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# Ingest Data
Store documents, conversations, and raw content into Hindsight to automatically extract and create memories.
When you **retain** content, Hindsight doesn't just store the raw text—it intelligently analyzes the content to extract meaningful facts, identify entities, and build a connected knowledge graph. This process transforms unstructured information into structured, queryable memories.
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:::info How Retain Works
Learn about fact extraction, entity resolution, and graph construction in the [Retain Architecture](/developer/retain) guide.
:::
:::tip Prerequisites
Make sure you've completed the [Quick Start](./quickstart) to install the client and start the server.
:::
## Store a Single Memory
## The Importance of Context
The `context` parameter is crucial for guiding how Hindsight extracts memories from your content. Think of it as providing a lens through which the system interprets the information.
**Why context matters:**
- **Steers memory extraction**: Context tells the memory bank what type of information to focus on and how to interpret ambiguous content
- **Improves relevance**: Memories extracted with proper context are more accurately categorized and easier to retrieve
- **Disambiguates meaning**: The same sentence can have different implications depending on context (e.g., "the project was terminated" means different things in a career vs. product context)
## Store with Context and Date
Always provide context and event dates for optimal memory extraction:
The `timestamp` defaults to the current time if not specified. Providing explicit timestamps enables temporal queries like "What happened last spring?"
### Response Fields
The retain response includes:
| Field | Type | Description |
|-------|------|-------------|
| `success` | bool | Whether the operation succeeded |
| `bank_id` | string | The memory bank ID |
| `items_count` | int | Number of items processed |
| `async` | bool | Whether processed asynchronously |
| `usage` | TokenUsage | Token usage metrics for LLM calls (synchronous only) |
The `usage` field contains token metrics for cost tracking:
- `input_tokens`: Tokens consumed by prompts
- `output_tokens`: Tokens generated by the LLM
- `total_tokens`: Sum of input and output tokens
Note: `usage` is only present for synchronous operations. Async operations (`async: true`) do not return usage metrics.
## Batch Ingestion
Store multiple items in a single request. **Batch ingestion is the recommended approach** as it significantly improves performance by reducing network overhead and allowing Hindsight to optimize the memory extraction process across related content.
The `document_id` groups related memories for later management.
## Store from Files
```bash
# Single file
hindsight memory retain-files my-bank document.txt
# Directory (recursive by default)
hindsight memory retain-files my-bank ./documents/
```
## Async Ingestion
For large batches, use async ingestion to avoid blocking: