Examples

See below for a comprehensive series of example notebooks and applications covering txtai.

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
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
Semantic Graphs	Explore topics, data connectivity and run network analysis
Topic Modeling with BM25	Topic modeling backed by a BM25 index

LLM

LLM chains, retrieval augmented generation (RAG), chat with your data, pipelines and workflows that interface with large language models (LLMs).

Notebook	Description
Prompt-driven search with LLMs	Embeddings-guided and Prompt-driven search with Large Language Models (LLMs)
Prompt templates and task chains	Build model prompts and connect tasks together with workflows
Build RAG pipelines with txtai	Guide on retrieval augmented generation including how to create citations
Integrate LLM frameworks	Integrate llama.cpp, LiteLLM and custom generation frameworks
Generate knowledge with Semantic Graphs and RAG	Knowledge exploration and discovery with Semantic Graphs and RAG

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

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

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

Scale

Run distributed txtai, integrate with the API and cloud endpoints.

Notebook	Description
API Gallery	Using txtai in JavaScript, Java, Rust and Go
Distributed embeddings cluster	Distribute an embeddings index across multiple data nodes
Embeddings in the Cloud	Load and use an embeddings index from the Hugging Face Hub
Custom API Endpoints	Extend the API with custom endpoints
API Authorization and Authentication	Add authorization, authentication and middleware dependencies to the API

Architecture

Deep dives into project architecture, data formats, benchmarks, and performance.

Notebook	Description
Anatomy of a txtai index	Deep dive into the file formats behind a txtai embeddings index
Embeddings components	Composable search with vector, SQL and scoring components
Customize your own embeddings database	Ways to combine vector indexes with relational databases
Building an efficient sparse keyword index in Python	Fast and accurate sparse keyword indexing
Benefits of hybrid search	Improve accuracy with a combination of semantic and keyword search
External database integration	Store metadata in PostgreSQL, MariaDB, MySQL and more
All about vector quantization	Benchmarking scalar and product quantization methods
External vectorization	Vectorization with precomputed embeddings datasets and APIs

Releases

New functionality added in major releases.

Notebook	Description
What’s new in txtai 4.0	Content storage, SQL, object storage, reindex and compressed indexes
What’s new in txtai 6.0	Sparse, hybrid and subindexes for embeddings, LLM improvements

Applications

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