> ## Documentation Index
> Fetch the complete documentation index at: https://docs.getprofile.org/llms.txt
> Use this file to discover all available pages before exploring further.

# Ingest Data

> Extract traits and memories from arbitrary text data

## Overview

Ingest arbitrary text data and extract user traits and memories synchronously. This endpoint allows you to process historical data, CRM notes, chat logs, emails, or any text containing user information.

Unlike the LLM proxy which processes conversations in real-time, this endpoint provides immediate extraction results, making it ideal for batch imports and offline processing.

## Use Cases

* **Historical chat log import** - Bulk import past conversations
* **CRM integration** - Extract user info from Salesforce/HubSpot notes
* **Email thread processing** - Analyze support ticket histories
* **Batch migrations** - Import user data from other systems
* **Offline processing** - Extract traits without live LLM calls

## Path Parameters

<ParamField path="id" type="string" required>
  Profile ID (internal UUID or external ID)
</ParamField>

## Request Body

<ParamField body="data" type="string" required>
  The text data to ingest (max 100KB). Can be any text containing user
  information.
</ParamField>

<ParamField body="source" type="string">
  Optional source identifier (e.g., "crm", "chat\_log", "email", "salesforce")
</ParamField>

<ParamField body="metadata" type="object">
  Optional metadata object for tracking (e.g., `{ "salesforceId": "abc123" }`)
</ParamField>

<ParamField body="extractTraits" type="boolean" default={true}>
  Whether to extract traits from the data
</ParamField>

<ParamField body="extractMemories" type="boolean" default={true}>
  Whether to extract memories from the data
</ParamField>

## Response

<ResponseField name="profile" type="object">
  The updated profile information
</ResponseField>

<ResponseField name="extracted" type="object">
  Extraction results

  <Expandable title="properties">
    <ResponseField name="traits" type="array">
      Array of extracted traits with `key`, `value`, `confidence`, and `action` (create/update)
    </ResponseField>

    <ResponseField name="memories" type="array">
      Array of extracted memories
    </ResponseField>

    <ResponseField name="stats" type="object">
      Statistics: `traitsCreated`, `traitsUpdated`, `memoriesCreated`
    </ResponseField>
  </Expandable>
</ResponseField>

<ResponseField name="source" type="string | null">
  The source passed in the request
</ResponseField>

<ResponseField name="metadata" type="object | null">
  The metadata passed in the request
</ResponseField>

## Response Example

<ResponseExample>
  ```json theme={null}
  {
    "profile": {
      "id": "550e8400-e29b-41d4-a716-446655440000",
      "externalId": "user-123",
      "summary": "Alex is a senior engineer at Acme Corp...",
      "createdAt": "2024-01-01T00:00:00Z",
      "updatedAt": "2024-01-15T10:30:00Z"
    },
    "extracted": {
      "traits": [
        {
          "key": "name",
          "value": "Alex",
          "confidence": 0.9,
          "action": "create"
        },
        {
          "key": "expertise_level",
          "value": "senior",
          "confidence": 0.85,
          "action": "update"
        },
        {
          "key": "interests",
          "value": ["TypeScript", "AI"],
          "confidence": 0.8,
          "action": "update"
        }
      ],
      "memories": [
        {
          "id": "mem-456",
          "content": "Works at Acme Corp",
          "type": "fact",
          "importance": 0.8,
          "createdAt": "2024-01-15T10:30:00Z"
        },
        {
          "id": "mem-457",
          "content": "Prefers technical communication",
          "type": "preference",
          "importance": 0.7,
          "createdAt": "2024-01-15T10:30:00Z"
        }
      ],
      "stats": {
        "traitsCreated": 1,
        "traitsUpdated": 2,
        "memoriesCreated": 2
      }
    },
    "source": "crm",
    "metadata": {
      "salesforceId": "abc123",
      "importedBy": "migration-script"
    }
  }
  ```
</ResponseExample>

## Examples

### Basic Usage

```bash theme={null}
curl -X POST https://api.yourserver.com/api/profiles/user-123/ingest \
  -H "Authorization: Bearer gp_your_key" \
  -H "Content-Type: application/json" \
  -d '{
    "data": "Alex is a senior engineer at Acme Corp. Prefers TypeScript and technical communication."
  }'
```

### CRM Data Import

```bash theme={null}
curl -X POST https://api.yourserver.com/api/profiles/user-123/ingest \
  -H "Authorization: Bearer gp_your_key" \
  -H "Content-Type: application/json" \
  -d '{
    "data": "Customer Alex Thompson from Acme Corp. Contact preference: email. Technical background, 10+ years experience. Currently evaluating our enterprise plan. Key decision maker for engineering tools.",
    "source": "crm",
    "metadata": {
      "salesforceId": "0035000000abc123",
      "accountName": "Acme Corp",
      "importDate": "2024-01-15"
    }
  }'
```

### Chat Log Import

```bash theme={null}
curl -X POST https://api.yourserver.com/api/profiles/user-123/ingest \
  -H "Authorization: Bearer gp_your_key" \
  -H "Content-Type: application/json" \
  -d '{
    "data": "User: Hi, I am Alex. I work as a senior engineer.\nAssistant: Nice to meet you!\nUser: I prefer concise, technical explanations.\nAssistant: Got it, I will keep responses technical and to the point.",
    "source": "chat_log",
    "metadata": {
      "sessionId": "sess_abc123",
      "platform": "web"
    }
  }'
```

### Extract Only Traits

```bash theme={null}
curl -X POST https://api.yourserver.com/api/profiles/user-123/ingest \
  -H "Authorization: Bearer gp_your_key" \
  -H "Content-Type: application/json" \
  -d '{
    "data": "Alex, senior software engineer, prefers TypeScript",
    "extractTraits": true,
    "extractMemories": false
  }'
```

## Error Responses

<ResponseExample>
  ```json 400 theme={null}
  {
    "error": {
      "message": "data is required and must be a non-empty string",
      "type": "invalid_request_error",
      "code": "invalid_data"
    }
  }
  ```
</ResponseExample>

<ResponseExample>
  ```json 400 theme={null}
  {
    "error": {
      "message": "data size exceeds maximum of 102400 bytes",
      "type": "invalid_request_error",
      "code": "data_too_large"
    }
  }
  ```
</ResponseExample>

<ResponseExample>
  ```json 404 theme={null}
  {
    "error": {
      "message": "Profile not found",
      "type": "not_found",
      "code": "profile_not_found"
    }
  }
  ```
</ResponseExample>

<ResponseExample>
  ```json 500 theme={null}
  {
    "error": {
      "message": "Trait extraction failed",
      "type": "internal_error",
      "code": "extraction_error"
    }
  }
  ```
</ResponseExample>

## Best Practices

1. **Data Size** - Keep data under 100KB per request. For larger datasets, split into multiple requests.

2. **Source Tracking** - Use the `source` field to track where data originated for audit trails.

3. **Metadata** - Include relevant metadata to link back to source systems (CRM IDs, session IDs, etc.).

4. **Batch Processing** - For bulk imports, process profiles sequentially to avoid rate limits.

5. **Error Handling** - Implement retry logic for transient failures (500 errors).

## Comparison with Live Proxy

| Feature             | Ingest Endpoint       | LLM Proxy                   |
| ------------------- | --------------------- | --------------------------- |
| **Use Case**        | Batch/historical data | Real-time conversations     |
| **Processing**      | Synchronous           | Background (async)          |
| **Returns Results** | Yes (immediate)       | No (extracts in background) |
| **Data Format**     | Any text              | OpenAI chat format          |
| **Rate Limit**      | Separate limit        | Standard proxy limit        |