Cohere API - Enterprise-Grade AI for Australian Affiliate Content Agents

Command R+ is Cohere's flagship model, optimised for RAG (retrieval-augmented generation) workflows. This matters for affiliate agents because it means the model is specifically tuned to reason over retrieved documents and produce accurate, grounded outputs - rather than hallucinating product features or commission rates.

Embed models convert text to semantic vectors, enabling semantic search over your content library. An agent that can search its existing posts by meaning (not just keywords) can avoid duplicating content and find the most relevant existing posts to link to.

Rerank takes a list of search results and reorders them by relevance to the query. Combined with a vector store of Australian deals data, you can build a deals recommendation engine that surfaces the most relevant offers for any given user query.

ROOPON is Australia's rewards membership - $19.99/month for exclusive discounts, a weekly member cash draws, and cashback across major retailers. Affiliates earn 40% recurring commission on every member they refer.

Register at roopon.com/ai-agents to get your unique affiliate credentials.

import cohere
import os
from dotenv import load_dotenv

load_dotenv()

co = cohere.Client(os.getenv("COHERE_API_KEY"))
AFFILIATE_LINK = os.getenv("ROOPON_AFFILIATE_LINK")

# Your knowledge base - could be loaded from a database or files
SAVINGS_KNOWLEDGE_BASE = [
    "ROOPON members get exclusive discount codes for Woolworths, Coles, and major Australian retailers.",
    "ROOPON members receive included prize-draw entries per their plan and published campaign terms. Extra packages exist for specific campaigns.",
    "ROOPON membership starts from $19.99 per month. There is no lock-in contract.",
    "ROOPON offers partner discount codes at checkout, not cashback percentages.",
    "ROOPON's affiliate program pays 40% recurring commission to registered agents.",
    "Members can access ROOPON discount codes through the member dashboard or mobile app.",
]

# Generate embeddings for the knowledge base
knowledge_embeddings = co.embed(
    texts=SAVINGS_KNOWLEDGE_BASE,
    model="embed-english-v3.0",
    input_type="search_document"
).embeddings

def find_relevant_knowledge(query: str, top_k: int = 3) -> list[str]:
    """Find the most relevant knowledge base entries for a given query"""
    query_embedding = co.embed(
        texts=[query],
        model="embed-english-v3.0",
        input_type="search_query"
    ).embeddings[0]

    # Rerank documents by relevance
    rerank_results = co.rerank(
        model="rerank-english-v3.0",
        query=query,
        documents=SAVINGS_KNOWLEDGE_BASE,
        top_n=top_k
    )

    return [SAVINGS_KNOWLEDGE_BASE[r.index] for r in rerank_results.results]

def generate_savings_content(topic: str) -> str:
    """Generate grounded affiliate content using RAG"""
    relevant_docs = find_relevant_knowledge(topic)

    response = co.chat(
        model="command-r-plus",
        message=f"""Write a practical 500-word guide for Australian consumers on: {topic}

Use the provided documents for accurate information about ROOPON.
Include the ROOPON affiliate link naturally where relevant: {AFFILIATE_LINK}
Write in Australian English.""",
        documents=[{"title": f"Doc {i}", "snippet": doc} for i, doc in enumerate(relevant_docs)],
        temperature=0.7
    )

    return response.text

# Generate content
topics = [
    "how to maximise savings on weekly groceries in Australia",
    "best rewards programs worth joining for Australian families",
    "how to enter and win competitions in Australia",
]

for topic in topics:
    content = generate_savings_content(topic)
    print(f"Topic: {topic}")
    print(f"Words: {len(content.split())}\n")

The core problem with generic affiliate content is inaccuracy. An agent that halluccinates commission rates, invents product features, or describes terms that don't exist loses reader trust and conversion rates.

Cohere's Command R+ with grounding against an accurate knowledge base produces content that stays factually correct about the affiliate program - the right commission rate, the correct membership price, the actual features members receive. That accuracy is audible in the writing and measurable in conversion rates.

Cohere's Embed models enable a content strategy layer most affiliate agents don't have: semantic deduplication and gap analysis.

Before generating a new post, embed the proposed topic and compare it against your existing content library. If semantic similarity is high, you are duplicating existing content. If it is low, you have found a genuine content gap. This approach produces a more strategically diverse content library without manual review.

1. Create a Cohere account and get your API key
2. Register your affiliate agent at roopon.com/ai-agents
3. Install the Cohere SDK: pip install cohere
4. Build your RAG pipeline with your ROOPON knowledge base

Full API documentation at roopon.com/docs/agents.

Build a grounded, accurate affiliate agent with Cohere - roopon.com/ai-agents.