# Using Python

#### Best Practice: Use a Python Virtual Environment

To avoid dependency conflicts and keep your environment clean, create and activate a Python virtual environment before installing any packages:

```bash
python3 -m venv venvsource venv/bin/activate
```

#### Install Dependencies

```bash
pip install llama-cpp-python pymilvus "pymilvus[model]"
```

#### Install Alith

<pre class="language-bash"><code class="lang-bash"><strong>python3 -m pip install alith -U
</strong></code></pre>

#### Set Environment Variables

For OpenAI/ChatGPT API:

```bash
export PRIVATE_KEY=<your wallet private key>
export OPENAI_API_KEY=<your openai api key>
```

For other OpenAI-compatible APIs (DeepSeek, Gemini, etc.):

```bash
export PRIVATE_KEY=<your wallet private key>
export LLM_API_KEY=<your api key>
export LLM_BASE_URL=<your api base url>
```

#### Step 1: Run the Inference Server

> **Note:** The public address of the private key you expose to the inference server is the `LAZAI_IDAO_ADDRESS`. Once the inference server is running, the URL must be registered using the `add_inference_node` function in Alith. This can only be done by LazAI admins.

**Local Development**

For OpenAI/ChatGPT API:

```python
from alith.inference import run
 
"""Run the server and use the following command to test the server
 
curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-H "X-LazAI-User: 0xc3e98E8A9aACFc9ff7578C2F3BA48CA4477Ecf49" \
-H "X-LazAI-Nonce: 123456" \
-H "X-LazAI-Signature: HSDGYUSDOWP123" \
-H "X-LazAI-Token-ID: 1" \
-d '{
  "model": "gpt-3.5-turbo",
  "messages": [
    {"role": "system", "content": "You are a helpful assistant"},
    {"role": "user", "content": "What is the capital of France?"}
  ],
  "temperature": 0.7,
  "max_tokens": 100
}'
"""
server = run(model="gpt-3.5-turbo", settlement=True, engine_type="openai")

```

For other OpenAI-compatible APIs (DeepSeek, Gemini, etc.):

```python
from alith.inference import run
 
# Example: Using DeepSeek model from OpenRouter
server = run(settlement=True, engine_type="openai", model="deepseek/deepseek-r1-0528")
```

**Production Deployment on Phala TEE Cloud**

For production-ready applications, deploy your inference server on [Phala TEE Cloud ](https://docs.phala.network/phala-cloud/references/tee-cloud-cli) for enhanced security and privacy. Once deployed, you will receive an inference URL that needs to be registered using the `add_inference_node` function by LazAI admins.

You can also use the existing inference nodes.

#### Step 2: Request Inference via LazAI Client

```python
from alith import Agent, LazAIClient
 
# 1. Join the iDAO, register user wallet on LazAI and deposit fees (Only Once)
LAZAI_IDAO_ADDRESS = "0xc3e98E8A9aACFc9ff7578C2F3BA48CA4477Ecf49" # Replace with your own address
client = LazAIClient()

try:
    client.get_user(client.wallet.address)
    print("User already exists")
except Exception:
    print("User does not exist, adding user")
    client.add_user(10000000)
    client.deposit_inference(LAZAI_IDAO_ADDRESS, 1000000)
# 2. Request the inference server with the settlement headers and DAT file id
file_id = 11  # Use the File ID you received from the Data Contribution step
url = client.get_inference_node(LAZAI_IDAO_ADDRESS)[1]
print("url", url)
agent = Agent(
    # Note: replace with your model here
    model="gpt-3.5-turbo",

    base_url=f"{url}/v1",
    # Extra headers for settlement and DAT file anchoring
    extra_headers=client.get_request_headers(LAZAI_IDAO_ADDRESS, file_id=file_id),
)
print(agent.prompt("summarize it"))
```

***

### Security & Privacy

* **Your data never leaves your control.** Inference is performed in a privacy-preserving environment, using cryptographic settlement and secure computation.
* **Settlement headers** ensure only authorized users and nodes can access your data for inference.
* **File ID** links your inference request to the specific data you contributed, maintaining a verifiable chain of custody.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.lazai.network/private-data-inference/lazai-api/using-python.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.