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Author details building semantic search with OpenAI embeddings

By PulseAugur Editorial · [1 sources] ·

The author details their experience building a semantic search system for an API pipeline to improve LLM accuracy. They explain that semantic search matches meaning rather than exact keywords, differentiating it from traditional lexical search. The process involved using OpenAI's text-embedding-3-large model, reducing vector dimensions to 2,000 for performance, and implementing an indexing strategy like IVFFlat, HNSW, or DiskANN to speed up queries. AI

IMPACT Provides practical insights for developers integrating LLMs and semantic search into applications.

RANK_REASON Blog post detailing the implementation of a specific technical feature (semantic search) using existing tools.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Author details building semantic search with OpenAI embeddings

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Blog post detailing the implementation of a specific technical feature (semantic search) using existing tools.
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product, infra
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Aditya Negandhi ·

    Semantic Search: What It Is and How I Built It

    <p>A while back I got a fun one at work: add semantic search to our API pipeline so our LLM could answer customer questions more accurately, in real time. I had a rough idea of how semantic search worked (shoutout to my ML professor, that class finally paid off), but I had no clu…

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