Product Recommendation Agent

import { BimpeAI } from "@bimpeai/sdk";

const bimpe = new BimpeAI({ apiKey: process.env.BIMPEAI_API_KEY! });

const page = await bimpe.workflows.list({ scope: "public", search: "product recommendation" });
const workflow = page.data[0];
console.log(workflow.id, workflow.name);

import os
from bimpeai import BimpeAI

client = BimpeAI(api_key=os.environ["BIMPEAI_API_KEY"])

page = client.workflows.list(scope="public", search="product recommendation")
workflow = page.data[0]
print(workflow.id, workflow.name)

const workflow = await bimpe.workflows.create({
  name: "Product recommender",
  system_prompt: "You recommend relevant products from the catalogue based on each customer's needs and budget.",
});
console.log(workflow.id);

workflow = client.workflows.create(
    name="Product recommender",
    system_prompt="You recommend relevant products from the catalogue based on each customer's needs and budget.",
)
print(workflow.id)

const agent = await bimpe.agents.create(
  {
    name: "Product recommender",
    description: "Recommends products from the catalogue based on customer needs.",
    workflow_id: workflow.id,
  },
  { idempotencyKey: "create-recommender-agent-v1" },
);

console.log(agent.id);

agent = client.agents.create(
    name="Product recommender",
    description="Recommends products from the catalogue based on customer needs.",
    workflow_id=workflow.id,
    idempotency_key="create-recommender-agent-v1",
)

print(agent.id)

// Live catalogue
await bimpe.agents.knowledgeBases.create(agent.id, {
  type: "url",
  name: "Product catalogue",
  url: "https://example.com/catalogue.json",
});

// Recommendation rules
await bimpe.agents.knowledgeBases.create(agent.id, {
  type: "text",
  name: "Recommendation rules",
  content:
    "Always surface 2-3 options across different price points.\n" +
    "Highlight the best-value option.\n" +
    "Do not recommend items marked Out of stock.\n" +
    "If budget is under £50, focus on the budget range first.",
});

# Live catalogue
client.agents.knowledge_bases.create(agent.id, {
    "type": "url",
    "name": "Product catalogue",
    "url": "https://example.com/catalogue.json",
})

# Recommendation rules
client.agents.knowledge_bases.create(agent.id, {
    "type": "text",
    "name": "Recommendation rules",
    "content": (
        "Always surface 2-3 options across different price points.\n"
        "Highlight the best-value option.\n"
        "Do not recommend items marked Out of stock.\n"
        "If budget is under £50, focus on the budget range first."
    ),
})

const { channels } = await bimpe.agents.getTestCode(agent.id);
// A tester opens channels.whatsapp.url, or sends channels.whatsapp.start_message
// to channels.whatsapp.phone_number, to open a 24-hour test window.
console.log(channels.whatsapp.start_message, channels.whatsapp.url);

// Or inject a test message yourself:
await bimpe.conversations.send(agent.id, {
  message: "Looking for a gift under £50 for someone who loves coffee — any ideas?",
  channel_type: "whatsapp",
  channel_user_id: "<tester-whatsapp-number>",
  is_test_channel: true,
});

test_code = client.agents.get_test_code(agent.id)
# A tester opens .url, or sends .start_message to .phone_number, to open a 24-hour window.
print(test_code.channels.whatsapp.start_message, test_code.channels.whatsapp.url)

# Or inject a test message yourself:
client.conversations.send(
    agent.id,
    message="Looking for a gift under £50 for someone who loves coffee — any ideas?",
    channel_type="whatsapp",
    channel_user_id="<tester-whatsapp-number>",
    is_test_channel=True,
)

const channels = await bimpe.agents.channels.list(agent.id);
console.log("Channels:", channels.map((c) => c.type));

channels = client.agents.channels.list(agent.id)
print("Channels:", [c.type for c in channels])

import { BimpeAI } from "@bimpeai/sdk";

const bimpe = new BimpeAI({ apiKey: process.env.BIMPEAI_API_KEY! });

// 1. Find workflow
const page = await bimpe.workflows.list({ scope: "public", search: "product recommendation" });
const workflow = page.data[0];

// 2. Create agent
const agent = await bimpe.agents.create(
  {
    name: "Product recommender",
    description: "Recommends products from the catalogue based on customer needs.",
    workflow_id: workflow.id,
  },
  { idempotencyKey: "create-recommender-agent-v1" },
);

// 3. Add catalogue and rules knowledge bases
await bimpe.agents.knowledgeBases.create(agent.id, {
  type: "url",
  name: "Product catalogue",
  url: "https://example.com/catalogue.json",
});

await bimpe.agents.knowledgeBases.create(agent.id, {
  type: "text",
  name: "Recommendation rules",
  content:
    "Always surface 2-3 options across different price points.\n" +
    "Highlight the best-value option.\n" +
    "Do not recommend items marked Out of stock.",
});

// 4. Verify connected channels (configured in the dashboard)
const channels = await bimpe.agents.channels.list(agent.id);
console.log("Agent ID:", agent.id);
console.log("Channels:", channels.map((c) => c.type));

// 5. Stream messages in an active conversation
const controller = new AbortController();

for await (const event of bimpe.conversations.messages.stream(
  agent.id,
  "<conversation_id>",
  { signal: controller.signal },
)) {
  console.log(event.role, event.message);
}

import os
from bimpeai import BimpeAI

client = BimpeAI(api_key=os.environ["BIMPEAI_API_KEY"])

# 1. Find workflow
page = client.workflows.list(scope="public", search="product recommendation")
workflow = page.data[0]

# 2. Create agent
agent = client.agents.create(
    name="Product recommender",
    description="Recommends products from the catalogue based on customer needs.",
    workflow_id=workflow.id,
    idempotency_key="create-recommender-agent-v1",
)

# 3. Add catalogue and rules knowledge bases
client.agents.knowledge_bases.create(agent.id, {
    "type": "url",
    "name": "Product catalogue",
    "url": "https://example.com/catalogue.json",
})

client.agents.knowledge_bases.create(agent.id, {
    "type": "text",
    "name": "Recommendation rules",
    "content": (
        "Always surface 2-3 options across different price points.\n"
        "Highlight the best-value option.\n"
        "Do not recommend items marked Out of stock."
    ),
})

# 4. Verify connected channels (configured in the dashboard)
channels = client.agents.channels.list(agent.id)
print("Agent ID:", agent.id)
print("Channels:", [c.type for c in channels])

# 5. Stream messages in an active conversation
for event in client.conversations.messages.stream(agent.id, "<conversation_id>"):
    print(event.role, event.message)