Rust 實現類似 DeepSeek 的搜索工具

使用 Rust 構建一個高效、高性能的搜索工具需要結合異步 I/O、索引結構和查詢優化。以下是一個簡化實現的框架：

核心組件設計

索引結構

use std::collections::{HashMap, HashSet};
use tantivy::schema::{Schema, TEXT, STORED};
use tantivy::{doc, Index};struct TextIndex {schema: Schema,index: Index,doc_store: HashMap<u64, String>,
}

查詢處理器

async fn query_index(index: &TextIndex,query: &str,filters: Option<Vec<Filter>>
) -> Result<Vec<SearchResult>, Error> {let searcher = index.reader.searcher();let query_parser = QueryParser::for_index(&index, vec![index.schema.get_field("content")?]);let query = query_parser.parse_query(query)?;let top_docs = searcher.search(&query, &TopDocs::with_limit(10))?;// ...結果處理邏輯
}

性能優化技術

異步任務調度

use tokio::sync::mpsc;
use rayon::prelude::*;async fn parallel_query(queries: Vec<String>,index: Arc<TextIndex>
) -> Vec<Vec<SearchResult>> {queries.par_iter().map(|q| {tokio::runtime::Handle::current().block_on(query_index(&index, q))}).collect()
}

內存管理

struct MemoryPool {buffers: Vec<Vec<u8>>,current_size: usize,max_size: usize,
}impl MemoryPool {fn acquire(&mut self, size: usize) -> Option<Vec<u8>> {if self.current_size + size <= self.max_size {let buf = self.buffers.pop().unwrap_or_else(|| vec![0; size]);self.current_size += size;Some(buf)} else {None}}
}

完整工作流程

1. 初始化索引構建器

fn build_index(documents: Vec<Document>) -> TextIndex {let mut schema_builder = Schema::builder();let content = schema_builder.add_text_field("content", TEXT | STORED);let schema = schema_builder.build();let index = Index::create_in_ram(schema.clone());// ...填充索引邏輯
}

1. 啟動網絡服務

use warp::Filter;async fn run_server(index: Arc<TextIndex>) {let search = warp::path("search").and(warp::query()).and_then(move |params| handle_search(params, index.clone()));warp::serve(search).run(([127, 0, 0, 1], 3030)).await;
}

1. 結果排序算法