RAG Systems Prototypes

This repository contains prototypes for Retrieval-Augmented Generation, Vector Embeddings, Semantic Search, and Context Management, aligned with IBM RAG Developer Badge.

Blueprint Overview

Based on IBM RAG docs (ibm.com/developer/rag) and Hugging Face embeddings, we'll build RAG pipelines for enhanced generation.

Prototypes

1. Retrieval-Augmented Generation: Integrate retrieval with LLMs.
2. Vector Embeddings: Generate and store embeddings for documents.
3. Semantic Search: Query vectors for relevant docs.
4. Context Management: Dynamic context assembly.

Tech Stack

• Python 3.8+
• Libraries: Transformers (Hugging Face), FAISS, LangChain.
• Dependencies: Install via pip install transformers faiss-cpu langchain.

Key Components

• Embedding Model: from transformers import AutoModel; model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
• RAG Pipeline: class RAGSystem: def retrieve(self, query): ...

Implementation Notes

• Files Added: embeddings.py (vector generation), semantic_search.py (query handling), context_management.py (dynamic context), rag_pipeline.py (full RAG integration), test_embeddings.py (unit tests).
• How to Run: Install deps, run python rag_pipeline.py for demos. Tests: python test_embeddings.py.
• Trade-offs: Uses GPT-2 for simplicity; scale to larger models for production.