Use Cases

The following sections introduce common txtai use cases. A comprehensive set of over 50 example notebooks and applications are also available.

Semantic Search

Build semantic/similarity/vector/neural search applications.

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.

Get started with the following examples.

Notebook	Description
Introducing txtai ▶️	Overview of the functionality provided by txtai
Similarity search with images	Embed images and text into the same space for search
Build a QA database	Question matching with semantic search
Semantic Graphs	Explore topics, data connectivity and run network analysis

LLM Orchestration

Prompt-driven search, retrieval augmented generation (RAG), pipelines and workflows that interface with large language models (LLMs).

Integrate conversational search, LLM chains and self-critique.

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

Language Model Workflows

Language model workflows, also known as semantic workflows, connect language models together to build intelligent applications.

While LLMs are powerful, there are plenty of smaller, more specialized models that work better and faster for specific tasks. This includes models for extractive question-answering, automatic summarization, text-to-speech, transcription and translation.

Notebook	Description
Run pipeline workflows ▶️	Simple yet powerful constructs to efficiently process data
Building abstractive text summaries	Run abstractive text summarization
Transcribe audio to text	Convert audio files to text
Translate text between languages	Streamline machine translation and language detection