ospipe 0.1.0

OSpipe: RuVector-enhanced personal AI memory system integrating with Screenpipe
Documentation

OSpipe

RuVector-enhanced personal AI memory for Screenpipe

Crates.io docs.rs License: MIT Rust WASM

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%.

OSpipe ships as a Rust crate, a TypeScript SDK, and a WASM library. It runs natively on Windows, macOS, and Linux, and can run entirely in the browser via WebAssembly at bundles as small as 11.8KB.

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

Features

  • Semantic Vector Search -- HNSW index via ruvector-core with 61us p50 query latency
  • 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
  • Hybrid Search -- weighted combination of semantic vector similarity and keyword term overlap
  • Query Router -- automatically routes queries to the optimal backend (Semantic, Keyword, Graph, Temporal, or Hybrid)
  • WASM Support -- runs entirely in the browser with bundles from 11.8KB (micro) to 350KB (full)
  • TypeScript SDK -- @ruvector/ospipe for Node.js and browser integration
  • Configurable Quantization -- 4-tier age-based compression: f32 -> int8 -> product -> binary
  • Cross-Platform -- native builds for Windows, macOS, Linux; WASM for browsers

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

Rust

Add OSpipe to your Cargo.toml:

[dependencies]
ospipe = { path = "examples/OSpipe" }

Create a pipeline, ingest frames, and search:

use ospipe::config::OsPipeConfig;
use ospipe::pipeline::ingestion::IngestionPipeline;
use ospipe::capture::{CapturedFrame, CaptureSource, FrameContent, FrameMetadata};

fn main() -> ospipe::error::Result<()> {
    // Initialize with default configuration
    let config = OsPipeConfig::default();
    let mut pipeline = IngestionPipeline::new(config)?;

    // Ingest a screen capture
    let frame = CapturedFrame::new_screen(
        "Firefox",
        "Meeting Notes - Google Docs",
        "Discussion about authentication: we decided to use JWT with refresh tokens",
        0,
    );
    let result = pipeline.ingest(frame)?;
    println!("Ingest result: {:?}", result);

    // Ingest an audio transcription
    let audio = CapturedFrame::new_audio(
        "Built-in Microphone",
        "Let's revisit the login flow next sprint",
        Some("Alice"),
    );
    pipeline.ingest(audio)?;

    // Search semantically
    let query_embedding = pipeline.embedding_engine().embed("auth token discussion");
    let results = pipeline.vector_store().search(&query_embedding, 5)?;

    for hit in &results {
        println!("Score: {:.4} | {:?}", hit.score, hit.metadata);
    }

    // Print pipeline statistics
    let stats = pipeline.stats();
    println!(
        "Ingested: {} | Deduped: {} | Denied: {} | Redacted: {}",
        stats.total_ingested, stats.total_deduplicated,
        stats.total_denied, stats.total_redacted
    );

    Ok(())
}

TypeScript

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

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

// Ingest a captured frame
await client.ingest({
  source: "screen",
  app: "Chrome",
  window: "Jira Board",
  content: "Sprint 14 planning: migrate auth to OAuth2",
});

// Semantic search
const results = await client.queryRuVector(
  "what did I discuss in the meeting about authentication?"
);

for (const hit of results) {
  console.log(`[${hit.score.toFixed(3)}] ${hit.metadata.text}`);
}

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());

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

RuVector Crate Integration

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 Phase 2
ruvector-attention Content prioritization and relevance weighting Phase 3
ruvector-gnn Learned search improvement via graph neural nets Phase 3
ruqu-algorithms Quantum-inspired search acceleration Phase 4

Configuration

OsPipeConfig

Top-level configuration with nested subsystem configs. All fields have sensible defaults.

use ospipe::config::OsPipeConfig;

let config = OsPipeConfig::default();
// config.data_dir        = "~/.ospipe"
// config.capture         = CaptureConfig { ... }
// config.storage         = StorageConfig { ... }
// config.search          = SearchConfig { ... }
// config.safety          = SafetyConfig { ... }

CaptureConfig

Field Type Default Description
fps f32 1.0 Frames per second for screen capture
audio_chunk_secs u32 30 Duration of audio chunks in seconds
excluded_apps Vec<String> ["1Password", "Keychain Access"] Applications excluded from capture
skip_private_windows bool true Skip windows marked as private/incognito

StorageConfig

Field Type Default Description
embedding_dim usize 384 Dimensionality of embedding vectors
hnsw_m usize 32 HNSW M parameter (max connections per layer)
hnsw_ef_construction usize 200 HNSW ef_construction (index build quality)
hnsw_ef_search usize 100 HNSW ef_search (query-time accuracy)
dedup_threshold f32 0.95 Cosine similarity threshold for deduplication
quantization_tiers Vec<QuantizationTier> 4 tiers (see below) Age-based quantization schedule

