> ## 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.

# Search & Recall

> Endpoints for querying knowledge graphs and retrieving data

These endpoints query your knowledge graphs. For the full parameter reference, see [Search Basics](/guides/search-basics).

## Recall

**`POST /api/v1/recall`** — Retrieve information from the knowledge graph.

Auto-routes the query to the best retrieval strategy. This is the primary search endpoint.

```bash theme={null}
curl -X POST https://your-tenant.aws.cognee.ai/api/v1/recall \
  -H "X-Api-Key: your-key" \
  -H "Content-Type: application/json" \
  -d '{"query": "What entities are in my data?"}'
```

**`GET /api/v1/recall`** — Retrieve recall history for the authenticated user.

## Search

**`POST /api/v1/search`** — Search for nodes in the graph database.

Provides direct control over the retrieval strategy. Accepts a `search_type` parameter to select a specific search mode.

```bash theme={null}
curl -X POST https://your-tenant.aws.cognee.ai/api/v1/search \
  -H "X-Api-Key: your-key" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "What did Einstein develop?",
    "search_type": "GRAPH_COMPLETION",
    "datasets": ["physics_data"]
  }'
```

Available search types are documented in [Search Types](/core-concepts/main-operations/legacy-operations/search).

**`GET /api/v1/search`** — Retrieve search history for the authenticated user.

## Visualize

**`GET /api/v1/visualize`** — Generate an HTML visualization of a dataset's knowledge graph.

Requires a `dataset_id` query parameter (UUID). Returns a self-contained HTML page with an interactive graph.

```bash theme={null}
curl "https://your-tenant.aws.cognee.ai/api/v1/visualize?dataset_id=<uuid>" \
  -H "X-Api-Key: your-key"
```

See also the [Knowledge Graph UI](/cognee-cloud/ui/knowledge-graph) for the built-in visualization.

**`POST /api/v1/visualize/multi`** — Generate a combined visualization from multiple users' datasets.

<Info>
  `recall` is recommended for most use cases. Use `search` when you need to specify a particular retrieval strategy.
</Info>