AstraDB
A custom vector database with ANN indexing, semantic search, and Retrieval-Augmented Generation (RAG) pipelines for AI-powered contextual querying.
AstraDB is a custom-built vector database designed for modern AI retrieval systems where semantic understanding matters more than traditional keyword search. The system implements Approximate Nearest Neighbor (ANN) indexing to perform efficient vector similarity search over high-dimensional embeddings, enabling fast and scalable semantic retrieval.
The project also integrates Retrieval-Augmented Generation (RAG) pipelines using embeddings and local Large Language Models (LLMs) to provide context-aware responses. Instead of relying solely on a model's internal knowledge, AstraVector retrieves the most relevant information from stored vectors and feeds it into the generation pipeline for accurate and grounded answers.
Stack
- Language: C++
- Database Architecture: Custom Vector Database
- Search: ANN Indexing and Vector Similarity Search
- AI: Embeddings + Local LLM Integration
- Architecture: Retrieval-Augmented Generation (RAG)
- API: REST API
- Features: Semantic Search, Metadata Filtering, Contextual Retrieval
Notable bits
The database is optimized for AI-powered applications that require fast and accurate semantic retrieval. It supports embedding storage, nearest-neighbor search, and context-aware retrieval pipelines that integrate directly with local LLMs.
The REST API exposes endpoints for vector insertion, similarity search, metadata filtering, and AI-assisted querying, making the system suitable for AI assistants, document retrieval systems, and knowledge bases.
Highlights
- Built a custom vector database implementing ANN indexing and semantic search.
- Developed Retrieval-Augmented Generation (RAG) pipelines using embeddings and local LLMs.
- Designed REST APIs for vector storage and semantic retrieval workflows.
- Supports context-aware querying for AI applications.
- Optimized for high-dimensional vector similarity search.