Dnomia Knowledge

Local knowledge engine for codebases. Indexes your markdown and source code into a single SQLite database with hybrid search (FTS5 keyword + vector semantic), knowledge graph, and developer interaction tracking.

Built for Claude Code via MCP (Model Context Protocol). Works entirely on your machine. Your code never leaves your computer.

Why

Developer tools treat every session as a blank slate. You search for the same function, re-read the same config file, and re-discover the same architectural pattern across dozens of conversations. Grep finds exact strings but misses semantic matches. Embeddings find related concepts but miss exact terms. Neither remembers what you searched for last week.

Existing solutions don't fit solo developers:

• GitHub Copilot / Cursor indexing is cloud-dependent and opaque. Your code leaves your machine.
• RAG pipelines require infrastructure (vector DBs, embedding APIs, chunking services) that cost money and attention.
• IDE search is per-project, per-session, with no memory of what matters to you.

dnomia-knowledge runs entirely on your machine: one SQLite database, one embedding model, hybrid search that combines keyword precision with semantic recall. It tracks which files you actually read and edit, boosts search results by your usage patterns, and syncs automatically on every commit. No cloud, no API keys, no infrastructure to manage.

What it does

Hybrid search across your projects. Not just grep, not just embeddings. FTS5 (BM25) and sqlite-vec (cosine KNN) run in parallel, merged with Reciprocal Rank Fusion. Finds code and documentation that keyword search misses and vector search misranks.

Knowledge graph over your codebase. Chunks are connected by markdown links, shared tags, categories, import statements, and semantic similarity. Community detection (Louvain) and PageRank surface the structure of your project.

Interaction tracking learns what matters to you. Every file you read and edit is logged. Search results are boosted by your actual usage patterns. Trace analytics show which files are hot, which knowledge gaps exist, and which areas are decaying.

Cross-project search lets you query across all your indexed repositories at once. Related projects can be linked via config for unified search results.

Continuous indexing keeps everything fresh. Git post-commit hooks and a periodic job (launchd on macOS) re-index changed files automatically. No daemon, no persistent memory usage.

Knowledge lifecycle (schema v4, opt-in) adds confidence scoring, supersession, and contradiction detection on top of the index. Chunks decay when ignored and strengthen when you read or edit them. Duplicate slugs and code-symbol collisions surface automatically. Ranking can be tuned by confidence, superseded chunks drop out of default search, and the lifecycle MCP tool exposes state to Claude Code. Inspired by the LLM Wiki v2 proposal extending Karpathy's LLM Wiki concept. Enable with [lifecycle] enabled = true in .knowledge.toml and DNOMIA_KNOWLEDGE_LIFECYCLE=1 for the hook.

Lifecycle at a glance

# See contradictions (e.g. duplicate slugs across posts)
dnomia-knowledge contradictions --project my-site

# Inspect a chunk's confidence and event history
dnomia-knowledge confidence 1234

# Mark an outdated doc as superseded by its rewrite
dnomia-knowledge supersede 1234 5678 --yes

# Apply daily decay sweep (or install launchd via --plist)
dnomia-knowledge forget --project my-site

From Claude Code via MCP: lifecycle(chunk_id, action='info') shows state, action='reinforce' bumps confidence, action='supersede' and action='restore' edit the supersession pointer.

Quick start

# Clone and install
git clone https://github.com/ceaksan/dnomia-knowledge.git
cd dnomia-knowledge
python3.11 -m venv .venv
source .venv/bin/activate
pip install -e .

# Index your first project
dnomia-knowledge index /path/to/your/project

# Search
dnomia-knowledge search "authentication middleware"

# See what files you access most
dnomia-knowledge trace hot

The embedding model (intfloat/multilingual-e5-base, ~500MB) downloads automatically on first run.

Connect to Claude Code

Add to ~/.claude/settings.json under mcpServers:

{
  "dnomia-knowledge": {
    "command": "/path/to/dnomia-knowledge/.venv/bin/python",
    "args": ["-m", "dnomia_knowledge.server"],
    "env": {
      "DNOMIA_KNOWLEDGE_PROJECT": "my-project"
    }
  }
}

Claude Code now has access to 6 MCP tools: search, index_project, project_info, graph_query, read_file, and fetch_and_index.

