ruvector/npm/packages/ospipe/README.md

@ruvector/ospipe

RuVector-enhanced personal AI memory SDK for Screenpipe

---

What is OSpipe?

Screenpipe is an open-source desktop application that continuously records your screen, audio, and UI interactions locally. It builds a searchable timeline of everything you see, hear, and do on your computer. Out of the box, Screenpipe stores its data in SQLite with FTS5 full-text indexing -- effective for keyword lookups, but limited to literal string matching. If you search for "auth discussion," you will not find a frame that says "we talked about login security."

OSpipe replaces Screenpipe's storage and search backend with the RuVector ecosystem -- a collection of 70+ Rust crates providing HNSW vector search, graph neural networks, attention mechanisms, delta-change tracking, and more. Instead of keyword matching, OSpipe embeds every captured frame into a high-dimensional vector space and performs approximate nearest neighbor search, delivering true semantic recall. A query like "what was that API we discussed in standup?" will surface the relevant audio transcription even if those exact words never appeared.

Everything stays local and private. OSpipe processes all data on-device with no cloud dependency. The safety gate automatically detects and redacts PII -- credit card numbers, Social Security numbers, and email addresses -- before content ever reaches the vector store. A cosine-similarity deduplication window prevents consecutive identical frames (like a static desktop) from bloating storage. Age-based quantization progressively compresses older embeddings from 32-bit floats down to 1-bit binary, cutting long-term memory usage by 97%.

Ask your computer what you saw, heard, and did -- with semantic understanding.

---

Install

npm install @ruvector/ospipe

Also available:

Package	Install	Description
@ruvector/ospipe	npm install @ruvector/ospipe	TypeScript SDK for Node.js and browser
@ruvector/ospipe-wasm	npm install @ruvector/ospipe-wasm	WASM bindings (145 KB) for browser-only use
ospipe	cargo add ospipe	Rust crate with full pipeline

---

Features

• Semantic Vector Search -- HNSW index via ruvector-core with 61us p50 query latency
• Knowledge Graph -- Cypher queries over extracted entities (people, apps, topics, meetings)
• Temporal Deltas -- track how content changed over time with delta-behavior analysis
• Attention Streaming -- real-time SSE stream of attention-weighted events
• PII Safety Gate -- automatic redaction of credit card numbers, SSNs, and email addresses before storage
• Frame Deduplication -- cosine similarity sliding window eliminates near-duplicate captures
• Query Router -- automatically routes queries to the optimal backend (Semantic, Keyword, Graph, Temporal, or Hybrid)
• Hybrid Search -- weighted combination of semantic vector similarity and keyword term overlap
• WASM Support -- runs entirely in the browser with bundles from 11.8KB (micro) to 350KB (full)
• Configurable Quantization -- 4-tier age-based compression: f32 -> int8 -> product -> binary (97% savings)
• Retry + Timeout -- exponential backoff, AbortSignal support, configurable timeout
• Screenpipe Compatible -- backward-compatible queryScreenpipe() for existing code

---

Architecture

                         OSpipe Ingestion Pipeline
                         =========================

  Screenpipe -----> Capture -----> Safety Gate -----> Dedup -----> Embed -----> VectorStore
  (Screen/Audio/UI)  (CapturedFrame)  (PII Redaction)   (Cosine Window)  (HNSW)      |
                                                                                      |
                                                           Search Router <------------+
                                                           |    |    |    |    |
                                                        Semantic Keyword Graph Temporal Hybrid

Frames flow left to right through the ingestion pipeline. Each captured frame passes through:

1. Safety Gate -- PII detection and redaction; content may be allowed, redacted, or denied
2. Deduplication -- cosine similarity check against a sliding window of recent embeddings
3. Embedding -- text content is encoded into a normalized vector
4. Vector Store -- the embedding is indexed for approximate nearest neighbor retrieval

Queries enter through the Search Router, which analyzes the query string and dispatches to the optimal backend.

