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FastAPI Profiling Service v2 — NVIDIA NIM powered.
Endpoints:
POST /profile → Profile company + compute score (single pipeline)
GET /health → Service health check
Security:
Bearer token authentication (shared secret with Node.js orchestration layer)
"""
import logging
from contextlib import asynccontextmanager
from fastapi import FastAPI, HTTPException, Depends
from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
from pydantic import BaseModel
from typing import Optional
from config import settings
from profiler import generate_profile
from scorer import compute_score
from hallucination_guard import validate_score_grounded
logging.basicConfig(level=getattr(logging, settings.LOG_LEVEL.upper(), logging.INFO))
logger = logging.getLogger(__name__)
# ─── Auth ─────────────────────────────────────────────────────
security = HTTPBearer()
def verify_token(credentials: HTTPAuthorizationCredentials = Depends(security)):
if credentials.credentials != settings.PYTHON_AI_SERVICE_SECRET:
raise HTTPException(status_code=401, detail="Invalid authentication")
return True
# ─── Models ───────────────────────────────────────────────────
class CompanyInput(BaseModel):
id: Optional[str] = None
name: str
industry: str = ""
employee_count: Optional[int] = None
description: str = ""
website_text: str = ""
linkedin_description: str = ""
tech_stack: list[str] = []
ai_job_count: int = 0
pain_signals: list[str] = []
service_match: Optional[str] = None
class ContactInput(BaseModel):
full_name: str = ""
email: Optional[str] = None
email_verified: bool = False
linkedin_personal_url: Optional[str] = None
social_profiles: dict = {}
class ProfileRequest(BaseModel):
company: CompanyInput
contacts: list[ContactInput] = []
trace_id: str = ""
# ─── App ──────────────────────────────────────────────────────
@asynccontextmanager
async def lifespan(app: FastAPI):
logger.info("🚀 AI Profiling Service v2 starting...")
logger.info(f" NVIDIA NIM: {settings.NVIDIA_NIM_BASE_URL}")
logger.info(f" Models: GPT OSS → Gemma 3 → LLaMA 70B → LLaMA 8B → Deterministic")
yield
logger.info("AI Profiling Service shutting down")
app = FastAPI(
title="AI Lead Profiling Service",
version="2.0.0",
lifespan=lifespan,
)
# ─── Endpoints ────────────────────────────────────────────────
@app.get("/health")
async def health():
return {
"status": "healthy",
"version": "2.0.0",
"models": {
"primary": "nvidia/llama-3.1-nemotron-ultra-253b-v1",
"secondary": "google/gemma-3-27b-it",
"tertiary": "meta/llama-3.3-70b-instruct",
"fast": "meta/llama-3.1-8b-instruct",
},
}
@app.post("/profile")
async def profile_company(request: ProfileRequest, _auth: bool = Depends(verify_token)):
"""
Full profiling pipeline:
1. LLM generates profile (chain-of-thought, grounded)
2. LLM extracts signals for scoring
3. Code computes score deterministically
4. Both are validated for hallucinations
"""
company_data = request.company.model_dump()
contacts_data = [c.model_dump() for c in request.contacts]
trace_id = request.trace_id
try:
# Step 1: Generate profile (LLM with grounding)
profile = await generate_profile(company_data, trace_id)
# Step 2: Compute score (LLM extracts signals → code computes)
score = await compute_score(company_data, profile, contacts_data, trace_id)
# Step 3: Validate score consistency
score_validation = validate_score_grounded(score, profile)
if not score_validation["is_valid"]:
logger.warning(f"Score validation issues: {score_validation['issues']}")
return {
"profile": profile,
"score": score,
"validation": {
"profile_grounded": profile.get("grounding_score", 0),
"profile_consistent": profile.get("is_consistent", True),
"score_valid": score_validation["is_valid"],
"score_issues": score_validation.get("issues", []),
},
"meta": {
"model_used": profile.get("llm_model", "unknown"),
"is_fallback": profile.get("is_fallback", False),
"tokens_used": profile.get("tokens_used", 0),
"trace_id": trace_id,
},
}
except Exception as e:
logger.error(f"Profiling failed for {company_data.get('name')}: {e}")
raise HTTPException(status_code=500, detail=str(e))
# ─── Run ──────────────────────────────────────────────────────
if __name__ == "__main__":
import uvicorn
uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=False)
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