## About reranking

Cohere's reranking models are available via the Rerank endpoint. This endpoint provides a powerful semantic boost to the search quality of any keyword or vector search system.

This quickstart guide shows you how to perform reranking with the Rerank endpoint.

::::::steps{titleSize="h2"}
:::::step{title="Setup"}
First, install the Cohere Python SDK with the following command.

```bash
pip install -U cohere
```

Next, import the library and create a client.

::::tabs
:::tab{title="Cohere Platform"}
```python PYTHON
import cohere

co = cohere.ClientV2(
    "COHERE_API_KEY"
)  # Get your free API key here: https://dashboard.cohere.com/api-keys
```
:::

:::tab{title="Private Deployment"}
```python PYTHON
import cohere

co = cohere.ClientV2(
    api_key="",  # Leave this blank
    base_url="<YOUR_DEPLOYMENT_URL>",
)
```
:::

:::tab{title="Bedrock"}
```python PYTHON
import cohere

co = cohere.BedrockClientV2(
    aws_region="AWS_REGION",
    aws_access_key="AWS_ACCESS_KEY_ID",
    aws_secret_key="AWS_SECRET_ACCESS_KEY",
    aws_session_token="AWS_SESSION_TOKEN",
)

# Get the model name: https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html
```
:::

:::tab{title="SageMaker"}
```python PYTHON
import cohere

co = cohere.SagemakerClientV2(
    aws_region="AWS_REGION",
    aws_access_key="AWS_ACCESS_KEY_ID",
    aws_secret_key="AWS_SECRET_ACCESS_KEY",
    aws_session_token="AWS_SESSION_TOKEN",
)
```
:::

:::tab{title="Azure AI"}
```python PYTHON
import cohere

co = cohere.ClientV2(
    api_key="AZURE_API_KEY",
    base_url="AZURE_ENDPOINT",  # example: "https://cohere-command-r-plus-08-2024-xyz.eastus.models.ai.azure.com/"
)
```
:::
::::
:::::

:::step{title="Retrieved Documents"}
First, define the list of documents to be reranked.

```python PYTHON
documents = [
    "Reimbursing Travel Expenses: Easily manage your travel expenses by submitting them through our finance tool. Approvals are prompt and straightforward.",
    "Working from Abroad: Working remotely from another country is possible. Simply coordinate with your manager and ensure your availability during core hours.",
    "Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance.",
    "Performance Reviews Frequency: We conduct informal check-ins every quarter and formal performance reviews twice a year.",
]
```
:::

:::::step{title="Reranking"}
Then, perform reranking by passing the documents and the user query to the Rerank endpoint.

::::tabs
:::tab{title="Cohere Platform"}
```python PYTHON
# Add the user query
query = "Are there fitness-related perks?"

# Rerank the documents

results = co.rerank(
    model="rerank-v4.0-pro", query=query, documents=documents, top_n=2
)

for result in results.results:
    print(result)
```
:::

:::tab{title="Private Deployment"}
```python PYTHON
# Add the user query
query = "Are there fitness-related perks?"

# Rerank the documents
results = co.rerank(
    model="rerank-v4.0-pro", query=query, documents=documents, top_n=2
)

for result in results.results:
    print(result)
```
:::

:::tab{title="Bedrock"}
```python PYTHON
# Add the user query
query = "Are there fitness-related perks?"

# Rerank the documents

results = co.rerank(
    model="YOUR_MODEL_NAME", query=query, documents=documents, top_n=2
)

for result in results.results:
    print(result)
```
:::

:::tab{title="SageMaker"}
```python PYTHON
# Add the user query
query = "Are there fitness-related perks?"

# Rerank the documents
results = co.rerank(
    model="YOUR_ENDPOINT_NAME",
    query=query,
    documents=documents,
    top_n=2,
)

for result in results.results:
    print(result)
```
:::

:::tab{title="Azure AI"}
```python PYTHON
# Add the user query
query = "Are there fitness-related perks?"

# Rerank the documents

results = co.rerank(
    model="model",  # Pass a dummy string
    query=query,
    documents=documents,
    top_n=2,
)

for result in results.results:
    print(result)
```
:::
::::

```mdx wordWrap
document=None index=2 relevance_score=0.115670934
document=None index=1 relevance_score=0.01729751
```
:::::
::::::

## Further Resources

- [Rerank endpoint API reference](/api)
- [Documentation on reranking](/guides/embeddings-vectors-search-retrieval-text-embeddings-reranking-overview)
- [LLM University chapter on reranking](https://cohere.com/llmu/reranking)

## Related pages

- [Retrieval augmented generation (RAG) - quickstart](./cohere-platform-v2-get-started-quickstart-rag-quickstart.md)
- [Semantic search - quickstart](./cohere-platform-v2-get-started-quickstart-sem-search-quickstart.md)
- [Text generation - quickstart](./cohere-platform-v2-get-started-quickstart-text-gen-quickstart.md)
- [Tool use & agents - quickstart](./cohere-platform-v2-get-started-quickstart-tool-use-quickstart.md)
- [Audio Transcription - quickstart](./cohere-platform-v2-get-started-quickstart-audio-transcription-quickstart.md)
- [Document Parsing - quickstart](./cohere-platform-v2-get-started-quickstart-parse-quickstart.md)

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