Private RAG Pipeline Engineering for Complex Documents & Enterprise Knowledge
Service Summary
I build production-grade Retrieval-Augmented Generation (RAG) pipelines that enable teams to query dense PDFs, spreadsheets, research papers, and SQL databases in natural English. Built with layout-aware document partitioning, semantic chunking, and persistent vector databases, my pipelines achieve high accuracy without leaking confidential files to external clouds.
What You Receive
Layout-Aware Partitioning (OCR)
Hi-res extraction that correctly preserves table grids, column structures, and figure hierarchies in complex PDFs.
Semantic Chunking & Re-Ranking
Context-aware text splitting combined with cross-encoder re-ranking to prioritize the most relevant passages.
Vector Store Engineering
Persistent, Dockerized vector databases (pgvector, ChromaDB, Weaviate, Qdrant) with volume backup configurations.
Interactive Query API & UI
FastAPI query endpoints alongside user-friendly web dashboards (AnythingLLM, OpenWebUI, or custom React interfaces).
Transparent Fixed-Price Packages
Clear deliverables with no surprise hourly invoices. Includes free post-launch support.
Silver
Basic RAG setup for text documents and lightweight search.
Delivery: 3–4 Business Days
- PDF & Markdown Ingestion Scripts
- Semantic Chunking Strategy
- ChromaDB Vector Store
- Local or OpenAI Embedding Setup
- FastAPI Query Endpoint
- 30 Days Warranty Support
Gold
Advanced RAG for scientific tables, multi-column PDFs, and chat UI.
Delivery: 5–7 Business Days
- Hi-Res Layout OCR for Complex Tables
- Hybrid Search (Keyword BM25 + Vector Semantic)
- PostgreSQL + pgvector or Weaviate Setup
- AnythingLLM / OpenWebUI Integration
- Docker Compose One-Command Launch
- Source Citation Attribution in Answers
- 60 Days Breakdown Support
Diamond
Enterprise knowledge pipeline with dynamic document sync and multi-agent reasoning.
Delivery: 10–14 Business Days
- Full Enterprise RAG Architecture
- Automated Directory Watch & Sync Daemon
- Cross-Encoder Re-Ranking Pipeline
- Multi-Modal Support (Images + Documents)
- Role-Based Access Control & RBAC Schema
- On-Premises Hardware Tuning (NVIDIA CUDA)
- 90 Days Priority Support
Private RAG for Biological Research Documents
Designed a layout-aware document ingestion pipeline allowing researchers to query hundreds of complex biological PDFs with cited answers and zero cloud leaks.
Outcome: 100% private, self-hosted deployment running locally with zero recurring SaaS fees.
Frequently Asked Questions
Why do standard out-of-the-box RAG tools fail on complex documents?
Standard text extractors flatten multi-column PDFs and strip table structures into incoherent strings. When naive chunking splits text mid-sentence or mid-table, the embeddings lose context, leading to poor retrieval and hallucinations. My pipelines use visual layout analysis (OCR) to preserve table structures before vectorization.
Can this system run entirely on an offline machine without internet access?
Yes. By containerizing local embedding models (such as all-MiniLM-L6-v2) and local LLMs (via Ollama or llama.cpp) inside Docker Compose, the entire pipeline operates with 100% air-gapped privacy.
What vector databases do you recommend for production?
For existing PostgreSQL environments, pgvector is ideal because it keeps relational data and vectors in one place. For dedicated local setups, ChromaDB or Qdrant provide fast, zero-maintenance local persistence with native Docker support.
Technologies & Protocols Used