Popular GitHub Repositories for Semantic Database

21 Lessons, Get Started Building with Generative AI ? https://microsoft.github.io/generative-ai-for-beginners/

For developers, who are building real-time data-driven applications, Redis is the preferred, fastest, and most feature-rich cache, data structure server, and document and vector query engine.

A lightning-fast search engine API bringing AI-powered hybrid search to your sites and applications.

The all-in-one Desktop & Docker AI application with built-in RAG, AI agents, No-code agent builder, and more.

Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. ?Docker-friendly.?Always in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time data APIs, and more.

Free universal database tool and SQL client

10 Lessons to Get Started Building AI Agents

LlamaIndex is the leading framework for building LLM-powered agents over your data.

The Memory layer for AI Agents

下一代 Node.js 和 TypeScript 对象关系映射 (ORM) 工具，支持 PostgreSQL、MySQL、MariaDB、SQL Server、SQLite、MongoDB 和 CockroachDB 数据库。

Opiniated RAG for integrating GenAI in your apps ? Focus on your product rather than the RAG. Easy integration in existing products with customisation! Any LLM: GPT4, Groq, Llama. Any Vectorstore: PGVector, Faiss. Any Files. Anyway you want.

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Your AI second brain. Self-hostable. Get answers from the web or your docs. Build custom agents, schedule automations, do deep research. Turn any online or local LLM into your personal, autonomous AI (gpt, claude, gemini, llama, qwen, mistral). Get started - free.

Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

the AI-native open-source embedding database

???AI-driven database tool and SQL client, The hottest GUI client, supporting MySQL, Oracle, PostgreSQL, DB2, SQL Server, DB2, SQLite, H2, ClickHouse, and more.

AI orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.

Chat with your database or your datalake (SQL, CSV, parquet). PandasAI makes data analysis conversational using LLMs and RAG.

? Chat with your SQL database ?. Accurate Text-to-SQL Generation via LLMs using RAG ?.

CVPR 2025 论文和开源项目合集