Initial commit: RA-H Open Source Edition

Local-first knowledge management system with BYO API keys.

Features:
- 3-panel UI (Nodes | Focus | Helpers)
- SQLite + sqlite-vec for vector search
- Agent system (Easy/Hard mode orchestrators)
- Content extraction (YouTube, PDF, web)
- Integrate workflow for connection discovery
- Dimension system with auto-assignment

Tech stack:
- Next.js 15 + TypeScript + Tailwind CSS
- Anthropic (Claude) + OpenAI (GPT) via Vercel AI SDK

Setup:
  npm install && npm rebuild better-sqlite3
  scripts/dev/bootstrap-local.sh
  npm run dev

MIT License

src/config/workflows/integrate.ts

@@ -0,0 +1,71 @@
+export const INTEGRATE_WORKFLOW_INSTRUCTIONS = `You are executing the INTEGRATE workflow for the currently focused node.
+
+MISSION
+Find meaningful connections across the user's knowledge graph and append an Integration Analysis to the node.
+
+YOU HAVE DIRECT WRITE ACCESS via updateNode. The tool automatically appends – you cannot overwrite existing content.
+
+WORKFLOW STEPS
+
+0. PLAN (MANDATORY)
+   - Call think to outline your approach for steps 1–4
+   - Focus on what entities/concepts to extract and how to search for them
+
+1. RETRIEVE & GROUND THE NODE
+   - Call getNodesById for the focused node
+   - Identify what type of thing this is (person, project, paper, idea, video, tweet, technique, etc.)
+   - Extract key entities: specific names, projects, concepts, techniques mentioned
+   - Summarize the core insight in one sentence
+
+2. SEARCH THE DATABASE FOR CONNECTIONS
+   DO NOT reference pinned context yet. Search the ENTIRE database:
+   
+   a) Obvious structural connections:
+      - If names mentioned → queryNodes to find existing nodes about those people
+      - If projects mentioned → queryNodes to find those project nodes
+      - If specific techniques/tools mentioned → search for those exact terms
+   
+   b) Thematic connections:
+      - Use searchContentEmbeddings with key concepts from step 1
+      - Look for shared themes, complementary ideas, contradictions
+      - Prefer nodes with high-signal relevance over weak matches
+   
+   - Aim for 3–8 strong connections, not 20 weak ones
+   - Check existing edges with queryEdge to avoid duplicating connections
+
+3. CONTEXTUALIZE WITH PINNED NODES
+   NOW review the supplied PINNED CONTEXT:
+   - Why might this node matter given the user's focus areas?
+   - Does it advance any themes visible in pinned nodes?
+   - Keep this brief – 1–2 sentences maximum
+
+4. APPEND INTEGRATION ANALYSIS
+   Call updateNode ONCE with ONLY the new section (do NOT include existing content):
+   
+   ---
+   ## Integration Analysis
+   
+   [2–3 sentences: what this node is, why it matters, core insight]
+   
+   **Database Connections:**
+   - [NODE:123:"Title"] — [why: authorship/shared concept/dependency/contradiction]
+   - [NODE:456:"Title"] — [why: ...]
+   - [continue for 3–8 connections found in step 2]
+   
+   **Relevance:** [1–2 sentences connecting to user's pinned context themes]
+   
+   CRITICAL: Send ONLY this new section. The tool will automatically append it to existing content.
+   
+   After ONE successful updateNode call, IMMEDIATELY move to step 5. Do NOT call updateNode again.
+
+5. RETURN SUMMARY
+   Reply with: Task / Actions / Result / Nodes / Follow-up (≤120 words)
+
+CRITICAL RULES
+- Search the FULL database, not just pinned nodes
+- Use entities from step 1 to guide searches in step 2
+- Call updateNode EXACTLY ONCE - after success, move to step 5 immediately
+- Keep total tool calls ≤ 18 (be efficient)
+- Adapt to any node type – don't assume it's always a paper or video
+
+The goal: integrate this node into the knowledge graph through meaningful, database-wide connections.`;