mirror of
https://github.com/supabase/supabase.git
synced 2026-10-10 03:45:06 +03:00
Charis
68cb1a1870
* docs: add cursor rule for embedding generation process Add documentation for cursor IDE about how docs embeddings are generated, including the workflow for creating and uploading semantic search content. * feat: improve API reference metadata upload with descriptive content - Add preembeddings script to run codegen before embedding generation - Enhance OpenApiReferenceSource to generate more descriptive content including parameters, responses, path information, and better structured documentation * feat: add Management API references to searchDocs GraphQL query - Add ManagementApiReference GraphQL type and model for API endpoint search results - Integrate Management API references into global search results - Update test snapshots and add comprehensive test coverage for Management API search * style: format
98 lines
3.0 KiB
TypeScript
98 lines
3.0 KiB
TypeScript
import { type RootQueryTypeSearchDocsArgs } from '~/__generated__/graphql'
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import { convertPostgrestToApiError, type ApiErrorGeneric } from '~/app/api/utils'
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import { Result } from '~/features/helpers.fn'
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import { openAI } from '~/lib/openAi'
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import { supabase, type DatabaseCorrected } from '~/lib/supabase'
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import { GuideModel } from '../guide/guideModel'
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import {
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DB_METADATA_TAG_PLATFORM_CLI,
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ReferenceCLICommandModel,
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} from '../reference/referenceCLIModel'
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import { ReferenceManagementApiModel } from '../reference/referenceManagementApiModel'
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import { ReferenceSDKFunctionModel, SDKLanguageValues } from '../reference/referenceSDKModel'
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import { TroubleshootingModel } from '../troubleshooting/troubleshootingModel'
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import { SearchResultInterface } from './globalSearchInterface'
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export abstract class SearchResultModel {
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static async search(
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args: RootQueryTypeSearchDocsArgs,
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requestedFields: Array<string>
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): Promise<Result<SearchResultModel[], ApiErrorGeneric>> {
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const query = args.query.trim()
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const includeFullContent = requestedFields.includes('content')
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const embeddingResult = await openAI().createContentEmbedding(query)
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return embeddingResult.flatMapAsync(async (embedding) => {
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const matchResult = new Result(
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await supabase().rpc('search_content', {
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embedding,
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include_full_content: includeFullContent,
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max_result: args.limit ?? undefined,
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})
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)
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.map((matches) =>
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matches
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.map(createModelFromMatch)
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.filter((item): item is SearchResultInterface => item !== null)
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)
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.mapError(convertPostgrestToApiError)
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return matchResult
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})
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}
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}
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function createModelFromMatch({
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type,
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page_title,
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href,
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content,
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metadata,
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subsections,
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}: DatabaseCorrected['public']['Functions']['search_content']['Returns'][number]): SearchResultInterface | null {
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switch (type) {
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case 'markdown':
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return new GuideModel({
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title: page_title,
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href,
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content,
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subsections,
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})
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case 'reference':
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const { language } = metadata
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if (language && SDKLanguageValues.includes(language)) {
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return new ReferenceSDKFunctionModel({
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title: page_title,
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href,
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content,
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language,
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methodName: metadata.methodName,
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})
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} else if (metadata.platform === DB_METADATA_TAG_PLATFORM_CLI) {
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return new ReferenceCLICommandModel({
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title: page_title,
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href,
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content,
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subsections,
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})
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// TODO [Charis 2025-06-09] replace with less hacky check
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} else if (metadata.subtitle?.startsWith('Management API Reference')) {
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return new ReferenceManagementApiModel({
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title: page_title,
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href,
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content,
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})
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} else {
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return null
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}
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case 'github-discussions':
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return new TroubleshootingModel({
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title: page_title,
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href,
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content,
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})
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default:
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return null
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}
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}
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