egutierrez
9ee8daa295
- 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.
85 lines
2.6 KiB
Python
85 lines
2.6 KiB
Python
# 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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