Context

Context is who this chat is with, and what your app is. Set it only on the first turn. It sticks for the whole conversation. Sending it again on a later turn is a 422. Each field can be up to 4,000 characters.

user_context — the shopper (preferences, budget, size).
application_context — context on your app, where this conversation is taking place

const stream = await client.conversations.createTurnStream({
  message: {
    role: "user",
    parts: [{ type: "text", text: "Weekend outfits" }],
  },
  context: {
    user_context:
      "25 year old software engineer in Williamsburg. Prefers minimalist styles, budget under $200.",
    application_context: "Shopping assistant inside a banking app.",
  },
});

stream = client.conversations.create_turn_stream(
    message={
        "role": "user",
        "parts": [{"type": "text", "text": "Weekend outfits"}],
    },
    context={
        "user_context": "25 year old software engineer in Williamsburg. Prefers minimalist styles, budget under $200.",
        "application_context": "Shopping assistant inside a banking app.",
    },
)

curl -N -X POST https://api.trychannel3.com/v1/conversations \
  -H "x-api-key: $CHANNEL3_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "message": {
      "role": "user",
      "parts": [{"type": "text", "text": "Weekend outfits"}]
    },
    "context": {
      "user_context": "25 year old software engineer in Williamsburg. Prefers minimalist styles, budget under $200.",
      "application_context": "Shopping assistant inside a banking app."
    }
  }'

For hard catalog rules on a single turn (in-stock, a set of retailers), use filters. To bind the chat to a saved set of brands, websites, categories, or products, pass config.collection_id on every turn — Channel3 tells the model what that collection covers so empty results can be explained. See Collections.

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