@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

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 -- [email protected] 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

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
});

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

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

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