59 lines
No EOL
2.7 KiB
Markdown
59 lines
No EOL
2.7 KiB
Markdown
# Architecture
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> Understanding Cognee's storage architecture and system components
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# Cognee Architecture
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## Why multiple stores
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No single database can handle all aspects of memory. Cognee combines three complementary storage systems. Each one plays a different role, and together they make your data both **searchable** and **connected**.
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* **Relational store** — Tracks your documents, their chunks, and provenance
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(i.e. where each piece of data came from and how it's linked to the source).
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* **Vector store** — Holds embeddings for semantic similarity
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(i.e. numerical representations that let Cognee find conceptually related text, even if the wording is different).
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* **Graph store** — Captures entities and relationships in a knowledge graph
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(i.e. nodes and edges that let Cognee understand structure and navigate connections between concepts).
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Cognee ships with lightweight defaults that run locally, and you can swap in production-ready backends when needed (see [Setup](/getting-started/installation)).
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## What is stored where
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Roughly speaking:
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* The **relational store** handles document-level metadata and provenance.
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* The **vector store** contains semantic fingerprints of chunks and [DataPoints](./building-blocks/datapoints).
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* The **graph store** captures higher-level structure in the form of entities and relationships.
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There is some overlap: for efficiency, parts of the same information may be indexed in more than one store.
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## How they are used
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The stores play different roles depending on the phase:
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* The **relational store** matters most during *cognification*, keeping track of documents, chunks, and where each piece of information comes from.
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* The **vector** and **graph** stores come into play during *search and retrieval*:
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* **Semantic searches** (vector): find conceptually related passages based on embeddings
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* **Structural searches** (graph): explore entities and relationships using Cypher directly
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* **Hybrid searches** (vector + graph): combine both perspectives to surface results that are contextually rich and structurally precise.
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<Columns cols={3}>
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<Card title="Main Operations" icon="play" href="/core-concepts/main-operations/add">
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See how Add, Cognify, and Search use the storage systems
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</Card>
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<Card title="Building Blocks" icon="puzzle" href="/core-concepts/building-blocks/datapoints">
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Learn about DataPoints, Tasks, and Pipelines that feed into storage
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</Card>
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<Card title="Search" icon="search" href="/core-concepts/main-operations/search">
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Explore different query types and modes that leverage the architecture
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</Card>
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</Columns>
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---
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> To find navigation and other pages in this documentation, fetch the llms.txt file at: https://docs.cognee.ai/llms.txt |