> This page is for version v2 API (default).
> For other versions, use one of these documentation indexes:
> - v2 API (default): https://docs.cohere.com/v2/llms.txt
> - v1 API: https://docs.cohere.com/v1/llms.txt

> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.cohere.com/llms.txt.
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# Retrieval augmented generation (RAG) - quickstart

> A quickstart guide for performing retrieval augmented generation (RAG) with Cohere's Command models (v2 API).

## About retrieval augmented generation

Retrieval augmented generation (RAG) enables an LLM to ground its responses on external documents, thus improving the accuracy of its responses and minimizing hallucinations.

The Chat endpoint comes with built-in RAG capabilities such as document grounding and citation generation.

This quickstart guide shows you how to perform RAG with the Chat endpoint.

## Setup

First, install the Cohere Python SDK with the following command.

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

Next, import the library and create a client.

#### Cohere Platform

**`PYTHON`**

```python PYTHON
import cohere

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

#### Private Deployment

**`PYTHON`**

```python PYTHON
import cohere

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

#### Bedrock

**`PYTHON`**

```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
```

#### SageMaker

**`PYTHON`**

```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",
)
```

#### Azure AI

**`PYTHON`**

```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/"
)
```

## Documents

First, define the documents that will passed as the context for RAG. These documents are typically retrieved from sources such as vector databases via semantic search, or any system that can retrieve unstructured data given a user query.

Each document is a `data` object that can take any number of fields e.g. `title`, `url`, `text`, etc.

**`PYTHON`**

```python PYTHON
documents = [
    {
        "data": {
            "text": "Reimbursing Travel Expenses: Easily manage your travel expenses by submitting them through our finance tool. Approvals are prompt and straightforward."
        }
    },
    {
        "data": {
            "text": "Working from Abroad: Working remotely from another country is possible. Simply coordinate with your manager and ensure your availability during core hours."
        }
    },
    {
        "data": {
            "text": "Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance."
        }
    },
]
```

## Response Generation

Next, call the Chat API by passing the documents in the `documents` parameter. This tells the model to run in RAG-mode and use these documents as the context in its response.

#### Cohere Platform

**`PYTHON`**

```python PYTHON
# Add the user query
query = "Are there health benefits?"

# Generate the response
response = co.chat(
    model="command-a-plus-05-2026",
    messages=[{"role": "user", "content": query}],
    documents=documents,
)

# Display the response
print(response.message.content[0].text)
```

#### Private Deployment

**`PYTHON`**

```python PYTHON
# Add the user query
query = "Are there health benefits?"

# Generate the response
response = co.chat(
    model="command-a-plus-05-2026",
    messages=[{"role": "user", "content": query}],
    documents=documents,
)

# Display the response
print(response.message.content[0].text)
```

#### Bedrock

**`PYTHON`**

```python PYTHON
# Add the user query
query = "Are there health benefits?"

# Generate the response
response = co.chat(
    model="YOUR_MODEL_NAME",
    messages=[{"role": "user", "content": query}],
    documents=documents,
)

# Display the response
print(response.message.content[0].text)
```

#### SageMaker

**`PYTHON`**

```python PYTHON
# Add the user query
query = "Are there health benefits?"

# Generate the response
response = co.chat(
    model="YOUR_ENDPOINT_NAME",
    messages=[{"role": "user", "content": query}],
    documents=documents,
)

# Display the response
print(response.message.content[0].text)
```

#### Azure AI

**`PYTHON`**

```python PYTHON
# Add the user query
query = "Are there health benefits?"

# Generate the response
response = co.chat(
    model="model",  # Pass a dummy string
    messages=[{"role": "user", "content": query}],
    documents=documents,
)

# Display the response
print(response.message.content[0].text)
```

```mdx wordWrap
Yes, there are health benefits. We offer gym memberships, on-site yoga classes, and comprehensive health insurance.
```

## Citation Generation

The response object contains a `citations` field, which contains specific text spans from the documents on which the response is grounded.

**`PYTHON`**

```python PYTHON
if response.message.citations:
    for citation in response.message.citations:
        print(citation, "\n")
```

```mdx wordWrap
start=14 end=88 text='gym memberships, on-site yoga classes, and comprehensive health insurance.' document_ids=['doc_1'] 

```

## Further Resources

* [Chat endpoint API reference](https://docs.cohere.com/reference/chat)
* [Documentation on RAG](https://docs.cohere.com/docs/retrieval-augmented-generation-rag)
* [LLM University module on RAG](https://cohere.com/llmu#rag)