324  txtai(1) - Kailash Tool Reference

324.1 txtai

txtai - txtai — all-in-one embeddings database for semantic search and LLM orchestration.

Layer OPS
Category o-03-vector-stores-retrieval
Safety class safe
Licence Apache-2.0
Upstream github.com/neuml/txtai

324.1.1 NAME

txtai - txtai — all-in-one embeddings database for semantic search and LLM orchestration.

324.1.2 SYNOPSIS & usage

Derived from the upstream README at generation time; verify against --help where the tool is installed.

# Get started in a couple lines
import txtai

embeddings = txtai.Embeddings()
embeddings.index(["Correct", "Not what we hoped"])
embeddings.search("positive", 1)
#[(0, 0.29862046241760254)]
# app.yml
embeddings:
    path: sentence-transformers/all-MiniLM-L6-v2

324.1.3 DESCRIPTION (upstream)

txtai is an all-in-one AI framework for semantic search, LLM orchestration and language model workflows. The key component of txtai is an embeddings database, which is a union of vector indexes (sparse and dense), graph networks and relational databases. This foundation enables vector search and/or serves as a powerful knowledge source for large language model (LLM) applications. Build autonomous agents, retrieval augmented generation (RAG) processes, multi-model workflows and more. Summary of txtai features: - 🔎 Vector search with SQL, object storage, topic modeling, graph analysis and multimodal indexing - 📄 Create embeddings for text, documents, audio, images and video - 💡 Pipelines power

324.1.4 CONFIGURATION (detected)

Config-surface markers detected in the upstream README (2 matches, e.g. app.yml, app.yml). A full config reference page for this tool is queued under the config-page policy — this line is the detection record, not the page.

324.1.5 SEE ALSO

ref: pin - verified 2026-10-14