Vectorboard is an open-source framework for optimizing and evaluating embedding and retrieval-based machine learning models, particularly those built around RAG (Retrieval-Augmented Generation).
RAG is a methodology that enhances machine learning models by combining generative and retrieval-based aspects.
Importance of Good Embeddings Good embeddings are vital for the successful execution of RAG applications. They serve as the basis for retrieving contextually relevant information.
How Vectorboard Helps
Vectorboard simplifies the complex task of optimizing these embeddings by trying different hyperparameters (chunk size, overlap, splitting function, embedding algorithm, etc,) in a structured framework.
In the end, Vectorboard provides you with a Results Dashboard that allows you to compare the performance of different embeddings and hyperparameters, ran on your own data.
| General | |
|---|---|
| Relese date | Sep, 2023 |
| Author | Vectorboard |
| Repository | https://github.com/VectorBoard/vectorboard/ |
| Type | Eval and Hyperparameter Optimization for embeddings |
Vectorboard - Resources
Resources to get stared with Vectorboard:
- Vectorboard GitHub Repository
- Documentation Documentation for Vectorboard
Vectorboard Libraries
A curated list of libraries and technologies to help you build great projects with Vectorboard.
- Vectorboard Python Library Documantation - The official Python library for Vectorboard.
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