Enterprise knowledge retrieval pipeline across 2TB+ structured and unstructured data sources using LangChain, FAISS, ChromaDB, and AWS SageMaker.
Production-grade Retrieval-Augmented Generation (RAG) pipeline designed for enterprise-scale knowledge retrieval. This system processes and indexes 2TB+ of structured and unstructured documents, providing context-aware search and AI-powered answers grounded in organizational knowledge.
Built and deployed at Verticiti and Reallytics.ai for clients in healthcare, finance, and enterprise operations.
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? Document Ingestion ?
? PDF, DOCX, HTML, CSV, JSON, Databases, APIs ?
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? Processing Pipeline ?
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? ? Parser ?? ? Chunker ?? ? Cleaner ?? ? Embedder ? ?
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? FAISS ? ? ChromaDB ? ? PG-Vector ?
? (Dense Index) ? ? (Hybrid) ? ? (Structured) ?
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? Retrieval Orchestrator ?
? - Multi-index fusion ?
? - Re-ranking (Cross-Encoder) ?
? - Context window optimization ?
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? LLM Generation Layer ?
? - GPT-4 / Claude / LLaMA ?
? - Grounded responses with citations ?
? - Hallucination detection ?
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? FastAPI Serving Layer ?
? - REST API endpoints ?
? - Streaming responses ?
? - Authentication & rate limiting ?
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- Multi-Source Ingestion: Parse PDF, DOCX, HTML, CSV, JSON, and database content with intelligent chunking strategies
- Hybrid Search: Combine dense vector search (FAISS) with sparse retrieval for optimal recall
- Cross-Encoder Re-Ranking: Two-stage retrieval with bi-encoder + cross-encoder for precision
- Multi-Vector Store: FAISS for speed, ChromaDB for hybrid search, PG-Vector for structured data
- Context Window Optimization: Intelligent truncation and relevance-prioritized context assembly
- Grounded Responses: LLM outputs include source citations and confidence scores
- Hallucination Detection: Post-generation validation against retrieved context
- Scalable Deployment: AWS SageMaker endpoints with auto-scaling and A/B testing
| Category | Technologies |
|---|---|
| Framework | LangChain, Python |
| Vector Stores | FAISS, ChromaDB, PG-Vector, Pinecone |
| Embeddings | OpenAI Ada-002, HuggingFace sentence-transformers |
| LLMs | GPT-4, Claude, LLaMA, Mistral |
| Re-Ranking | Cross-Encoder (ms-marco), Cohere Rerank |
| Cloud | AWS SageMaker, Lambda, S3 |
| Data | PostgreSQL, Redis (caching), Apache Airflow |
| API | FastAPI, WebSockets (streaming) |
| Metric | Value |
|---|---|
| Document corpus size | 2TB+ |
| Indexing throughput | 10K docs/hour |
| Query latency (p95) | < 500ms |
| Retrieval recall@10 | 92% |
| Answer accuracy (RAGAS) | 87% |
| API cost reduction vs hosted models | 40% |
Source Code: The production source code for this project is maintained in a private repository due to proprietary and client confidentiality requirements. This repository documents the architecture, design decisions, and technical approach. For code-level discussions or collaboration inquiries, feel free to reach out.
Rehan Malik - CTO @ Reallytics.ai