brags 0.0.5

Build-your-own Retrieval-Augmented Generation (RAG) system

brags

brags (Build-your-own RAG System) is a Python package that makes it easy to spin up a custom Retrieval-Augmented Generation (RAG) pipeline.
It combines Python for the RAG logic and a background Go file watcher that monitors your documents folder, so your vector database is always up to date.

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Features

• Config-driven RAG setup (rag_config.yaml)
• Pluggable embeddings (HuggingFace, OpenAI, etc.)
• Flexible LLM providers (OpenAI, Gemini, Ollama, HuggingFace)
• Multiple vector stores (FAISS, Chroma, Qdrant, Pinecone, Weaviate)
• Two file watcher modes:
• Persistent (event-driven) → watches changes in real time via fsnotify
• Cron (polling-based) → scans folder at regular intervals
• Chunking and reranking options
• Hallucination checking with embedding similarity or LLM-based fact checking
• Configurable logging & monitoring

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Installation

Clone the repo and install using Poetry or pip:

git clone https://github.com/omkar-wagholikar/brags.git
cd brags
pip install -e .

Build the Go watcher binary (required for background file monitoring):

cd go
./build.sh

This will generate the watcher binary that brags runs in the background.

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Quick Start

1. Copy the example config:

cp brags/rag_config.example.yaml brags/rag_config.yaml

2. Edit rag_config.yaml with your model, embeddings, and file watcher preferences:

file_watcher:
  type: "persistent"   # Options: persistent, cron
  watch_dir: "./watched"
  pattern: "*.txt"
  cron_schedule: "*/3 * * * * *"  # Only for cron watcher
  debounce_seconds: 1             # Only for persistent watcher

3. Run your RAG system:

python -m brags.main

The Go watcher will start in the background, monitor your documents folder, and update your vector DB whenever files change.

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Project Structure

brags/                # Python package
go/                   # Go watchers + Python callback
tests/                # Unit tests
vector_db/            # Local FAISS indexes
rag_config.yaml       # Main configuration file

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Configuration

All behavior is controlled via rag_config.yaml. Sections include:

• llm → provider, model, API keys
• embedding → embedding model & dimensions
• vector_store → FAISS, Chroma, etc.
• chunking → chunk size, overlap, splitter
• reranking → reranker model
• hallucination_checker → method + provider
• logging → level and log file path
• file_watcher → watcher type, path, debounce/cron config

See rag_config.example.yaml for details.

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Testing

Run unit tests:

pytest tests

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Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines, and check CHANGELOG.md for updates.

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License

This project is licensed under the MIT License.

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