2024 · Creator

Specialized Technical Assistant (RAG)

Python-based RAG pipeline that lets you build a domain-specific AI assistant grounded in your own documentation.

The challenge

Internal docs and runbooks pile up across Confluence, Notion, and GitHub. Engineers ask the same questions weekly and search returns nothing useful.

The result

A Python RAG pipeline grounded in your own documentation — chunking, embedding, retrieval, and citation built around a domain-specific assistant. Answers cite the source doc so you can audit the response.

Year: 2024
Role: Creator
Stack: 6 teches
Status: Published

Overview

This project is a reference implementation of a Retrieval-Augmented Generation (RAG) pipeline using Python, LangChain, and an OpenAI-compatible LLM. Feed it your documentation, blog posts, or code and ask natural-language questions — it answers using only your content.

Architecture

Ingestion — parse Markdown/HTML docs, chunk by heading
Embedding — embed chunks with Sentence Transformers
Retrieval — pgvector for ANN search (HNSW index)
Generation — Claude / GPT-4 with retrieved context injected into system prompt
Evaluation — RAGAS metrics for faithfulness and context recall