lugacore 0.1.0a1

A Naive RAG SDK using Gemini for embeddings, Qdrant for retrieval, and document loaders for building RAG piplines from scratch.

LugaCore

A lightweight, modular Retrieval-Augmented-Generation (RAG) system built from fundamental python libraries for efficient, verifiable Q&A.

📘 Overview

This project is a RAG framework built without reliance on high-level orchestration libraries. it demonstrates a ground-up implementation of the key RAG components.

✨ Features

Custom Document Processing:

• Self-implemented Text Splitter: Recursive,semantic text splitting with token awareness logic for optimal chunk boundaries.
• Custom Document Loader: Direct .docx parsing via python-docx, preserving paragraphs.

Intelligent Retrieval:

• Hybrid Search Engine: combines dense semantic and sparse keyword retrieval within Qdrant for flexible query resolution.
• Qdrant Integration: Native support for:
  • Vector indexing and collection management
  • Batch upserts and attribute-based payload filtering
  • Hybrid retrieval experimentation (dense + sparse)

Gemini LLM Integration

• LLM-Driven Generation: Uses Gemini exclusively for answer synthesis, summarization, and context-aware responses.
• Context injection: Dynamically augments user queries with retrieved context chunks.
• Conversation Flow: Uses Gemini chat SDK for multi-turn conversations keeping track of user queries and RAG responses to improve relevance.

Modular & Transparent

• 100% open and debuggable - designed for learning, research, and low-level RAG experimentation.

🚀 Installation & Quick Start

Follow these steps to install LangCore from PyPI and start using your RAG powered by Gemini.

1. Create project directory (if not already created)

# setup
mkdir my_project
cd my_project

2. Create and activate Virtual environment

# create a virtual environment
python -m venv virt

#Activate it (windows)
source virt/scripts/activate

3. Install LugaCore from PyPI

 pip install lugacore

4. Verify Installation

pip list | grep lugacore

5. Quick Start Example

from lugacore import LugaCore

#Initialize RAG pipeline
rag = LugaCore(
  qdrant_api_key="YOUR_QDRANT_API_KEY",
  gemini_api_key="YOUR_GEMINI_API_KEY",
  qdrant_url="YOUR_QDRANT_URL"
)

# Ingest documents
rag.load_document(filepath_or_buffer="path/to/your/file.docx")

#Query your knowledge base
response = rag.ask("What is physics?")
print(response)

🧩 Planed Features

• Re-ranking.
• Knowledge Graph
• Caching layer
• Multimodal support (images,audio, video).
• Agentic pipelines
• Async & Sync support