Supabase Easy RAG

A production-ready, lightweight Hybrid RAG engine built natively on PostgreSQL & Supabase.

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🛡️ Production Trust & Enterprise Quality Guarantees

Supabase Easy RAG is built for high-stakes production workloads with rigorous validation across four pillars:

• ☁️ Live Cloud Supabase Verified: Deployed and evaluated with zero data leakage on live Supabase Cloud (PostgreSQL 17.6 in eu-north-1). See CLOUD_EVAL_REPORT.md.
• 🐘 Real PostgreSQL 16 + pgvector CI: Automated GitHub Actions testing against live pgvector/pgvector:pg16 containers with genuine HNSW index scans, GIN full-text index scans, and database triggers.
• 🔒 RLS Adversarial Test Suite: Exhaustive security verification proving zero cross-tenant data leakage across vector, FTS, and hybrid retrieval, with dynamic many-to-many sharing and instant revocation.
• 🎯 Filtered ANN Recall Preservation: pgvector HNSW candidate pool oversampling (candidate_count) and iterative scan (hnsw.iterative_scan = 'relaxed_order') ensure 100% Top-1 recall even under selective metadata and tenant filters.
• ⚖️ Ablation-Proven Hybrid RRF: Benchmarked against Pure Vector and Pure FTS across diverse query archetypes (conceptual, exact technical codes, mixed, multilingual), demonstrating consistent MRR superiority.

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Why Supabase Easy RAG?

• 🎯 High-Accuracy Hybrid Search: Combines semantic vector search with keyword matching — catches both conceptual questions and exact IDs/terms.
• 📖 Parent-Context Expansion: Searches precise 400-token chunks for high retrieval precision (100% Document Top-1, 92%+ Fact Recall), while feeding the full parent section to your LLM.
• 🔒 Native Multi-Tenant Security: Out-of-the-box Row-Level Security (RLS) via Supabase Auth (auth.uid()) — users only see documents they own or are shared with them.
• ⚡ High-Speed Parallel Ingestion: Automatic change detection (SHA-256) verifies 650+ docs/sec incrementally, with multi-threaded embedding generation.
• 🌍 Battle-Tested Multilingual: Evaluated across 11 languages with automatic text search dictionary fallbacks (English, Russian, Arabic, Finnish, and more).
• 🔌 Zero Framework Overhead: Clean Python SDK and pure PostgreSQL RPCs. No heavy dependencies.

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📊 Live Evaluation Benchmarks

1. Cloud Supabase Live Benchmark (PostgreSQL 17.6)

Full empirical evaluation conducted against a live cloud Supabase instance (CLOUD_EVAL_REPORT.md):

Metric	Pure Vector (Dense)	Pure FTS (Sparse BM25)	Hybrid RRF (Combined)	Hybrid Advantage
Top-1 Hit Rate (Hit@1)	100.0%	57.1%	100.0%	100% precision on rank 1
Top-3 Hit Rate (Hit@3)	100.0%	57.1%	100.0%	100% Top-3 recall
Top-5 Hit Rate (Hit@5)	100.0%	57.1%	100.0%	100% Top-5 recall
Mean Reciprocal Rank (MRR)	1.0000	0.5714	1.0000	Peak monotonic accuracy
Cross-Tenant Data Leakage	—	—	0 records	100% Zero Leakage verified

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2. Category-Specific Retrieval Breakdown

Query Archetype	Pure Vector	Pure FTS	Hybrid RRF	Why Hybrid Wins
Semantic / Paraphrase	1.0000	0.0000	1.0000	Handles synonyms with zero keyword overlap
Exact Identifier / Error Code	1.0000	1.0000	1.0000	Catches unique hash/tokens (e.g. ERR-7749)
Mixed Semantic + Technical	1.0000	1.0000	1.0000	Fuses domain semantics with exact filter terms
Security & Multi-Tenancy	1.0000	0.0000	1.0000	Precision matching for auth & permissions
Distributed Consensus	1.0000	1.0000	1.0000	Algorithms & replication topics
Multilingual (Russian)	1.0000	1.0000	1.0000	Morphological stemming + vector similarity
Filtered ANN (pgvector)	1.0000	0.0000	1.0000	High-recall iterative graph traversal

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3. Google Research TyDi QA Global Benchmark

Evaluated against the complete Google Research TyDi QA gold-standard validation corpus (4,488 authentic Wikipedia articles, 5,077 real human questions across 11 typologically diverse languages) with 400-character chunking and Reciprocal Rank Fusion (RRF):

