Conversational Recommender / E-Commerce Assistant

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

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

Trait Schema Example

{
  "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"
  }
}

Proxy Integration

Set up automatic preference injection for your chat interface

Memories API

Store purchase events and preference confirmations

​Scenario

​Extraction

​Injection

​Impact

​Implementation

​Trait Schema Example

​Related Resources