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Dabarqus
Overview :
Dabarqus is a Retrieval-Augmented Generation (RAG) framework that allows users to provide private data to large language models (LLMs) in real time. This tool facilitates the storage of various data sources (such as PDFs, emails, and raw data) into semantic indices, referred to as 'memory repositories,' through REST APIs, SDKs, and CLI tools. Dabarqus supports LLM-style prompts, enabling users to interact with memory repositories in a straightforward manner without needing to construct special queries or learn a new query language. Additionally, it allows for the creation and utilization of multiple semantic indices (memory repositories), organizing data by topics, categories, or other grouping methods. The product background of Dabarqus emphasizes its aim to simplify the integration of private data with AI language models, enhancing the efficiency and accuracy of data retrieval.
Target Users :
The target audience for Dabarqus includes developers and data scientists who need to integrate private datasets into AI language models to enhance their performance and accuracy. Dabarqus simplifies the data integration and querying process, enabling these professionals to efficiently develop and deploy applications based on language models.
Use Cases
Developers use Dabarqus to integrate internal PDF documents into chatbots for more accurate information retrieval.
Data scientists leverage Dabarqus to store research data in memory repositories for use in machine learning models.
Businesses employ Dabarqus to consolidate customer service records into language models for more personalized customer service.
Features
Supports integration of various data sources, including PDFs, emails, and raw data.
Interacts with memory repositories using LLM-style prompts without the need for a special query language.
Offers a REST API for easy integration with existing development tools.
Supports the creation and management of multiple semantic indices (memory repositories).
Provides SDKs in Python and JavaScript for seamless project integration.
Delivers LLM-compatible outputs, enabling seamless collaboration with LLM providers like ChatGPT and Ollama.
Supports deployment on Linux, macOS, and Windows platforms.
How to Use
1. Download and install the Dabarqus client.
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2. Store data sources (e.g., PDFs, emails) in the designated memory repository using CLI or API.
3. Query the memory repository using LLM-style prompts to retrieve relevant information.
4. Utilize Dabarqus's REST API and SDK to integrate the retrieved data into existing applications.
5. Create and manage multiple semantic indices (memory repositories) as needed.
6. Integrate Dabarqus's output with other LLM providers.
7. Deploy and use Dabarqus on Linux, macOS, and Windows platforms.
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