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Author builds long-term memory for REXA using PostgreSQL and pgvector

By PulseAugur Editorial · [1 sources] ·

The author details how they implemented long-term memory for their REXA project using PostgreSQL and the pgvector extension. This allows REXA to store and recall past interactions, enhancing its conversational capabilities. The implementation leverages Mastodon for hosting and Hackaday as a platform for sharing the technical details of this memory system. AI

IMPACT Demonstrates a practical application of vector databases for enhancing AI agent memory and recall capabilities.

RANK_REASON This is a technical blog post detailing the implementation of a specific feature (memory) for a project, rather than a new product release or significant industry event.

Read on Mastodon — mastodon.social →

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

Author builds long-term memory for REXA using PostgreSQL and pgvector

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5 / 100
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Tool
This is a technical blog post detailing the implementation of a specific feature (memory) for a project, rather than a new product release or significant industry event.
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product, infra
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COVERAGE [1]

  1. Mastodon — mastodon.social TIER_1 English(EN) · [email protected] ·

    How I Built Memory for REXA Using PostgreSQL and pgvector One thing I wanted REXA to eventually have was long-term memory . The idea is simple: when I explicitl

    How I Built Memory for REXA Using PostgreSQL and pgvector One thing I wanted REXA to eventually have was long-term memory . The idea is simple: when I explicitly tell REXA something like: “Remember that I prefer PostgreSQL for my backend projects.” REXA should be able to save tha…

    LINKS dev.to/…/how-i-implemented-memory-in-my-a…

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