Dabarqus

Dabarqus is an all-in-one RAG (Retrieval-Augmented Generation) native (Linux, macOS, and Windows) server designed to deliver state-of-the-art RAG on-premise at a fraction of the cost of building from scratch.

In our experience with custom RAG solutions, we've encountered three major challenges:

1. Ensuring data privacy and control
2. Simplifying setup across different environments
3. Integrating RAG capabilities seamlessly with LLMs

How Dabarqus addresses these challenges:

1. Local and Private: Dabarqus runs entirely on your own hardware - be it a PC, laptop, server, or owned-cloud infrastructure. Your data never leaves your control.
2. Zero Dependencies: Dabarqus is a standalone C++ application with everything built-in. No external dependencies, no installation complexities.
3. Easy Integration: Dabarqus features a REST API with JSON output, facilitating smooth integration with existing systems and LLMs.

Quick start

Linux

1. Unzip the Dabarqus file into a folder

unzip Dabarqus-linux-DOWNLOADED_VERSION.zip
cd Dabarqus-linux-DOWNLOADED_VERSION
./bin/barq service install

2. Open a browser and go to http://localhost:6568/admin

macOS

1. Unzip the Dabarqus file into a folder

unzip Dabarqus-linux-DOWNLOADED_VERSION.zip
cd Dabarqus-linux-DOWNLOADED_VERSION
./bin/barq service install

2. Open a browser and go to http://localhost:6568/admin

Windows

1. Double click the Dabarqus-windows-DOWNLOADED_VERSION.exe and install
2. Double click the Dabarqus icon

About Dabarqus

Key features of Dabarqus:

1. Comprehensive Solution: Dabarqus integrates all essential components for RAG in one package:

   • Vector database for efficient storage and retrieval
   • Embedding model for converting text to vector representations
   • Ingestion and retrieval utilities for seamless data management
   • Built-in chatbot accessible via browser

2. Modern REST API: Simplifies integration and usage by providing a fully documented, modern REST API.

3. Intelligent Querying: Send queries or chat inputs to receive relevant documents, ranked by relevance to your input.

4. Enhanced AI Compatibility: The search results can be easily used with various AI models to generate more informed responses.

5. Cross-Platform Compatibility: Dabarqus runs as an OS service, ensuring seamless operation across Windows, Linux, and macOS.

For developers, we've also created barq, a command-line interface that interacts with Dabarqus via a REST API, providing a familiar and efficient tool for integration and management.

Dabarqus streamlines the RAG implementation process, offering a robust, flexible, and user-friendly solution that prioritizes data privacy and eliminates dependency issues. Whether you're working on a small-scale project or a large enterprise solution, Dabarqus provides the tools you need for efficient knowledge storage and retrieval, all while keeping your data secure and under your control.

Architecture

The application has three components: a backend service (called Dabarqus) that runs as an OS service at startup, a command line interface (called barq) for developers, and an Electron UI (called ODOBO) for end users.

• Dabarqus is the engine of the application. barq and ODOBO access it via a REST API.
• Dabarqus is installed as a Windows, Linux or MacOS service that runs at all times.
• barq is installed alongside Dabarqus, and is placed in the system PATH for easy access from a command line.
• ODOBO is installed as a desktop application on Windows and MacOS.

Barq - Command-line interface to Dabarqus

To install: barq service install

To uninstall: barq service uninstall

Using with the CLI

Store

Usage: barq store --input-path <path to folder> --memory-bank "<memory bank name>"

Example: barq store --input-path C:\docs --memory-bank documents

Retrieve

Usage: barq retrieve --memory-bank "<memory bank name>"

• Example: barq retrieve --memory-bank documents
• Example: barq retrieve --memory-bank documents --query "Tell me about the documents" --query-limit 3 This will display three answers to the query from the 'documents' memory bank

API - REST interface to Dabarqus

Method	Endpoint	Description	Parameters
GET	/health or /api/health	Check the health status of the service	None
GET	/admin/*	Serve the admin application	None
GET	/odobo/*	Serve the Odobo application	None
GET	/api/models	Retrieve available AI models	None
GET	/api/model/metadata	Get metadata for a specific model	modelRepo, filePath (optional)
GET	/api/downloads	Get information about downloaded items	modelRepo (optional), filePath (optional)
GET	/api/downloads/enqueue	Enqueue a new download	modelRepo, filePath
GET	/api/downloads/cancel	Cancel a download	modelRepo, filePath
GET	/api/downloads/remove	Remove a downloaded item	modelRepo, filePath
GET	/api/inference	Get information about inference items	alias (optional)
GET	/api/inference/start	Start an inference	alias, modelRepo, filePath, address (optional), port (optional), contextSize (optional), gpuLayers (optional), chatTemplate (optional)
GET	/api/inference/stop	Stop an inference	alias
GET	/api/inference/status	Get the status of an inference	alias (optional)
GET	/api/inference/reset	Reset an inference	alias
GET	/api/inference/restart	Restart the current inference	None
GET	/api/hardware or /api/hardwareinfo	Get hardware information	None
GET	/api/silk	Get memory status	None
GET	/api/silk/enable	Enable memories	None
GET	/api/silk/disable	Disable memories	None
GET	/api/silk/memorybanks	Get memory banks information	None
GET	/api/silk/memorybank/activate	Activate a memory bank	memorybank
GET	/api/silk/memorybank/deactivate	Deactivate a memory bank	memorybank, all
GET	/api/silk/query	Perform a semantic query	(Parameters handled by Silk retriever)
GET	/api/silk/health	Check the health of the Silk retriever	None
GET	/api/silk/model/metadata	Get model metadata from the Silk retriever	(Parameters handled by Silk retriever)
GET	/api/shutdown	Initiate server shutdown	None
POST	/api/utils/log	Write to log	JSON body with log details
POST	/api/silk/embedding	Get an embedding from the Silk retriever	(Parameters handled by Silk retriever)

Using the API

• Example: curl http://localhost:6568/api/silk/query?q=Tell%20me%20about%20the%20documents&limit=3&memorybank=docs