3 repository-uri
Awesome GitHub RepositoriesRAG Optimizations
Database features specifically optimized for retrieval-augmented generation pipelines, including reranking.
Distinct from Vector Memory Stores: Focuses on the database's optimization for RAG rather than as a general memory store for agents
Explore 3 awesome GitHub repositories matching data & databases · RAG Optimizations. Refine with filters or upvote what's useful.
Awesome RAG Optimizations GitHub Repositories
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- semi-technologies/weaviate
semi-technologies/weaviate
16,337Vezi pe GitHubWeaviate is a cloud-native vector database and distributed vector store designed to save high-dimensional vectors alongside structured data. It functions as a hybrid search engine that combines vector similarity, keyword matching, and structured metadata filtering within a single query. The system is optimized for retrieval-augmented generation, integrating vector search with generative AI and reranking to power question-and-answer workflows. It distinguishes itself through the ability to merge semantic search with traditional keyword queries and structured metadata filters to improve result
Provides a vector store optimized for RAG, integrating similarity search with generative AI workflows.
Go - lancedb/lancedb
lancedb/lancedb
9,031Vezi pe GitHubLanceDB is a vector database and columnar data store designed to function as a versioned dataset manager and vector search engine. It serves as a high-performance backend for indexing and retrieving high-dimensional embeddings, providing the foundation for machine learning data pipelines. The system distinguishes itself through a combination of cloud-native object storage and immutable version tracking, allowing for data time-travel and reproducible AI experiments. It integrates hybrid search capabilities, merging dense vector similarity with BM25 full-text search and SQL-like scalar filters
Implements specialized database features optimized for high-performance indexing and retrieval within RAG pipelines.
HTMLapproximate-nearest-neighbor-searchimage-searchnearest-neighbor-search - aiming-lab/simplemem
aiming-lab/SimpleMem
2,972Vezi pe GitHubSimpleMem is a persistent memory system for AI assistants designed to maintain context across different user chat sessions. It functions as a memory server and multimodal vector database that stores and retrieves information from text, images, audio, and video. The project features a context compression engine that distills interaction histories into compact units to reduce token consumption. It utilizes a distributed memory orchestrator and worker-thread parallel processing to reduce latency when querying large-scale dialogue datasets. The system implements a hybrid indexing approach combin
Includes a diagnostic framework to optimize the precision of memories recovered in RAG pipelines.
Python
