> For clean Markdown of any page, append `.md` to the page URL.
> For a complete documentation index, see https://docs.sarvam.ai/llms.txt.
> For full documentation content in one file, see https://docs.sarvam.ai/llms-full.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.sarvam.ai/_mcp/server.

# How to list your chat messages

> Defines your entire conversation.

The `messages` parameter defines your entire conversation so far. This is how you "teach" the model what has happened in the chat.

Each message is an object with two fields:

| **Key**   | **Value**                              |
| --------- | -------------------------------------- |
| `role`    | `"system"`, `"user"`, or `"assistant"` |
| `content` | The message text (string)              |

### Why is this important?

* The model uses the conversation history to generate **context-aware replies**.
* The order of the messages matters, the model reads them **top to bottom**.
* Including previous assistant responses helps the model maintain **coherence** and **memory**.

### Roles explained:

* **`system`**  (Optional, but Recommended)\
  Sets initial behavior, tone, or instructions for the assistant.\
  *Example:* `"You are a helpful assistant."`

* **`user`**  (Required)\
  Represents questions, requests, or inputs from the user.\
  *Example:* `"Tell me about Indian classical music."`

* **`assistant`**  (Optional, only for context in multi-turn)\
  Contains previous replies from the model, which help it stay consistent in tone and content.\
  *Example:* `"Indian classical music is one of the oldest musical traditions..."`

### Example: Listing messages in a conversation

First, install the SDK:

```bash
pip install -Uqq sarvamai
```

Then use the following Python code:

```python
from sarvamai import SarvamAI

# Initialize the SarvamAI client with your API key
client = SarvamAI(api_subscription_key="YOUR_SARVAM_API_KEY")

# Example 1: Default example - single "user" message which is required (no prior context)
response = client.chat.completions(
    model="sarvam-105b",
    messages=[
        {"role": "user", "content": "Hey, what is the capital of India?"}
    ],
)

print(response.choices[0].message.content)
```

```python
# Example 2: Multi-turn example: maintaining conversation context
from sarvamai import SarvamAI

client = SarvamAI(api_subscription_key="YOUR_SARVAM_API_KEY")

response = client.chat.completions(
    model="sarvam-105b",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Tell me about Indian classical music."},
        {"role": "assistant", "content": "Indian classical music is one of the oldest musical traditions in the world, with roots in ancient texts like the Natya Shastra."},
        {"role": "user", "content": "What are the two main styles?"}
    ],
)

# Receive assistant's reply as output
print(response.choices[0].message.content)
```

> Defines your entire conversation.