RAG & Vector Databases

Build production RAG systems that transform any source into searchable knowledge.

What is RAG?

Retrieval-Augmented Generation = Vector Database + Retrieval + LLM

The Problem: 70% of RAG development is data preprocessing.

The Solution: Skill Seekers automates it all—extract, chunk, embed, store.

Quick Selector

Your Goal	Integration	Best For
Python RAG pipeline	LangChain	Most popular, flexible
Query/chat engine	LlamaIndex	Document Q&A focus
Local development	Chroma	Easy setup, embeddings included
Production cloud	Pinecone	Serverless, scalable
Enterprise self-hosted	Weaviate	GraphQL, modular AI
High performance	Qdrant	Rust engine, filtering
GPU acceleration	FAISS	Facebook AI, billions of vectors
Enterprise NLP	Haystack	Pipelines, agent framework

One Command, Any Source

# From documentation
skill-seekers scrape --format langchain --config react.json

# From GitHub repo
skill-seekers scrape --format langchain --github owner/repo

# From PDF
skill-seekers scrape --format langchain --pdf manual.pdf

# From codebase
skill-seekers analyze --format langchain --directory ./project

How It Works

┌─────────────┐     ┌──────────────┐     ┌─────────────┐     ┌─────────┐
│   Source    │────▶│Skill Seekers │────▶│ Vector DB   │────▶│   LLM   │
│(Any Source) │     │(Chunk/Embed) │     │(Pinecone/  │     │(Answer) │
└─────────────┘     └──────────────┘     │ Chroma/etc) │     └─────────┘
                                          └─────────────┘

Tutorial

5-Minute RAG Pipeline →

Next Steps

LangChain - Get started with Python RAG
Choose a Vector Database - Store your embeddings