Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'
This commit is contained in:
ruv
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vendor/ruvector/examples/OSpipe/.github-ci-stub.yml
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vendor/ruvector/examples/OSpipe/.github-ci-stub.yml
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|
||||
# OSpipe Cross-Platform Build Matrix
|
||||
# Copy to .github/workflows/ospipe.yml to activate
|
||||
name: OSpipe Build
|
||||
on:
|
||||
push:
|
||||
paths: ['examples/OSpipe/**']
|
||||
pull_request:
|
||||
paths: ['examples/OSpipe/**']
|
||||
|
||||
jobs:
|
||||
build:
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
include:
|
||||
- os: macos-latest
|
||||
target: aarch64-apple-darwin
|
||||
name: macOS ARM64
|
||||
- os: macos-13
|
||||
target: x86_64-apple-darwin
|
||||
name: macOS x64
|
||||
- os: windows-latest
|
||||
target: x86_64-pc-windows-msvc
|
||||
name: Windows x64
|
||||
- os: ubuntu-latest
|
||||
target: x86_64-unknown-linux-gnu
|
||||
name: Linux x64
|
||||
- os: ubuntu-latest
|
||||
target: wasm32-unknown-unknown
|
||||
name: WASM
|
||||
runs-on: ${{ matrix.os }}
|
||||
name: ${{ matrix.name }}
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- uses: dtolnay/rust-toolchain@stable
|
||||
with:
|
||||
targets: ${{ matrix.target }}
|
||||
- name: Build
|
||||
run: cargo build -p ospipe --target ${{ matrix.target }} --release
|
||||
- name: Test
|
||||
run: cargo test -p ospipe
|
||||
if: matrix.target != 'wasm32-unknown-unknown'
|
||||
- name: Upload artifact
|
||||
uses: actions/upload-artifact@v4
|
||||
if: matrix.target != 'wasm32-unknown-unknown'
|
||||
with:
|
||||
name: ospipe-${{ matrix.target }}
|
||||
path: |
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||||
target/${{ matrix.target }}/release/libospipe*
|
||||
target/${{ matrix.target }}/release/ospipe*
|
||||
if-no-files-found: ignore
|
||||
- name: Upload WASM artifact
|
||||
uses: actions/upload-artifact@v4
|
||||
if: matrix.target == 'wasm32-unknown-unknown'
|
||||
with:
|
||||
name: ospipe-wasm
|
||||
path: target/wasm32-unknown-unknown/release/ospipe.wasm
|
||||
if-no-files-found: ignore
|
||||
1986
vendor/ruvector/examples/OSpipe/ADR-OSpipe-screenpipe-integration.md
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1986
vendor/ruvector/examples/OSpipe/ADR-OSpipe-screenpipe-integration.md
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File diff suppressed because it is too large
Load Diff
73
vendor/ruvector/examples/OSpipe/Cargo.toml
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vendor/ruvector/examples/OSpipe/Cargo.toml
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||||
[package]
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||||
name = "ospipe"
|
||||
version = "0.1.0"
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||||
edition = "2021"
|
||||
rust-version = "1.77"
|
||||
license = "MIT"
|
||||
description = "OSpipe: RuVector-enhanced personal AI memory system integrating with Screenpipe"
|
||||
authors = ["Ruvector Team"]
|
||||
repository = "https://github.com/ruvnet/ruvector"
|
||||
|
||||
[dependencies]
