🍯 HoneyHive Cookbooks

A comprehensive collection of integration examples for AI observability and evaluation with HoneyHive

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📋 Overview

This repository contains a collection of cookbooks and examples for integrating various tools, frameworks, and services with HoneyHive for comprehensive AI observability and evaluation. Each cookbook provides practical guidance and code examples to help you implement effective tracing and evaluation for your AI systems.

🧰 Available Cookbooks

🔍 RAG (Retrieval-Augmented Generation)

Cookbook	Description
qdrant-cookbook	Integration with Qdrant vector database for RAG pipelines
zilliz-honeyhive	Integration with Zilliz (Milvus) vector database
rag-chromadb-cookbook-python	Integration with ChromaDB for RAG pipelines

🔗 Framework Integrations

Cookbook	Description
langchain-python	Integration examples with LangChain in Python
langchain-typescript	Integration examples with LangChain in TypeScript
llamaindex-python	Integration with LlamaIndex in Python
nextjs-quickstart	Basic Next.js integration with HoneyHive
nextjs-quickstart-with-sentry	Next.js integration with both HoneyHive and Sentry

💼 Domain-Specific Evaluations

Cookbook	Description
claims-summarizer-python	Process and summarize claims data using Python
claims-transcript-summarizer-js	Process and summarize transcript data for claims
text2sql-evals	Evaluate Text-to-SQL model performance

🎓 Academic Benchmarks

Cookbook	Description
putnam-evaluation-python	Evaluation examples using Putnam dataset
putnam-evaluation-async-python	Asynchronous evaluation with Putnam dataset

📚 Getting Started & Learning

Cookbook	Description
observability-tutorial-python	Basic observability tutorial in Python
observability-tutorial-ts	Basic observability tutorial in TypeScript

🤖 LLM Provider Integrations

Cookbook	Description
mistral-cookbook	Integration with Mistral AI's models and API

🚀 Getting Started

Each cookbook contains its own README with specific instructions. To get started:

1. HoneyHive Account: Sign up at honeyhive.ai and get your API key
2. Clone the Repository:

   git clone https://github.com/honeyhiveai/cookbook.git
   cd cookbook

3. Choose a Cookbook: Navigate to the cookbook that matches your use case and follow its README

🛠️ Requirements

Depending on the cookbook you're using, you'll need:

• Python 3.8+ for Python examples
• Node.js for JavaScript and TypeScript examples
• Jupyter Notebook support for notebook examples
• API Keys for relevant services (HoneyHive, OpenAI, etc.)

👥 Contributing

We welcome contributions from the community! To contribute:

1. Fork the repository
2. Create your feature branch: git checkout -b feature/new-example
3. Commit your changes: git commit -m 'Add a new example'
4. Push to the branch: git push origin feature/new-example
5. Submit a pull request

🤝 Support

For questions or issues:

• Open an issue
• Contact the HoneyHive team at [email protected]
• Visit honeyhive.ai

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