Tags: #vector-database

An open-source vector database designed as data infrastructure for AI applications, simplifying embedding management and semantic search.

An open-source platform for building, deploying, and orchestrating AI agents and agentic workflows with 1,000+ integrations and LLMs.

An open-source knowledge engine designed to build personalized and dynamic memory for AI agents with minimal code.

A high-performance, cloud-native vector database built for scalable Approximate Nearest Neighbor (ANN) search and AI applications.

An all-in-one AI framework for semantic search, LLM orchestration, and language model workflows, built around an embeddings database.

A modular framework for building advanced Agentic RAG systems using LangGraph, featuring intelligent query processing, conversation memory, and human-in-the-loop clarification.

LEANN is an innovative vector database enabling fast, accurate, and 100% private RAG on personal devices with 97% storage savings.

A customizable AI chatbot template built with LangChain and LangGraph, enabling users to ingest PDF documents, store embeddings, and answer queries using an LLM.

A comprehensive, full-stack guide to Retrieval-Augmented Generation (RAG) technology, covering theory, practice, and engineering best practices for building LLM applications.

An open-source, cloud-native vector database enabling semantic search at scale by combining vector similarity with structured filtering and integrated AI models.

Qdrant is a high-performance, massive-scale vector database and search engine designed for next-generation AI applications.

Learn to design, train, and deploy a real-time financial advisor LLM system using a hands-on, three-pipeline approach.

An intelligent dialogue system leveraging RAG technology to build Q&A services based on your custom knowledge base.

Deep Lake is an AI data runtime and database optimized for deep learning, offering multimodal data storage, querying, vector search, and streaming for LLM and deep learning applications.

Enhances AI coding agents like Claude Code with semantic search, providing deep, cost-effective context from the entire codebase.

An open-source, embedded retrieval library and multimodal AI lakehouse designed for fast, scalable vector search and data management in AI/ML applications.

A high-performance AI data platform for real-time search, recommendation, and machine learning inference at any scale.

An AI-native database optimized for LLM applications, offering incredibly fast hybrid search across dense vectors, sparse vectors, tensors, and full-text data.

An AI-native search database that unifies relational, vector, text, JSON, and GIS data in a single engine, enabling hybrid search and in-database AI workflows.

AutoFlow is an open-source, Graph RAG-based conversational knowledge base tool leveraging TiDB Serverless Vector Storage for intelligent search and instant responses.

A cloud-native distributed vector database designed for efficient similarity search of embedding vectors in AI applications.

HelixDB is an open-source, Rust-built graph-vector database that consolidates multiple data models to simplify AI application development.

A modern, AI-native management tool for Milvus vector databases, offering multi-cluster management, data exploration, and AI-driven operations.

A comprehensive collection of Jupyter Notebooks and sample applications designed to help users learn and experiment with Pinecone vector databases and common AI patterns.

An interactive learning platform providing tutorials and demos to master Milvus for building AI applications with unstructured data.

An open-source platform leveraging LLMs and vector databases to enable deep research, intelligent Q&A, and comprehensive reporting on private enterprise data.

A Python library for representing, transmitting, storing, and retrieving multimodal data, designed for AI applications.

A universal UI and tool suite designed to simplify the management, visualization, and interaction with vector databases like Pinecone, Chroma, Qdrant, and Weaviate.

A declarative, transactional Python library for building multimodal AI applications with incremental data storage, transformation, indexing, and orchestration.

A collaborative, spreadsheet-like platform for building, experimenting with, and evaluating AI applications, especially those leveraging Retrieval-Augmented Generation (RAG).

PostgresML integrates machine learning and AI capabilities, including GPU acceleration and large language models, directly into PostgreSQL, eliminating the need for separate systems and data transfers.

An open-source, self-hostable toolkit for testing, experimenting with, and evaluating prompts, large language models (LLMs), and vector databases.