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Setup

export DEEPSEEK_API_KEY="sk-..."

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Basic Usage

from definable.model import DeepSeekChat
from definable.model.message import Message

model = DeepSeekChat(id="deepseek-chat")
response = model.invoke(
    messages=[Message(role="user", content="Hello!")],
    assistant_message=Message(role="assistant", content=""),
)
print(response.content)

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Parameters

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id
str
default:"deepseek-chat"
Model identifier. Common values: deepseek-chat, deepseek-reasoner.
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api_key
str
DeepSeek API key. Defaults to the DEEPSEEK_API_KEY environment variable.
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base_url
str
default:"https://api.deepseek.com"
DeepSeek API base URL.
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temperature
float
Sampling temperature.
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max_tokens
int
Maximum output tokens.

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Reasoning Support

DeepSeek models can produce reasoning content alongside their response: 
from definable.model.message import Message

model = DeepSeekChat(id="deepseek-reasoner")
response = model.invoke(
    messages=[Message(role="user", content="Solve: what is 15! / 13!?")],
    assistant_message=Message(role="assistant", content=""),
)
print(response.reasoning_content)  # Step-by-step reasoning
print(response.content)            # Final answer

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Streaming

from definable.model.message import Message

for chunk in model.invoke_stream(
    messages=[Message(role="user", content="Explain quantum computing.")],
    assistant_message=Message(role="assistant", content=""),
):
    if chunk.content:
        print(chunk.content, end="", flush=True)

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Async Usage

from definable.model.message import Message

response = await model.ainvoke(
    messages=[Message(role="user", content="Hello!")],
    assistant_message=Message(role="assistant", content=""),
)
DeepSeek does not support native structured outputs (supports_native_structured_outputs = False). Structured output requests are handled via prompt engineering with JSON Schema instructions.