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docs: comprehensive README update for v1.0.1
- Updated intro to mention reranking and qmd lineage - Architecture diagram shows orchestrator, 4-lane search, LLM cache - Example outputs from real demo vault (not placeholders) - Explain example shows Intent: Conceptual + reranker lane - Quick start: intelligence opt-in section, CMake note - Fixed test count to 270
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README.md

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[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)
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[![GitHub release](https://img.shields.io/github/v/release/devwhodevs/engraph)](https://github.com/devwhodevs/engraph/releases)
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engraph turns your markdown vault into a searchable knowledge graph that AI agents can query through [MCP](https://modelcontextprotocol.io). It combines semantic embeddings, full-text search, and wikilink graph traversal into a single local binary. No API keys, no cloud — everything runs on your machine.
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engraph turns your markdown vault into a searchable knowledge graph that AI agents can query through [MCP](https://modelcontextprotocol.io). It combines semantic embeddings, full-text search, wikilink graph traversal, and LLM-powered reranking into a single local binary. Same model stack as [qmd](https://github.com/tobi/qmd). No API keys, no cloud — everything runs on your machine.
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<p align="center">
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<img src="assets/demo.gif" alt="engraph demo: 4-lane hybrid search with LLM intelligence, person context bundles, Metal GPU" width="800">
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Your vault (markdown files)
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┌─────────────────────────────────────────┐
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│ engraph index │
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│ │
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│ Walk → Chunk → Embed → Store → Graph
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│ │
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│ SQLite: files, chunks, FTS5, vectors, │
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│ edges, centroids, tags
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└─────────────────────────────────────────┘
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┌─────────────────────────────────────────────
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│ engraph index
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│ Walk → Chunk → Embed (llama.cpp) → Store
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│ SQLite: files, chunks, FTS5, vectors,
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│ edges, centroids, tags, LLM cache
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└─────────────────────────────────────────────
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┌─────────────────────────────────────────┐
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│ engraph serve │
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│ │
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│ MCP Server (stdio) + File Watcher │
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│ │
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│ 13 tools: search, read, list, context, │
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│ who, project, create, append, move... │
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└─────────────────────────────────────────┘
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┌─────────────────────────────────────────────┐
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│ engraph serve │
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│ │
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│ MCP Server (stdio) + File Watcher │
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│ │
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│ Search: Orchestrator → 4-lane retrieval │
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│ → Reranker → Two-pass RRF fusion │
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│ │
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│ 13 tools: search, read, list, context, │
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│ who, project, create, append, move... │
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└─────────────────────────────────────────────┘
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Claude / Cursor / any MCP client
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# Pre-built binaries (macOS arm64, Linux x86_64)
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# → https://github.com/devwhodevs/engraph/releases
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# From source
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# From source (requires CMake for llama.cpp)
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cargo install --git https://github.com/devwhodevs/engraph
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```
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```
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```
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1. [0.03] 02-Areas/Development/Auth Architecture.md > ## OAuth Flow #a1b2c3
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The OAuth 2.0 implementation uses PKCE for public clients...
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1. [0.04] 02-Areas/Development/Auth-Architecture.md > # Auth Architecture #6e1b70
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OAuth 2.0 with PKCE for all client types. Session tokens stored in HTTP-only cookies...
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2. [0.02] 01-Projects/Backend/API Design.md > ## Authentication #d4e5f6
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All endpoints require Bearer token auth. Tokens issued by...
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2. [0.04] 01-Projects/API-Design.md > # API Design #e3e350
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All endpoints require Bearer token authentication. Tokens are issued by the OAuth 2.0...
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3. [0.02] 03-Resources/People/Sarah Chen.md #789abc
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Lead on the auth rewrite. Key decisions documented in...
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3. [0.04] 03-Resources/People/Sarah-Chen.md > # Sarah Chen #4adb39
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Senior Backend Engineer. Tech lead for authentication and security systems...
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```
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Note how result #3 was found via **graph expansion** — Sarah's note doesn't mention "auth system" directly, but she's linked from the auth architecture doc.
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Note how result #3 was found via **graph expansion** — Sarah's note doesn't mention "auth system" directly, but she's linked from the auth architecture doc via `[[Sarah Chen]]`.
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**Connect to Claude Code:**
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Now Claude can search your vault, read notes, build context bundles, and create new notes — all through structured tool calls.
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**Enable intelligence (optional, ~1.3GB download):**
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```bash
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engraph configure --enable-intelligence
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# Downloads Qwen3-0.6B (orchestrator) + Qwen3-Reranker (cross-encoder)
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# Adds LLM query expansion + 4th reranker lane to search
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```
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## Example usage
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**Hybrid search with score breakdown:**
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**4-lane search with intent classification:**
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```bash
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engraph search "project deadlines" --explain
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engraph search "how does authentication work" --explain
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```
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```
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Intent: Exploratory
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1. [0.04] 01-Projects/API-Design.md > # API Design #e3e350
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All endpoints require Bearer token authentication...
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Intent: Conceptual
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--- Explain ---
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1. [0.04] 01-Projects/Q2 Planning.md > ## Milestones #abc123
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Semantic: 0.018 | FTS: 0.015 | Graph: 0.008 | Rerank: 0.014
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Q2 deliverables: auth rewrite by April 15, API v2 by May 1...
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01-Projects/API-Design.md
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RRF: 0.0387
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semantic: rank #2, raw 0.38, +0.0194
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rerank: rank #2, raw 0.01, +0.0194
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02-Areas/Development/Auth-Architecture.md
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RRF: 0.0384
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semantic: rank #1, raw 0.51, +0.0197
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rerank: rank #4, raw 0.00, +0.0187
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```
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The orchestrator classified the query as **Conceptual** (boosting semantic lane weight). The reranker scored each result for relevance as the 4th RRF lane.
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**Rich context for AI agents:**
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```bash
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- Vault graph: bidirectional wikilink + mention edges with multi-hop expansion
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- Placement correction learning from user file moves
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- Configurable model overrides for multilingual support
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- 271 unit tests, CI on macOS + Ubuntu
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- 270 unit tests, CI on macOS + Ubuntu
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## Roadmap
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