rag_initio

Simple Retrieval-Augmented Generation (RAG) with Langchain and Groq. Answers questions based on sample_knowledge.txt.

Key Features:

• Loads knowledge from sample_knowledge.txt.
• Chunks text and generates embeddings using Sentence Transformers.
• Stores embeddings in Chroma vector database.
• Retrieves top 3 relevant chunks for queries.
• Uses Groq Llama 3 model (llama-3-70b-versatile) via Langchain for answer generation.
• Employs RetrievalQA chain for question answering.
• Secure API key handling via GROQ_API_KEY environment variable.
• Prints query, answer, and source documents.

Usage:

1. Clone the repository (if applicable).
2. Install dependencies:

   pip install -U langchain langchain-community langchain-groq sentence-transformers chromadb

3. Set your Groq API key:

   export GROQ_API_KEY="your_groq_api_key_here"

   (Replace with your actual key.)

e.g., python rag_example.py

Install Packages:

pip install -U langchain langchain-community langchain-groq sentence-transformers chromadb