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Import requestsdef encode_image (image_url, project, api_key)

Project + : + api_key}, json= { image Image_url } ).json () [vector]# here, we get an image url from the documents from earlier and encode them!vector. Relevance ai's mission is to accelerate the development of artificial intelligence products using vectors We see vectors as a technology that will change the future so we want to make using vectors as easy as possible.

Identifying strength & weaknesses of vector search below, we use an example of vector search where an individual searches for an sku However, the search results encode the letters and fail to realize/return the right sku We also attach a code example using the vector ai client for those interested in trying this out. Welcome to the official documentation of the vecdb api

Glossary to special qualitative cloud + vecdb api terminology an example of a document in relevance ai:

List of urls for our api references Vector search is the process of finding the most similar vectors to itself (also known as nearest neighbors of similarity search). Datasets in vecdb are known in other applications as tables or collections. Concepts about vectorswhat are vectors

Vectors are a list of numbers that meaningfully and uniquely represent data See below for an example.

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