> ## 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.

# Conversational Recommender / E-Commerce Assistant

> Chat-based product finders with persistent style and preference memory

## Scenario

Chat-based product finder on an e-commerce site or marketplace that remembers user preferences across sessions.

## Extraction

From on-site chat, searches, clicks, and purchases, GetProfile maintains:

* `style_preferences[]`: "minimalist", "streetwear", "pastel colors"
* `constraints`: budget, size, materials to avoid (e.g. wool)
* `brand_affinities[]` and `brand_avoidances[]`
* `decision_speed`: impulse buyer vs researcher

This comes from conversation like "I hate wool sweaters" or "I usually spend under \$100".

## Injection

For each conversation turn:

* GetProfile injects a compact preference block:
  * "User likes minimalist, neutral colors, hates wool, typical budget under \$80, prefers sustainable brands."

* It injects a few recent **preference-confirming memories** (click/purchase events) to help retrieval.

The LLM then uses this as:

* Additional filters in its retrieval/system prompt ("avoid wool, budget under 80"),
* Guidance for how to present results ("3 options, sorted by sustainability and price").

## Impact

* Fewer irrelevant suggestions, higher conversion, and a user who feels understood without filling forms.

## Implementation

<CodeGroup>
  ```typescript TypeScript theme={null}
  import OpenAI from 'openai';

  const client = new OpenAI({
  apiKey: process.env.GETPROFILE_API_KEY,
  baseURL: 'https://api.yourserver.com/v1',
  defaultHeaders: {
  'X-GetProfile-Id': userId,
  'X-Upstream-Key': process.env.OPENAI_API_KEY,
  },
  });

  // Product recommendation request
  const response = await client.chat.completions.create({
  model: 'gpt-5',
  messages: [
  {
  role: 'system',
  content: 'You are a helpful shopping assistant. Recommend products that match the user\'s style and budget preferences.',
  },
  {
  role: 'user',
  content: 'I need a new winter jacket.',
  },
  ],
  });
  // GetProfile injects style preferences, budget constraints, and past purchases

  ```
</CodeGroup>

## Trait Schema Example

```json theme={null}
{
  "style_preferences": {
    "type": "array",
    "items": {
      "type": "string"
    },
    "description": "Style categories the user prefers"
  },
  "budget_range": {
    "type": "object",
    "properties": {
      "min": {
        "type": "number"
      },
      "max": {
        "type": "number"
      }
    },
    "description": "Typical spending range"
  },
  "size_preferences": {
    "type": "array",
    "items": {
      "type": "string"
    },
    "description": "Preferred sizes"
  },
  "material_avoidances": {
    "type": "array",
    "items": {
      "type": "string"
    },
    "description": "Materials the user dislikes or is allergic to"
  },
  "brand_affinities": {
    "type": "array",
    "items": {
      "type": "string"
    },
    "description": "Brands the user prefers"
  },
  "decision_speed": {
    "type": "string",
    "enum": ["impulse", "researcher", "comparison-shopper"],
    "description": "How quickly the user makes purchase decisions"
  }
}
```

## Related Resources

<CardGroup cols={2}>
  <Card title="Proxy Integration" icon="arrow-right-arrow-left" href="/openai-compatibility">
    Set up automatic preference injection for your chat interface
  </Card>

  <Card title="Memories API" icon="brain" href="/api-reference/memories/create">
    Store purchase events and preference confirmations
  </Card>
</CardGroup>