retrieval

Diagnose the performance of your RAG🩺

StarListify is a Python package that classifies GitHub stars history into organized category lists based on user-defined criteria.

Run, quantize, and fine-tune LLMs on Apple Silicon. Pure Rust, no Python, no CUDA, no ONNX

Cottontail DB is a column store vector database aimed at multimedia retrieval. It allows for classical boolean as well as vector-space retrieval (nearest neighbour search) used in similarity search using a unified data and query model.

[ACL 2021] Learning Relation Alignment for Calibrated Cross-modal Retrieval

[ICLR 2025] Official code repository for "TULIP: Token-length Upgraded CLIP"

Train linear embedding adapters with triplet loss to align retrieval embeddings with your queries (RAG).

Fine-tuning black-box OpenAI embedding models

[ACL 2026] CoCoA: Collaborative Chain-of-Agents for Parametric-Retrieved Knowledge Synergy

Vector compression with TurboQuant codecs for embeddings, retrieval, and KV-cache. 10x compression, pure NumPy core — optional GPU acceleration via PyTorch (CUDA/MPS) or MLX (Metal).

Recurrence Meets Transformers for Universal Multimodal Retrieval

Cognitive-structured Multimodal Agent (CMA-Harness): a memory-centric agent for long-horizon multimodal understanding, generation, and editing — externalizing visual history into episodic memory with selective retrieval. Includes the M2CA-Bench benchmark.

chew through any source into clean datasets! a fast ingestion, RAG & fine-tuning toolkit

Visual tool to compare 6 RAG chunking strategies side-by-side with grading and query selection

Context Runtime — a database query planner for LLM context. Decides what a model sees before it answers; plans it, runs it through reused substrate, and learns from the outcome.

Lightning-fast RAG for AI agents. ONNX-powered, 4-layer fusion, MCP server. No PyTorch.

🔰 A Comprehensive RAG repository covering basic vanilla RAG techniques, advanced retrieval methods, hybrid search fusion approaches, hands-on reranking techniques with code + explanation 📚✨