Use Cases

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

Build semantic/similarity/vector/neural search applications.

demo

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.

search search

Get started with the following examples.

NotebookDescription
Introducing txtai ▶️Overview of the functionality provided by txtaiOpen In Colab
Similarity search with imagesEmbed images and text into the same space for searchOpen In Colab
Build a QA databaseQuestion matching with semantic searchOpen In Colab
Semantic GraphsExplore topics, data connectivity and run network analysisOpen In Colab

LLM Orchestration

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

Chains

Integrate LLM chains (known as workflows in txtai), multiple LLM agents and self-critique.

llm

See below to learn more.

NotebookDescription
Prompt templates and task chainsBuild model prompts and connect tasks together with workflowsOpen In Colab
Integrate LLM frameworksIntegrate llama.cpp, LiteLLM and custom generation frameworksOpen In Colab

Retrieval augmented generation

Retrieval augmented generation (RAG) reduces the risk of LLM hallucinations by constraining the output with a knowledge base as context. RAG is commonly used to “chat with your data”.

rag rag

A novel feature of txtai is that it can provide both an answer and source citation.

NotebookDescription
Prompt-driven search with LLMsEmbeddings-guided and Prompt-driven search with Large Language Models (LLMs)Open In Colab
Build RAG pipelines with txtaiGuide on retrieval augmented generation including how to create citationsOpen In Colab

Language Model Workflows

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

flows flows

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.

NotebookDescription
Run pipeline workflows ▶️Simple yet powerful constructs to efficiently process dataOpen In Colab
Building abstractive text summariesRun abstractive text summarizationOpen In Colab
Transcribe audio to textConvert audio files to textOpen In Colab
Translate text between languagesStreamline machine translation and language detectionOpen In Colab