Claude Code hooks (optional)

Track file interactions automatically by adding hooks to ~/.claude/settings.json:

{
  "hooks": {
    "PreToolUse": [
      {
        "matcher": "Read|Grep",
        "hooks": [
          {
            "type": "command",
            "command": "/path/to/.venv/bin/python -m dnomia_knowledge.hooks.pre_tool_use"
          }
        ]
      }
    ],
    "PostToolUse": [
      {
        "matcher": "Read|Edit",
        "hooks": [
          {
            "type": "command",
            "command": "/path/to/.venv/bin/python -m dnomia_knowledge.hooks.post_tool_use"
          }
        ]
      }
    ]
  }
}

The PreToolUse hook redirects large file reads (>300 lines) to the knowledge base search. The PostToolUse hook logs every Read/Edit for interaction tracking and search ranking.

Project configuration

Create .knowledge.toml in your project root to control what gets indexed:

[project]
name = "my-project"
type = "saas"              # content | saas | static

[content]
paths = ["docs/"]
extensions = [".md", ".mdx"]

[code]
preset = "python"          # web | python | django | mixed
paths = ["src/"]
max_chunk_lines = 50

[graph]
enabled = true
edge_types = ["link", "tag", "semantic", "import"]
semantic_threshold = 0.75

[indexing]
ignore_patterns = ["node_modules", "dist", "__pycache__", ".venv"]
max_file_size_kb = 500

[injection]
enabled = false            # experimental; off by default
max_hint_tokens = 500
cache_ttl_seconds = 300
hot_limit = 3
recent_limit = 3

Without .knowledge.toml, defaults to indexing .md and .mdx files only.

Experimental: passive context injection

With [injection] enabled = true, the server augments the search tool description on every tools/list call with a compact hint listing the top hot files and recent edits for DNOMIA_KNOWLEDGE_PROJECT. The model sees this passively, without calling search first. Hint is capped at 500 tokens, cached 5 minutes, falls back silently on error. See ADR-001.

CLI reference

Indexing

dnomia-knowledge index <path>              # Index a project directory
dnomia-knowledge index <path> --full       # Force full reindex (skip incremental)
dnomia-knowledge index-all                 # Index all registered projects
dnomia-knowledge index-all --changed       # Only projects with changes since last index

Search

dnomia-knowledge search <query>                    # Search all projects
dnomia-knowledge search <query> -p my-project      # Filter by project
dnomia-knowledge search <query> -d code            # Filter by domain (code|content|all)
dnomia-knowledge search <query> --lang python      # Filter by language

Trace analytics

dnomia-knowledge trace hot                 # Most accessed files (reads + edits + searches)
dnomia-knowledge trace gaps                # Searches that returned zero results
dnomia-knowledge trace decay               # Files with declining activity over time
dnomia-knowledge trace queries             # Most frequent search patterns

All trace commands accept --project/-p, --days/-d (default 30), and --limit/-l (default 20).

Git history analysis

dnomia-knowledge git-sync <path>           # Sync git log into the database
dnomia-knowledge analyze churn             # Most modified files by insertions + deletions
dnomia-knowledge analyze hotspots          # Directory-level churn aggregation
dnomia-knowledge analyze crossover         # Fuse git churn with trace read data

Crossover analysis assigns signals to files based on change frequency vs read frequency:

Signal	Meaning
BLIND	High churn, zero reads. Changing but never consulted.
TURBULENT	High churn, low reads. Unstable and under-monitored.
HOT	High churn, high reads. Core active area.
STABLE	Low churn, high reads. Settled reference code.
ZOMBIE	Zero churn, some reads. Read but never touched.
COLD	Low churn, low reads. Inactive.