|
||||
# Serialization (cross-platform)
|
||||
serde = { workspace = true }
|
||||
serde_json = { workspace = true }
|
||||
|
||||
# Error handling and utilities (cross-platform)
|
||||
thiserror = { workspace = true }
|
||||
tracing = { workspace = true }
|
||||
|
||||
# Time and UUID (cross-platform)
|
||||
chrono = { version = "0.4", features = ["serde"] }
|
||||
uuid = { version = "1.11", features = ["v4", "serde", "js"] }
|
||||
|
||||
# Math (cross-platform)
|
||||
rand = { workspace = true }
|
||||
|
||||
# Native-only: RuVector ecosystem (path dependencies)
|
||||
# These crates pull in platform-specific code (mmap, tokio, ring, etc.) that
|
||||
# does not compile for wasm32-unknown-unknown.
|
||||
[target.'cfg(not(target_arch = "wasm32"))'.dependencies]
|
||||
ruvector-core = { version = "2.0", path = "../../crates/ruvector-core" }
|
||||
ruvector-filter = { version = "2.0", path = "../../crates/ruvector-filter" }
|
||||
ruvector-cluster = { version = "2.0", path = "../../crates/ruvector-cluster" }
|
||||
ruvector-delta-core = { version = "0.1", path = "../../crates/ruvector-delta-core", features = ["serde"] }
|
||||
ruvector-router-core = { version = "2.0", path = "../../crates/ruvector-router-core" }
|
||||
ruvector-graph = { version = "2.0", path = "../../crates/ruvector-graph", default-features = false }
|
||||
ruvector-gnn = { version = "2.0", path = "../../crates/ruvector-gnn", default-features = false }
|
||||
cognitum-gate-kernel = { version = "0.1", path = "../../crates/cognitum-gate-kernel", default-features = true }
|
||||
ruqu-algorithms = { version = "2.0.5", path = "../../crates/ruqu-algorithms", default-features = false }
|
||||
ruvector-attention = { version = "2.0", path = "../../crates/ruvector-attention", default-features = false }
|
||||
|
||||
# HTTP server dependencies (native only)
|
||||
axum = { version = "0.7", features = ["json"] }
|
||||
tower-http = { version = "0.6", features = ["cors"] }
|
||||
tower = { version = "0.5" }
|
||||
tokio = { workspace = true }
|
||||
tracing-subscriber = { version = "0.3", features = ["env-filter"] }
|
||||
|
||||
# WASM-only dependencies
|
||||
[target.'cfg(target_arch = "wasm32")'.dependencies]
|
||||
wasm-bindgen = { workspace = true }
|
||||
js-sys = { workspace = true }
|
||||
serde-wasm-bindgen = "0.6"
|
||||
getrandom = { version = "0.2", features = ["js"] }
|
||||
console_error_panic_hook = { version = "0.1", optional = true }
|
||||
|
||||
[target.'cfg(target_arch = "wasm32")'.dev-dependencies]
|
||||
wasm-bindgen-test = "0.3"
|
||||
|
||||
[dev-dependencies]
|
||||
tokio = { workspace = true }
|
||||
uuid = { version = "1.11", features = ["v4"] }
|
||||
|
||||
[features]
|
||||
default = ["console_error_panic_hook"]
|
||||
|
||||
[lib]
|
||||
crate-type = ["cdylib", "rlib"]
|
||||
|
||||
[[bin]]
|
||||
name = "ospipe-server"
|
||||
path = "src/bin/ospipe-server.rs"
|
||||
required-features = []
|
||||
666
vendor/ruvector/examples/OSpipe/README.md
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vendor/ruvector/examples/OSpipe/README.md
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||||
# OSpipe
|
||||
|
||||
**RuVector-enhanced personal AI memory for Screenpipe**
|
||||
|
||||
[](https://crates.io/crates/ospipe)
|
||||
[](https://docs.rs/ospipe)
|
||||
[](LICENSE)
|
||||
[](https://www.rust-lang.org/)
|
||||
[](https://webassembly.org/)
|
||||
|
||||
---
|
||||
|
||||
## What is OSpipe?
|
||||
|
||||
[Screenpipe](https://github.com/mediar-ai/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](https://github.com/ruvnet/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`:
|
||||
|
||||
```toml
|
||||
[dependencies]
|
||||
ospipe = { path = "examples/OSpipe" }
|
||||
```
|
||||
|
||||
Create a pipeline, ingest frames, and search:
|
||||
|
||||
```rust
|
||||
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
|
||||
|
||||
```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)
|
||||
|
||||
```javascript
|
||||
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
|
||||
|
||||
<details>
|
||||
<summary>Full Configuration Reference</summary>
|
||||
|
||||
### `OsPipeConfig`
|
||||
|
||||
Top-level configuration with nested subsystem configs. All fields have sensible defaults.
|
||||
|
||||
```rust
|
||||
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
|
||||
|
||||
```rust
|
||||
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(),
|
||||
],
|
||||
},
|
||||
};
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
---
|
||||
|
||||
## Safety Gate
|
||||
|
||||
<details>
|
||||
<summary>PII Detection Details</summary>
|
||||
|
||||
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):
|
||||
```rust
|
||||
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:
|
||||
|
||||
```javascript
|
||||
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`.
|
||||
|
||||
</details>
|
||||
|
||||
---
|
||||
|
||||
## Advanced Configuration
|
||||
|
||||
<details>
|
||||
<summary>WASM Deployment</summary>
|
||||
|
||||
### 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:
|
||||
|
||||
```javascript
|
||||
// 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
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>Cross-Platform Build</summary>
|
||||
|
||||
### Build Targets
|
||||
|
||||
```bash
|
||||
# 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.
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>Quantization Tiers</summary>
|
||||
|
||||
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
|
||||
|
||||
```rust
|
||||
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.
