We've just released the initial version of our open-source vector embedding pipeline. It's designed to embed large volumes of data. While embedding a few documents for Q&A is straightforward, consistently ingesting gigabytes of unstructured data is a whole different ballgame.
By using our API, you can embed raw data and store vectors in your vector database, sidestepping the complexities of cloud infrastructure.
Now, in true YC spirit, we're launching before everything is polished. Our Github repo (https://github.com/dgarnitz/vectorflow
) is a work in progress, and we're eager to get it in front of the community early on.
Check out our Github repo and let us know what you think. Your feedback, suggestions, and critiques will be highly appreciated as we continue to refine and develop.
We've just released the initial version of our open-source vector embedding pipeline. It's designed to embed large volumes of data. While embedding a few documents for Q&A is straightforward, consistently ingesting gigabytes of unstructured data is a whole different ballgame.
By using our API, you can embed raw data and store vectors in your vector database, sidestepping the complexities of cloud infrastructure.
Now, in true YC spirit, we're launching before everything is polished. Our Github repo (https://github.com/dgarnitz/vectorflow ) is a work in progress, and we're eager to get it in front of the community early on.
Check out our Github repo and let us know what you think. Your feedback, suggestions, and critiques will be highly appreciated as we continue to refine and develop.