⚡ PDX: A Library for Fast Vector Search and Indexing on CPUs (x86, ARM) — for Python and C++. Index millions of vectors in seconds. Search them in milliseconds.
PaveDB is an inspectable retrieval database for AI applications: text-backed vector search with source provenance and query replay, embedded or over HTTP. Website: https://pavedb.org/
Automated video advertisement content analysis system using Sentence Transformers and cosine similarity for yes/no question evaluation. Features text embedding with all-mpnet-base-v2, batch processing, vector indexing, and performance evaluation against human-coded ground truth data.
Flask-based web application designed for detecting objects in images and retrieving visually similar images from a dataset (2021)
Milvus integration vector pipeline
Experimental framework for evaluating TiDB’s vector search capabilities with LangChain-based LLM retrieval workflows. Includes setup scripts, indexing pipelines, and retrieval benchmarks to test hybrid query performance and relevance scoring on TiDB’s vector database engine.
Local-RAG indexes selected project folders using a local embedding model storing chunked vectors in Weaviate. Enables coding agents to traverse a code graph and perform semantic queries against the codebase (supports Word, PDF and OCR)
Document intelligence & RAG engine. 7 API endpoints, FAISS vector indexing, semantic chunking, parallel LLM inference with VRAM monitoring. 24 docs/min throughput. Pub+sub NATS integration for bidirectional knowledge flow with Cerebro
Dependency-free Elixir client for PaveDB’s HTTP API, covering collections, text/file/vector ingestion, search, shared scope, query replay, documents, chunks, and health.
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