stanleychen111 downloadsAI-assisted ingest, chat, lint, and git-backed wiki that compounds knowledge from 15+ raw formats — Markdown, PDF, Office docs, images, and more.
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ContextOS is an Obsidian plugin for maintaining a Karpathy-style LLM Wiki. It helps turn raw source notes into a persistent, structured wiki that compounds over time instead of re-deriving knowledge from scratch on every query.
Retry-After, and time out slow requests with a configurable limit.The plugin defaults to this structure:
raw/ # immutable source notes
raw/assets/ # source attachments
wiki/ # LLM-maintained wiki pages
wiki/index.md # content index
wiki/log.md # newest-first ingest/chat/lint log
All paths are configurable in the plugin settings.
.md, .txt, .csv, .tsv, .json, .yaml, .yml, .log, .ts, .js, .py, .go, .rs, .java, .cpp, .sql, .sh.html, .htm.doc, .docx, .rtf.xls, .xlsx.ppt, .pptx.pdf.png, .jpg, .jpeg, .webp, .gifPDFs and PPTX files are parsed directly when they contain readable text. PDF pages, PPTX slides, and image files only use vision OCR when text is not directly extractable.
Install dependencies:
npm install
Build the plugin:
npm run build
Copy the files from build/ into your Obsidian vault plugin directory:
build/main.js → <your-vault>/.obsidian/plugins/contextos/main.js
build/styles.css → <your-vault>/.obsidian/plugins/contextos/styles.css
build/manifest.json → <your-vault>/.obsidian/plugins/contextos/manifest.json
Enable ContextOS in Obsidian community plugin settings.
Open the plugin settings and configure:
Configure one or more LLM providers under the Providers section. Each provider has a type (OpenAI, Anthropic, Gemini, DeepSeek, Groq, Ollama, or OpenAI-compatible), API key, API URL, and model.
Use the Operation routing bar to assign specific providers to each operation:
| Operation | Description |
|---|---|
| Default provider | Fallback when no specific provider is configured for an operation |
| Text operations | Ingest, lint, and save-to-wiki |
| Chat operations | Chat panel conversations |
| Vision operations | PDF OCR, image OCR |
For example: use a cheap model (e.g. GPT-4.1 Mini) for text operations, a powerful model (e.g. Claude Opus) for chat, and a vision-capable model for OCR.
Third-party OpenAI-compatible providers can be used as long as the URL points directly to their /v1/chat/completions endpoint. Use Test connection in settings to check whether the configured endpoint returns HTTP 2xx — the connection is also auto-tested when you enter an API key.
Customize the system prompts for ingest, chat, and lint. Leave empty to use the built-in defaults.
Replace the LLM-based page selection step with vector similarity search. Configure under Embeddings:
| Backend | Description |
|---|---|
| None (default) | Current LLM-based page selection. No changes needed. |
| Ollama | Uses a local Ollama embedding model (e.g. mxbai-embed-large). Stores vectors in wiki/.embeddings/. |
| OpenAI | Uses OpenAI's embeddings API (text-embedding-3-small or similar). Stores vectors in wiki/.embeddings/. |
| Qdrant | Full vector database via Qdrant Cloud. Embeddings and search are delegated to Qdrant. |
When enabled, embeddings are computed automatically on ingest and used on chat queries. Falls back to LLM-based selection if the embeddings folder is empty or the backend is unreachable.
Automatically commit wiki changes after each change plan is applied. Configure under the Git section:
| Setting | Description |
|---|---|
| Git integration | Off, local-only, or remote (SSH) synchronization. |
| Connection method | SSH manual setup (paste your remote URL) or SSH auto-generate key (creates an Ed25519 keypair). |
| Remote URL | SSH remote URL (e.g. [email protected]:user/repo.git). |
| Auto push after commit | Push to the remote after each local commit. |
| Test connection | Verify git is installed and the remote is reachable. |
When using SSH auto-generate key, the plugin creates an Ed25519 keypair in ~/.ssh/ and displays the public key — copy it to your GitHub/GitLab SSH keys settings. Remote URL entry auto-tests the connection after you finish typing.