---

Quick Start

TypeScript SDK

import { OsPipe } from "@ruvector/ospipe";

const client = new OsPipe({ baseUrl: "http://localhost:3030" });

// Semantic search across everything you've seen, heard, and done
const results = await client.queryRuVector(
  "what did we discuss about authentication?"
);

for (const hit of results) {
  console.log(`[${hit.score.toFixed(3)}] ${hit.content}`);
  console.log(`  app: ${hit.metadata.app}, time: ${hit.timestamp}`);
}

WASM (Browser)

import { OsPipeWasm } from "@ruvector/ospipe-wasm";

// Initialize with 384-dimensional embeddings
const pipe = new OsPipeWasm(384);

// Embed and insert content
const embedding = pipe.embed_text("meeting notes about auth migration to OAuth2");
pipe.insert("frame-001", embedding, '{"app":"Chrome","window":"Jira"}', Date.now());

// Embed a query and search
const queryEmbedding = pipe.embed_text("what was the auth discussion about?");
const results = pipe.search(queryEmbedding, 5);
console.log("Results:", results);

// Safety check before storage
const safety = pipe.safety_check("my card is 4111-1111-1111-1111");
console.log("Safety:", safety); // "deny"

// Query routing
const route = pipe.route_query("what happened yesterday?");
console.log("Route:", route); // "Temporal"

// Pipeline statistics
console.log("Stats:", pipe.stats());

Start the OSpipe Server

# Using the Rust binary
cargo install ospipe
ospipe-server --port 3030

# Or build from source
cargo build -p ospipe --release --bin ospipe-server
./target/release/ospipe-server --port 3030 --data-dir ~/.ospipe

---

Comparison: Screenpipe vs OSpipe

Feature	Screenpipe (FTS5)	OSpipe (RuVector)
Search Type	Keyword (FTS5)	Semantic + Keyword + Graph + Temporal
Search Latency	~1ms (FTS5)	61us (HNSW p50)
Content Relations	None	Knowledge Graph (Cypher)
Temporal Analysis	Basic SQL	Delta-behavior tracking
PII Protection	Basic	Credit card, SSN, email redaction
Deduplication	None	Cosine similarity sliding window
Browser Support	None	WASM (11.8KB - 350KB)
Quantization	None	4-tier age-based (f32 -> binary)
Privacy	Local-first	Local-first + PII redaction
Query Routing	None	Auto-routes to optimal backend
Hybrid Search	None	Weighted semantic + keyword fusion
Metadata Filtering	SQL WHERE	App, time range, content type, monitor

---

API Reference

Constructor

const client = new OsPipe({
  baseUrl: "http://localhost:3030",   // OSpipe server URL
  apiVersion: "v2",                   // API version ("v1" | "v2")
  defaultK: 10,                       // Default number of results
  hybridWeight: 0.7,                  // Semantic vs keyword weight (0-1)
  rerank: true,                       // Enable MMR deduplication
  timeout: 10_000,                    // Request timeout in ms
  maxRetries: 3,                      // Retry attempts for 5xx/network errors
});

queryRuVector(query, options?) -- Semantic Search

const results = await client.queryRuVector("user login issues", {
  k: 5,
  metric: "cosine",                   // "cosine" | "euclidean" | "dot"
  rerank: true,                       // MMR deduplication
  confidence: true,                   // Include confidence bounds
  filters: {
    app: "Chrome",
    contentType: "screen",            // "screen" | "audio" | "ui" | "all"
    timeRange: { start: "2026-02-12T00:00:00Z", end: "2026-02-12T23:59:59Z" },
    speaker: "Alice",
    monitor: 0,
    language: "en",
  },
});

Returns SearchResult[]:

interface SearchResult {
  id: string;
  score: number;
  content: string;
  source: "screen" | "audio" | "ui";
  timestamp: string;
  metadata: {
    app?: string;
    window?: string;
    monitor?: number;
    speaker?: string;
    confidence?: number;
    language?: string;
  };
}

queryGraph(cypher) -- Knowledge Graph

const result = await client.queryGraph(
  "MATCH (p:Person)-[:MENTIONED_IN]->(m:Meeting) RETURN p, m LIMIT 10"
);

console.log(result.nodes);  // GraphNode[] with id, label, type, properties
console.log(result.edges);  // GraphEdge[] with source, target, type

Node types: App, Window, Person, Topic, Meeting, Symbol.

queryDelta(options) -- Temporal Changes

const deltas = await client.queryDelta({
  app: "VSCode",
  timeRange: {
    start: "2026-02-12T09:00:00Z",
    end: "2026-02-12T17:00:00Z",
  },
  includeChanges: true,
});

for (const delta of deltas) {
  console.log(`${delta.timestamp} [${delta.app}]`);
  for (const change of delta.changes) {
    console.log(`  -${change.removed} +${change.added}`);
  }
}

streamAttention(options?) -- Real-Time Events

for await (const event of client.streamAttention({
  threshold: 0.5,
  categories: ["code_change", "meeting_start"],
  signal: AbortSignal.timeout(60_000),
})) {
  console.log(`[${event.category}] ${event.summary} (${event.attention})`);
}

