Deepseeko1 delivers a focused approach to integrating large language models into production workflows through its Deepseek API. This reference documentation outlines endpoints, request formats, and best practices for reliable implementation.
Engineers and product teams use Deepseek API docs to design scalable applications, manage token usage, and optimize response quality across diverse scenarios.
| Resource | Type | Purpose | Authentication | Typical Latency |
|---|---|---|---|---|
| /v1/chat/completions | Endpoint | Conversational inference | Bearer token | Low to medium |
| /v1/embeddings | Endpoint | Vector representation | Bearer token | Low |
| Temperature | Parameter | Controls randomness | Optional | N/A |
| Top-p sampling | Parameter | Dynamic nucleus filtering | Optional | N/A |
Getting Started with Deepseek API
Deepseek API docs guide you through account setup, key generation, and initial requests. You configure endpoints with minimal boilerplate and standard HTTP libraries.
The quickstart section provides curl examples, request payload templates, and expected response shapes to accelerate integration.
Authentication and Rate Limits
Setting API Keys
Pass your secret key in the Authorization header as Bearer token to authenticate every call securely.
Rate Limit Windows
Requests per minute thresholds vary by tier, and 429 responses include retry-after hints to guide backoff strategies.
Endpoints and Request Patterns
Chat Completions
The /v1/chat/completions endpoint supports multi-turn dialog, tool usage, and structured output modes with configurable max tokens.
Embeddings
The /v1/embeddings endpoint maps input text to high-dimensional vectors suitable for semantic search and clustering tasks.
Performance Tuning
Temperature and Sampling
Adjust temperature to balance creativity and determinism, and choose between top-p and top-k for controlled generation.
Token Management
Monitor input and output token counts to optimize cost, and set stop sequences to truncate generation at desired boundaries.
Operational Best Practices
- Rotate keys periodically and restrict IP origins in your dashboard.
- Monitor quota usage via dashboard alerts to avoid service disruption.
- Version your payload schema to handle future API updates smoothly.
- Validate responses with schema checks before downstream processing.
- Cache embeddings for repeated queries to reduce latency and cost.
FAQ
Reader questions
How do I handle intermittent 5xx errors from Deepseek API?
Implement exponential backoff with jitter, log failed request IDs, and use idempotency keys where supported to safely retry.
Can I stream responses for real-time UI updates?
Yes, set stream to true and process server-sent events to incrementally render token-level output in your application.
What should I do if my context window is exceeded?
Shorten input length, filter irrelevant passages, or split tasks into smaller subqueries to stay within the model context limit.
Are embeddings cached or stored by Deepseek?
Embedding vectors are not retained by default unless you explicitly enable storage; review data retention settings in your account dashboard.