Cohere Toolkit provides pre-built components for rapidly building and deploying Retrieval-Augmented Generation (RAG) applications. It targets developers and users aiming to create LLM-based products with features like conversational history and customizable data sources. The toolkit simplifies RAG development by offering a modular architecture and deployment guides.

How It Works

The toolkit features a backend API and multiple client-side interfaces, including web apps and a Slack bot. The backend supports various model providers (Cohere, SageMaker, Azure, Bedrock, HuggingFace, local models) and customizable tools/data sources. This modular design allows users to swap components and integrate different LLMs and data connectors, enhancing flexibility and extensibility for diverse RAG use cases.

Quick Start & Requirements

• Local Setup: Requires Docker, Docker-compose >= 2.22, and Poetry.
• Installation: git clone https://github.com/cohere-ai/cohere-toolkit.git && cd cohere-toolkit && make first-run
• Cloud Setup: GitHub Codespaces is supported.
• Details: Cohere Toolkit Setup

Highlighted Details

• Supports multiple LLM providers including Cohere, SageMaker, Azure, Bedrock, HuggingFace, and local models.
• Includes pre-built integrations for data sources like Google Drive, Gmail, Slack, and GitHub.
• Offers customizable web UIs (Next.js) and a Slack bot interface.
• Provides guides for deploying services on AWS, GCP, and Azure.

Maintenance & Community

Contributions are welcomed. Further details on contributing can be found in the documentation.

Licensing & Compatibility

The repository does not explicitly state a license in the provided README.

Limitations & Caveats

The README does not specify licensing details, which may impact commercial use or integration into closed-source projects. The setup requires specific versions of Docker and Docker Compose.