Knowledge graph

dnomia-knowledge graph rebuild             # Rebuild all edges for a project
dnomia-knowledge graph communities         # Run Louvain community detection + PageRank

Continuous indexing

dnomia-knowledge install-hooks             # Git post-commit hooks on all projects
dnomia-knowledge install-hooks --uninstall
dnomia-knowledge install-launchd           # macOS launchd job (every 5 min)
dnomia-knowledge install-launchd --uninstall

Other

dnomia-knowledge project-info              # List all projects with stats
dnomia-knowledge read-file <path>          # Smart file reading with chunk awareness
dnomia-knowledge export                    # CSV export of all chunks

How it works

Search pipeline

Query
  -> embed with "query: " prefix (768d vector, cached)
  -> FTS5 BM25 search (keyword matching)
  -> sqlite-vec KNN search (semantic similarity)
  -> RRF merge (k=60): score = sum(1/(k + rank + 1))
  -> Fallback: prefix matching if both return empty
  -> Interaction boost: re-rank by read/edit frequency (30-day window)
  -> Return top N with snippets

Indexing pipeline

Project directory
  -> Scan: filter by extension, size, .gitignore, config patterns
  -> For each changed file (MD5 hash comparison):
      -> .md/.mdx -> heading-based chunker (##/### splits, frontmatter)
      -> code -> Tree-sitter AST chunker (functions, classes, methods)
      -> Embed passages (batch=8, "passage: " prefix)
      -> Atomic transaction: delete old + insert chunks + insert vectors
  -> Build graph edges (link, tag, category, semantic, import)
  -> Update project metadata + git commit hash

Continuous indexing

git commit -> post-commit hook -> file lock -> background reindex
launchd (every 5 min) -> index-all --changed -> git HEAD comparison -> reindex

Only one index process runs at a time. File locks prevent concurrent embedding model loads (protects 8GB RAM machines).

Architecture

Single SQLite database with three search layers:

Layer	Technology	Purpose
Keyword	FTS5 (BM25)	Porter stemmer, unicode61 tokenizer
Semantic	sqlite-vec	Cosine KNN on 768d normalized vectors
Graph	NetworkX	Louvain communities, PageRank, BFS traversal

Embedding model: intfloat/multilingual-e5-base (768d). Lazy loaded on first search, auto-unloads after 10 minutes idle. Runs on 8GB RAM.

Code parsing: Tree-sitter with language pack. Extracts functions, classes, methods, structs, interfaces, enums with proper boundaries. Falls back to sliding-window chunking for unsupported languages.

Module structure

src/dnomia_knowledge/
  server.py        MCP server (6 tools, thread-safe singletons)
  store.py         SQLite persistence, schema v3, migrations, triggers
  search.py        Hybrid FTS5 + vector, RRF merge, interaction boost
  indexer.py       Scan -> chunk -> embed -> store pipeline
  graph.py         Edge builder, Louvain community detection, PageRank
  embedder.py      Lazy sentence-transformer, LRU cache, auto-unload
  cli.py           Rich CLI with 10+ commands
  registry.py      .knowledge.toml config loader (Pydantic v2)
  models.py        Chunk, SearchResult, IndexResult, InteractionType
  chunker/
    md_chunker.py  Heading-based markdown splitter
    ast_chunker.py Tree-sitter AST chunker with fallback
    languages.py   Per-language AST node type mappings
  hooks/
    pre_tool_use.py   Redirects large file reads to search
    post_tool_use.py  Logs read/edit interactions

Data model

Table	Purpose
projects	Registered projects with path, type, graph config, last indexed commit
chunks	Indexed content and code pieces with metadata
chunks_vec	sqlite-vec virtual table for vector embeddings (768d)
chunks_fts	FTS5 virtual table mirroring chunk content
file_index	Per-file MD5 hash tracking for incremental indexing
edges	Knowledge graph edges (link, tag, category, semantic, import)
chunk_interactions	Read/edit/search_hit tracking for boost and analytics
search_log	Query history for gap analysis and pattern tracking
git_commits	Parsed git log entries
git_file_changes	Per-file diff stats from git history

Triggers auto-sync FTS5 on chunk insert/update/delete. Vector cleanup triggers on chunk delete.

Environment variables

Variable	Default	Description
DNOMIA_KNOWLEDGE_DB	~/.local/share/dnomia-knowledge/knowledge.db	Database path
DNOMIA_KNOWLEDGE_PROJECT	(none)	Default project for MCP search

Requirements

• Python 3.11+
• macOS or Linux (launchd is macOS only, git hooks work everywhere)
• ~500MB disk for embedding model (downloaded once)
• 8GB RAM minimum (embedding model loads lazily)

Development

pip install -e ".[dev]"
python -m pytest tests/ -v       # 276 tests
ruff check src/ tests/           # Linting

License

MIT