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).

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