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| import * as reduce from 'lodash/reduce' | |
| import * as get from 'lodash/get' | |
| import * as isMatch from 'lodash/isMatch' | |
| export default (nextProps, state) => { | |
| if (nextProps.mutationLoading) { | |
| return null | |
| } | |
| const prevProps = get(state, '_prevProps', false) |
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| // Use your MDX content with this component. | |
| import React from 'react' | |
| import MDX from '@mdx-js/runtime' | |
| import components from '../utils/markdown/markdown' | |
| // Renders a cimple loading spinner as a test | |
| import Loading from './Loading' | |
| const mdxComponents = { |
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| { | |
| "tools": [ | |
| { | |
| "type": "function", | |
| "function": { | |
| "name": "codebase_search", | |
| "description": "Find snippets of code from the codebase most relevant to the search query.\nThis is a semantic search tool, so the query should ask for something semantically matching what is needed.\nIf it makes sense to only search in particular directories, please specify them in the target_directories field.\nUnless there is a clear reason to use your own search query, please just reuse the user's exact query with their wording.\nTheir exact wording/phrasing can often be helpful for the semantic search query. Keeping the same exact question format can also be helpful.", | |
| "parameters": { | |
| "type": "object", | |
| "properties": { |
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| async run() { | |
| const vpc = new sst.aws.Vpc('AppVPC') | |
| const database = new sst.aws.Aurora('AppDB', { | |
| engine: 'postgres', | |
| scaling: { | |
| min: '1 ACU', | |
| max: '10 ACU', | |
| }, | |
| dataApi: true, |
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