🚀 FindaLedge: Simple Ensemble Search for RAG 🔍

FindaLedge is a Python library for building robust, hybrid search backends for Retrieval-Augmented Generation (RAG) and LLM applications. It unifies vector and keyword search, manages document ingestion, and provides a simple, powerful API.

✨ Build powerful RAG search backends with ease! ✨

🇯🇵 日本語版 README はこちら (Click here for Japanese README)

🤔 Why FindaLedge?

• Vector search (semantic) and keyword search (BM25) each have strengths and weaknesses.
• FindaLedge combines both (ensemble search) for best accuracy, with zero setup hassle.
• Handles all the plumbing: document loading, chunking, embedding, index sync, result fusion (RRF), and more!

✨ Features

⚙️ Supported Environment

🛠️ Quick Start

1. Install (with uv & venv recommended)

# Create and activate venv
python -m venv .venv
.venv\Scripts\Activate.ps1  # (Windows Powershell)

# Install uv (if not yet)
pip install uv

# Install dependencies
uv pip install -r requirements.txt
# or: uv pip install .

2. Set Environment Variables (optional)

$env:OPENAI_API_KEY="sk-..."  # For OpenAI
$env:FINDALEDGE_EMBEDDING_MODEL_NAME="text-embedding-3-small"
$env:FINDALEDGE_PERSIST_DIR="./my_data"

3. Basic Usage

from findaledge import FindaLedge

ledge = FindaLedge()
ledge.add_document("docs/manual.txt")
results = ledge.search("What is the main topic?")
for r in results:
    print(r.document.page_content, r.score)

4. Run Tests

.venv\Scripts\Activate.ps1
uv pip install -r requirements.txt
pytest

🏗️ Architecture (Layered)

@startuml
FindaLedge --> Finder
FindaLedge --> DocumentLoader
FindaLedge --> DocumentSplitter
FindaLedge --> EmbeddingModelFactory
FindaLedge --> ChromaDocumentStore
FindaLedge --> BM25sStore
Finder --> SearchResult
@enduml

🧑‍💻 Main API (Class Table)

📖 Documentation

• Usage Guide
• Architecture
• Requirements
• Function Spec

🧪 Testing

• All tests pass (pytest/uv, Windows Powershell)
• Run: pytest
• Coverage: pytest-cov enabled

🤝 Contributing

Contributions welcome! Fork, branch, PR, and let's build better RAG search together 🚀

📜 License

MIT License. See LICENSE.