Advanced
RAG System
A production-grade architecture that ingests documents, generates robust embeddings, and retrieves precise semantic context to power accurate, human-like AI answers.
From Document to Answer.
A four-stage pipeline that handles ingestion, embedding, retrieval, and generation with full observability.
Ingest Docs
Upload PDFs, Markdown, and plain text files. Processed automatically via Celery background jobs.
Embed Data
Generate robust embeddings using text-embedding-3-small and securely store in Supabase pgvector.
Retrieve Context
Hybrid search combining dense (HNSW) and sparse (GIN) indexes, fused with Reciprocal Rank Fusion.
Answer Human
Stream accurate, human-like responses token-by-token with fully traceable inline citations.
Agentic
Chain-of-Thought.
See exactly what the AI is doing before it answers. Query classification, semantic retrieval, cross-encoder re-ranking, and validation steps are all exposed in a transparent chain-of-thought panel.
Smart Routing
Simple greetings bypass retrieval entirely for instant answers, saving compute.
Query Expansion
Uses HyDE (Hypothetical Document Embeddings) to drastically improve recall.
> User query: "How do the embedding models work?"
> Classifying intent: RAG_REQUIRED
> Rewriting query resolving pronouns...
> Generating HyDE document representation...
> Vector search (HNSW) over 1,420 chunks
> Keyword search (GIN) fallback executed
> Applying Reciprocal Rank Fusion (RRF)...
> Re-ranking top 10 chunks via FlashRank local cross-encoder...
> Initiating real-time SSE stream generation...
Core Capabilities
Engineered to handle your private data at scale, securely.
AI Chat with Citations
Conversations stream token-by-token. Every answer includes inline citations linked back to the exact source chunk with text previews.
Multi-part Resolution
Complex questions break into sub-queries. Follow-up questions resolve pronouns seamlessly based on conversational history.
Real-Time Metrics
At-a-glance visibility into processed documents, total chunks, token usage, estimated costs, and real-time step durations.
Built for Production
A modern, scalable technical stack meticulously chosen for robust document retrieval and agentic coordination.
Core Technologies
Implementation Details
Identity & Security
- •Clerk Authentication
- •Postgres Row-Level Security (RLS)
- •Redis API Rate Limiting
- •LangSmith Tracing & Observability
Advanced Retrieval
- •pgvector (HNSW) Dense Search
- •Full-text (GIN) Sparse Fallback
- •FlashRank Local Cross-Encoder
- •Reciprocal Rank Fusion (RRF)
Agentic & Async Ops
- •LangGraph State Machines
- •Async SSE Generation Streaming
- •Celery Background Document Jobs
- •Dynamic Query Routing