Objective: Develop an enhanced search tool capable of recursively searching through files in a specified directory and its subdirectories. This tool will search for files containing a specified search term within the filename, metadata, or contents, with case-insensitive matching.
Key Features:
Advanced Search Capabilities: Utilize semantic search powered by a local RAG to index and search contents. The search will consider various file types and metadata attributes.
Integration of Local and Online Data: Seamlessly integrate data from local files with relevant online content to provide enriched search results. Users can choose to start with local data and pull supplementary information from online sources or vice versa.
User Interface: Offer both a command-line interface for advanced users and a graphical user interface for ease of use, including search parameter configuration and real-time results display.
Performance: Implement efficient indexing and caching strategies to handle large datasets with minimal performance impact.
Use Cases:
Academics researching historical documents could find references and additional resources by searching through both local copies of primary sources and enriched online databases.
Software developers could use the tool to locate specific code snippets that are both locally available and in public repositories.
Technologies Used: Python, Elasticsearch for indexing
Objective: Develop an enhanced search tool capable of recursively searching through files in a specified directory and its subdirectories. This tool will search for files containing a specified search term within the filename, metadata, or contents, with case-insensitive matching.
Key Features:
Advanced Search Capabilities: Utilize semantic search powered by a local RAG to index and search contents. The search will consider various file types and metadata attributes. Integration of Local and Online Data: Seamlessly integrate data from local files with relevant online content to provide enriched search results. Users can choose to start with local data and pull supplementary information from online sources or vice versa. User Interface: Offer both a command-line interface for advanced users and a graphical user interface for ease of use, including search parameter configuration and real-time results display. Performance: Implement efficient indexing and caching strategies to handle large datasets with minimal performance impact.
Use Cases:
Academics researching historical documents could find references and additional resources by searching through both local copies of primary sources and enriched online databases. Software developers could use the tool to locate specific code snippets that are both locally available and in public repositories. Technologies Used: Python, Elasticsearch for indexing