diff --git a/README.md b/README.md index d6953ede..c721dd39 100644 --- a/README.md +++ b/README.md @@ -23,7 +23,7 @@ **An AI coding assistant skill.** Type `/graphify` in Claude Code, Codex, OpenCode, Cursor, Gemini CLI, GitHub Copilot CLI, VS Code Copilot Chat, Aider, OpenClaw, Factory Droid, Trae, Hermes, Kiro, or Google Antigravity - it reads your files, builds a knowledge graph, and gives you back structure you didn't know was there. Understand a codebase faster. Find the "why" behind architectural decisions. -Fully multimodal. Drop in code, PDFs, markdown, screenshots, diagrams, whiteboard photos, images in other languages, or video and audio files - graphify extracts concepts and relationships from all of it and connects them into one graph. Videos are transcribed with Whisper using a domain-aware prompt derived from your corpus. YAML/YML files (Kubernetes, Kustomize, Helm, config) are indexed for semantic extraction. 25 languages supported via tree-sitter AST (Python, JS, TS, Go, Rust, Java, C, C++, Ruby, C#, Kotlin, Scala, PHP, Swift, Lua, Zig, PowerShell, Elixir, Objective-C, Julia, Verilog, SystemVerilog, Vue, Svelte, Dart). +Fully multimodal. Drop in code, PDFs, markdown, screenshots, diagrams, whiteboard photos, images in other languages, or video and audio files - graphify extracts concepts and relationships from all of it and connects them into one graph. Videos are transcribed with Whisper using a domain-aware prompt derived from your corpus. YAML/YML files (Kubernetes, Kustomize, Helm, config) are indexed for semantic extraction. SQL files are AST-extracted deterministically — tables, views, functions, foreign keys, and FROM/JOIN relationships map directly into the graph with no LLM needed. 25 languages supported via tree-sitter AST (Python, JS, TS, Go, Rust, Java, C, C++, Ruby, C#, Kotlin, Scala, PHP, Swift, Lua, Zig, PowerShell, Elixir, Objective-C, Julia, Verilog, SystemVerilog, Vue, Svelte, Dart). > Andrej Karpathy keeps a `/raw` folder where he drops papers, tweets, screenshots, and notes. graphify is the answer to that problem - 71.5x fewer tokens per query vs reading the raw files, persistent across sessions, honest about what it found vs guessed. @@ -353,7 +353,7 @@ Works with any mix of file types: | Type | Extensions | Extraction | |------|-----------|------------| -| Code | `.py .ts .js .jsx .tsx .mjs .go .rs .java .c .cpp .rb .cs .kt .scala .php .swift .lua .zig .ps1 .ex .exs .m .mm .jl .vue .svelte` | AST via tree-sitter + call-graph (cross-file for all languages) + Java extends/implements + docstring/comment rationale | +| Code | `.py .ts .js .jsx .tsx .mjs .go .rs .java .c .cpp .rb .cs .kt .scala .php .swift .lua .zig .ps1 .ex .exs .m .mm .jl .vue .svelte .sql` | AST via tree-sitter + call-graph (cross-file for all languages) + Java extends/implements + docstring/comment rationale. SQL: tables, views, functions, foreign keys, FROM/JOIN edges (requires `pip install graphifyy[sql]`) | | Docs | `.md .mdx .html .txt .rst .yaml .yml` | Concepts + relationships + design rationale via Claude | | Office | `.docx .xlsx` | Converted to markdown then extracted via Claude (requires `pip install graphifyy[office]`) | | Papers | `.pdf` | Citation mining + concept extraction |