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
neuml/txtai
txtai is a comprehensive AI framework designed for semantic search, LLM orchestration, and language model workflows. At its core is an embeddings database, which integrates vector indexes (sparse and dense), graph networks, and relational databases. This foundation supports powerful vector search and serves as a knowledge source for large language model applications. The framework enables users to build autonomous agents, implement Retrieval Augmented Generation (RAG) processes, and create multi-model workflows. Key features include vector search with multimodal indexing, embedding generation for various data types (text, images, audio, video), and pipelines for LLM prompts, Q&A, transcription, summarization, and more. It also supports workflows to combine pipelines and agents that intelligently connect these components to solve complex problems. txtai offers Web and Model Context Protocol (MCP) APIs with bindings for multiple languages, emphasizing local operation and scalability. It is built on Python 3.10+, Hugging Face Transformers, Sentence Transformers, and FastAPI.
https://github.com/neuml/txtai
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