The project's README from github.com/deepseek-ai/deepseek-recipe. As one markdown file: README.md.
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deepseek-recipe is a collection of Rust libraries and Python bindings that uniformly convert API requests in different formats into the Conversation format, render conversations into prompts for DeepSeek models, encode conversations into token IDs with an attached tokenizer, and convert model output into responses in the corresponding format. Use these components to connect an inference backend to API services that support multiple formats. The caller must provide model inference, tool execution, and HTTP transport.
Getting started · Streaming response · Use with tokenizer · License
prefix continuation.temperature,
top_p, and output token limits. After the caller encodes the prompt,
ConversationRequest::resolve_max_tokens checks the prompt and max_tokens
against the context limit and supplies the max_tokens default when the
request sets none: 8192 outside thinking mode, 65536 in thinking mode, and
131072 in thinking mode with max effort.reasoning_tokens when the stream processor has an
attached tokenizer: the processor encodes the reasoning content with it to
count the tokens.response_format of type
json_schema or regex, and Responses text.format of type json_schema.
Conversion validates a JSON Schema, and rendering writes it into the prompt; a
regex is passed through as ResponseFormat::Regex. Conversion also selects
the OutputConstraint of InferenceOptions: the tool call block of a request
with tools and a tool choice other than none, otherwise the JSON, JSON
Schema, or regex of its response format. The caller decides whether its
backend enforces it by constrained decoding.[Unsupported Image]. The component's ReqwestImageFetcher
connects to any address by default, so a service that fetches URLs from
untrusted requests should install a DestinationFilter.apply_patch custom tool. V4.1 supports deferred tool loading in
Messages and client-executed tool search in Responses.logprobs and top_logprobs (Chat Completions) and
top_logprobs (Responses) are converted into InferenceOptions. The caller
supplies the backend's values in InferenceChunk::Tokens; with an attached
tokenizer, the Chat Completions and Responses adapters report them. The
library does not compute probabilities.The examples below convert a Chat Completions request into a DeepSeek V4.1
prompt. The library resolves the model version from the request's model name
and accepts DeepSeek-V4-Pro and DeepSeek-V4.1-Flash by default; register
other names, such as deepseek-flash, with
ConversionOptions::with_model_alias.
Python 3.10+:
python3 -m pip install deepseek-recipe
from deepseek_recipe import ChatCompletionRequest, ConversionOptions, DeepseekV41Encoding
request = ChatCompletionRequest({
"model": "DeepSeek-V4.1-Flash",
"messages": [{"role": "user", "content": "Hello"}],
})
converted = request.convert(ConversionOptions())
rendered = DeepseekV41Encoding().render_conversation(converted.conversation)
print(rendered.prompt)
cargo add [email protected] [email protected] serde_json@1
See the development guide for source builds and image dependencies.
use deepseek_recipe::openai::ChatCompletionRequest;
use deepseek_recipe::request::{ConversionOptions, ProtocolRequest};
use deepseek_recipe_encoding::PromptEncoding;
use deepseek_recipe_encoding::v4::dsv41::DeepseekV41Encoding;
use serde_json::json;
fn main() -> Result<(), Box<dyn std::error::Error>> {
let request: ChatCompletionRequest = serde_json::from_value(json!({
"model": "DeepSeek-V4.1-Flash",
"messages": [{"role": "user", "content": "Hello"}],
}))?;
let converted = request.convert(ConversionOptions::default())?;
let rendered = DeepseekV41Encoding::new().render_conversation(&converted.conversation);
println!("{}", rendered.prompt);
Ok(())
}
Run the demo from the repository root to render prompts from requests and parse model output into protocol responses:
cargo run -p encoding-decoding-demo --locked
Open http://127.0.0.1:7778.
Rust and Python both support converting model output into streaming responses. See the Streaming response. To encode conversations into token IDs or decode backend token IDs, see use with tokenizer.
| Package | Purpose |
|---|---|
deepseek-recipe |
Protocol conversion and model output parsing. |
deepseek-recipe-core |
Shared conversation, message, image, and tool types. |
deepseek-recipe-encoding |
DeepSeek V4 and V4.1 prompt rendering and token encoding. |
deepseek-recipe-image |
Image fetching and preprocessing. |
deepseek-recipe-macros |
The prompt! macro used by prompt rendering. |
deepseek-recipe-python |
Python bindings, imported as deepseek_recipe. |
| encoding-decoding-demo | A web interface that renders prompts, highlights special tokens, and parses complete model output into Chat Completions, Responses, or Messages responses. |
| server-rs | An Axum API example with mock inference. |
| server-py | A FastAPI example with mock inference. |
Project code and public documentation are licensed under the MIT License. Bundled tokenizer notices are in static/tokenizers/README.md.
deepseek-recipe is a collection of Rust libraries and Python bindings that uniformly convert API requests in different formats into the Conversation format, render conversations into prompts for DeepSeek models, encode conversations into token IDs with an attached tokenizer…