How to Intermediate · 4 min read

How to integrate Cohere reranker in RAG

Q: How to integrate Cohere reranker in RAG

Use the cohere Python SDK to rerank retrieved documents by sending them along with the query to the rerank endpoint. Integrate this reranking step between retrieval and generation in your RAG pipeline to improve the relevance of context passed to your LLM.

Quick answer

Use the cohere Python SDK to rerank retrieved documents by sending them along with the query to the rerank endpoint. Integrate this reranking step between retrieval and generation in your RAG pipeline to improve the relevance of context passed to your LLM.

PREREQUISITES

Python 3.8+
Cohere API key
pip install cohere>=5.0.0
OpenAI API key (for generation step)
pip install openai>=1.0

Setup

Install the cohere SDK and openai SDK for generation. Set your API keys as environment variables for secure access.

Install Cohere SDK: pip install cohere
Install OpenAI SDK: pip install openai
Set environment variables COHERE_API_KEY and OPENAI_API_KEY

bash

pip install cohere openai

Step by step

This example demonstrates a simple RAG pipeline where you first retrieve documents (mocked here), then use Cohere's reranker to reorder them based on relevance to the query, and finally generate an answer using OpenAI's gpt-4o-mini model with the top-ranked context.

python

import os
import cohere
from openai import OpenAI

# Initialize clients
cohere_client = cohere.Client(api_key=os.environ["COHERE_API_KEY"])
openai_client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

# Sample query and retrieved documents
query = "What are the benefits of renewable energy?"
documents = [
    "Renewable energy reduces greenhouse gas emissions.",
    "Fossil fuels are limited and polluting.",
    "Solar and wind power are sustainable energy sources.",
    "Renewable energy can create jobs in new sectors."
]

# Step 1: Use Cohere reranker to rank documents by relevance
rerank_response = cohere_client.rerank(
    query=query,
    documents=documents,
    top_n=len(documents)  # rerank all
)

# Extract reranked documents
reranked_docs = [item.document for item in rerank_response]

# Step 2: Prepare context from top 2 documents
top_docs = reranked_docs[:2]
context = "\n".join(top_docs)

# Step 3: Generate answer with OpenAI GPT-4o-mini using context
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": f"Answer the question using the context below:\n{context}\nQuestion: {query}"}
]

response = openai_client.chat.completions.create(
    model="gpt-4o-mini",
    messages=messages
)

print("Answer:", response.choices[0].message.content)

output

Answer: Renewable energy offers several benefits including reducing greenhouse gas emissions, providing sustainable energy sources like solar and wind, and creating jobs in emerging sectors.

Common variations

You can customize the reranking by adjusting top_n to limit how many documents to rerank or return. Use async calls if your application requires concurrency. For generation, you can swap gpt-4o-mini with other OpenAI models or use Anthropic's Claude models. Also, integrate real retrieval systems like Pinecone or FAISS instead of mocked documents.

python

import asyncio
import cohere
from openai import OpenAI

async def async_rerank_and_generate(query, documents):
    cohere_client = cohere.Client(api_key=os.environ["COHERE_API_KEY"])
    openai_client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

    # Async rerank
    rerank_response = await cohere_client.rerank.acreate(
        query=query,
        documents=documents,
        top_n=len(documents)
    )

    reranked_docs = [item.document for item in rerank_response]
    context = "\n".join(reranked_docs[:3])

    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": f"Use this context to answer:\n{context}\nQuestion: {query}"}
    ]

    response = await openai_client.chat.completions.acreate(
        model="gpt-4o-mini",
        messages=messages
    )

    return response.choices[0].message.content

# Usage example
# asyncio.run(async_rerank_and_generate(query, documents))

Troubleshooting

If you get authentication errors, verify your COHERE_API_KEY and OPENAI_API_KEY environment variables are set correctly.
If reranking returns empty or errors, check your network connection and ensure you are using the latest cohere SDK version.
For slow responses, consider caching reranked results or limiting top_n.

✅

Key Takeaways

Use Cohere's rerank endpoint to reorder retrieved documents by relevance before generation.
Integrate reranking between retrieval and generation in your RAG pipeline for better answer quality.
Set API keys securely via environment variables and use official SDKs for best compatibility.

Verified 2026-04 · gpt-4o-mini

Verify ↗