How to build a DeepSeek agent

 DeepSeek, a powerful AI tool, has demonstrated excellent performance and application potential in various fields. Building DeepSeek intelligent entities can provide intelligent solutions for enterprises and individuals. The specific steps for building a DeepSeek agent are as follows:

1. Prepare the environment and configure API

1. Obtain API access

The construction of DeepSeek's intelligent body must be done through a collaborative platform, which can be divided into two ways.

① Local deployment:

Download the open source tool Ollama (supports Windows/macOS/Linux) and load the DeepSeek model via the command line.

Hit hard

ollama run deepseek-r1 # Download and start the model service.

② Call the cloud API:

Register a third-party platform account (such as Silicon Flow, Volcano Engine, Wenxin Intelligent Platform) and generate an API key in the console. For example, the working path of Silicon Mobility Platform is User Center → API Management → Generate New Key.

2. Build a development environment

① Install the necessary toolchain.

Hit hard

# It is recommended to use Anaconda to manage the Python environment.

conda create -n deepseek python=3.10

conda activate deepseek

pip install ollama chatbox # Core dependent libraries

② API key setting

For cloud calls, enter the API key and terminal address (e.g. api.siliconflow.cn/v1) provided by the platform to tools such as Chatbox.

2. Agent instantiation and function configuration

1. Local model startup

Start the model service through Ollama and test the response.

Hit hard

ollama run deepseek-r1

>>> Hello, please introduce the features of DeepSeek.

# The model returns a structured response.

2. Cloud API call example (Python)

Python

Get request

def query_deepseek(prompt, API key):

Header = {"Authentication": f"Owner {api_key}"}

Data = {

"Model": "deepseek-r1",

"Message": [{"Role": "User", "Content": Prompt}]

}

Response = request.post("https://api.siliconflow.cn/v1/chat/completions", json=data, header=header)

Returns response.json()["choices"][0]["message"]["content"]

api_key = "sk-xxx" # Replace with your actual key

print(query_deepseek("How to optimize your customer service process?", api_key))

3. Customize your knowledge base and adapt scenarios

1. Data preparation specification

Convert business data to standard format:

Jason

// knowledgebase.json

{

"qa_pairs": [

{"question": "Product return policy", "answer": "Support for 7-day returns without reason..."},

{"question": "Technical support channel", "answer": "You can contact our online customer service or call 400..."}

]

}

2. Connect data via configuration file

yaml

# settings.yaml

Model path: ./models/deepseek-r1

Data source: ./knowledge_base.json

response_threshold: 0.8 # Confidence Filtering

4. System Integration and Deployment Solutions

1. Lightweight Local Deployment

Use Chatbox to implement graphical interaction.

Hit hard

chatbox --model deepseek-r1 --api http://localhost:11434 # Connect to local Ollama service.

Docker container deployment:

Dockerfile

Copy knowledge_base.json /app/data/ from ollama/ollama

CMD ["ollama", "run", "deepseek-r1", "--data", "/app/data"]

2. Enterprise-level cloud integration

High concurrency is achieved through API Gateway.

Python

Flask Example

Import Flask from Flask, Request

app = flask(__name__)

@app.route('/chat', method=['posts'])

def handle_query():

user input = request.json['text']

return query_deepseek(user_input, api_key)

if __name__ == '__main__':

app.run(host='0.0.0.0', port=5000)

Performance Optimization Suggestions:

Enable caching mechanism (Redis/Memcached)

Use asynchronous framework (FastAPI/Celery)

Configure load balancing (Nginx)

5. Scenario-based application path

1. Individual developer

Recommended toolchain: Ollama + Chatbox + Postman

Typical application areas: Intelligent writing assistant, local knowledge-based Q&A system.

2. Enterprise users

Recommended architecture:

Mermaid

Graph LR

A [Business system] --> B [API gateway]

B --> C [DeepSeek agent cluster]

C --> D [CRM/ERP database]

6. Maintenance and optimization

Monitoring metrics: Response delay (target <800ms), intent recognition accuracy (regular AB testing).

Iteration strategy: Update knowledge base data weekly and evaluate the need for model upgrades quarterly.

Security specifications: Store sensitive information in conversation records and set content filtering rules (sensitive word library)

With the above standardized process, developers can complete the basic environment setup in 1 hour and realize business scenario docking in 3 days. If the requirements are complex, it is recommended to refer to the official DeepSeek documentation or request support from certified service providers (e.g. Silicon Mobility's technical service team).