mindvault 0.5.3

Local-first second brain RAG system — chat with your own data

MindVault

A local-first second brain that turns your AI conversation exports, Obsidian notes, and documents into a searchable, conversational memory system.

Everything runs on your machine. No data leaves.

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What it does

• Ingests Anthropic conversation exports, Obsidian vaults, PDFs, and plain text files
• Indexes content into a multi-layer memory system (raw → compressed → structured → linked)
• Remembers entities, decisions, and goals extracted from every chat
• Retrieves using hybrid scoring — summaries first, raw text only when needed
• Chats interactively with six reasoning modes powered by a council of AI voices
• Searches the web automatically when your memory doesn't have a confident answer
• Saves sessions — resume any previous conversation exactly where you left off

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Quick start

Prerequisites

• Python 3.11+
• Ollama with two models:

ollama pull nomic-embed-text   # vector search
ollama pull llama3.2           # chat and summarization
ollama serve                   # start Ollama if not running

Install

git clone https://github.com/calebthecm/MindVault
cd MindVault
python -m venv .venv && source .venv/bin/activate
pip install -e .               # installs deps + registers the mindvault CLI

Or without the CLI shortcut:

pip install -r requirements.txt

First run

mindvault setup                # or: python -m mindvault setup

Add your data

Drop your Anthropic export folder into the project directory (any folder starting with data-). PDFs and .txt/.md files go anywhere — point the ingester at them manually.

• Anthropic export: claude.ai → Settings → Export Data

Index and chat

mindvault ingest               # index everything
mindvault chat                 # start talking to your brain

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Running MindVault

Three equivalent ways to run it — use whichever you prefer:

# After pip install -e . (recommended)
mindvault
mindvault chat
mindvault ingest

# As a Python module (no install needed)
python -m mindvault
python -m mindvault chat
python -m mindvault ingest

# Legacy script (still works)
python mindvault.py
python mindvault.py chat
python mindvault.py ingest

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Commands

mindvault                           chat (default)
mindvault chat                      interactive REPL
mindvault chat --resume             resume last session
mindvault chat --resume <id>        resume specific session
mindvault ingest                    auto-discover and index all exports
mindvault ingest ./folder/          index a specific folder
mindvault ingest --force            re-index even if already processed
mindvault notes                     regenerate Obsidian notes
mindvault setup                     first-run configuration wizard
mindvault stats                     show index and session statistics
mindvault sessions                  list resumable sessions
mindvault consolidate               merge near-duplicate memories

During a chat session:

Shift+Tab            cycle reasoning mode
/help                show all commands
/web <query>         search the web (DuckDuckGo, no API key needed)
/search <term>       search memory without LLM — shows scored results
/note <text>         quick-capture a note (indexed on next ingest)
/forget <topic>      suppress matching chunks from future retrieval
/mode [name]         show or switch mode (CHAT, PLAN, DECIDE, DEBATE, REFLECT, EXPLORE)
/sources             show which memories were used in the last answer
/remember <fact>     save a fact to this session
/private             toggle private vault inclusion
/resume              interactive session picker
/clear               clear conversation history
/quit, /exit         end session (compresses and saves automatically)

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Web search

MindVault searches the web automatically when memory confidence is low, or on demand:

/web what is the current price of ETH?
/web latest news on local SEO in 2025

Uses DuckDuckGo — no API key, no Docker, no setup. Configure in config.py:

WEB_SEARCH_AUTO_THRESHOLD = 0.45   # auto-search when best memory score is below this
WEB_SEARCH_MAX_RESULTS    = 5      # results to include in context

Set WEB_SEARCH_AUTO_THRESHOLD = 0 to disable auto-search.

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Reasoning modes

MindVault has six modes, cycled with Shift+Tab in the prompt bar.

Mode	What it does
💬 CHAT	Standard RAG — retrieve memories, synthesize an answer
📋 PLAN	Break the task into structured, actionable steps
🗳 DECIDE	Five-voice council votes; tally + majority verdict shown
⚖ DEBATE	FOR vs AGAINST, then a moderated verdict
🔍 REFLECT	Deep synthesis — what does your brain really know about this?
🕸 EXPLORE	Graph traversal — follows memory links to surface surprises

The council is five internal voices with distinct personalities:

Voice	Orientation
📊 The Analyst	Evidence-first, skeptical, quantitative
🚀 The Visionary	Big-picture, creative, optimistic
🔧 The Pragmatist	What's actionable right now
😈 The Devil	Challenges every assumption, finds the flaw
📜 The Historian	Patterns across time; what past memory reveals

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How it works

Memory layers

Layer	What	Used for
Raw	Original text chunks	Fallback when summaries aren't confident enough
Compressed	LLM-generated summaries per session/document	Primary retrieval context
Structured	Extracted entities (persons, projects, decisions, goals)	Entity-boosted retrieval
Linked	Relationships between memories via shared entities + wikilinks	Graph traversal in EXPLORE mode
Web	Live DuckDuckGo results	Augments memory for current/unknown topics

Retrieval scoring

score = 0.5 × embedding_similarity
      + 0.2 × entity_overlap
      + 0.2 × recency
      + 0.1 × importance

Compressed summaries are searched first. Raw chunks are only fetched when confidence drops below the threshold. EXPLORE mode additionally walks memory_links to pull in related neighbors.

Session lifecycle

1. During chat: turns saved live + entities extracted per exchange (background)
2. On exit: LLM compresses the session into a 2–4 sentence summary
3. Summary embedded and stored in the compressed memory layer
4. Resume anytime with --resume or /resume during chat

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Configuration

All settings in mindvault/config.py:

Variable	Default	What it controls
LLM_MODEL	llama3.2	Model for summarization, chat, extraction
EMBEDDING_MODEL	nomic-embed-text	Vector search embeddings
CHAT_TOP_K	8	Chunks retrieved per query
COMPRESSED_SCORE_THRESHOLD	0.75	Below this, also fetch raw chunks
WEB_SEARCH_AUTO_THRESHOLD	0.45	Auto web search below this memory score (0 = off)
SUGGEST_FOLLOWUPS	True	Suggest follow-up questions after each answer
WRITE_SESSIONS_TO_VAULT	True	Write session summary notes to Obsidian on exit
CHAT_INCLUDE_PRIVATE	False	Include private vault by default

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Storage

Path	What
brain.db	SQLite: ingestion tracking, entities, links, importance scores
.qdrant/	Qdrant: vector index (raw + compressed collections)
sessions/	Compressed chat sessions (.json.gz)
notes/	Quick-captured notes via /note (indexed on next ingest)
My Brain/	Obsidian vault — business, projects, general knowledge
Private Brain/	Obsidian vault — personal content (separate collection)
data-*/	Export folders (excluded from git)

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Privacy

• All processing is local by default.
• Web search uses DuckDuckGo's anonymous API — no account, no tracking.
• My Brain and Private Brain are in separate Qdrant collections — private content is never implicitly included in responses.
• .gitignore excludes all personal data: vaults, exports, sessions, databases.

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Requirements

qdrant-client       vector database
httpx               HTTP client (LLM + web requests)
python-dotenv       .env file loading
pypdf               PDF ingestion
prompt_toolkit      TUI and interactive input
rich                markdown rendering in terminal
ddgs                web search (no API key)
trafilatura         web page content extraction