Examples

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

Pipelines

Transform data with language model backed pipelines.

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

Workflows

Efficiently process data at scale.

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

Model Training

Train NLP models.

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

Applications

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