Where Haystack meets real vector DBs and models

What it does This repository is a curated collection of Jupyter notebooks demonstrating concrete integrations with the Haystack AI framework. Each notebook acts as a small, runnable recipe for connecting Haystack to specific model providers, vector databases, retrieval techniques, or experimental features. It functions as a community-driven appendix to the official documentation.

The interesting bit The project treats examples like actual cookbook entries: every notebook is indexed in index.toml with topics and an experimental flag, so you can browse by ingredient rather than guessing from filenames. Contributors can even ship a new recipe directly from Google Colab without touching a local terminal.

Key highlights

• Covers specific integrations with model providers, vector databases, and retrieval techniques
• Most notebooks are small, focused demos rather than sprawling applications
• Community contributions accepted via PR, including direct saves from Colab
• Indexed in index.toml with titles, topics, and experimental-feature flags

Verdict Worth a bookmark if you are building with Haystack and prefer to start from working code rather than abstract documentation. If you do not use Haystack, there is nothing here for you.