Dabarqus

Dabarqus is a stand alone application that implements a complete RAG solution. It is designed to be easy to use and easy to integrate with your existing applications. Dabarqus includes a REST API, a command-line interface, and an admin dashboard.

Why Create Dabarqus

If you're a developer, building a basic RAG solution is pretty straightforward. There are tons of tutorials and how-to's as well as Python code to reuse. But, if you're deploying your RAG solution within a company, or for end-user PCs, you will also have to figure out some potentially tricky deployment and maintenance issues. That means also deploying Python, a vector database, the right embedding AI model, and possible licensing challenges. Dabarqus was created to address these issues with a stand-alone, all-in-one solution with no dependencies. It's written in low-level C++ with built in vector search capabilities, flexibility to use the embedding AI model that's best for your use case, and a REST API for easy development integration.

Table of Contents

1. Quick Start
   • Ubuntu
   • macOS
   • Windows
2. Features
3. Barq - Command-line Interface
   • Using with the CLI
     • Store
     • Retrieve
4. API - REST Interface
   • Using the API
5. Examples

Quick start

Ubuntu

Dabarqus works on CPU only, or can use NVIDIA CUDA for higher performance. For the CUDA (aka nvidia cublas) version, you will need to install the NVIDIA driver. The CPU version does not require any additional software. Note that to use the CUDA version, you will need to have an NVIDIA GPU with CUDA support, and to download the CUDA version of Dabarqus.

0. To install NVIDIA drivers on Ubuntu (if you have an NVIDIA GPU), run the following command:

   sudo ubuntu-drivers install

1. Unzip the Dabarqus file into a folder

   unzip Dabarqus-linux-DOWNLOADED_VERSION.zip
   cd Dabarqus-linux-DOWNLOADED_VERSION
   chmod +x ./bin/*
   ./bin/barq service install

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

macOS

For package file downloads, do the following:

1. Double click the Dabarqus-macos-DOWNLOADED_VERSION.pkg and install
2. After installation, open your browser and navigate to http://localhost:6568/admin

For zip file downloads, do the following:

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 or navigate to http://localhost:6568/admin

Features

1. Ingest documents, databases, and APIs: Ingest diverse data sources like PDFs*, emails, and raw data.

   • No matter where your data resides, Dabarqus can make it available to your LLM

2. LLM-Style Prompting: Use simple, LLM-style prompts when speaking to your memory banks.

   • Dabarqus will retrieve relevant data using the same prompt you give your LLM
   • No need to construct special queries or learn a new query language

3. REST API: Comprehensive control interface for downloading models, prompting semantic indexes, and even LLM inference.

   • REST is a standard interface that enjoys wide adoption, so your team doesn't need to learn a new, complex system
   • Allows comprehensive integration with existing development tools for easy adoption

4. Multiple Semantic Indexes (Memory Banks): Group your data into separate semantic indexes (memory banks).

   • Keep your data organized by subject matter, category, or whatever grouping you like
   • Memory banks are portable, so you can create and use them wherever you like

5. SDKs: Native SDKs in Python and Javascript.

   • Easily integrates with Python and Javascript projects

6. LLM-Friendly Output: Produces LLM-ready output that works with ChatGPT, Ollama, and any other LLM provider

   • Works seamlessly with the LLM of your choice

7. Admin Dashboard: Monitor performance, test memory banks, and make changes in an easy-to-use UI

   • Easy access to Dabarqus features
   • Monitor app performance with real-time graphs

8. Mac, Linux, and Windows Support: Runs natively with zero dependencies on all platforms: MacOS (Intel or Metal), Linux, and Windows (CPU or GPU)

   • Runs on whatever platform you use

9. LLM Inference: Chat with LLM models right through the Dabarqus API/SDKs

   • Built-in chatbot capabilities for use in your applications

*Dabarqus Professional Edition is required for email, messaging and API support.

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

Examples

Examples of Dabarqus in action can be found in this repo under examples.

• PythonPDFChatbot-RESTAPI: An example chatbot program using Dabarqus via the REST API to chat with your PDFs.
• PythonPDFChatbot-PythonSDK: An example chatbot program using Dabarqus via the Python SDK to chat with your PDFs.
• StoreFiles: A Python example of storing documents in to a memory bank (semantic index) using the Python SDK

Notes:

1. Dabarqus Professional Edition is required for email, messaging and API support.