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.

📜 Table of Contents

• The short answer
• What it covers
• Build a RAG app in minutes
• Practical tips
• FAQ

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

pip install txtai

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:

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.

❓ 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.