Put supported source files under the configured raw folder, such as Markdown, text, CSV/TSV, code, HTML, PDF, images, DOC/DOCX, XLS/XLSX, PPT/PPTX, or RTF.
Run the command:
Ingest changed raw files into ContextOS
The command scans the configured raw folder and processes all new or changed supported raw files. Text/code-like files are read directly, HTML is converted to readable text, Office documents, spreadsheets, presentations, and RTF files are extracted locally, and text-layer PDFs are extracted directly. Scanned or image-only PDF pages and image-only PPTX slides use vision OCR (pages are OCR'd concurrently — configurable with OCR page concurrency), and supported image files are sent to the configured vision model for OCR before the extracted text is ingested. Files that have already been successfully applied are skipped until their content changes.
When Auto ingest raw file changes is enabled, the plugin watches the configured raw folder for supported file creations and modifications. After a short debounce, it runs the same ingest pipeline and automatically applies validated changes without opening the review modal. It also polls the raw folder on the configured interval to catch files changed outside Obsidian (which do not fire file events). Auto ingest is disabled by default.
The command flow is:
Open the chat panel from the ribbon (the chat icon) or run:
Query ContextOS
This opens a chat panel docked in the right sidebar. Ask questions in natural language and the plugin answers from your wiki: it reads the index first and, for larger wikis, asks the model which pages are relevant and drills into only those (index-first retrieval). If embeddings are configured, page selection uses vector similarity search instead of an extra LLM call. Replies stream token-by-token with a three-dot loading indicator, then re-render as full Markdown. You can Copy a reply or Save to wiki to file a worthwhile answer back as a page through the reviewed change-plan flow (the exchange is also recorded in the log), so explorations compound over time.
The panel keeps multiple conversations:
Run:
Lint ContextOS
The plugin asks the model to reconcile the wiki with the current raw sources and to look for stale claims, contradictions, missing cross-references, important concepts without pages, and data gaps. Because the wiki is a synthesis distilled from many sources, a page whose raw source was removed is usually revised — its now-unsupported claims dropped — rather than deleted; a page is deleted only when nothing of value would remain. Deletions are proposed in the change plan and applied only after you review them (following Karpathy's model: sources are immutable, and the wiki is reconciled during lint rather than automatically when a source is deleted).
Run:
Undo last ContextOS change
Reverses the most recently applied change plan — deletes files that were created, restores files that were modified, and recreates files that were deleted. A confirmation notice shows the plan summary and operation counts before reverting. History is kept for the last 20 change sets.
indexPath and logPath must stay inside the configured wiki folder.This plugin sends selected vault content to the OpenAI-compatible chat completions endpoint configured in the plugin settings. During ingest, it sends new or changed raw text extracted from supported source files, including Markdown, text/code, HTML, PDFs, Office documents, spreadsheets, presentations, and RTF files; when OCR is needed, it sends rendered PDF page images, embedded PPTX slide images, or supported image files to the configured model. Wiki index/log context is included. When you chat, it sends the wiki index plus the pages selected for each turn along with the recent conversation messages; saving a chat answer and the lint command also send relevant wiki context. The Test connection button sends a real completion request ("ping") and verifies the provider returns non-empty content, reporting status, finish reason, and a content preview. No network request is made until you configure an API URL and API key and run a command or click the test button.
The API key is stored locally in Obsidian plugin data and is sent as an Authorization header only to the configured API URL. If you configure a third-party OpenAI-compatible endpoint, your API key and selected vault content are sent to that provider.
The plugin does not include telemetry, analytics, ads, or a self-update mechanism.
Run tests:
npm test
Build:
npm run build
The generated main.js is intentionally ignored by git and should not be committed.