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Semantic search and workflows powered by language models

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Semantic search and workflows powered by language models

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txtai is an open-source platform for semantic search and workflows powered by language models.


Traditional search systems use keywords to find data. Semantic search has an understanding of natural language and identifies results that have the same meaning, not necessarily the same keywords.


txtai builds embeddings databases, which are a union of vector indexes and relational databases. This enables similarity search with SQL. Embeddings databases can stand on their own and/or serve as a powerful knowledge source for large language model (LLM) prompts.

Semantic workflows connect language models together to build intelligent applications.


Integrate vector search, conversational search, automatic summarization, transcription, translation and more.

Summary of txtai features:

  • 🔎 Similarity search with SQL, object storage, topic modeling, graph analysis, multiple vector index backends (Faiss, Annoy, Hnswlib) and support for external vector databases
  • 📄 Create embeddings for text, documents, audio, images and video
  • 💡 Pipelines powered by language models that run question-answering, labeling, transcription, translation, summarization, LLM prompts and more
  • ↪️️ Workflows to join pipelines together and aggregate business logic. txtai processes can be simple microservices or multi-model workflows.
  • ⚙️ Build with Python or YAML. API bindings available for JavaScript, Java, Rust and Go.
  • ☁️ Cloud-native architecture that scales out with container orchestration systems (e.g. Kubernetes)

The following applications are powered by txtai.


Application Description
txtchat Conversational search and workflows for all
paperai Semantic search and workflows for medical/scientific papers
codequestion Semantic search for developers
tldrstory Semantic search for headlines and story text

txtai is built with Python 3.7+, Hugging Face Transformers, Sentence Transformers and FastAPI

Why txtai?


In addition to traditional search systems, a growing number of language model backed solutions are available, so why txtai?

  • Up and running in minutes with pip or Docker
# Get started in a couple lines
from txtai.embeddings import Embeddings

embeddings = Embeddings({"path": "sentence-transformers/all-MiniLM-L6-v2"})
embeddings.index([(0, "Correct", None), (1, "Not what we hoped", None)])"positive", 1)
#[(0, 0.2986203730106354)]
  • Built-in API makes it easy to develop applications using your programming language of choice
# app.yml
    path: sentence-transformers/all-MiniLM-L6-v2
CONFIG=app.yml uvicorn "txtai.api:app"
curl -X GET "http://localhost:8000/search?query=positive"
  • Run local - no need to ship data off to disparate remote services
  • Work with micromodels all the way up to large language models (LLMs)
  • Low footprint - install additional dependencies and scale up when needed
  • Learn by example - notebooks cover all available functionality



The easiest way to install is via pip and PyPI

pip install txtai

Python 3.7+ is supported. Using a Python virtual environment is recommended.

See the detailed install instructions for more information covering optional dependencies, environment specific prerequisites, installing from source, conda support and how to run with containers.



The examples directory has a series of notebooks and applications giving an overview of txtai. See the sections below.

Semantic Search

Build semantic/similarity/vector/neural search applications.

Notebook Description
Introducing txtai ▶️ Overview of the functionality provided by txtai Open In Colab
Build an Embeddings index with Hugging Face Datasets Index and search Hugging Face Datasets Open In Colab
Build an Embeddings index from a data source Index and search a data source with word embeddings Open In Colab
Add semantic search to Elasticsearch Add semantic search to existing search systems Open In Colab
Similarity search with images Embed images and text into the same space for search Open In Colab
Distributed embeddings cluster Distribute an embeddings index across multiple data nodes Open In Colab
What's new in txtai 4.0 Content storage, SQL, object storage, reindex and compressed indexes Open In Colab
Anatomy of a txtai index Deep dive into the file formats behind a txtai embeddings index Open In Colab
Custom Embeddings SQL functions Add user-defined functions to Embeddings SQL Open In Colab
Model explainability Explainability for semantic search Open In Colab
Query translation Domain-specific natural language queries with query translation Open In Colab
Build a QA database Question matching with semantic search Open In Colab
Embeddings components Composable search with vector, SQL and scoring components Open In Colab
Semantic Graphs Explore topics, data connectivity and run network analysis Open In Colab
Topic Modeling with BM25 Topic modeling backed by a BM25 index Open In Colab
Prompt-driven search with LLMs Embeddings-guided and Prompt-driven search with Large Language Models (LLMs) Open In Colab
Embeddings in the Cloud Load and use an embeddings index from the Hugging Face Hub Open In Colab


Transform data with language model backed pipelines.

Notebook Description
Extractive QA with txtai Introduction to extractive question-answering with txtai Open In Colab
Extractive QA with Elasticsearch Run extractive question-answering queries with Elasticsearch Open In Colab
Extractive QA to build structured data Build structured datasets using extractive question-answering Open In Colab
Apply labels with zero shot classification Use zero shot learning for labeling, classification and topic modeling Open In Colab
Building abstractive text summaries Run abstractive text summarization Open In Colab
Extract text from documents Extract text from PDF, Office, HTML and more Open In Colab
Text to speech generation Generate speech from text Open In Colab
Transcribe audio to text Convert audio files to text Open In Colab
Translate text between languages Streamline machine translation and language detection Open In Colab
Generate image captions and detect objects Captions and object detection for images Open In Colab
Near duplicate image detection Identify duplicate and near-duplicate images Open In Colab
API Gallery Using txtai in JavaScript, Java, Rust and Go Open In Colab


Efficiently process data at scale.

Notebook Description
Run pipeline workflows ▶️ Simple yet powerful constructs to efficiently process data Open In Colab
Transform tabular data with composable workflows Transform, index and search tabular data Open In Colab
Tensor workflows Performant processing of large tensor arrays Open In Colab
Entity extraction workflows Identify entity/label combinations Open In Colab
Workflow Scheduling Schedule workflows with cron expressions Open In Colab
Push notifications with workflows Generate and push notifications with workflows Open In Colab
Pictures are a worth a thousand words Generate webpage summary images with DALL-E mini Open In Colab
Run txtai with native code Execute workflows in native code with the Python C API Open In Colab
Prompt templates and task chains Build model prompts and connect tasks together with workflows Open In Colab

Model Training

Train NLP models.

Notebook Description
Train a text labeler Build text sequence classification models Open In Colab
Train without labels Use zero-shot classifiers to train new models Open In Colab
Train a QA model Build and fine-tune question-answering models Open In Colab
Train a language model from scratch Build new language models Open In Colab
Export and run models with ONNX Export models with ONNX, run natively in JavaScript, Java and Rust Open In Colab
Export and run other machine learning models Export and run models from scikit-learn, PyTorch and more Open In Colab


Series of example applications with txtai. Links to hosted versions on Hugging Face Spaces also provided.

Application Description
Basic similarity search Basic similarity search example. Data from the original txtai demo. 🤗
Book search Book similarity search application. Index book descriptions and query using natural language statements. Local run only
Image search Image similarity search application. Index a directory of images and run searches to identify images similar to the input query. 🤗
Summarize an article Summarize an article. Workflow that extracts text from a webpage and builds a summary. 🤗
Wiki search Wikipedia search application. Queries Wikipedia API and summarizes the top result. 🤗
Workflow builder Build and execute txtai workflows. Connect summarization, text extraction, transcription, translation and similarity search pipelines together to run unified workflows. 🤗


Full documentation on txtai including configuration settings for pipelines, workflows, indexing and the API.

Further Reading



For those who would like to contribute to txtai, please see this guide.

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