TxtAI (12,800 Stars) 2026: The All-in-One Embeddings Database for Semantic Search and RAG

TxtAI (12,800 stars) is an all-in-one embeddings database: semantic search, LLM orchestration and workflow in one Python library. Here is how to build a RAG app with it.

💡 What You Will Learn

TxtAI (12,800 stars) is an all-in-one embeddings database: semantic search, LLM orchestration and workflow in one Python library. Here is how to build a RAG app with it.

## The short answer **neuml/txtai** (12,800 stars, Python) is an all-in-one embeddings database. It combines semantic search, LLM orchestration, and workflow in a single library - no separate vector database or search service required. It runs on SQLite by default, which keeps setup trivial. ## What it covers - **Semantic search**: embed documents and search by meaning - **RAG pipeline**: retrieve + generate in one API - **LLM orchestration**: agents, tools, and prompt pipelines - **Workflow**: build multi-step data pipelines declaratively - **Scalable**: from SQLite on a laptop to external vector stores ## Build a RAG app in minutes ```bash pip install txtai ``` ```python from txtai import RAG rag = RAG() rag.index(["Semantic Kernel is Microsoft SDK for AI agents.", "MCP standardizes AI tool access."]) print(rag("What does MCP standardize?")) # MCP standardizes AI tool access. ``` For more control, build the pipeline explicitly: ```python from txtai import Embeddings, LLM embeddings = Embeddings(path="sentence-transformers/all-MiniLM-L6-v2") embeddings.index(["docs", ...]) llm = LLM("hf:Qwen/Qwen2.5-7B-Instruct") results = embeddings.search("query", limit=3) answer = llm(f"Answer using: {results}") ``` ## Practical tips - Start with the default SQLite backend - it handles millions of vectors fine on one machine. - Use the built-in `txtai.app` to try semantic search interactively. - For production scale, plug in an external vector database via the `vectors` config. ## FAQ **Is it a vector database?** It embeds and stores vectors itself (SQLite by default), and can connect to external stores when needed. **Does it include an LLM?** It orchestrates LLMs (Hugging Face, OpenAI-compatible) rather than bundling one. **Is it free?** Yes - Apache-2.0 open source.