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

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.

See below to learn more.

Notebook	Description
Prompt templates and task chains	Build model prompts and connect tasks together with workflows
Integrate LLM frameworks	Integrate llama.cpp, LiteLLM and custom generation frameworks
Build knowledge graphs with LLMs	Build knowledge graphs with LLM-driven entity extraction

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".

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

Notebook	Description
Build RAG pipelines with txtai	Guide on retrieval augmented generation including how to create citations
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

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