Vector indexes for image similarity search in Azure AI Search?
I was going through the Azure AI Studio and trying to create an Ai Search against images, but it only accepts document file types. Images such as *.png's are not permitted. I can generate vectorizations of images based on this: https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/image-retrieval?tabs=csharp#call-the-vectorize-image-api If I use that API to retrieve image vectors is it possible to populate a AI Search vector database with these results? I.e. bypass it's own vector embedding and use my vectors? I'm still learning how to translate my rough knowledge of AI into Azure services, so appreciate helping me get oriented. My goal is to process a collection of images, generate vectors, store the vectors in a vector index(what I understand AI Search to be), and then on demand at a later time individual process a user supplied image and perform a vector search on that image against the AI Search index to find similar images. Note I am not asking about performing OCR. I understand images embedded in documents can have some analysis performed on them to extract features, but AFAIK it doesn't create a vector embedding of the actual image that would be appropriate for a similarity search.
RAG and AI search beyond basics
Hi All, I was wondering if someone has experience with a bit more complex RAG + AI Search scenarios and would be able to suggest the best approach. Scenario one AI search consists of 20 car articles, e.g. Each article has 5,000 words, it's chucked into 1,500 tokes, and all this is indexed in AI search in total 1,000 entries (roughly each article is 50 chunks). These are made-up numbers just for laying down the basics of my scenario. If each article describes one car, RAG and AI search works perfectly for questions what is the price range for Tesla cars, What colours are available for BMW 5. We run the user's query against AI search, put relevance threshold 2 and take the 5 most relevant articles. but How to execute questions that require knowledge of more than 5 articles E.g. Give me a list of all cars described? If we apply the above logic, RAG will consist only of 5 most relevant chanks and we what we need here is almost to submit all 20 articles to AI, in order get all the cars. What is the best strategy for this UC Scenario 2 Image: we have an AI search with all BBC articles indexed. How to if approach if a user asks give me a summary of the 5 latest articles published on BBC? Running this query against AI search will return not return most recent articles it will return articles that have 5 latest articles published as content Thanks in advance
