> ## Documentation Index > Fetch the complete documentation index at: https://docs.cognee.ai/llms.txt > Use this file to discover all available pages before exploring further. # Architecture > Understanding Cognee's storage architecture and system components # Cognee Architecture Cognee Architecture ## Why multiple stores 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**. * **Relational store** — Tracks your documents, their chunks, and provenance (i.e. where each piece of data came from and how it's linked to the source). * **Vector store** — Holds embeddings for semantic similarity (i.e. numerical representations that let Cognee find conceptually related text, even if the wording is different). * **Graph store** — Captures entities and relationships in a knowledge graph (i.e. nodes and edges that let Cognee understand structure and navigate connections between concepts). Cognee ships with lightweight defaults that run locally, and you can swap in production-ready backends when needed (see [Setup](/getting-started/installation)). ## What is stored where Roughly speaking: * The **relational store** handles document-level metadata and provenance. * The **vector store** contains semantic fingerprints of chunks and [DataPoints](./building-blocks/datapoints). * The **graph store** captures higher-level structure in the form of entities and relationships. There is some overlap: for efficiency, parts of the same information may be indexed in more than one store. ## How they are used The stores play different roles depending on the phase: * The **relational store** matters most during permanent-memory ingestion, keeping track of documents, chunks, and where each piece of information comes from. * The **vector** and **graph** stores come into play during *recall and retrieval*: * **Semantic searches** (vector): find conceptually related passages based on embeddings * **Structural searches** (graph): explore entities and relationships using Cypher directly * **Hybrid searches** (vector + graph): combine both perspectives to surface results that are contextually rich and structurally precise. See how Remember, Improve, and Recall use the storage systems Learn about DataPoints, Tasks, and Pipelines that feed into storage Explore the default retrieval flow built on top of the architecture