How can you improve the accuracy of your vector database and RAG systems?

Mar 6, 2024

We propose a new approach that will not only improve your data retrieval to the point you only get relevant results but will also help you, as a developer, to reduce the amount of maintenance work you have to do on RAG systems on top of vectorized databases. For example, decrease the continuous work on adjusting chunking lengths and thresholds.

The reason we are here is that Blar identified three major problems regarding RAG systems on top of vectorized databases: 

  • They give you the wrong answer when they don’t know (because they retrieve the closest vector). 

  • Thresholds and chunking get complex when keeping data up to date (once the developer finds the magic number for one topic, it rapidly becomes obsolete as new data comes in).

  • They are great for superficial queries but not for complex ones that require multi-hoping. 

Besides these problems, the developer is the one who needs to vectorize and index their information, a process that can become time-consuming and complex. To fix this situation, Blar proposes a new way of saving your data and a new way of retrieving it. How does it work?

  • You, the developer, access our console and drag and drop all the RAW files you plan to feed to the LLM. 

  • You connect our API to your stack. And you are ready to go!

  • Blar will handle the chunking, indexing, and storing processes while dramatically improving retrieval accuracy. 

Blar retrieves only relevant answers because we leverage graph structures to access your data. Our novelty comes in how we build these graph structures to make them fast and scalable while keeping the high accuracy that graph databases are known for. Besides how to build it, our second novelty comes in the retrieval system itself, conformed by three pieces:

  • Keyword search for speed

  • Traditional semantic search

  • Graph traverser for multi-hop complex queries

Blar is in its early days. Initially, we support the PDF format and are still working on the data governance layer. That said, we welcome you to be part of the next-generation databases and retrieval systems that help developers reduce their workload on maintenance while improving the retrieved information relevance. 

Try Blar today! 🚀

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© 2024 Blar AI, INC

From SF to the 🌎

From SF to the 🌎

© 2024 Blar AI, INC

© 2024 Blar AI, INC

From SF to the 🌎