Open Source Python Search Software

DocFetcher is an Open Source desktop search application: It allows you to search the contents of files on your computer. — You can think of it as Google for your local files. The application runs on Windows, Linux and Mac OS X.

An advanced command-line tool designed to brute force directories and files in webservers, AKA web path scanner. Wordlist is a text file, each line is a path. About extensions, unlike other tools, dirsearch only replaces the %EXT% keyword with extensions from -e flag. For wordlists without %EXT% (like SecLists), -f | --force-extensions switch is required to append extensions to every word in wordlist, as well as the /. To use multiple wordlists, you can separate your wordlists with commas. Example: wordlist1.txt,wordlist2.txt. Default values for dirsearch flags can be edited in the configuration file: default.conf. The thread number (-t | --threads) reflects the number of separated brute force processes. And so the bigger the thread number is, the faster dirsearch runs. By default, the number of threads is 30, but you can increase it if you want to speed up the progress.

truffleHog searches through git repositories for high entropy strings and secrets, digging deep into commit history. TruffleHog runs behind the scenes to scan your environment for secrets like private keys and credentials, so you can protect your data before a breach occurs. Secrets can be found anywhere, so TruffleHog scans more than just code repositories, including SaaS and internally hosted software. With support for custom integrations and new integrations added all the time, you can secure your secrets across your entire environment. TruffleHog is developed by a team entirely comprised of career security experts. Security is our passion and primary concern, and all features are developed with best practices in mind. TruffleHog enables you to track and manage secrets within our intuitive management interface, including links to exactly where secrets have been found. TruffleHog runs quietly in the background, continuously scanning your environment for secrets.

rag-search is a lightweight Retrieval-Augmented Generation API service designed to provide structured semantic search and answer generation through a simple FastAPI backend. The project integrates web search, vector embeddings, and reranking logic to retrieve relevant context before passing it to a language model for response generation. It is built to be easily deployable, requiring only environment configuration and dependency installation to run a functional RAG service. The system supports configurable filtering, scoring thresholds, and reranking options, allowing developers to fine-tune retrieval quality. Its architecture is modular, separating handlers, services, and utilities to support customization and extension. Overall, rag-search serves as a practical starter backend for teams building AI search or question-answering applications on their own data.

turbovec is a Rust-based vector index with Python bindings for fast similarity search. It is built around TurboQuant, a quantization approach designed to reduce vector storage while preserving useful distance information. The project targets workloads where embedding search needs to be compact, efficient, and practical to integrate into Python applications. It avoids a separate training phase for the quantizer, which can simplify setup compared with systems that require codebook learning. TurboVec is useful for developers building retrieval, ranking, semantic search, recommendation, or AI memory systems. Its main value is combining Rust performance with a Python-facing workflow for modern vector search experiments and applications.