enterprise_rag.py source

"""Enterprise RAG System with LLM-as-a-Judge

Hybrid-retrieval RAG with an LLM judge that scores every answer for faithfulness, relevance and toxicity.
"""

from portfolio import Metric, Project


class EnterpriseRag(Project):
    status = "wip"
    featured = True
    stack = [
        "Python",
        "FastAPI",
        "React",
        "TypeScript",
        "LangChain",
        "PostgreSQL",
        "Qdrant",
        "Docker",
    ]
    metrics = []
    links = {
        "repo": "https://github.com/maaz7409/SmartRAG",
    }
    demo = None

    highlights = [
        "Hybrid retrieval: dense embeddings (text-embedding-3-small) in Qdrant, combined with BM25 keyword search.",
        "LLM-as-a-Judge: a second model with Pydantic structured outputs scores each answer for faithfulness, context relevance and toxicity.",
        "Observability: async PostgreSQL logging of query trails, latency and scores.",
        "Frontend: React + TypeScript with streaming answers, citations and Recharts analytics.",
        "Shipping: Docker Compose, with CI/CD on GitHub Actions.",
    ]