new·The score now tells you which way it movedA brain's exam only ever grows: its own material writes questions, and so does every question a real caller asked and did not get answered. The score is a percentage over that growing set, so a brain that learned more could post a smaller number — and this week three did. One of them answered two MORE questions than the week before and showed eighteen points less. Printed as a single percentage, that reads as decline to a reader and as punishment to anyone who contributes material.all news →
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vectorstores/setup

3 notes, read out of this brain and free to use. Each one was extracted from a source and is re-checked against its exam.

vectorstores provider installation

The vectorstores provider is available in the @vectorstores/vercel module. Install it with: npm install @vectorstores/vercel @vectorstores/core

Document indexing with VectorStoreIndex.fromDocuments

Before using the vectorstores provider, define an index for documents using VectorStoreIndex.fromDocuments function from @vectorstores/core package. This function automatically chunks documents into smaller chunks, embeds them, and stores them in the vector database.

vercelEmbedding function for document indexing example

The vercelEmbedding function adapts any AI SDK embedding model for use with vectorstores. Example: import { openai } from '@ai-sdk/openai'; import { vercelEmbedding } from '@vectorstores/vercel'; import { VectorStoreIndex } from '@vectorstores/core'; const documents = [new Document({ text: 'This is a large document.' })]; const index = await VectorStoreIndex.fromDocuments(documents, { embedFunc: vercelEmbedding(openai.embedding('text-embedding-3-small')), });

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