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Postgres Semantic Search Tutorial Uses OpenAI Embeddings

By PulseAugur Editorial · [1 sources] ·

This article details how to implement semantic search in PostgreSQL using the pgvector extension. It explains the process using OpenAI's ada-002 embedding model, which has 1536 dimensions. The guide covers using an ivfflat index for cosine distance and the <=> operator for nearest-neighbor queries, suggesting a 0.75 similarity threshold to filter out irrelevant results. AI

IMPACT Provides a practical guide for developers to integrate AI-powered semantic search into existing database infrastructure.

RANK_REASON The cluster describes a technical tutorial and implementation guide for a specific software/database feature, which falls under research or technical documentation. [lever_c_demoted from research: ic=1 ai=0.7]

Read on Mastodon — fosstodon.org →

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

Postgres Semantic Search Tutorial Uses OpenAI Embeddings

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The cluster describes a technical tutorial and implementation guide for a specific software/database feature, which falls under research or technical documentation. [lever_c_demoted from research: …
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COVERAGE [1]

  1. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    How could you implement semantic search in Postgres with Sequel + pgvector? We're glad you asked! Here is an article about OpenAI ada-002 (1536 dims), ivfflat i

    How could you implement semantic search in Postgres with Sequel + pgvector? We're glad you asked! Here is an article about OpenAI ada-002 (1536 dims), ivfflat index for cosine distance, <=> for nearest-neighbor queries, 0.75 similarity threshold to drop noise. https:// go.upgrade…

    LINKS ombulabs.ai/…/semantic-search.html

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