SearchConfig

Field Type Default Description
default_k usize 10 Default number of results to return
hybrid_weight f32 0.7 Semantic vs keyword weight (1.0 = pure semantic, 0.0 = pure keyword)
mmr_lambda f32 0.5 MMR diversity vs relevance tradeoff
rerank_enabled bool false Whether to enable result reranking

SafetyConfig

Field Type Default Description
pii_detection bool true Enable PII detection (emails)
credit_card_redaction bool true Enable credit card number redaction
ssn_redaction bool true Enable SSN redaction
custom_patterns Vec<String> [] Custom substring patterns that trigger denial

Example: Custom Configuration

use ospipe::config::*;
use std::path::PathBuf;

let config = OsPipeConfig {
    data_dir: PathBuf::from("/var/lib/ospipe"),
    capture: CaptureConfig {
        fps: 0.5,
        audio_chunk_secs: 60,
        excluded_apps: vec![
            "1Password".into(),
            "Signal".into(),
            "Bitwarden".into(),
        ],
        skip_private_windows: true,
    },
    storage: StorageConfig {
        embedding_dim: 768,      // Use a larger model
        hnsw_m: 48,              // More connections for better recall
        hnsw_ef_construction: 400,
        hnsw_ef_search: 200,
        dedup_threshold: 0.98,   // Stricter deduplication
        ..Default::default()
    },
    search: SearchConfig {
        default_k: 20,
        hybrid_weight: 0.8,      // Lean more toward semantic
        mmr_lambda: 0.6,
        rerank_enabled: true,
    },
    safety: SafetyConfig {
        pii_detection: true,
        credit_card_redaction: true,
        ssn_redaction: true,
        custom_patterns: vec![
            "INTERNAL_ONLY".into(),
            "CONFIDENTIAL".into(),
        ],
    },
};

Safety Gate

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

Safety Decisions

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

Detected PII Patterns

Credit Cards -- sequences of 13-16 digits (with optional spaces or dashes):

4111111111111111       -> [CC_REDACTED]
4111 1111 1111 1111    -> [CC_REDACTED]
4111-1111-1111-1111    -> [CC_REDACTED]

Social Security Numbers -- XXX-XX-XXXX format:

123-45-6789            -> [SSN_REDACTED]

Email Addresses -- word@domain.tld patterns:

user@example.com       -> [EMAIL_REDACTED]
admin@company.org      -> [EMAIL_REDACTED]

Custom Patterns -- configurable substring deny list. When a custom pattern is matched, the entire frame is denied (not just redacted):

let config = SafetyConfig {
    custom_patterns: vec!["TOP_SECRET".to_string(), "CLASSIFIED".to_string()],
    ..Default::default()
};

WASM Safety API

The WASM bindings expose a simplified safety classifier:

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"

The WASM classifier also detects sensitive keywords: password, secret, api_key, api-key, apikey, token, private_key, private-key.

Advanced Configuration

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

Build Targets

# Native (current platform)
cargo build -p ospipe --release

# WASM (browser)
cargo build -p ospipe --target wasm32-unknown-unknown --release

# Generate JS bindings
wasm-pack build examples/OSpipe --target web --release

# Windows (cross-compile)
cross build -p ospipe --target x86_64-pc-windows-gnu --release

# macOS ARM (cross-compile)
cross build -p ospipe --target aarch64-apple-darwin --release

# macOS Intel (cross-compile)
cross build -p ospipe --target x86_64-apple-darwin --release

# Linux ARM (cross-compile)
cross build -p ospipe --target aarch64-unknown-linux-gnu --release

Conditional Compilation

OSpipe uses conditional compilation to separate native and WASM dependencies:

  • Native (cfg(not(target_arch = "wasm32"))) -- links against ruvector-core, ruvector-filter, ruvector-cluster, ruvector-delta-core, ruvector-router-core, and cognitum-gate-kernel
  • WASM (cfg(target_arch = "wasm32")) -- uses wasm-bindgen, js-sys, serde-wasm-bindgen, and getrandom with the js feature

The src/wasm/helpers.rs module contains pure Rust functions (cosine similarity, hash embedding, safety classification, query routing) that compile on all targets and are tested natively.

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

Custom Tiers

use ospipe::config::{StorageConfig, QuantizationTier, QuantizationMethod};

let storage = StorageConfig {
    quantization_tiers: vec![
        QuantizationTier { age_hours: 0,    method: QuantizationMethod::None },
        QuantizationTier { age_hours: 12,   method: QuantizationMethod::Scalar },
        QuantizationTier { age_hours: 72,   method: QuantizationMethod::Product },
        QuantizationTier { age_hours: 360,  method: QuantizationMethod::Binary },
    ],
    ..Default::default()
};

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.

