vstash 0.3.1

Local document memory with instant semantic search. Drop any file. Ask anything. Get an answer in under a second.

vstash

Local document memory with instant semantic search.

Drop any file. Ask anything. Get an answer fast.

pip install vstash
vstash add paper.pdf notes.md https://example.com/article
vstash search "what's the main argument about X?"

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Why vstash?

Most RAG tools are slow, cloud-dependent, or require a running server. vstash is none of those things.

Layer	Technology	Why
Embeddings	FastEmbed (ONNX Runtime)	~700 chunks/s, fully in-process, no server
Vector store	sqlite-vec	Single .db file, cosine similarity, zero deps
Keyword search	FTS5 (SQLite)	Exact matches, porter stemming, built into SQLite
Hybrid ranking	Reciprocal Rank Fusion	Best of both: semantic + keyword, no training needed
Chunking	Semantic-first	Markdown headers & paragraphs, with token-bounded fallback
Inference	Cerebras / Ollama / OpenAI	~2,000 tok/s via Cerebras, or 100% local via Ollama
Parsing	markitdown	PDF, DOCX, PPTX, XLSX, HTML, Markdown, URLs

Philosophy: extreme speed at every layer. Zero cloud required for search.

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Install

pip install vstash

Or from source:

git clone https://github.com/stffns/vstash
cd vstash
pip install -e .

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Quick start

Search (free, no API key needed)

Semantic search works 100% locally — no inference backend required:

vstash add report.pdf
vstash add ~/docs/notes.md
vstash add https://arxiv.org/abs/2310.06825
vstash search "what is the proposed method?"

Ask (requires an LLM backend)

To get natural language answers, configure an inference backend:

# Option A: Fully local with Ollama (free, private)
# Install Ollama: https://ollama.com
ollama pull llama3.2

# Option B: Fast with Cerebras (free tier available)
export CEREBRAS_API_KEY=your_key_here

# Option C: OpenAI or any compatible API
export OPENAI_API_KEY=your_key_here

Then:

vstash ask "summarize the key findings"
vstash chat   # interactive Q&A session

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Python SDK

New in v0.3.0. Use vstash as a building block in your own agents and pipelines:

from vstash import Memory

mem = Memory(project="my_agent")
mem.add("docs/spec.pdf")

# Semantic search — free, no LLM
chunks = mem.search("deployment strategy", top_k=5)
for c in chunks:
    print(c.text, c.score)

# Search + LLM answer
answer = mem.ask("What are the system requirements?")

# Management
mem.list()                # → list[DocumentInfo]
mem.stats()               # → StoreStats
mem.remove("docs/old.pdf")

The Memory class supports project/collection scoping, context managers, and works with any configured inference backend. See the full API in vstash/memory.py.

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Commands

vstash add <file/dir/url>   Add documents to memory
vstash ask "<question>"     Answer a question from your documents
vstash search "<query>"     Semantic search without LLM (free, local)
vstash chat                 Interactive Q&A session
vstash list                 Show all documents in memory
vstash stats                Memory statistics (docs, chunks, DB size)
vstash forget <file>        Remove a document from memory
vstash watch <dir>          Auto-ingest on file changes
vstash export               Export chunks as JSONL for training data curation
vstash config               Show current configuration
vstash-mcp                  Start MCP server (for Claude Desktop integration)

Filtering with metadata

vstash supports hierarchical metadata via frontmatter or CLI flags:

vstash add notes.md --collection research --project ml-survey --tags "attention,transformers"
vstash list --project ml-survey
vstash ask "what architectures were compared?" --project ml-survey
vstash export --project ml-survey --format jsonl

Documents with YAML frontmatter are parsed automatically:

---
project: ml-survey
layer: literature-review
tags: [attention, transformers]
---

# My Research Notes
...

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MCP Server — Claude Desktop Integration

vstash includes a built-in MCP server that gives Claude Desktop persistent document memory across sessions.

