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Overview

A common nusance when building AI apps is context embeddings. If you use a traditional stack you generall have to store an embedding, vectorize it, store the vector, then when you query a vector you’ll get another reference to the file which you then have to fetch again. Pinata’s solution is much more elegant. With Pinata’s file vectoring you can upload a file and vector it at the same time.

const upload = await pinata.upload.private
  .file(file)
  .group("GROUP_ID")
  .vectorize()

When it comes time to query a vector, you have the option to either list your query results and judge the matching score, or just return the highest scoring file itself.

const { data, contentType } = await pinata.files.private
  .queryVectors({
    groupId: "GROUP_ID",
    query: "Hello World!",
    returnFile: true
  })

This enables develops to build AI applications faster and with less code!

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Pinata Vector Storage: Public Beta Limits

During the public beta, Pinata Vector Storage has the following limits:

• File Vectorization Limit: You can vectorize up to 10,000 files.
• Index Limit: You can create a maximum of 5 indexes, managed using Pinata Groups.
• Results Limit: You can query a vector and get a max of 20 files returned in one request.

What this means

You can organize your vector embeddings into up to 5 searchable indexes using Pinata Groups. Across all these groups, you can store a total of 10,000 vector embeddings corresponding to files stored on Pinata.

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Vectorizing Files

There are two ways you can vectorize file uploads, and with both options files must be part of a group in order for vectors and queries to work.

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Vectorize on Upload

If you use the Files SDK you can vectorize a file on upload.

const upload = await pinata.upload.private
  .file(file)
  .group("GROUP_ID")
  .vectorize()

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Vectorize After Upload

If you already have a file that’s been uploaded and it’s part of a group then you can vectorize it.

const update = await pinata.files.private.vectorize("FILE_ID")

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Querying Vectors

After a file has been vectorized and it’s part of a group, you can query vectors for a given group.

const results = await pinata.files.private.queryVectors({
  groupId: "52681e41-86f4-407b-8f79-33a7e7e5df68",
  query: "Hello World"
})

This will return the following type:

type VectorizeQueryResponse = {
	count: number;
	matches: VectorQueryMatch[];
};

type VectorQueryMatch = {
	file_id: string;
	cid: string;
	score: number;
};

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Returning the Top Match File

A unique feature in the SDK is if you pass in the returnFile flag then you will get the file and it’s contents rather than just the reference to the file.

const { data, contentType }  = await pinata.files.private.queryVectors({
  groupId: "52681e41-86f4-407b-8f79-33a7e7e5df68",
  query: "Hello World",
  returnFile: true
})

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Deleting Vectors

If at any point you need to delete vectors for a file you can do so with the deleteVectors method in the SDK.

const update = await pinata.files.private.deleteVectors("FILE_ID")