vector-store

Harnessing the Memory Power of the Camelids

Python SDK for vector search, semantic search, RAG, embeddings, and AI agents on Oracle AI Database

Semantic product search on Databricks

🗲 A high-performance on-disk dictionary.

just testing langchain with llama cpp documents embeddings

A simple way to convert and manage files in vector storage.

Chat with your PDFs using AI! This Streamlit app uses RAG, LangChain, FAISS, and OpenAI to let you ask questions and get answers with page and file references.

Vector search examples with ScyllaDB

The AI Assistant uses OpenAI's GPT models and Langchain for agent management and memory handling. With a Streamlit interface, it offers interactive responses and supports efficient document search with FAISS. Users can upload and search pdf, docx, and txt files, making it a versatile tool for answering questions and retrieving content.

MemPack is a blazing-fast, lightweight alternative to heavy vector databases: a knowledge pack with an ANN index for fast semantic search.

Model Context Protocol (MCP) server for local vector storage & semantic search (ChromaDB, OCR, async ingestion).

Chat with your PDFs using AI! This Streamlit app uses RAG, LangChain, FAISS, and OpenAI to let you ask questions and get answers with page and file references.

LlamaIndex integration for Infino — vector, BM25, hybrid (RRF), and SQL retrieval over one engine on object storage.

Sub-second RAG regression testing. Define golden questions, detect lost chunks in CI. pytest for your RAG pipeline.

AI Data Assistant empowers data professionals by speaking both human language and machine langauges (SQL/Python)

This is a toy Web application written in Flask, featuring a Medical Assistant Chatbot powered by Large Language Models (LLMs) and Retrieval Augmented Generation (RAG).

Retail-RAG: A Python-based Retrieval-Augmented Generation (RAG) system for business insights using OpenAI GPT and FAISS. Ingests retail data, generates embeddings, and enables semantic search for financial, customer, and operational insights. Scalable API layer for real-time data-driven decision-making.

A powerful Retrieval Augmented Generation (RAG) application built with NVIDIA AI endpoints and Streamlit. This solution enables intelligent document analysis and question-answering using state-of-the-art language models, featuring multi-PDF processing, FAISS vector store integration, and advanced prompt engineering.