Setup

1. Install vstash:

pip install vstash

2. Add to Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "vstash": {
      "command": "vstash-mcp"
    }
  }
}

If using pyenv, use the full path: "command": "/path/to/.pyenv/versions/3.x.x/bin/vstash-mcp"

3. Restart Claude Desktop.

Available MCP Tools

Tool	Description
vstash_add(path)	Ingest a file, directory, or URL into memory
vstash_ask(query, top_k)	Semantic search + LLM-generated answer with sources
vstash_search(query, top_k)	Raw retrieval without LLM — returns chunks with scores
vstash_list()	List all ingested documents
vstash_stats()	Database statistics (doc count, chunks, size)
vstash_forget(source)	Remove a document from memory

Make sure your ~/.vstash/vstash.toml includes the API key under [cerebras] (or your chosen backend), since MCP servers don't inherit shell environment variables.

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Configuration

vstash looks for vstash.toml in your current directory, then ~/.vstash/vstash.toml, then falls back to defaults.

[inference]
backend = "cerebras"       # "cerebras" | "ollama" | "openai"
model   = "llama3.1-8b"

[cerebras]
api_key = ""               # or set CEREBRAS_API_KEY env var

[ollama]
host  = "http://localhost:11434"
model = "llama3.2"

[embeddings]
model = "BAAI/bge-small-en-v1.5"   # 384 dims, ~700 chunks/s

[chunking]
size    = 1024    # max tokens per chunk
overlap = 128     # token overlap (used in fixed-window fallback)
top_k   = 5       # chunks retrieved per query

[storage]
db_path = "~/.vstash/memory.db"

Embedding models

Model	Dims	Speed	Quality
BAAI/bge-small-en-v1.5	384	~700 chunks/s	Great
BAAI/bge-base-en-v1.5	768	~300 chunks/s	Excellent
nomic-ai/nomic-embed-text-v1.5	768	~300 chunks/s	Excellent

Changing the embedding model requires re-ingesting all documents (dimensions must match).

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How it works

Ingestion pipeline

file/URL
  → markitdown         (parse to plain text)
  → _split_by_headers  (Markdown sections)
  → _split_by_paragraphs (paragraph boundaries)
  → _fixed_window      (fallback for oversized paragraphs)
  → _merge_small       (merge tiny chunks < 80 tokens)
  → FastEmbed ONNX     (embed each chunk, ~700 chunks/s)
  → sqlite-vec         (store vectors)
  → FTS5               (index text for keyword search)

Semantic chunking preserves document structure: Markdown headers stay with their body content, paragraphs aren't torn mid-sentence, and tiny fragments are merged to avoid low-quality embeddings.

Search pipeline

query
  → FastEmbed ONNX     (embed query)
  → sqlite-vec         (top-k×10 vector candidates by cosine similarity)
  → FTS5               (top-k×10 keyword candidates by BM25)
  → RRF                (merge rankings: score = Σ 1/(60+rank))
  → top-k results      (default: 5 chunks)
  → LLM                (optional: Cerebras, Ollama, or OpenAI)

Reciprocal Rank Fusion (k=60, vec_weight=0.6, fts_weight=0.4) ensures that semantic queries find conceptually related chunks while exact keyword queries are never missed.

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Privacy

Component	Data leaves machine?
Embeddings (FastEmbed)	Never — fully local ONNX
Vector store (sqlite-vec)	Never — local .db file
Semantic search	Never — local embeddings + SQLite
Inference (Cerebras/OpenAI)	Yes — query + retrieved chunks sent to API
Inference (Ollama)	Never — fully local

For full privacy, use backend = "ollama" or skip inference entirely and use vstash search instead of vstash ask.

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Supported file types

PDF, DOCX, PPTX, XLSX, Markdown, TXT, HTML, CSV, Python, JavaScript, TypeScript, Go, Rust, Java — and any URL.

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Roadmap

• Phase 1 ✅: Core — ingest, embed, hybrid search, answer
• Phase 2 ✅: Usability — MCP server, collections/namespaces, watch mode, frontmatter metadata, export, semantic chunking
• Phase 3 ✅: Python SDK — from vstash import Memory
• Phase 4: Sync — cr-sqlite CRDT peer-to-peer sync, multiple profiles, REST API

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Easter Egg

In a 2018 Cornell paper "Local Homology of Word Embeddings", researchers used the variable v_stash (p. 11) to refer to the "vector of the word stash" — making this the first documented use of the exact term in the context of AI/embeddings.

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License

MIT