| |
| """ |
| FastAPI application with Agentic Adaptive RAG System. |
| |
| Backends: Qdrant (hybrid search), Neo4j (knowledge graph), Mem0 (memory) |
| Agent: LangGraph state machine with self-reflective retrieval loop |
| """ |
|
|
| from fastapi import FastAPI |
| from fastapi.middleware.cors import CORSMiddleware |
| import logging |
| import sys |
| import os |
|
|
| sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) |
|
|
| from src.api.routes import router, app_state |
|
|
| logging.basicConfig( |
| level=logging.INFO, |
| format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' |
| ) |
| logger = logging.getLogger(__name__) |
|
|
| app = FastAPI( |
| title="CVE-KGRAG Agentic RAG API", |
| description="Hybrid search + knowledge graph + agentic RAG with long-term memory", |
| version="3.0.0-agentic", |
| ) |
|
|
| app.add_middleware( |
| CORSMiddleware, |
| allow_origins=["*"], |
| allow_credentials=True, |
| allow_methods=["*"], |
| allow_headers=["*"], |
| ) |
|
|
| app.include_router(router, prefix="/api/v1") |
|
|
|
|
| @app.on_event("startup") |
| async def startup(): |
| logger.info("CVE-KGRAG Agentic RAG API starting up ...") |
|
|
| |
| try: |
| from src.generators.rag_system import CVERAGSystem |
| rag_system = CVERAGSystem() |
| app_state["rag_system"] = rag_system |
| stats = rag_system.get_collection_stats() |
| total = stats.get("total_documents", 0) |
| if total == 0: |
| logger.warning("Qdrant collections empty. Run: python -m src.generators.rag_system --build") |
| else: |
| logger.info("Qdrant ready: %s total chunks %s", f"{total:,}", stats.get("collections", {})) |
| except Exception as e: |
| logger.error("Qdrant init failed: %s", e) |
| app_state["rag_system"] = None |
|
|
| |
| try: |
| from src.constructors.neo4j_graph_service import Neo4jGraphService |
| graph_service = Neo4jGraphService() |
| graph_service.driver.verify_connectivity() |
| app_state["graph_service"] = graph_service |
| neo4j_stats = graph_service.get_stats() |
| logger.info("Neo4j ready: CVE nodes=%s", f"{neo4j_stats.get('CVE', 0):,}") |
| except Exception as e: |
| logger.warning("Neo4j unavailable: %s. Graph endpoints → 503.", e) |
| app_state["graph_service"] = None |
|
|
| |
| try: |
| from llms.factory import LLMFactory |
| from src.generators.rag_config import get_llm_config |
| llm_config = get_llm_config() |
| if llm_config.get("enabled"): |
| app_state["llm_client"] = LLMFactory.create_from_config(llm_config) |
| if app_state["llm_client"]: |
| info = app_state["llm_client"].get_model_info() |
| logger.info("LLM ready: provider=%s model=%s", info.get("provider"), info.get("model_name")) |
| from src.agents.llm_runnable import BaseLLMRunnable |
| app_state["llm_runnable"] = BaseLLMRunnable(app_state["llm_client"]) |
| else: |
| logger.warning("LLM config enabled but init failed.") |
| app_state["llm_runnable"] = None |
| else: |
| logger.info("LLM not enabled. Set LLM_ENABLED=true.") |
| app_state["llm_client"] = None |
| app_state["llm_runnable"] = None |
| except Exception as e: |
| logger.warning("LLM client unavailable: %s", e) |
| app_state["llm_client"] = None |
| app_state["llm_runnable"] = None |
|
|
| |
| try: |
| from src.agents.reranker import load_reranker |
| app_state["reranker"] = load_reranker() |
| if app_state["reranker"] is not None: |
| logger.info("Reranker ready: backend=%s threshold=%.2f", |
| app_state["reranker"].backend, app_state["reranker"].threshold) |
| except Exception as e: |
| logger.warning("Reranker init failed: %s", e) |
| app_state["reranker"] = None |
|
|
| |
| try: |
| from src.agents.memory import CVEKGMemory |
| memory = CVEKGMemory() |
| app_state["memory"] = memory |
| logger.info("Mem0 memory: %s", "enabled" if memory.enabled else "disabled") |
| except Exception as e: |
| logger.warning("Mem0 init failed: %s — memory disabled", e) |
| app_state["memory"] = None |
|
|
| |
| try: |
| from pathlib import Path |
| from src.agents.cheat_sheet import init_cheat_sheet |
| project_root = Path(__file__).resolve().parents[2] |
| app_state["cheat_sheet"] = init_cheat_sheet(project_root) |
| logger.info("Cheat sheet loaded ✓") |
| except Exception as e: |
| logger.warning("Cheat sheet init failed: %s — running without taxonomy hints", e) |
| app_state["cheat_sheet"] = None |
|
|
| |
| try: |
| from src.agents.tools import create_agent_tools |
| if app_state["rag_system"]: |
| app_state["agent_tools"] = create_agent_tools( |
| app_state["rag_system"], |
| app_state.get("graph_service"), |
| llm_client=app_state.get("llm_client"), |
| cheat_sheet=app_state.get("cheat_sheet"), |
| ) |
| logger.info("Agent tools: %d ready", len(app_state["agent_tools"])) |
| else: |
| app_state["agent_tools"] = [] |
| except Exception as e: |
| logger.warning("Agent tools init failed: %s", e) |
| app_state["agent_tools"] = [] |
|
|
| |
| try: |
| from src.agents.llm_runnable import get_chat_model |
| app_state["chat_model"] = get_chat_model(app_state.get("llm_client")) |
| if app_state["chat_model"] is not None: |
| logger.info("Chat model adapter ready (ReAct retrieve enabled)") |
| else: |
| logger.info("No chat model adapter — retrieve will use legacy pipeline") |
| except Exception as e: |
| logger.warning("Chat model adapter init failed: %s", e) |
| app_state["chat_model"] = None |
|
|
| |
| try: |
| from src.agents.adaptive_rag import compile_agent_graph |
| if app_state["rag_system"]: |
| app_state["agent_graph"] = compile_agent_graph( |
| llm_client=app_state.get("llm_client"), |
| rag_system=app_state["rag_system"], |
| graph_service=app_state.get("graph_service"), |
| memory=app_state.get("memory"), |
| llm_runnable=app_state.get("llm_runnable"), |
| reranker=app_state.get("reranker"), |
| cheat_sheet=app_state.get("cheat_sheet"), |
| agent_tools=app_state.get("agent_tools"), |
| chat_model=app_state.get("chat_model"), |
| ) |
| logger.info("Agent graph compiled ✓ (10 nodes, self-reflective, adaptive retrieval)") |
| else: |
| app_state["agent_graph"] = None |
| except Exception as e: |
| logger.warning("Agent graph compilation failed: %s", e) |
| app_state["agent_graph"] = None |
|
|
| logger.info("API ready — endpoints: /api/v1/{health,search,query,query/agent,query/stream,summary,stats,graph/*}") |
|
|
|
|
| @app.on_event("shutdown") |
| async def shutdown(): |
| logger.info("CVE-KGRAG Agentic RAG API shutting down ...") |
|
|
|
|
| if __name__ == "__main__": |
| import uvicorn |
| uvicorn.run( |
| "src.api.main:app", |
| host="0.0.0.0", |
| port=8000, |
| reload=True, |
| log_level="info", |
| ) |
|
|
