DeepSeek provides several APIs designed to let developers integrate and interact with their AI models efficiently. The primary offerings include a standard chat completion API, an embeddings API, and customization tools for fine-tuning models. These APIs are built around RESTful principles, using JSON for requests and responses, and are accessible via HTTP endpoints. They cater to common use cases like text generation, semantic analysis, and task-specific model adaptation, making them versatile for developers working on applications ranging from chatbots to data analysis tools.
The chat completion API is the core interface for text generation. It allows developers to send prompts and receive model-generated responses, similar to other modern language model APIs. For example, a typical request might include a messages array with user and system roles, along with parameters like temperature (to control randomness) or max_tokens (to limit response length). The endpoint, such as POST /v1/chat/completions, returns a JSON object containing the generated text and metadata. This API supports streaming for real-time interactions, which is useful for applications like live chatbots or interactive assistants. Developers can also specify stop sequences to halt generation at predefined points, ensuring output aligns with specific formatting needs.
For applications requiring semantic understanding, DeepSeek offers an embeddings API. This converts text into high-dimensional vector representations, enabling tasks like similarity comparison, clustering, or search. A request to an endpoint like POST /v1/embeddings might include a input field with the target text and a model parameter specifying the embedding variant (e.g., text-embedding-3). The response provides a vector that can be stored or compared using cosine similarity. This is particularly useful for building recommendation systems or document retrieval tools. Additionally, DeepSeek provides customization APIs for fine-tuning models on proprietary data. Developers can upload datasets, trigger training jobs via endpoints like POST /v1/fine_tuning/jobs, and deploy custom model versions. These APIs include tools for monitoring training progress and managing model versions, giving teams control over performance optimization.
Documentation for these APIs includes details on authentication (typically via API keys), rate limits, and error codes. For example, a 429 status code indicates rate-limiting, while a 401 signifies invalid credentials. DeepSeek also provides client libraries in languages like Python to simplify integration. By focusing on these core APIs, the platform balances flexibility with ease of use, allowing developers to adapt the tools to their specific needs without unnecessary complexity.