Introduction: A New Way to Search Your Files
Searching for the right file shouldn’t feel like digging through an endless junk drawer. Traditional keyword search often fails because it only matches exact words. That’s where Semtools, a Rust-based CLI tool, steps in. By using semantic search, it understands the meaning behind your query—so when you type “presentation on annual revenue,” it can still find “Q4 Financials.” No complex setup, no vector database—just fast, accurate results right from your command line.
Semantic search offers a powerful alternative. It is a technology that understands the contextual meaning and intent behind a search query, rather than simply matching keywords. This approach is much more like asking a human assistant to find a file. It allows users to search for what they mean, not just the exact words they have used. Semtools is a new tool that brings this advanced search capability directly to the command line, offering a fast and simple solution for local file systems.
1. What is Semantic Search? (And Why You Need It)
Semantic search is a data searching technique focused on understanding the meaning of a query. It moves beyond simple keyword matching. The goal is to deliver more relevant results by considering relationships between words, the searcher’s intent, and the context of the content.
A key distinction exists between semantic search and its traditional counterpart. A keyword search for “running shoes” would only look for documents containing those exact words. In contrast, a semantic search engine would understand that “running shoes” is related to “athletic footwear,” “sneakers,” and brands like Nike or Adidas. It would return relevant results even if the keywords are not present. The technology also recognizes subtle but critical differences in meaning, such as distinguishing a search for “chocolate milk” from one for “milk chocolate”.
The value of this approach is significant. By understanding the meaning behind even complex or ambiguous queries, semantic search delivers a much-improved user experience. Users are more likely to find exactly what they are looking for on the first try, leading to enhanced engagement and efficiency. The technology shifts the burden from the user to the search system, which is a transformative change for document retrieval.
The following table highlights the fundamental differences between the two search paradigms.
| Characteristic | Keyword Search | Semantic Search |
| How it finds results | Relies on exact term matching. | Focuses on conceptual similarity and user intent. |
| Understanding | Treats words as isolated units. | Understands words in context, their relationships, and nuanced meaning. |
| User Experience | Can miss highly relevant information if keywords are not present. | Finds what you mean, not just what you say, leading to more relevant results. |
2. The Vector Database Conundrum
Semantic search is commonly powered by a technology known as a vector database. These are specialized databases that store and search numerical representations of data, called “vectors” or “embeddings”. An embedding is a numerical array that captures the semantic meaning of text, images, or other data types. Data points with similar meanings are located closer together in the vector space.
The standard approach for implementing semantic search involves a multi-step process. First, documents are converted into these numerical embeddings using a machine learning model. Second, these embeddings are stored and indexed in a vector database. When a user enters a query, it is also converted into an embedding. The system then searches the database for the document embeddings that are geometrically closest to the query embedding. This method allows for fast retrieval of conceptually similar content.
This traditional workflow, however, comes with a notable drawback for personal or small-scale use. It requires the setup of a separate database, which consumes system resources and adds a layer of complexity. The initial process of indexing and storing all the document embeddings can be time-consuming, requiring a separate application or service to run. The project aifs, for example, relies on this exact process by using a vector database to create a persistent index file in a directory. The complexity and overhead of this traditional approach create friction, particularly for a user who simply wants a quick, local search without a complicated setup.
3. Introducing Semtools: The Frictionless Solution
Semtools is a powerful command-line tool designed to address the challenges of traditional semantic search for local files.4 It offers a high-performance solution for document parsing and semantic search, built with the Rust programming language for speed and reliability.
It is important to clarify that the user query refers to a specific Rust-based CLI tool. The name “Semtools” has been used for other projects as well. For example, a popular R package called semTools is a collection of functions for statistical analysis in structural equation modeling. There is also a Ruby gem with a similar name used for ontology-based operations and similarity calculations. These are distinct projects with different purposes. The tool at the center of this report is the Rust-based command-line interface, which focuses on document processing and semantic search for file systems.
The design philosophy behind the Rust CLI is simplicity and efficiency. It works seamlessly with standard Unix pipelines, allowing users to chain commands together for a flexible and powerful workflow. It handles multiple document formats, including PDF, DOCX, and PPTX. This approach offers a frictionless way to perform advanced searches without the need for a complex, external system.
4. The Secret to Speed: How Semtools Works Its Magic
The core of Semtools’s design is its end-to-end search pipeline. Unlike other tools that require separate steps for parsing and searching, Semtools combines these functions into a single, cohesive workflow.4 This is accomplished by using two main tools:
parse and search. The parse tool handles document conversion, turning files like PDFs or DOCX into a markdown format. The output is then seamlessly fed into the search tool using a Unix pipe, enabling a powerful and intuitive command like parse document.pdf | search "error handling". This integrated design simplifies the user experience by providing an all-in-one toolkit for document processing and retrieval.
