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SIFS is a Rust-based code search tool designed specifically for AI Agents, editor integration, and local development workflows. It uses a hybrid search architecture combining semantic embedding and BM25 algorithm, achieving an excellent NDCG@10 score of 0.8444 in the Semble benchmark while maintaining extremely low query latency.
Section 01
SIFS is a Rust-based code search tool designed specifically for AI Agents, editor integration, and local development workflows. It uses a hybrid search architecture combining semantic embedding and BM25 algorithm, achieving an excellent NDCG@10 score of 0.8444 in the Semble benchmark while maintaining extremely low query latency.
Section 02
With the continuous improvement of large language model capabilities, AI Agents are playing an increasingly important role in software development. However, for Agents to effectively assist in development, they first need to quickly understand and retrieve relevant content from codebases. Traditional code search tools either rely on simple text matching and cannot handle semantic-level queries, or depend on cloud services, which have latency and privacy issues.
The design goal of SIFS is to provide a fast and intelligent local code search solution. It is fully developed in Rust, leveraging Rust's performance advantages and memory safety, while supporting multiple usage modes including command-line tools, Rust libraries, and Model Context Protocol (MCP) servers.
Section 03
SIFS uses a hybrid search architecture that combines semantic search and the traditional BM25 algorithm to balance the understanding ability of natural language queries and the precision of symbol matching.
Section 04
SIFS uses minishlab/potion-code-16M as the default embedding model, which is a Model2Vec-compatible encoder designed specifically for code. Model2Vec is a lightweight embedding model format; compared to traditional Transformer models, it is smaller in size and faster in inference, making it ideal for local deployment. SIFS directly reads model tensors and tokenizer files via a local Model2Vec loader, ensuring that the query path is entirely completed within the Rust process without relying on external services.
Section 05
For scenarios that do not require semantic understanding or environments with limited network access, SIFS provides a pure BM25 search mode. BM25 is a classic information retrieval algorithm that calculates relevance based on term frequency and inverse document frequency. This mode does not load any models and runs completely offline, making it ideal for package manager smoke tests and first-run checks.
Section 06
Hybrid search is a core feature of SIFS. It first performs semantic search and BM25 search separately, then uses Reciprocal Rank Fusion to normalize and merge the two result lists. The system also dynamically adjusts weights based on query characteristics: for queries containing a large number of symbols (such as function names, variable names), it increases the weight of BM25; for natural language descriptive queries, it relies more on semantic search.
Section 07
SIFS has been fully evaluated using the Semble benchmark suite. This test set includes 63 open-source repositories, 19 programming languages, and 1251 annotated search tasks.
Section 08
In the full benchmark test, SIFS achieved the following results:
Compared to other solutions, SIFS is slightly lower in search quality than CodeRankEmbed Hybrid (0.8617) and Semble (0.8544), but has a significant advantage in query latency. SIFS's P50 latency is only 0.0017 ms, far lower than CodeRankEmbed Hybrid's 16.9 ms and Semble's 1.3 ms.
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