Amanatal11/rag_history_agent

A Retrieval-Augmented Generation (RAG) system focused on Ethiopian history. It indexes local text documents, retrieves the most relevant information based on user queries, and uses a Groq-hosted large language model (LLM) to generate clear, concise, and accurate answers. # Ethiopian History RAG Assistant A lightweight Retrieval-Augmented Generation (RAG) application that answers questions about Ethiopian history using a local text corpus, a persistent Chroma vector database, and a Groq-hosted LLM. The project ships with a Streamlit UI and an optional CLI. ## Features - Ingest `.txt` sources from `data/`, chunk and embed them with `sentence-transformers/all-MiniLM-L6-v2`. - Persist embeddings in Chroma (`chroma_db/`) for fast reloads. - Retrieve top-k relevant chunks via semantic similarity with deduplication and thresholding. - Build a concise, source-aware prompt for the LLM. - Generate succinct answers using `langchain_groq.ChatGroq` authenticated via `GROQ_API_KEY`. - Streamlit UI and CLI entry points. ## Architecture - `app.py`: Streamlit UI for querying and displaying answers. - `vectordb_and_ingestion.py`: `VectorDBManager` handles chunking, embedding, persistence, and retrieval. - `prompt_builder.py`: Builds compact prompts from retrieved chunks with minimal source hints. - `retrieval_and_response.py`: CLI pipeline mirroring the app (load/ingest → retrieve → prompt → answer). - `logger.py`: Minimal console logger shared across modules. - `wiki_fetcher.py`: Helper to fetch Wikipedia pages into `data/` as `.txt`. ## Prerequisites - Python 3.12+ - A Groq API key ## Quickstart ```bash # 1) Create and activate a virtual environment (recommended) python3 -m venv venv source venv/bin/activate # Windows: venv\Scripts\activate # 2) Install dependencies pip install -r requirements.txt # 3) Configure environment # Create .env and set GROQ_API_KEY printf "GROQ_API_KEY=your_key_here\n" > .env # 4) (Optional) Seed/expand the corpus with Wikipedia content python -c "from wiki_fetcher import fetch_and_save_page; fetch_and_save_page('Axum Empire')" # 5) Run the Streamlit app streamlit run code/app.py ``` ## Screenshots Answer found from context ( Answer not found (out-of-scope question): ### CLI Usage ```bash python -m code.r …