vstash 0.17.4

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

vstash

Local hybrid retrieval engine that beats ColBERTv2 on BEIR SciFact with BGE-small.

Single SQLite file. Zero cloud dependencies. Sub-25ms at 10K chunks.

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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Retrieval Quality

Evaluated on the BEIR benchmark — the standard for comparing retrieval systems:

Dataset	vstash (NDCG@10)	ColBERTv2	BM25	Dense-only
SciFact (5K docs)	0.726	0.693 (+4.8%)	0.665 (+9.2%)	0.653 (+11.2%)
NFCorpus (3.6K docs)	0.359	0.344 (+4.4%)	0.325 (+10.5%)	0.338 (+6.2%)
SciDocs (25K docs)	0.194	0.154 (+26.2%)	0.158 (+23.0%)	0.163 (+19.2%)
FiQA (57K docs)	0.392	0.356 (+10.0%)	0.236 (+65.8%)	0.402 (−2.5%)
ArguAna (8.7K docs)	0.437	0.463 (−5.6%)	0.315 (+38.7%)	0.584 (−25.2%)

Same embedding model (BGE-small 384d) across all comparisons. Adaptive RRF improves all 5 datasets vs fixed weights. Results reproducible via python -m experiments.beir_benchmark.

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Why vstash?

Layer	Technology	Why
Embeddings	FastEmbed (ONNX Runtime)	~700 chunks/s, fully local, no server
Vector store	sqlite-vec	Single .db file, cosine similarity, zero deps
Keyword search	FTS5 (SQLite)	Exact matches, built into SQLite
Hybrid ranking	Reciprocal Rank Fusion	Semantic + keyword fusion — beats both alone
Scoring	Frequency + temporal decay	Results improve with usage, adaptive maturity gate
Dedup	Intra-document MMR	Diverse sections from long docs, not redundant chunks
Inference	Local auto-detect / Cloud	Ollama, LM Studio, Cerebras, OpenAI — all optional

Zero cloud required for search. Inference is optional.

What's new in v0.17

• Dynamic chunk_size — Memory(chunk_size=2048) or vstash add --chunk-size 2048. Per-document override without modifying config. Validation: overlap < chunk_size.
• Adaptive RRF — IDF-based weight adjustment per query. Rare terms boost keyword search, common terms boost vector search. Long queries relax distance cutoff. Improves all 5 BEIR datasets.
• 615 tests across 28 test modules (+ 6 benchmark regression tests).

What's new in v0.16

• Local-first LLM auto-detect — New default backend "local" probes for Ollama, LM Studio, or any OpenAI-compatible server. Zero config needed — just start a local server and vstash ask works.
• Search --explain — Diagnostic flag showing why each chunk ranked where it did: vector distance, FTS rank, RRF breakdown, frequency/decay scoring, and MMR penalty.
• 612 tests across 27 test modules, all passing on Python 3.10–3.12.

What's new in v0.15

• Unified DB resolution — CLI, MCP server, SDK, and reindex all share the same 6-level database resolution chain. Fixes bugs where different entry points could silently operate on different databases.
• Federated context expansion — --all-profiles now expands adjacent chunks per-store before merging, matching single-profile answer quality.
• 592 tests across 27 test modules, all passing on Python 3.10–3.12.

What's new in v0.14

• Document reconstruction — get_document_chunks(path) retrieves all chunks for a document in order. Available in Python SDK and as MCP tool.

What's new in v0.13

• Direct chunk retrieval — get_chunk(id) and get_chunks(ids) for O(1) access to specific chunks by database ID. Enables downstream apps (spaced repetition, pinned references) to retrieve knowledge atoms without re-running search.

What's new in v0.12

• Cross-session journal — vstash journal save/recall/log/prune for lightweight agent memory across sessions. Append-only entries with semantic recall, project tags, and time-window filtering.
• Transcript parsing — automatically extract structured journal entries from conversation logs.

What's new in v0.11

• Multi-profile support — isolated databases per profile with vstash profile create/list/delete/active.
• Federated search — query across all profiles simultaneously with cross-profile deduplication.
• Profile resolution chain — --profile flag → VSTASH_PROFILE env → default.

What's new in v0.10

• Hybrid code splitting — 3-tier backend: tree-sitter AST → parso AST → regex fallback. Each backend gracefully degrades to the next.
• 25+ languages — tree-sitter support for C, C++, Ruby, PHP, Swift, Kotlin, Scala, Lua, R, C#, Bash, Zig, Elixir, Erlang, Haskell, OCaml, Dart, Vue, Svelte (plus all previously supported).
• Optional install — pip install vstash[treesitter] for tree-sitter, or use parso (Python) + regex (6 languages) by default.

What's new in v0.9

• Auto-generated titles — vstash remember generates descriptive slugs when no --title is provided.
• Forget remembered text — vstash forget "text://<title>" removes text ingested via remember.

What's new in v0.8

• Multilingual embeddings — search in any language. Cross-lingual similarity improves ~40%.
• vstash reindex — switch embedding models without re-ingesting.
• Intra-document MMR dedup — replaces hard per-document dedup. Semantically diverse sections from the same long document now surface in results.

