This repository provides a collection of tutorials, examples, and guides for utilizing the Groq API, targeting developers looking to integrate Groq's LLM capabilities into their applications. It offers practical demonstrations of advanced features like function calling, RAG, and multimodal processing, enabling users to build sophisticated AI-powered solutions.

How It Works

The cookbook showcases various techniques for interacting with Groq's API, leveraging popular libraries and frameworks such as LangChain, Instructor, Gradio, and Streamlit. It demonstrates how to implement features like guardrails for content filtering, function calling for tool integration, retrieval-augmented generation (RAG) for context-aware responses, and multimodal processing for image analysis. The examples are designed to be practical and easily adaptable for real-world use cases.

Quick Start & Requirements

• Installation: No explicit installation instructions are provided; examples are typically run as Python scripts.
• Prerequisites: A Groq API key is required, obtainable by creating an account on the Groq website. Python environment is assumed.
• Resources: Examples cover a range of functionalities, from basic API calls to more complex integrations requiring specific libraries.
• Links:
  • Groq Account: https://console.groq.com/
  • Contribution Guidelines: https://github.com/groq/groq-api-cookbook/blob/main/CONTRIBUTING.md

Highlighted Details

• Extensive coverage of function calling with multiple use cases (eCommerce, SQL, Stock Market, Parallel).
• Demonstrations of Retrieval-Augmented Generation (RAG) with various data sources and benchmarking.
• Examples for JSON mode and structured output generation using libraries like Instructor.
• Multimodal capabilities showcased with batch image processing and single image analysis.
• Integrations with popular frameworks like Gradio, Streamlit, LangChain, CrewAI, and LiteLLM Proxy.

Maintenance & Community

The project is community-driven, with contributions welcomed. A Discord server is available for developer discussions and collaboration. Issues can be reported via GitHub.

Licensing & Compatibility

The repository does not explicitly state a license in the provided README. Users should verify licensing for any code snippets or examples used in commercial or closed-source applications.

Limitations & Caveats

The README does not specify any limitations or known bugs. The project appears to be a collection of examples rather than a standalone library, so direct integration might require adapting code from the examples.