API Reference

Rust API

Core Types

Type Module Description
OsPipeConfig config Top-level configuration
CaptureConfig config Capture subsystem settings
StorageConfig config HNSW and quantization settings
SearchConfig config Search weights and defaults
SafetyConfig config PII detection toggles
CapturedFrame capture A captured screen/audio/UI frame
CaptureSource capture Source enum: Screen, Audio, Ui
FrameContent capture Content enum: OcrText, Transcription, UiEvent
FrameMetadata capture Metadata (app, window, monitor, confidence, language)
OsPipeError error Unified error type

Pipeline

Type / Function Module Description
IngestionPipeline::new(config) pipeline::ingestion Create a new pipeline
IngestionPipeline::ingest(frame) pipeline::ingestion Ingest a single frame
IngestionPipeline::ingest_batch(frames) pipeline::ingestion Ingest multiple frames
IngestionPipeline::stats() pipeline::ingestion Get ingestion statistics
IngestResult pipeline::ingestion Enum: Stored, Deduplicated, Denied
PipelineStats pipeline::ingestion Counters for ingested/deduped/denied/redacted
FrameDeduplicator pipeline::dedup Cosine similarity sliding window

Storage

Type / Function Module Description
VectorStore::new(config) storage::vector_store Create a new vector store
VectorStore::insert(frame, embedding) storage::vector_store Insert a frame with its embedding
VectorStore::search(query, k) storage::vector_store Top-k nearest neighbor search
VectorStore::search_filtered(query, k, filter) storage::vector_store Search with metadata filters
SearchResult storage::vector_store Result with id, score, metadata
SearchFilter storage::vector_store Filter by app, time range, content type, monitor
StoredEmbedding storage::vector_store Stored vector with metadata and timestamp
EmbeddingEngine::new(dim) storage::embedding Create an embedding engine
EmbeddingEngine::embed(text) storage::embedding Generate a normalized embedding
EmbeddingEngine::batch_embed(texts) storage::embedding Batch embedding generation
cosine_similarity(a, b) storage::embedding Cosine similarity between two vectors

Search

Type / Function Module Description
QueryRouter::new() search::router Create a query router
QueryRouter::route(query) search::router Route a query to optimal backend
QueryRoute search::router Enum: Semantic, Keyword, Graph, Temporal, Hybrid
HybridSearch::new(weight) search::hybrid Create a hybrid search with semantic weight
HybridSearch::search(store, query, emb, k) search::hybrid Combined semantic + keyword search

Safety

Type / Function Module Description
SafetyGate::new(config) safety Create a safety gate
SafetyGate::check(content) safety Check content, return safety decision
SafetyGate::redact(content) safety Redact and return cleaned content
SafetyDecision safety Enum: Allow, AllowRedacted(String), Deny { reason }

WASM API (OsPipeWasm)

Method Parameters Returns Description
new(dimension) usize OsPipeWasm Constructor
insert(id, embedding, metadata, timestamp) &str, &[f32], &str, f64 Result<(), JsValue> Insert a frame
search(query_embedding, k) &[f32], usize JsValue (JSON array) Semantic search
search_filtered(query_embedding, k, start, end) &[f32], usize, f64, f64 JsValue (JSON array) Time-filtered search
is_duplicate(embedding, threshold) &[f32], f32 bool Deduplication check
embed_text(text) &str Vec<f32> Hash-based text embedding
batch_embed(texts) JsValue (Array) JsValue (Array) Batch text embedding
safety_check(content) &str String Returns "allow", "redact", or "deny"
route_query(query) &str String Returns "Semantic", "Keyword", "Graph", or "Temporal"
len() -- usize Number of stored embeddings
stats() -- String (JSON) Pipeline statistics

Testing

# Run all 56 tests
cargo test -p ospipe

# Run with verbose output
cargo test -p ospipe -- --nocapture

# Run only integration tests
cargo test -p ospipe --test integration

# Run only unit tests (embedding, WASM helpers)
cargo test -p ospipe --lib

# 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

Test Coverage

Test Category Count Module
Configuration 2 tests/integration.rs
Capture frames 3 tests/integration.rs
Embedding engine 6 src/storage/embedding.rs
Vector store 4 tests/integration.rs
Deduplication 2 tests/integration.rs
Safety gate 6 tests/integration.rs
Query routing 4 tests/integration.rs
Hybrid search 2 tests/integration.rs
Ingestion pipeline 5 tests/integration.rs
Cosine similarity 3 tests/integration.rs
WASM helpers 18 src/wasm/helpers.rs
Total 56

Related

  • ADR: OSpipe Screenpipe Integration -- Architecture Decision Record with full design rationale
  • Screenpipe -- Open-source local-first desktop recording + AI memory
  • RuVector -- 70+ Rust crates for vector search, graph neural networks, and attention mechanisms
  • @ruvector/ospipe -- TypeScript SDK (npm)
  • @ruvector/ospipe-wasm -- WASM package (npm)

License

Licensed under either of:

at your option.