Building a magical AI-powered semantic search from scratch
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A detailed end-to-end walkthrough on building an AI-powered semantic search over personal blog content. Covers converting MDX content into text chunks, generating 1536-dimensional embedding vectors via OpenAI's text-embedding-ada-002 API, storing them in a Supabase pgvector database, and querying with cosine similarity. Also covers crafting a GPT-3.5-turbo completion prompt with role-based prompting and prompt injection mitigations, streaming the response to the frontend using ReadableStream and Edge Functions, real-time MDX serialization of the streamed output, and mocking the OpenAI API for cost-effective testing and e2e validation.
Table of contents
The building blocks of an AI-powered semantic searchTransforming words to vectorsStreaming a human-readable response with GPTWorking on the UI of an AI-powered featureConclusion and Takeaways25 Impressions