Earlier versions

• v0.7 — Adaptive scoring maturity gate (γ), zero-cost cold start.
• v0.6 — Distance-based relevance signal (F1=0.952), document dedup, context expansion (±1 chunks).

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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, start any local LLM server — vstash auto-detects it:

# Option A: Ollama (auto-detected on port 11434)
ollama pull qwen3.5:9b

# Option B: LM Studio (auto-detected on port 1234 or 8080)
# Just load a model in the GUI

# Option C: Cloud backends (set in vstash.toml)
# inference.backend = "cerebras" + inference.model = "llama3.1-8b" + CEREBRAS_API_KEY env
# inference.backend = "openai"   + OPENAI_API_KEY env

Then:

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

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Python SDK

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")
mem.remember("OAuth uses PKCE for public clients", title="Auth Decision")

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

# Direct chunk access by ID (O(1) lookup)
chunk = mem.get_chunk(chunks[0].chunk_id)

# Full document reconstruction from chunks
all_chunks = mem.get_document_chunks("docs/spec.pdf")

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

# Cross-session journal
mem.journal_save("Decided to use FastAPI for the gateway")
entries = mem.journal_recall("architecture decisions")

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

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Commands

vstash add <file/dir/url>   Add documents to memory
vstash remember "<text>"    Ingest text directly (no file needed)
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 reindex              Re-embed all chunks with a new model
vstash watch <dir>          Auto-ingest on file changes
vstash export               Export chunks as JSONL for training data curation
vstash config               Show current configuration
vstash profile <cmd>        Manage named profiles (create, list, delete, active)
vstash journal <cmd>        Cross-session memory (save, recall, log, prune)
vstash-mcp                  Start MCP server (for Claude Desktop integration)

Filtering with metadata

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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Configuration

vstash looks for vstash.toml in your current directory, then ~/.vstash/vstash.toml, then falls back to sensible defaults. Run vstash config to see your active settings.

See the Configuration Reference for all options.

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Privacy

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

Search is always private. For fully private answers, use a local LLM (default) or skip inference entirely with vstash search.

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Supported File Types

PDF, DOCX, PPTX, XLSX, Markdown, TXT, HTML, CSV — and any URL.

Code files (25+ languages with tree-sitter): Python, JavaScript, TypeScript, Go, Rust, Java, C, C++, Ruby, PHP, Swift, Kotlin, Scala, Lua, R, C#, Bash, Zig, Elixir, Erlang, Haskell, OCaml, Dart, Vue, Svelte.

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Experiments

Experiment	Corpus	Key Result	Command
BEIR Benchmark	5 BEIR datasets, up to 57K docs	Beats BM25 5/5, ColBERTv2 4/5; NDCG@10=0.726 on SciFact	python -m experiments.beir_benchmark
ArXiv Retrieval	1,000 ML papers, 3 models	P@5=0.703, MRR=0.895	python -m experiments.arxiv_retrieval_bench
Dataset Discovery	954 HuggingFace datasets	91.4% discovery rate	python -m experiments.dataset_discovery
Answer Relevance	SciFact, NFCorpus	+8.3% answer quality vs Chroma (LLM judge)	python -m experiments.answer_relevance

The dataset discovery engine also has an interactive mode — describe what you need, get the right dataset:

python -m experiments.dataset_discovery --interactive
> time series forecasting for retail sales
1. walmart-sales-dataset (time-series-forecasting) — 0.87

Run all experiments: python -m experiments.run_all

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Documentation

Guide	Description
Configuration	Full TOML reference — all sections and options
How It Works	Ingestion pipeline, search pipeline, chunking strategies, RRF
Memory Scoring	Frequency + decay re-ranking — formula, tuning, disabling
MCP Server	MCP integration — 16 tools for any MCP-compatible client
Agent Integration	Claude Code, Claude Desktop, and other LLM agents
LangChain	VstashRetriever for chains and agents
Embedding Models	Model comparison and backend selection
Experiments	Retrieval benchmarks — hypotheses, results, conclusions

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Roadmap

• Phase 1 ✅: Core — ingest, embed, hybrid search, answer
• Phase 2 ✅: Usability — MCP server, collections, watch mode, metadata, export
• Phase 3 ✅: Python SDK — from vstash import Memory
• Phase 4 ✅: LangChain integration — VstashRetriever
• Phase 5 ✅: Memory scoring — frequency + temporal decay re-ranking
• Phase 6 ✅: Retrieval quality — distance-based relevance signal, document dedup, context expansion
• Phase 7 ✅: Multilingual — cross-lingual embeddings, vstash reindex, MMR dedup
• Phase 8 ✅: Hybrid code splitting — tree-sitter + parso + regex, 25+ languages
• Phase 9 ✅: Multi-profile — isolated databases, federated search, profile management
• Phase 10 ✅: Cross-session journal — save, recall, log, prune for agent memory
• Phase 11 ✅: Direct chunk API — get_chunk/get_chunks for O(1) retrieval by ID

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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