mirror of
https://github.com/deepseek-ai/deepseek-recipe.git
synced 2026-11-09 14:49:50 +08:00
114 lines
3.9 KiB
Markdown
114 lines
3.9 KiB
Markdown
# 流式响应
|
||
|
||
[English](streaming.md) | **中文**
|
||
|
||
`StreamProcessor` 把后端输出转换为 Messages、Chat Completions 或 Responses 事件。以下示例使用 mock 推理并打印 Chat Completions chunk。后端与 HTTP 传输由应用提供。思考模式下，只有为 processor 附加 tokenizer 后，它才会统计 usage 中的 `reasoning_tokens`，见[使用 tokenizer](tokenizer.zh.md#解码)。
|
||
|
||
Token 的 log probability 按解析器保留的原始字节归属。只报告完整落在输出原文片段内的 token；跨解析片段边界的 token 不报告概率。被删除的文本和 JSON 输出周围生成的替代空格不带概率。若自定义 tokenizer 的词表字节与解码文本不一致，该解码片段不报告概率。completion usage 仍按已处理的后端 token 计数。
|
||
|
||
## Rust
|
||
|
||
为你的应用添加依赖：
|
||
|
||
```sh
|
||
cargo add deepseek-recipe@0.2 serde_json@1 tokio-stream@0.1
|
||
cargo add tokio@1 --features macros,rt-multi-thread
|
||
```
|
||
|
||
将以下代码保存为 `src/main.rs`，然后运行 `cargo run`：
|
||
|
||
```rust
|
||
use deepseek_recipe::openai::ChatCompletionRequest;
|
||
use deepseek_recipe::request::{ConversionOptions, ProtocolRequest};
|
||
use deepseek_recipe::stream::{
|
||
InferenceChunk, InferenceFinishReason, PromptUsage, StreamProcessor,
|
||
};
|
||
use serde_json::json;
|
||
use tokio_stream::{StreamExt, iter};
|
||
|
||
#[tokio::main]
|
||
async fn main() -> Result<(), Box<dyn std::error::Error>> {
|
||
let body = json!({
|
||
"model": "DeepSeek-V4.1-Flash",
|
||
"messages": [{"role": "user", "content": "Hello"}],
|
||
"thinking": {"type": "disabled"},
|
||
});
|
||
|
||
// chunk 生成器使用转换后请求的最终设置。
|
||
let request: ChatCompletionRequest = serde_json::from_value(body)?;
|
||
let converted = request.convert(ConversionOptions::default())?;
|
||
let generator = ChatCompletionRequest::chunk_generator(
|
||
&converted,
|
||
"id-1".into(),
|
||
"DeepSeek-V4.1-Flash".into(),
|
||
);
|
||
let processor = StreamProcessor::new(generator, converted.parsing_options);
|
||
|
||
// 模拟后端的推理 chunk。
|
||
let inference = iter(vec![
|
||
InferenceChunk::Ready {
|
||
system_fingerprint: Some("fp-1".into()),
|
||
prompt_usage: PromptUsage {
|
||
prompt_tokens: 4,
|
||
prompt_cache_hit_tokens: 0,
|
||
},
|
||
},
|
||
InferenceChunk::Text {
|
||
content: "Hello!".into(),
|
||
token_count: 2,
|
||
},
|
||
InferenceChunk::Finish {
|
||
finish_reason: InferenceFinishReason::Stop,
|
||
},
|
||
]);
|
||
|
||
// 将其转换为协议 chunk。
|
||
let mut chunks = std::pin::pin!(processor.into_protocol_stream(inference));
|
||
while let Some(chunk) = chunks.next().await {
|
||
println!("{}", serde_json::to_string(&chunk?)?);
|
||
}
|
||
Ok(())
|
||
}
|
||
```
|
||
|
||
## Python
|
||
|
||
安装包：`python3 -m pip install deepseek-recipe`。
|
||
`push` 处理一个后端 chunk；`finish` 完成响应处理。
|
||
|
||
```python
|
||
from deepseek_recipe import (
|
||
ChatCompletionRequest,
|
||
ConversionOptions,
|
||
InferenceChunk,
|
||
InferenceFinishReason,
|
||
PromptUsage,
|
||
StreamProcessor,
|
||
)
|
||
|
||
request = ChatCompletionRequest({
|
||
"model": "DeepSeek-V4.1-Flash",
|
||
"messages": [{"role": "user", "content": "Hello"}],
|
||
"thinking": {"type": "disabled"},
|
||
})
|
||
converted = request.convert(ConversionOptions())
|
||
generator = ChatCompletionRequest.chunk_generator(converted, "id-1", "DeepSeek-V4.1-Flash")
|
||
processor = StreamProcessor(generator, converted.parsing_options)
|
||
|
||
# 模拟后端的推理 chunk。
|
||
chunks = []
|
||
for chunk in [
|
||
InferenceChunk.ready(system_fingerprint="fp-1", prompt_usage=PromptUsage(prompt_tokens=4)),
|
||
InferenceChunk.text("Hello!", token_count=2),
|
||
InferenceChunk.finish(InferenceFinishReason.Stop),
|
||
]:
|
||
chunks.extend(processor.push(chunk))
|
||
chunks.extend(processor.finish())
|
||
|
||
for chunk in chunks:
|
||
print(chunk.to_json())
|
||
processor.close()
|
||
```
|
||
|
||
支持范围见[项目 README](../README.zh.md)，API 约定见 [Rust API 文档](https://docs.rs/deepseek-recipe)。
|