ARES

RAG Assessment framework for Python providing reference-free evaluation of RAG pipelines using LLM-as-a-judge, measuring context relevancy, context recall, faithfulness, and answer relevancy with automatic test data generation.

Open-source document parsing framework from IBM with 97.9% accuracy in complex table extraction and excellent text fidelity. Self-hostable solution for converting PDFs, spreadsheets, and scanned images into structured data for RAG pipelines.

Open-source LLM app development platform with an intuitive interface that combines AI workflow, RAG pipeline, agent capabilities, model management, and observability features for rapid prototyping and production deployment.

Knowledge engine for AI agent memory and memory layer for AI agents. Replaces complex RAG pipelines with serverless, single-file memory supporting instant retrieval and long-term memory.

LEANN is a lightweight RAG-focused library for vector search on embeddings, achieving 97% storage savings via advanced compression and quantization techniques on personal devices. Implemented in Rust/Python, it supports efficient ANN indexing without full DB overhead. Ideal for embedded apps and private prototyping; far lighter than Milvus, more efficient on-device vs hnswlib.

ClickHouse is a columnar OLAP database with vector indexes (ANN via AMM, brute-force), supporting SQL queries over vectors + structured data at petabyte scale. Excels in aggregations with vectors. For analytics workloads with embeddings; faster ingestion than Postgres pgvector for big data.