This repository provides tutorials for Haystack, an open-source framework by deepset for building production-ready LLM applications, retrieval-augmented generation (RAG) pipelines, and intelligent search systems over large document collections. It targets developers and researchers looking to leverage state-of-the-art NLP models with flexibility and ease of use.

How It Works

The tutorials demonstrate building various LLM-powered applications using Haystack's pipeline-based architecture. This approach allows for modular construction of complex workflows, integrating components like retrievers, generators, and custom logic for tasks such as question answering, document classification, and function calling agents. The framework emphasizes composability and ease of experimentation with different NLP models.

Quick Start & Requirements

• Install: pip install farm-haystack
• Prerequisites: Python 3.8+, PyTorch, Transformers, Sentence-Transformers. Specific tutorials may require additional libraries or pre-trained models.
• Resources: Tutorials are designed to be runnable on standard development machines, with some potentially benefiting from GPU acceleration for larger models.
• Links: Tutorials are also published to the Haystack Website.

Highlighted Details

• Covers a wide range of LLM application patterns, from basic QA to advanced RAG evaluation and tool-calling agents.
• Demonstrates techniques for metadata filtering, hybrid retrieval, and conditional routing within pipelines.
• Includes examples for building chat applications and generating structured output with auto-correction.
• Provides guidance on creating custom components and serializing pipelines for deployment.

Maintenance & Community

Haystack is developed by deepset. Further community and contribution information can be found via their Contributing Guidelines.

Licensing & Compatibility

The Haystack framework is Apache 2.0 licensed, permitting commercial use and integration into closed-source projects.

Limitations & Caveats

The repository contains tutorials, not the core Haystack framework itself. Users will need to install the farm-haystack package separately. Some advanced tutorials may require significant computational resources or specific model downloads.