Blog | AI Whispr

Build fast, scalable vector embedding & semantic search API service with a simple configuration

Implementing vector embedding and semantic search for large document repositories is the first step in Retrieval Augmented...

Setting up a vector embedding pipeline with AIWhispr, Bert and Qdrant on MacOS.

This blog shows how to setup a vector embedding pipeline using AIWhispr , SBert sentence transformer model, Qdrant vector database. This...

AIWhispr 0.938 Release

We continue integrating new vector databases and embedding services towards our goal of a no/low-code semantic search tool that is easy...

Code Walkthrough : Adding support for indexing files on Google Cloud Storage

In the spirit of open source, we want to ensure that users, collaborators of AIWhispr also understand how to add their own custom...

Quick overview of AIWhispr design

AIWhispr takes a modular design approach, recognising that 4 distinct functions are coordinated to drive a scalable semantic search...

Setting up semantic search for files in Azure Storage: Part 1 (mount BlobStorage)

There are 4 ways to setup semantic search for your files stored in Azure containers. You can Mount the Azure container (blob storage) as...

Semantic search using AIWhispr with QDrant vector database

AIWhispr is a tool to enable AI powered semantic search on documents It is easy to install. Simple to configure. Can handle multiple file...

Document Retrieval with Semantic Search: Introducing AIWhispr

In today’s fast-paced world, where information accumulates rapidly, the need to retrieve and leverage past insights efficiently is...