HelixDB Launches: The Database for RAG & AI

HelixBD recently launched!

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^{"Unified Graph-Vector Database for AI Retrieval"}

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^{TL;DR: HelixDB is an open-source graph-vector database that brings structure to your un-structured data for RAG and AI applications. Currently, they have hundreds of developers building projects with HelixDB, including a Fortune 500 company.}

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https://youtu.be/V5viTRj2h68

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Founded by George Curtis & Xavier Cochran

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❌ The Problem

AI is changing at a rapid rate which is fundamentally changing technology. This new tech needs new infrastructure.

Everyone is trying to build AI applications, which often involve dedicated data retrieval for their specific use case. Building these retrieval systems is hard. Previously, they have relied solely on vector databases to retrieve semantic matches on tiny snippets of text data. But, this technology is shifting and is relying more heavily on connected data, which comes with better context.

But building these retrieval systems often involve:

1. Vector databases
2. Graph databases
3. Bespoke middleman/syncing software

These setups are complicated, take a lot of time, engineering expertise, and create huge amounts of overhead which makes maintaining them very time consuming and expensive.

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✅ Their Solution

HelixDB integrates semantic meaning (through its vector types) with relationships to other data (graph types), a similar model to how they structure information in their brains.

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This makes it the best solution for making AI retrieval engines for agents and LLMs.

How they currently do this:

1. Seamlessly integrate vector types into your knowledge graph
2. Type-safe query language that guides developers and agents to write correct queries before they are executed
3. Extreme speeds outperforming industry leaders by 1-3 orders of magnitude

How they are going to make it better:

1. MCP tools that allow agents to walk the graph themselves, deciding at each step how to traverse the graph based on the schema and available data.
2. Built-in ingestion pipelines for multi-modal data.
3. Built-in embedding-models bringing immediate structure to your knowledge graph.

To get setup, follow the guide in their README:
HelixDB Github

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Learn More

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^{🌐 Visit www.helix-db.com to learn more.}

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^{🤝 Ask: Intros to people/companies that are working on Graph/Hybrid RAG that could benefit from better performance or less overhead in their development cycles? Contact the founders here.}

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^{⭐ Give HelixDB a star on Github.}

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^{👣 Follow HelixDB on LinkedIn & X.}

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