# Project Log for Obsidian Dictionary #### **Key Progress** 1. **Testing Local Integration**: - Determined Khoj can only search indexed files within Obsidian. - Identified that dictionaries stored outside the Obsidian vault pose integration challenges. 2. **Estimated File Size**: - Lightweight dictionaries: 10,000 words (~10 MB) to 100,000 words (~100 MB) per language. - Extensive datasets require ~300 MB or more for multilingual support with detailed entries. 3. **Alternate Approach Considered**: - QuickAdd macro to launch a Python script for querying a local dictionary outside Obsidian. - Noted as a potential solution but not pursued further. --- #### **Pivot to Online Integration** - **New Direction**: - Develop a **custom Khoj agent** for online dictionary queries. - Trigger queries from the **Obsidian chat interface**. - Leverage online resources for lightweight, up-to-date definitions. - **Benefits of Pivot**: - Eliminates the need for local dictionary storage. - Simplifies setup and maintenance. - Utilizes online APIs for broader word coverage and richer data. --- #### **Next Steps** 1. Design a custom Khoj agent for online dictionary queries: - Identify suitable dictionary APIs (e.g., Oxford, Merriam-Webster, or Wiktionary). - Implement API integration with Khoj. 2. Configure the agent to process user prompts from the Obsidian chat. 3. Test end-to-end functionality: - Highlight a word → Trigger query in Khoj chat → Receive formatted definition. 4. Optionally, implement note creation for queried definitions. lernen #### **1. Pivot from Khoj AI** - Initial approach using **Khoj AI** for dictionary queries was abandoned due to: - Limitations in querying external files. - Need for a more direct and manageable local solution. #### **2. Shell Command Integration** - Successfully configured the **Obsidian Shell Commands** plugin to run a Python script. - Created a script that: - Queries a local dictionary file. - Creates a note in the "Dictionary" folder with the word’s definition, part of speech, and examples. #### **3. English Dictionary** - Selected **WordNet** as the source for English. - Preprocessed WordNet to create a compact JSON file (~30 MB) containing: - Definitions. - Example sentences. #### **4. German Dictionary** - Attempted to use **OpenThesaurus**, but it lacked definitions (only provided synonyms). - Pivoted to finding a more suitable local database solution. #### **5. Exploring New German Dictionary Options** - Considered several options: - **Wiktionary Lite**: Filtered and preprocessed German subset. - **Leipzig Corpora**: Lightweight and ready-to-use datasets (~10–30 MB). - **Dict.cc**: Bilingual (German-English) wordlists. - **Project Gutenberg**: Public domain historical dictionaries. --- ### **Current Focus** 1. **German Dictionary**: - Identified Project Gutenberg dictionaries, such as "A Complete Dictionary of the English and German Languages." - Preparing to download and preprocess a public domain dictionary for local use. 2. **Preprocessing Tools**: - Python scripts created to preprocess plain text and structured datasets into JSON format for quick querying. --- ### **Next Steps** 1. **Download and Preprocess German Dictionary**: - Finalize the source (e.g., Project Gutenberg, Leipzig Corpora). - Extract word entries, definitions, and examples. 2. **Refine Query Script**: - Ensure the script handles both German and English queries seamlessly. - Add support for fallback responses (e.g., "definition unavailable"). 3. **Testing**: - Test the complete workflow in Obsidian: Highlight a word → Run command → Generate note. 4. **Documentation**: - Document the workflow for easy future updates or expansions. ### **1. Decision to Use Python for Dictionary Notes** - **Objective:** To create dictionary notes in Obsidian from a JSONL file of dictionary entries. Each note would: - Follow a specific markdown structure. - Be automatically created in a designated folder. - Include metadata, definitions, examples, and additional content. - **Initial Challenges:** - Parsing a large JSONL file efficiently. - Generating markdown files with proper formatting. - Handling edge cases such as multiple definitions, empty fields, or missing data. --- ### **2. Initial Python Script** - **Features Implemented:** - Read the JSONL file. - Match the selected word from Obsidian with the `word` field in the JSONL file. - Create a markdown note in the specified folder using the given structure. - **Issues Identified:** - Incorrect file paths caused initial failures. - Unicode errors when writing non-ASCII characters to markdown files. - Handling missing or incomplete fields (e.g., no synonyms, translations, etc.). - Lack of feedback on script execution progress. --- ### **3. Iterative Improvements** - **Enhancements Made:** - **Error Logging:** Added a log file (`Python Error Log.md`) to record script execution details, including errors and successes. - **Empty Field Handling:** Updated the script to exclude headers with no content, improving readability of the notes. - **Unicode Handling:** Fixed issues by ensuring all file operations used UTF-8 encoding. - **Challenges Addressed:** - Some headers were still rendering when their content was empty. - Notes were not being created for all definitions when a word had multiple entries. --- ### **4. Handling Multiple Definitions** - **Problem Identified:** - Words with multiple definitions in the JSONL file (e.g., "Aal") only generated one note, often at random. - **Solution:** - Updated the Python script to: - Create separate notes for each definition, appending `_1`, `_2`, etc., to filenames. - Log each created note in a "Created Notes List." - **Remaining Challenge:** - Users had no way to select the correct note from the created options. --- ### **5. Integration with QuickAdd in Obsidian** - **Objective:** Allow users to choose the correct note when multiple definitions exist. - **Steps Taken:** - Updated the Python script to: - Log links to all created notes in the "Created Notes List" file. - Trigger the QuickAdd macro after generating notes. - Created a JavaScript script to: - Read the "Created Notes List." - Display a selection dialog (suggester) to the user. - Open the selected note. - **Challenges Addressed:** - Ensured Python and JavaScript scripts worked together seamlessly. - Fixed issues with the "Created Notes List" structure and QuickAdd's ability to parse it. --- ### **6. Debugging and Testing** - **Issues Encountered:** - The JavaScript script created a new note instead of opening the selected one. - The Python script failed to log all created notes correctly in some cases. - Empty notes were being created when data was missing. - **Solutions Implemented:** - Updated the Python script to skip empty definitions. - Fixed the JavaScript script to: - Open the selected note directly instead of creating a new one. - Display appropriate feedback for empty or invalid selections. - Conducted extensive testing to ensure smooth integration between Python and QuickAdd. --- ### **7. Current State** - **Functionality Achieved:** - The Python script: - Reads the JSONL file and generates markdown notes for selected words. - Handles multiple definitions by creating separate notes. - Logs created notes in a "Created Notes List." - The JavaScript script: - Displays a suggester dialog in Obsidian for selecting the correct note. - Opens the selected note directly. - **Outstanding Issues:** - Empty notes are still being logged in some cases. - Minor bugs with type not populating correctly in the frontmatter. --- ### **8. Future Enhancements** - **User-Friendly Dialogs:** Consider implementing a more robust user interface for selecting definitions. - **Error Handling:** Ensure all edge cases (e.g., malformed JSON entries, invalid file paths) are handled gracefully. - **Performance Optimization:** Optimize the Python script for larger JSONL files, ensuring quick response times. --- ### **Reflection** We've made significant progress since starting this project, addressing most of the major challenges along the way. The integration between Python and Obsidian (via QuickAdd) now works effectively, providing a flexible and automated way to create dictionary notes. While some minor issues remain, the foundation is solid, and we're close to achieving a fully functional solution. Let me know when you're ready to tackle the next steps!