[draft] Vector search#388
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Code Review
This pull request introduces AI embedding generation and caching capabilities. It adds a new EMBEDDING type to the GraphQL schema, implements a createEmbedding utility using Google Vertex AI with support for media chunking, and includes comprehensive tests. Feedback focuses on a breaking change in the createAIResponse utility that affects existing callers, the lack of handling for concurrent 'LOADING' states which could lead to redundant API calls, and opportunities to improve performance by parallelizing media chunk processing and reusing AI clients. Additionally, the AIEmbedding GraphQL type should include the text field to expose error messages.
| if ( | ||
| cached && | ||
| cached.status === 'SUCCESS' && | ||
| Array.isArray(cached.embeddings) && | ||
| cached.embeddings.length > 0 | ||
| ) { | ||
| return cached.embeddings as EmbeddingChunk[]; | ||
| } |
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The current logic only returns cached embeddings if the status is SUCCESS. If an embedding is currently being generated (status: 'LOADING'), this code will proceed to call createAIResponse, which likely creates a duplicate loading record and starts a redundant generation process. It should instead wait for the existing process to complete.
| fields: { | ||
| ...commonAiResponseFields, | ||
| }, |
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The AIEmbedding type is missing the text field. Since createEmbedding stores error messages in the text field when the status is ERROR, this field should be added to the GraphQL model to allow clients to retrieve error details.
fields: {
...commonAiResponseFields,
text: { type: GraphQLString },
},
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