I turned my housing society's WhatsApp group into memory with Cognee

说明与用途

作者将住宅小区的 WhatsApp 群聊导出转换为 Cognee 内存，构建知识图谱，能够快速返回带日期和发送者的精确答案，并默认屏蔽电话号码。

问题

A large WhatsApp group chat (164 members, 3,320 messages over 239 days) made it hard to retrieve specific information such as water supply schedules because messages were buried and required scrolling.

结果

The first sync ingested 239 days in ~14 minutes; a repeated import took 15 seconds with no changes; deleting a day emitted a _deleted row and reduced documents from 239 to 238. Queries returned precise, dated answers with masked senders.

工作流

• Export chat from WhatsApp (Android or iPhone) to a .txt or .zip file.
• Use cognee.remember with the whatsapp_source connector to ingest the export into Cognee.
• Query the knowledge graph with cognee.search specifying the dataset and node set for the desired group.
• Receive answers that include the date, sender (masked phone numbers), and message content.
• Re-import new exports; the connector detects unchanged days and only processes new or modified days, emitting _deleted rows for days fully removed.
• Mask phone numbers by default to protect privacy.
• Run tests on synthetic exports to validate parsing logic.

证据

My housing society has a WhatsApp group with 164 members. It's where the notices go out: water supply timings, maintenance work, decisions from meetings.

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The export I took had 3,320 messages spread over 239 days, and when I wanted to know something simple, like when the water was going to be off, my only option was to scroll and hope.

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Cognee works in three steps: you add data, cognify turns it into a knowledge graph, and search asks it questions.

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On my society's export, questions like this came back with dated answers, for example "water supply for one hour on August 8", instead of a vague summary of the whole chat.

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The first sync took about 14 minutes and the second took 15 seconds, because there was nothing to do.

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