Deepseek node connects to Deepseek’s large language models for processing natural language questions and generating answers. It is used to integrate LLM functionality into a pipeline through token-based API authentication.
Inputs
- Prompt – Text prompt for the model
- Questions – This port accepts user or system-generated questions or prompts. These are sent to the Deepseek model for processing
- Documents – Document objects for context
- System – System instructions for the model
Outputs
- Text – Generated text output
- Answers – This port returns the generated text output from the model. Responses can be routed to downstream nodes for further use
Configuration
Model Settings
- Model – Deepseek model to use
- Default – “deepseek-chat”
- Note – Available models include deepseek-chat, deepseek-coder
- Use deepseek-chat for general text generation and question answering
- Use deepseek-coder for programming-related tasks and code generation
- API Key – Deepseek API key
- Note – Required for authentication
- Temperature – Creativity/randomness level
- Default – 0.7
- Note – Range: 0.0-1.0
- Max Tokens – Maximum response length
- Default – 1024
- Note – Limits output size
Advanced Settings
- Top P – Nucleus sampling parameter
- Default – 0.95
- Note – Controls diversity
- Top K – Top-K sampling parameter
- Default – 40
- Note – Limits token selection
- System Prompt – Default system instructions
- Note – Sets model behavior
- Stop Sequences – Sequences to stop generation
- Default – []
- Note – Custom stop tokens
- Timeout – API request timeout
- Default – 60
- Note – In seconds
Example Usage
Basic Text Generation
{
"model": "deepseek-chat",
"apiKey": "your-api-key",
"temperature": 0.7,
"maxTokens": 1024,
"topP": 0.95
}
Code Generation with Deepseek Coder
For code generation tasks using the specialized coder model:
{
"model": "deepseek-coder",
"apiKey": "your-api-key",
"temperature": 0.3,
"maxTokens": 2048,
"systemPrompt": "You are an expert programmer. Generate clean, efficient, and well-documented code based on the requirements provided.",
"topP": 0.9,
"timeout": 120
}
Best Practices
Prompt Engineering
- Provide clear, specific instructions in your prompts
- Use system prompts to establish consistent behavior
- Include relevant context for knowledge-intensive tasks
- Structure prompts with clear sections for complex tasks
Performance Optimization
- Adjust temperature based on task requirements (lower for factual responses, higher for creative content)
- Set appropriate max tokens to avoid unnecessary processing
- Use streaming for responsive user interfaces
Troubleshooting
API Problems
- Authentication errors: Verify API key validity
- Rate limit exceeded: Implement request throttling or upgrade API tier
- Timeout errors: Increase timeout setting or reduce prompt/context size
Response Quality Issues
- Irrelevant responses: Refine prompts or adjust system instructions
- Inconsistent outputs: Lower temperature for more deterministic responses
- Truncated responses: Increase max tokens setting
For detailed technical information, refer to:
- Deepseek API Documentation
- Deepseek Model Capabilities
- Deepseek Connector Source Code ../../../aparavi-connectors/connectors/llm_deepseek/deepseek.py