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moved cognee documentation; updated links

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kanungle
2025-10-30 07:25:43 -07:00
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qdrant-landing/content/documentation/data-management/cognee.md → qdrant-landing/content/documentation/frameworks/cognee.md
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--- ---
title: Cognee title: "Cognee"
description: Cognee ships a Qdrant adapter and documents Qdrant as a preferred, built-in vector database option. That means you configure one URI and key, and Cognee's pipelines will read/write embeddings directly to Qdrant while building and querying the graph.
--- ---
# Cognee # Cognee
@@ -8,7 +9,7 @@ Embeddings make it easy to retrieve similar chunks of information — but most a
## Why Qdrant For The Memory Layer ## Why Qdrant For The Memory Layer
At runtime, Cognee's semantic memory layer requires fast and predictable lookups to surface candidates for graph reasoning, as well as tight control over metadata to ground multi-hop traversals. Qdrant's design aligns with those needs with its: At runtime, [Cognee](https://www.cognee.ai/)'s semantic memory layer requires fast and predictable lookups to surface candidates for graph reasoning, as well as tight control over metadata to ground multi-hop traversals. Qdrant's design aligns with those needs with its:
- Nearest-neighbor search for fast candidate recall. - Nearest-neighbor search for fast candidate recall.
- Expressive payload filtering to constrain by factors like timestamp windows, document type, or source tags. - Expressive payload filtering to constrain by factors like timestamp windows, document type, or source tags.
@@ -90,3 +91,4 @@ If you prefer not to run infrastructure, Cognee's hosted option — [cogwit](htt
- [Cognee Documentation](https://docs.Cognee.ai/getting-started/introduction) - [Cognee Documentation](https://docs.Cognee.ai/getting-started/introduction)
- [Cognee Source](https://github.com/topoteretes/Cognee) - [Cognee Source](https://github.com/topoteretes/Cognee)
- [Cognee Website](https://www.cognee.ai/)