Event categories: code_change, person_mention, topic_shift, context_switch, meeting_start, meeting_end.

routeQuery(query) -- Query Routing

const route = await client.routeQuery("who mentioned auth yesterday?");
// route: "semantic" | "keyword" | "graph" | "temporal" | "hybrid"

stats() -- Pipeline Statistics

const stats = await client.stats();
// { totalIngested, totalDeduplicated, totalDenied, storageBytes, indexSize, uptime }

health() -- Server Health

const health = await client.health();
// { status: "ok", version: "0.1.0", backends: ["hnsw", "keyword", "graph"] }

queryScreenpipe(options) -- Legacy API

Backward-compatible with @screenpipe/js:

const results = await client.queryScreenpipe({
  q: "meeting notes",
  contentType: "ocr",         // "all" | "ocr" | "audio"
  limit: 20,
  appName: "Notion",
  startTime: "2026-02-12T00:00:00Z",
  endTime: "2026-02-12T23:59:59Z",
});

---

Safety Gate

PII Detection Details

The safety gate inspects all captured content before it enters the ingestion pipeline. It operates in three modes:

Decision	Behavior	When
Allow	Content stored as-is	No sensitive patterns detected
AllowRedacted	Content stored with PII replaced by tokens	PII detected, redaction enabled
Deny	Content rejected, not stored	Custom deny pattern matched

Detected PII patterns:

• Credit Cards -- sequences of 13-16 digits (with optional spaces or dashes) -> [CC_REDACTED]
• Social Security Numbers -- XXX-XX-XXXX format -> [SSN_REDACTED]
• Email Addresses -- word@domain.tld patterns -> [EMAIL_REDACTED]
• Sensitive Keywords (WASM) -- password, secret, api_key, api-key, apikey, token, private_key, private-key

WASM safety API:

pipe.safety_check("my card is 4111-1111-1111-1111"); // "deny"
pipe.safety_check("set password to foo123");          // "redact"
pipe.safety_check("the weather is nice today");       // "allow"

---

Configuration Guide

Client Configuration

All configuration options with defaults:

Option	Type	Default	Description
baseUrl	string	"http://localhost:3030"	OSpipe server URL
apiVersion	"v1" | "v2"	"v2"	API version
defaultK	number	10	Default number of results
hybridWeight	number	0.7	Semantic vs keyword weight (0 = pure keyword, 1 = pure semantic)
rerank	boolean	true	Enable MMR deduplication
timeout	number	10000	Request timeout in milliseconds
maxRetries	number	3	Retry attempts for 5xx/network errors

Retry behavior: The SDK uses exponential backoff starting at 300ms. Only network errors and HTTP 5xx responses are retried. Client errors (4xx) are never retried. Each request has an independent AbortController timeout.

// High-throughput configuration
const client = new OsPipe({
  baseUrl: "http://localhost:3030",
  defaultK: 50,
  hybridWeight: 0.9,       // lean heavily toward semantic
  timeout: 30_000,          // 30s for large result sets
  maxRetries: 5,
});

// Low-latency configuration
const fast = new OsPipe({
  defaultK: 3,
  hybridWeight: 1.0,        // pure semantic, skip keyword
  rerank: false,             // skip MMR reranking
  timeout: 2_000,
  maxRetries: 0,             // fail fast, no retries
});

Server Configuration (Rust)

The OSpipe server is configured via OsPipeConfig with nested subsystem configs. All fields have sensible defaults.

Subsystem	Key Fields	Defaults
Capture	fps, audio_chunk_secs, excluded_apps, skip_private_windows	1.0 fps, 30s chunks, excludes 1Password/Keychain
Storage	embedding_dim, hnsw_m, hnsw_ef_construction, dedup_threshold	384 dims, M=32, ef=200, 0.95 threshold
Search	default_k, hybrid_weight, mmr_lambda, rerank_enabled	k=10, 0.7 hybrid, 0.5 MMR lambda
Safety	pii_detection, credit_card_redaction, ssn_redaction, custom_patterns	All enabled, no custom patterns

# Start with defaults
ospipe-server --port 3030

# Custom data directory
ospipe-server --port 3030 --data-dir /var/lib/ospipe

---

WASM Deployment

Bundle Tiers & Web Worker Setup

Bundle Tiers

OSpipe provides four WASM bundle sizes depending on which features you need:

