This repository provides a curated collection of example notebooks demonstrating the capabilities of the Haystack framework for building NLP applications. It targets developers and researchers looking for practical guidance on integrating various model providers, vector databases, and retrieval techniques within their projects. The cookbook serves as a valuable resource for learning and implementing advanced NLP workflows with Haystack.

How It Works

The cookbook showcases practical implementations of Haystack features through Jupyter notebooks. Each notebook typically focuses on a specific use case, such as integrating a particular LLM provider, utilizing a specific vector database for RAG, or demonstrating a novel retrieval strategy. This approach allows users to quickly grasp and adapt specific functionalities without needing to sift through extensive documentation.

Quick Start & Requirements

• Install: pip install farm-haystack[all] (or specific components)
• Prerequisites: Python 3.8+, potentially specific model provider API keys, and access to vector databases.
• Resources: Notebooks often require downloading models or datasets, which can vary in size.
• Links:
  • Haystack Docs: https://docs.haystack.deepset.ai/
  • Official Tutorials: https://haystack.deepset.ai/tutorials
  • Blog: https://haystack.deepset.ai/blog

Highlighted Details

• Demonstrates integration with diverse model providers (e.g., OpenAI, Hugging Face).
• Showcases various vector databases (e.g., Pinecone, Weaviate, FAISS).
• Illustrates different retrieval techniques and RAG patterns.
• Includes examples of experimental Haystack features.

Maintenance & Community

This repository is maintained by deepset AI. Contributions are encouraged via pull requests. Further community engagement can be found through deepset's official channels.

Licensing & Compatibility

The repository itself is likely governed by the license of the Haystack framework, which is typically Apache 2.0. This license is permissive and allows for commercial use and integration into closed-source projects.

Limitations & Caveats

The examples are designed for demonstration and may require adjustments for production environments. Some notebooks might rely on specific versions of dependencies or external services that could change over time. The "experimental" tag indicates features that are not yet stable.