feat: Implemented memory persistent

docker-compose.yaml

@@ -19,4 +19,4 @@ services:
       - .env
 
 volumes:
-  data:
+  data:

src/naliiabot/bot/agent/agent.py

@@ -1,8 +1,9 @@
 from langgraph.graph import StateGraph, START, END
 from langchain_core.messages import SystemMessage, ToolMessage, BaseMessage
+from langgraph.checkpoint.base import BaseCheckpointSaver
 from langgraph.checkpoint.memory import InMemorySaver
 from langchain_core.runnables import RunnableConfig
-from typing import Literal, Callable, Any
+from typing import Literal, Callable, Any, Union
 from .schemas import MessagesState
 from dataclasses import dataclass
 import logging
@@ -30,7 +31,7 @@ class Agent:
         self,
         model: Any,
         config: AgentConfig | None = None,
-        checkpointer: InMemorySaver | None = None, 
+        checkpointer: Union[BaseCheckpointSaver, None] = None,
         tools: list | None = None
     ):
         """
@@ -72,6 +73,19 @@ class Agent:
         """Expone el grafo para inspección en tests."""
         return self._build_agent()
 
+    async def ainvoke(self, state: dict, config: RunnableConfig | None = None) -> dict:
+        """
+        Versión asíncrona de invoke, necesaria para checkpointers de DB.
+        """
+        if not config:
+            config: RunnableConfig = {"configurable": {"thread_id": "default"}}
+
+        if "messages" not in state:
+            raise ValueError("State must contain 'messages' key")
+
+        # Llamamos al método ainvoke del grafo compilado
+        return await self._compiled_agent.ainvoke(state, config=config)
+
     def invoke(self, state: dict, config: RunnableConfig | None = None) -> dict:
         """
         Ejecuta el agente con el estado inicial.

src/naliiabotapi/api/dependencies.py

@@ -4,22 +4,34 @@ from naliiabot.bot.agent.agent import Agent, AgentConfig
 from naliiabot.bot.tools.naliia_tools import NaliiaTools
 from naliiabot.bot.factories.llm_factory import LLMFactory
 from naliiabot.bot.prompts.load_prompt import get_prompt_template
+from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
+from psycopg_pool import AsyncConnectionPool
+from psycopg.rows import dict_row
 from ..logger import logger
+from .settings import _settings
 
-MODEL_NAME = os.getenv("LLM_MODEL", "anthropic")
 
-def get_agent():
+_connection_string = f"postgresql://{_settings.DB_USER}:{_settings.DB_PASSWORD}@localhost:5432/{_settings.DB_NAME}"
+agent_instance: Agent | None = None
+db_pool = None
+
+
+async def get_agent() -> Agent:
     """
-    Dependency function to get an instance of the Agent class.
-
-    Returns:
-        Agent: An instance of the Agent class.
+    Dependencia simplificada que devuelve la instancia ya construida.
     """
+    if agent_instance is None:
+        raise RuntimeError("El Agente no ha sido inicializado en el lifespan.")
+    return agent_instance
 
-    NALIIA_PROMPT = get_prompt_template(
-        "NALIIA_PROMPT")
-    naliia_tools = NaliiaTools().get_tools()
-    config = AgentConfig(system_prompt=NALIIA_PROMPT)
-    model = LLMFactory(MODEL_NAME).get_model()
 
-    return Agent(model=model, config=config, tools=naliia_tools)
+def get_async_connection_pool():
+    return AsyncConnectionPool(
+        conninfo=_connection_string,
+        max_size=20,
+        open=False,
+        kwargs={
+            "autocommit": True,
+            "prepare_threshold": 0,
+            "row_factory": dict_row
+        })

src/naliiabotapi/api/settings.py

@@ -0,0 +1,14 @@
+import os
+from dotenv import load_dotenv
+
+
+class Settings:
+    DB_NAME = os.getenv("POSTGRES_DB", "db")
+    DB_USER = os.getenv("POSTGRES_USER", "postrges")
+    DB_PASSWORD = os.getenv("POSTGRES_PASSWORD", "password")
+
+    def __init__(self):
+        load_dotenv()
+
+
+_settings = Settings()

src/naliiabotapi/api/v1/webhooks/chat_hook.py

@@ -74,7 +74,7 @@ async def webhook_chat(request: Request, agent = Depends(get_agent)):
 
         messages = [HumanMessage(content=user_message)]
 
-        agent_response = agent.invoke({"messages": messages}, config=config)
+        agent_response = await agent.ainvoke({"messages": messages}, config=config)
         agent_response_content = agent_response["messages"][-1].content
         
         clean_jid = thread_id.split('@')[0]
@@ -93,4 +93,4 @@ async def webhook_chat(request: Request, agent = Depends(get_agent)):
         logger.error(f"Error processing webhook: {e}")
         return {"error": str(e)}
     
-    return {"status": "sent", "reply": agent_response_content}
+    return {"status": "sent", "reply": agent_response_content}

src/naliiabotapi/main.py

@@ -1,11 +1,43 @@
 from fastapi import FastAPI
 from naliiabotapi.api.v1.endpoints.chat import router as chat_router
 from naliiabotapi.api.v1.webhooks.chat_hook import router as chat_hook_router
+import naliiabotapi.api.dependencies as deps
+from contextlib import asynccontextmanager
+from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    deps.db_pool = deps.get_async_connection_pool()
+    await deps.db_pool.open()
+
+    async with deps.db_pool.connection() as conn:
+        checkpointer = AsyncPostgresSaver(conn)
+        await checkpointer.setup()
+
+        model_name = "anthropic"
+        naliia_prompt = deps.get_prompt_template("NALIIA_PROMPT")
+        naliia_tools = deps.NaliiaTools().get_tools()
+        config = deps.AgentConfig(system_prompt=naliia_prompt)
+        model = deps.LLMFactory(model_name).get_model()
+
+        deps.agent_instance = deps.Agent(
+            model=model,
+            config=config,
+            checkpointer=checkpointer,
+            tools=naliia_tools
+        )
+
+    yield
+
+    await deps.db_pool.close()
+
 
 app = FastAPI(
     title="NaliiaBot API",
     description="API for NaliiaBot, a chatbot that provides customer service and related topics.",
-    version="1.0.0"
+    version="1.0.0",
+    lifespan=lifespan
 )
 
 app.include_router(chat_router)