We solved RAG.
A dash of genius for your LLM.
What is RAG?
RAG is short for Retrieval Augmented Generation, which is a framework for making private data available to AI large language models in real time.
What is Dabarqus?
Ingest documents, databases and APIs
No matter where your data resides, Dabarqus can make it available to your LLM.
*check here for available file types in Dabarqus editions.LLM-Style Prompting
Dabarqus will retrieve relevant data using the same prompt that you give your LLM. No need to construct special queries or learn a new query language.
REST API
REST is a standard interface that enjoys wide adoption, so your team doesn’t learn a new, complex system. It allows comprehensive integration with existing development tools for easy adoption.
Multiple Semantic Indexes
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.
SDKs
Easily integrates with Python and Javascript projects. Above is a simple chatbot created using the Python SDK.
LLM-Friendly Output
Works seamlessly with the LLM of your choice.
How Dabarqus Works
Our all-in-one RAG solution is so simple that we can explain it in three steps:
Effortlessly Ingest diverse data sources (PDFs*, emails, raw data, etc) in semantic indexes called memory banks.
# Store documents in a memory bank
curl -X GET "${DABARQUS_SERVER}/api/silk/store" \
-H "Content-Type: application/json" \
--data-urlencode "input-path=/path/to/your/documents" \
--data-urlencode "memory-bank=my_documents"
# Store documents in a memory bank
barq store --input-path="/path/to/your/documents" --memory-bank=my_documents
Retrieve the most relevant information from your memory banks using LLM-Style Prompting.
No need to construct special queries — Dabarqus’s advanced query processing ensures you get precise, context-aware results every time.
# Retrieve information from the memory bank
curl -X GET "${DABARQUS_SERVER}/api/silk/query" \
-H "Content-Type: application/json" \
--data-urlencode "q=Tell me about the documents" \
--data-urlencode "limit=3" \
--data-urlencode "memorybank=my_documents"
# Retrieve information from the memory bank
barq retrieve --memory-bank=my_documents --query "When did I travel to Florida?"
Dabarqus gets you from zero to RAG in minutes and our REST API, SDKs, and CLI tool make integration simple across various platforms and languages.
Here’s an example of how easy it is with a Python-based chatbot we’ve put together using Dabarqus.
