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27f6252 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 | # src/api/main.py
"""
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 ...")
# ── 1. Qdrant RAG system ──────────────────────────────────────────────────
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
# ── 2. Neo4j graph service ────────────────────────────────────────────────
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
# ── 3. LLM client ─────────────────────────────────────────────────────────
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
# ── 4. Reranker (grading backend) ────────────────────────────────────────
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
# ── 5. Mem0 long-term memory ──────────────────────────────────────────────
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
# ── 5.5. Cheat-sheet taxonomy (CWE↔CAPEC↔MITRE) ─────────────────────────
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
# ── 6. Agent tools ────────────────────────────────────────────────────────
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"] = []
# ── 6.5. Chat-model adapter for ReAct retrieve ───────────────────────────
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
# ── 7. Compile agent graph ────────────────────────────────────────────────
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",
)
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