Examples

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

Autonomous agents, 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|
Build knowledge graphs with LLMs| Build knowledge graphs with LLM-driven entity extraction|
Advanced RAG with graph path traversal| Graph path traversal to collect complex sets of data for advanced RAG|
Advanced RAG with guided generation| Retrieval Augmented and Guided Generation|
RAG with llama.cpp and external API services| RAG with additional vector and LLM frameworks|
How RAG with txtai works| Create RAG processes, API services and Docker instances|
Speech to Speech RAG ▶️| Full cycle speech to speech workflow with RAG|
Analyzing Hugging Face Posts with Graphs and Agents| Explore a rich dataset with Graph Analysis and Agents|
Granting autonomy to agents| Agents that iteratively solve problems as they see fit|
Getting started with LLM APIs| Generate embeddings and run LLMs with OpenAI, Claude, Gemini, Bedrock and more|
Analyzing LinkedIn Company Posts with Graphs and Agents| Exploring how to improve social media engagement with AI|
Parsing the stars with txtai| Explore an astronomical knowledge graph of known stars, planets, galaxies|
Chunking your data for RAG| Extract, chunk and index content for effective retrieval|
Medical RAG Research with txtai| Analyze PubMed article metadata with RAG|
GraphRAG with Wikipedia and GPT OSS| Deep graph search powered 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|
Generative Audio| Storytelling with generative audio 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|

API

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|
OpenAI Compatible API| Connect to txtai with a standard OpenAI client library|

Architecture

Project architecture, data formats, external integrations, scale to production, 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|
Integrate txtai with Postgres| Persist content, vectors and graph data in Postgres|
Embeddings index format for open data access| Platform and programming language independent data storage with txtai|
Accessing Low Level Vector APIs| Build a vector database using txtai’s low-level 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|
What’s new in txtai 7.0| Semantic graph 2.0, LoRA/QLoRA training and binary API support|
What’s new in txtai 8.0| Agents with txtai|
What’s new in txtai 9.0| Learned sparse vectors, late interaction models and rerankers|

Applications

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

Application| Description|
---|---|---
Basic similarity search| Basic similarity search example. Data from the original txtai demo.| 🤗
Baseball stats| Match historical baseball player stats using vector search.| 🤗
Benchmarks| Calculate performance metrics for the BEIR datasets.| Local run only
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.| 🤗
Retrieval Augmented Generation| RAG with txtai embeddings databases. Ask questions and get answers from LLMs bound by a context.| Local run only
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.| 🤗