Vectordb Collection

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📦Normal

★★

“Neo4j graph construction from unstructured data using LLMs”

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💎Aqua

★★

“The pretty much "official" DSPy framework for Typescript”

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💎Aqua

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“A modular and comprehensive solution to deploy a Multi-LLM and Multi-RAG powered chatbot (Amazon Bedrock, Anthropic, HuggingFace, OpenAI, Meta, AI21, Cohere, Mistral) using AWS CDK on AWS”

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🔮Psychic

★★

“Benchmark for vector databases.”

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🔥Fire

★★

“A @ClickHouse fork that supports high-performance vector search and full-text search.”

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⚙️Steel

★★

“Full-text and semantic search on any Postgres”

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🔥Fire

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“Single-file memory layer for AI agents, sub mili-second RAG on Apple Silicon. Metal Optimized On-Device. No Server. No API. One File. Pure Swift”

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🔮Psychic

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“A collection of apps powered by the LangChain LLM framework.”

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🔥Fire

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“vsag is a vector indexing library used for similarity search.”

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“Advanced Retrieval-Augmented Generation (RAG) through practical notebooks, using the power of the Langchain, OpenAI GPTs ,META LLAMA3 ,Agents.”

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⚡Thunder

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“LangStream. Event-Driven Developer Platform for Building and Running LLM AI Apps. Powered by Kubernetes and Kafka.”

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⚙️Steel

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“In-memory vector store with efficient read and write performance for semantic caching and retrieval system. Redis for Semantic Caching.”

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💎Aqua

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“A modern desktop application for exploring, managing, and analyzing vector databases”

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📦Normal

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“Awesome List of Vector DB resources”

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🔥Fire

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“AI Native database for embedding vectors”

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🔮Psychic

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“chm to markdown and vectorDB”

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🔥Flame

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“AI Toolkit for Node.js (LLM, Image Generation, Embeddings, Vector Search)”

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⚔️Fighting

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“TinyVecDB is an ultra fast embedded vector database.”