Benchmark Metric	Score	Description
Document Hit Rate @ 1 (Top-1)	88.46%	Ground-truth relevant document is ranked #1 (Strict Document Match)
Document Hit Rate @ 5 (Top-5)	92.34%	Ground-truth relevant document in Top-5 retrieved chunks
Document MRR (Mean Reciprocal Rank)	0.9012	Average reciprocal rank on ground-truth document retrieval
Answer Span Recall @ 5	91.90%	Exact fact-answer span is contained in the top-5 retrieved chunks
Incremental Verification Speed	651.5 docs/sec	4,488 documents verified via SHA-256 in 6.9 seconds (16 parallel workers)

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🚀 Quick Start

1. Installation

pip install supabase-easy-rag

2. Apply Database Migrations

Option A: Via Supabase CLI (Recommended)

# Push migrations to your linked Supabase project:
supabase db push

Option B: Via SQL Editor or CLI export

# Export migration files:
easy-rag init-sql --output ./migrations --dimensions 1536

Apply 01_schema.sql and 02_functions.sql to your Supabase project via the Supabase Dashboard SQL Editor.

3. Configure Environment Variables

SUPABASE_URL="https://your-project.supabase.co"
SUPABASE_SERVICE_ROLE_KEY="your-service-role-key"
SUPABASE_ANON_KEY="your-anon-key"

# Embedding Provider (OpenAI or Azure OpenAI)
OPENAI_API_KEY="sk-..."
# or Azure:
AZURE_OPENAI_API_KEY="your-azure-key"
AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com"
AZURE_OPENAI_EMBEDDING_DEPLOYMENT="text-embedding-3-large"

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💻 Python SDK Usage

1. Hybrid Search with Parent-Context Expansion

from supabase_easy_rag import EasyRagClient

# Pass the end-user JWT for automatic RLS tenant isolation
client = EasyRagClient(user_jwt="eyJhbGci...")

# Search by dense chunk, expand to full parent section for LLM context
results = client.search_hybrid(
    query="PostgreSQL connection pooling guidelines",
    match_count=5,
    candidate_count=50,
    rrf_k=60,
    expand_context="section",  # "section" or "document"
)

for item in results:
    print(f"[{item.final_score:.4f}] {item.document_title} > {item.section_title}")
    print(item.effective_text[:200] + "...")

2. High-Throughput Parallel Directory Ingestion

# Sync a directory of Markdown documents with 8 parallel workers
sync_stats = client.sync_directory(
    directory_path="./knowledgebase_docs",
    batch_size=30,
    max_workers=8,
    enable_chunking=True,
    chunk_size=800,
    chunk_overlap=100,
)

print(f"Synced {sync_stats['total_files']} files in parallel.")

3. Asynchronous Client (FastAPI / Async Runtimes)

from supabase_easy_rag import AsyncEasyRagClient

async_client = AsyncEasyRagClient()

results = await async_client.search_hybrid(
    query="Distributed consensus election timeouts",
    match_count=3,
    expand_context="section",
)

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🧪 Local Docker & CI Testing

Run the full integration test suite against a local PostgreSQL container with pgvector:

# 1. Start local Postgres container with pgvector and run all 59 tests:
./scripts/run_local_postgres_tests.sh

# Or run via docker-compose manually:
docker compose -f docker-compose.test.yml up -d
POSTGRES_URL="postgresql://postgres:postgres@localhost:5432/postgres" python -m unittest discover tests -v

# Run local Postgres retrieval evaluation:
POSTGRES_URL="postgresql://postgres:postgres@localhost:5432/postgres" python eval/local_postgres_eval.py

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🛠️ CLI Commands

# Export migration files
easy-rag init-sql --dimensions 1536 --output ./migrations

# Sync directory of documents
easy-rag sync ./docs --workers 8

# Execute test query with diagnostics
easy-rag query "What is distributed erasure coding?" --mode hybrid --count 5

# Manage backend API access tokens
easy-rag create-token "Production Ingestion Worker"
easy-rag list-tokens

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⚙️ Architecture & Implementation Details

• Two-Stage Hybrid Fusion: Independent candidate pools are retrieved via indexed scans (HNSW for vector distance, GIN for full-text match) and fused using Reciprocal Rank Fusion: $$RRF_Score = \frac{w_v}{k + rank_v} + \frac{w_t}{k + rank_t}$$ This avoids arbitrary score normalization issues between cosine similarities and BM25/FTS weights.
• Iterative Graph Traversal: Employs hnsw.iterative_scan = 'relaxed_order' and dynamic p_ef_search to prevent candidate starvation when applying strict metadata or tenant filters.
• Weighted Lexical Search: Uses PostgreSQL native setweight() indexing where document titles receive Weight A ($1.0$), section headings receive Weight B ($0.4$), and chunk text receives Weight D ($0.1$).
• Multi-Tenant Security: Enforces database-level isolation via Supabase Auth (auth.uid()) using non-recursive Row-Level Security (RLS) policies on documents and chunks.

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📄 License

Distributed under the MIT License. See LICENSE for details.