Semtools accomplishes its primary goal—semantic search without a vector database—by generating embeddings “on the fly”. When a search query is executed, the tool generates the embeddings in real-time. It does not save them to disk or an external database. This approach eliminates the need for an initial indexing step and avoids the overhead of managing a persistent database. It represents a fundamental departure from the standard semantic search workflow.
The ability to perform this real-time embedding generation and search is not a limitation but a deliberate design choice made possible by leveraging state-of-the-art technologies. The search function uses model2vec embeddings for speed. This library can reduce the size of machine learning models by up to a factor of 50 and make them up to 500 times faster on a CPU.15 This performance boost makes it feasible to generate embeddings for a document in real time, bypassing the need for a pre-built index. For the subsequent similarity comparison, the tool uses
simsimd. This library provides highly optimized routines for computing distances between vectors, such as cosine similarity.simsimd can be up to 133 times faster than standard Python libraries like SciPy for this task. The combination of these two high-performance libraries is what allows Semtools to provide a fast semantic search experience without a vector database.
5. Semtools vs. the Competition
While Semtools is unique in its approach, it exists within a larger ecosystem of local semantic search tools. One prominent example is aifs (AI Filesystem), a tool built for local semantic search over folders.
The two tools represent two different design philosophies. aifs adheres to the traditional vector database approach. It processes all nested, supported files in a folder and then stores the embeddings in a persistent _.aifs file. This process makes subsequent searches extremely fast, as the index is already created. However, it requires an initial, one-time indexing step and creates a hidden file in the directory. This is a good solution for a static set of documents that will be searched repeatedly.
Semtools, on the other hand, is designed to be a lightweight, single-purpose building block for a Unix-based workflow. The tool’s efficiency, piping capabilities, and automation are central to its purpose. It is not designed to create a persistent index but rather to perform fast, on-demand searches. This makes it ideal for scripting, integrating into automated agents, or for quick, one-off queries. The table below summarizes these key differences.
| Characteristic | Semtools | AIFS |
| Core Technology | On-the-fly embeddings, no database. | Persistent vector database (chroma). |
| Indexing | No pre-indexing required; search is instant. | Indexes documents and saves embeddings to a hidden file (_.aifs). |
| Best Use Case | Fast, one-off searches; integration with Unix pipelines and scripting. | Repeated searches on a largely static document set; “set it and forget it” workflow. |
6. A Quick Guide to Getting Started
Getting started with Semtools is simple. The tool is available via cargo, which is Rust’s package manager. The installation process requires only a single command line:
cargo install semtools
Once installed, the tools are ready to use. The most basic and powerful command involves chaining the parse and search tools together using a pipe (|).
To search for a specific term inside a single PDF file:
parse document.pdf | search "error handling"
To search across multiple documents at once, a user can combine standard Unix commands. For example, to search for “financial projections” across all PDF files in a directory:
parse *.pdf | cat | search "financial projections"
The search tool also includes configurable options. Users can set a similarity threshold to control how similar a result must be to a query and a context window to adjust the surrounding text returned with a match. This design allows Semtools to be a flexible and powerful tool for a variety of use cases, from simple queries to complex scripted workflows.
7. Final Thoughts: Your New File System Sidekick
Semtools represents an innovative approach to local file search. It offers the power of semantic search without the complexity and resource overhead of a traditional vector database. The tool’s design, which leverages the speed of Rust and high-performance computing libraries, provides a fast and efficient solution for document processing and retrieval. By operating on the command line, it offers unparalleled flexibility for developers, system administrators, and anyone who wants to integrate powerful search capabilities into their automated workflows. Semtools is a versatile and reliable utility for anyone looking to bring the power of semantic understanding to their local file system.
🔗 Keep Exploring AI Tools & Insights
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- Kosmos 2.5: A New Standard in Document AI Technology
- Why Developers Love AdaFlow for LLM Workflow Optimization
- How LEANN Makes AI Vector Indexing Affordable and Private
- MobileCLIP2 Explained: Apple’s Powerful New AI Model
- Apple’s FastVLM Models with WebGPU: What You Need to Know
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🌍 External Resources on Semantic Search
- Introduction to Semantic Search (Pinecone)
- How Semantic Search Works (NVIDIA Blog)
- Vector Databases Explained (Weaviate)
👉 Want to see how semantic AI can supercharge your business workflows? Talk to Ossels AI and explore how we build intelligent, real-world AI solutions.
