💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
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Updated
May 9, 2024 - Python
💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding.
Retrieval and Retrieval-augmented LLMs
The open source platform for AI-native application development.
Providing enterprise-grade LLM-based development framework, tools, and fine-tuned models.
LLM App templates for RAG, knowledge mining, and stream analytics. Ready to run with Docker,⚡in sync with your data sources.
Distributed vector search for AI-native applications
The AI-native database built for LLM applications, providing incredibly fast full-text and vector search
Harness LLMs with Multi-Agent Programming
Generative AI reference workflows optimized for accelerated infrastructure and microservice architecture.
RAG (Retrieval Augmented Generation) Framework for building modular, open source applications for production by TrueFoundry
The framework for fast development and deployment of RAG systems.
Ship RAG based LLM web apps in seconds.
Longterm Memory for Autonomous Agents.
Fast, Accurate, Lightweight Python library to make State of the Art Embedding
Fractal Graph Desktop for Ai-Agents, Web-Browsing, Note-Taking, and Code.
An LLM-powered advanced RAG pipeline built from scratch
All-in-one infrastructure for building search, recommendations, and RAG. Trieve combines search language models with tools for tuning ranking and relevance.
Awesome-LLM-RAG: a curated list of advanced retrieval augmented generation (RAG) in Large Language Models
Build your own serverless AI Chat with Retrieval-Augmented-Generation using LangChain.js, TypeScript and Azure
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