Rust library for vector embeddings and reranking. Inspired by qdrant/fastembed.
-
Updated
Jan 12, 2026 - Rust
Rust library for vector embeddings and reranking. Inspired by qdrant/fastembed.
Suite of tools containing an in-memory vector datastore and AI proxy
Chat with Lex! A RAG app, using HyDE with milvus DB for vector store, VLLM for LLM inference, and FastEmbed for Embeddings!
Generating embedding for 1000s of PDF Documents, in Qdrant using FastEmbed with distributed Computing in Ray
🧠 Universal long-term memory for AI agents. GraphRAG-powered knowledge base with vector search + graph traversal. Privacy-first, local-only, MCP-compatible. Connect Claude, Copilot, or any AI assistant.
MedSage is a multimodal healthcare assistant that combines LLMs, vector search, and real-time reasoning to deliver fast, reliable medical insights. It supports symptom analysis, medical document Q&A, universal file RAG, multilingual interactions, and emergency SOS with live location.
⚡ Instantly index, deduplicate, and search your code, docs, and web content in a blazing-fast Qdrant vector DB for AI & RAG.
Using Qdrant, Fastembed, Google Cloud, OpenAI to build a Question Answer Cloud Based RAG System
ExFastembed is an Elixir wrapper around the fastembed-rs crate.
A high-performance, Rust-based in-memory vector store with FastEmbed integration for Python applications.
A library to gather structured statistics on the source code files in a software repository, generate embeddings and store in a vector database.
An AI + RAG system delivering culturally contextualized nutritional intelligence, personalized caloric guidance, and queryable knowledge over Indian Food Composition Tables (IFCT).
Demo for SenTrEv python package
A polars plugin for embedding DataFrames
Data Retrieval from Qdrant Vector DB based on RRF algo and metadata filtering
AI-Powered Employee Skill & Project Recommendation System An intelligent system that recommends roles and training programs based on employee skills, leveraging AI for personalized career development and project matching.
deadpool implementation for fastembed
Add a description, image, and links to the fastembed topic page so that developers can more easily learn about it.
To associate your repository with the fastembed topic, visit your repo's landing page and select "manage topics."