pathwaycom/llm-app
Ready-to-run cloud templates for building real-time RAG, AI pipelines, and enterprise search applications that synchronize with various live data sources.
Awesome Infra for AI › Vector Databases & Retrieval Infrastructure
EmbeddedLLM/JamAIBase
JamAI Base is an open-source Retrieval-Augmented Generation (RAG) backend platform meticulously designed for developing and managing AI applications. It integrates an embedded database (SQLite) and an embedded vector database, LanceDB, providing managed memory and robust RAG capabilities. The platform streamlines LLM, vector embeddings, and reranker orchestration and management through a convenient, intuitive, spreadsheet-like user interface and a simple REST API. This user-friendly approach allows for defining data requirements using natural language prompts, simplifying complex data operations. Key features include "Generative Tables" for dynamic data generation by LLMs, "Action Tables" for real-time interactions and complex workflow orchestration, and "Knowledge Tables" for structured data and document repositories, enhancing LLM contextual understanding. "Chat Tables" simplify intelligent chatbot development with context-aware interactions and seamless RAG integration. JamAI Base emphasizes an innovative RAG technique, offering built-in features like query rewriting, hybrid search, reranking, structured RAG content management, and adaptive chunking. Its serverless design, built on LanceDB, ensures scalability and optimal performance, supporting various LLMs such as OpenAI GPT-4, Anthropic Claude 3, and Meta Llama3. The platform's declarative paradigm simplifies development by focusing on "what" to achieve rather than "how," making it accessible to users with varying technical expertise. It aims to provide a comprehensive, developer-friendly environment for building and iterating on AI applications.
https://github.com/EmbeddedLLM/JamAIBase
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