
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nombre="Asistente Inteligente",
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descripcion="",
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system_prompt="",
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rol="asistente",
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objetivos=[],
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max_iterations=0,
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memoria=memoria,
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mcp=registry
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)
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logger.success("[OK] Agente LLM listo.")
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return agente
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# ⚡ Función simple
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async def responder(prompt: str, agente: AgenteAI) -> str:
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logger.info(f"[Petición] Prompt recibido: {prompt[:50]}...")
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respuesta = await agente.interactuar_en_bucle(prompt=prompt, stream=False)
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logger.debug(f"[Respuesta] {respuesta[:100]}...")
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return respuesta
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# 🔁 Función en streaming
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async def responder_stream(prompt: str, agente: AgenteAI) -> AsyncGenerator[str, None]:
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logger.info(f"[Streaming] Prompt recibido: {prompt[:50]}...")
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async for token in agente.interactuar_en_bucle(prompt=prompt, stream=True):
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yield token
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@@ -0,0 +1,35 @@
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from fastapi import WebSocket, APIRouter, WebSocketDisconnect
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from backend.domains.llms.llm_chat_srvc import construir_agente_llm
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from src.Logger.logger_db import LoggerDB, logger
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from entrypoint.init_db import db_credencial
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import json
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LoggerDB(db_credencial, "logger_llm_ws", created_by="sistema")
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router = APIRouter()
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agente = construir_agente_llm()
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@router.websocket("/ws/chat")
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async def chat_ws(websocket: WebSocket):
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await websocket.accept()
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try:
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data = await websocket.receive_text()
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parsed = json.loads(data)
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prompt = parsed.get("prompt")
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if not prompt:
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await websocket.send_text("⚠️ Prompt vacío.")
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await websocket.close()
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return
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# ✅ Solución: hacer await antes de iterar
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respuesta_gen = await agente.interactuar_en_bucle(prompt=prompt, stream=True)
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async for token in respuesta_gen:
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await websocket.send_text(token)
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await websocket.close()
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except WebSocketDisconnect:
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logger.info("🔌 WebSocket desconectado por el cliente.")
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except Exception as e:
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logger.exception("❌ Error en WebSocket:")
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await websocket.close()
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+4
-1
@@ -3,6 +3,8 @@
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from fastapi import FastAPI
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from fastapi.middleware.cors import CORSMiddleware
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from backend.router_v1 import router
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from backend.domains.llms import llm_chat_ws_endpoint_v1
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app = FastAPI(
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title="Fitz Backend",
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@@ -21,4 +23,5 @@ app.add_middleware(
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# Incluye las rutas de tu API
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app.include_router(router, prefix="/api/v1", tags=["v1"])
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app.include_router(router, prefix="/api/v1", tags=["v1"])
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app.include_router(llm_chat_ws_endpoint_v1.router)
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@@ -4,9 +4,10 @@ from fastapi import APIRouter
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from backend.domains.experiments import charts_examples_endpoint_v1 as charts
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from backend.domains.experiments import ping_endpoint_v1
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from backend.domains.text_manager import text_manager_endpoint_v1
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from backend.domains.llms import llm_chat_endpoint_v1
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router = APIRouter()
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router.include_router(ping_endpoint_v1.router, prefix="/ping")
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router.include_router(text_manager_endpoint_v1.router, prefix="/text_manager")
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router.include_router(charts.router, prefix="/charts")
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router.include_router(llm_chat_endpoint_v1.router, prefix="/llm", tags=["Agente LLM"]) # ← Nuevo router
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