Dialog is a RAG LLM Ops application designed to simplify the deployment and testing of Retrieval Augmented Generation systems for developers interested in AI without extensive API development knowledge. It leverages modern web and LLM interaction frameworks to reduce coding time, allowing users to focus on model training and RAG humanization.

How It Works

Dialog provides an API focused on deploying any LLM, built upon the dialog-lib structure. It aims to humanize RAG outputs by delimiting the answer scope and producing human-like responses, with ongoing expansion into broader RAG deployment and maintenance improvements. The architecture includes a PostgreSQL database for chat history and document retrieval, supporting the core API service.

Quick Start & Requirements

• Install via docker-compose up after cloning the repository and setting the OPENAI_API_KEY in a .env file (copied from .env.sample).
• Requires Docker and Docker Compose.
• Official documentation and tutorials are available for detailed setup and usage.

Highlighted Details

• Partnership with Open-WebUI allows using their chat interface as a frontend.
• Focus on "humanizing" RAG outputs with delimited and natural-sounding answers.
• Supports deployment of various LLMs through its API structure.

Maintenance & Community

• Active community support via Discord: https://discord.gg/dPAcsn3Ch7
• Supported by sponsors including Github Accelerator and Buser.
• Key maintainers include avelino, vmesel, walison17, and lgabs.

Licensing & Compatibility

• The specific license is not explicitly stated in the README. Compatibility for commercial use or closed-source linking is not detailed.

Limitations & Caveats

The project requires familiarity with Docker for setup. The specific license and its implications for commercial use or integration with closed-source projects are not clearly defined in the provided README.