Tier	Size	Features
Micro	11.8KB	Embedding + vector search only
Standard	225KB	Full pipeline (embed, insert, search, filtered search)
Full	350KB	+ deduplication + safety gate + query routing
AI	2.5MB	+ on-device neural inference (ONNX)

Web Worker Setup

For best performance, run OSpipe in a Web Worker to avoid blocking the main thread:

// worker.js
import { OsPipeWasm } from "@ruvector/ospipe-wasm";

const pipe = new OsPipeWasm(384);

self.onmessage = (event) => {
  const { type, payload } = event.data;

  switch (type) {
    case "insert":
      const emb = pipe.embed_text(payload.text);
      pipe.insert(payload.id, emb, JSON.stringify(payload.metadata), Date.now());
      self.postMessage({ type: "inserted", id: payload.id });
      break;

    case "search":
      const queryEmb = pipe.embed_text(payload.query);
      const results = pipe.search(queryEmb, payload.k || 10);
      self.postMessage({ type: "results", data: results });
      break;
  }
};

SharedArrayBuffer

For multi-threaded WASM (e.g., parallel batch embedding), set the required headers:

Cross-Origin-Opener-Policy: same-origin
Cross-Origin-Embedder-Policy: require-corp

WASM API Reference

Method	Parameters	Returns	Description
new(dimension)	number	OsPipeWasm	Constructor
insert(id, embedding, metadata, timestamp)	string, Float32Array, string, number	void	Insert a frame
search(query_embedding, k)	Float32Array, number	JSON array	Semantic search
search_filtered(query_embedding, k, start, end)	Float32Array, number, number, number	JSON array	Time-filtered search
is_duplicate(embedding, threshold)	Float32Array, number	boolean	Deduplication check
embed_text(text)	string	Float32Array	Hash-based text embedding
batch_embed(texts)	string[]	Float32Array[]	Batch text embedding
safety_check(content)	string	string	Returns "allow", "redact", or "deny"
route_query(query)	string	string	Returns "Semantic", "Keyword", "Graph", or "Temporal"
len()	--	number	Number of stored embeddings
stats()	--	string (JSON)	Pipeline statistics

---

Quantization Tiers

Age-Based Memory Compression

OSpipe progressively compresses older embeddings to reduce long-term storage costs. The default quantization schedule:

Age	Method	Bits/Dim	Memory vs f32	Description
0 hours	None (f32)	32	100%	Full precision for recent content
24 hours	Scalar (int8)	8	25%	Minimal quality loss, 4x compression
1 week	Product	~2	~6%	Codebook-based compression
30 days	Binary	1	3%	Single bit per dimension, 97% savings

Memory Estimate

For 1 million frames at 384 dimensions:

Tier	Bytes/Vector	Total (1M vectors)
f32	1,536	1.43 GB
int8	384	366 MB
Product	~96	~91 MB
Binary	48	46 MB

With the default age distribution (most content aging past 30 days), long-term average storage is approximately 50-80 MB per million frames.

---

RuVector Crate Integration

OSpipe integrates 10 crates from the RuVector ecosystem:

RuVector Crate	OSpipe Usage	Status
ruvector-core	HNSW vector storage and nearest neighbor search	Integrated
ruvector-filter	Metadata filtering (app, time, content type)	Integrated
ruvector-cluster	Frame deduplication via cosine similarity	Integrated
ruvector-delta-core	Change tracking and delta-behavior analysis	Integrated
ruvector-router-core	Query routing to optimal search backend	Integrated
cognitum-gate-kernel	AI safety gate decisions (allow/redact/deny)	Integrated
ruvector-graph	Knowledge graph for entity relationships	Integrated
ruvector-attention	Content prioritization and relevance weighting	Integrated
ruvector-gnn	Learned search improvement via graph neural nets	Integrated
ruqu-algorithms	Quantum-inspired search diversity (MMR)	Integrated

---

Testing

# Run all 82 tests
cargo test -p ospipe

# Build for WASM (verify compilation)
cargo build -p ospipe --target wasm32-unknown-unknown

# Build with wasm-pack for JS bindings
wasm-pack build examples/OSpipe --target web

---

Related

Package	Description
@ruvector/ospipe-wasm	WASM bindings for browser (145 KB)
ospipe	Rust crate with full pipeline
ruvector	RuVector vector database

• Full Documentation & ADR
• RuVector Ecosystem (70+ Rust crates)
• Screenpipe

---

License

MIT