|
||||
|
||||
</details>
|
||||
|
||||
---
|
||||
|
||||
## 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
|
||||
|
||||
```bash
|
||||
# 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](./ADR-OSpipe-screenpipe-integration.md) -- Architecture Decision Record with full design rationale
|
||||
- [Screenpipe](https://github.com/mediar-ai/screenpipe) -- Open-source local-first desktop recording + AI memory
|
||||
- [RuVector](https://github.com/ruvnet/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:
|
||||
|
||||
- MIT License ([LICENSE-MIT](../../LICENSE-MIT) or http://opensource.org/licenses/MIT)
|
||||
- Apache License, Version 2.0 ([LICENSE-APACHE](../../LICENSE-APACHE) or http://www.apache.org/licenses/LICENSE-2.0)
|
||||
|
||||
at your option.
|
||||
3
vendor/ruvector/examples/OSpipe/dist/checksums.sha256
vendored
Normal file
3
vendor/ruvector/examples/OSpipe/dist/checksums.sha256
vendored
Normal file
@@ -0,0 +1,3 @@
|
||||
4f4c747c3a363e7f41c50ec065b316afff5c26a0daf62aabedfc4285e4206131 ospipe-server-linux-arm64
|
||||
f9627349e486a0a57e55299dd254dda09f4032c1b82270f15c37d56c404dfc57 ospipe-server-linux-x86_64
|
||||
5a14e46829bb6e8395d43bbc9ed1d485af3db726e3e75e6f86844d655b2f70e9 ospipe-server-windows-x86_64.exe
|
||||
BIN
vendor/ruvector/examples/OSpipe/dist/npm/ruvector-ospipe-0.1.0.tgz
vendored
Normal file
BIN
vendor/ruvector/examples/OSpipe/dist/npm/ruvector-ospipe-0.1.0.tgz
vendored
Normal file
Binary file not shown.
BIN
vendor/ruvector/examples/OSpipe/dist/npm/ruvector-ospipe-wasm-0.1.0.tgz
vendored
Normal file
BIN
vendor/ruvector/examples/OSpipe/dist/npm/ruvector-ospipe-wasm-0.1.0.tgz
vendored
Normal file
Binary file not shown.
BIN
vendor/ruvector/examples/OSpipe/dist/ospipe-server-linux-arm64
vendored
Executable file
BIN
vendor/ruvector/examples/OSpipe/dist/ospipe-server-linux-arm64
vendored
Executable file
Binary file not shown.
BIN
vendor/ruvector/examples/OSpipe/dist/ospipe-server-linux-x86_64
vendored
Executable file
BIN
vendor/ruvector/examples/OSpipe/dist/ospipe-server-linux-x86_64
vendored
Executable file
Binary file not shown.
BIN
vendor/ruvector/examples/OSpipe/dist/ospipe-server-windows-x86_64.exe
vendored
Executable file
BIN
vendor/ruvector/examples/OSpipe/dist/ospipe-server-windows-x86_64.exe
vendored
Executable file
Binary file not shown.
111
vendor/ruvector/examples/OSpipe/src/bin/ospipe-server.rs
vendored
Normal file
111
vendor/ruvector/examples/OSpipe/src/bin/ospipe-server.rs
vendored
Normal file
@@ -0,0 +1,111 @@
|
||||
//! OSpipe REST API server binary.
|
||||
//!
|
||||
//! Starts the OSpipe HTTP server with a default pipeline configuration.
|
||||
//! The server exposes semantic search, query routing, health, and stats endpoints.
|
||||
//!
|
||||
//! ## Usage
|
||||
//!
|
||||
//! ```bash
|
||||
//! ospipe-server # default port 3030
|
||||
//! ospipe-server --port 8080 # custom port
|
||||
//! ospipe-server --data-dir /tmp/ospipe # custom data directory
|
||||
//! ```
|
||||
|
||||
use std::sync::Arc;
|
||||
use tokio::sync::RwLock;
|
||||
|
||||
fn main() {
|
||||
// Parse CLI arguments
|
||||
let args: Vec<String> = std::env::args().collect();
|
||||
let mut port: u16 = 3030;
|
||||
let mut data_dir: Option<String> = None;
|
||||
|
||||
let mut i = 1;
|
||||
while i < args.len() {
|
||||
match args[i].as_str() {
|
||||
"--port" | "-p" => {
|
||||
if i + 1 < args.len() {
|
||||
port = args[i + 1].parse().unwrap_or_else(|_| {
|
||||
eprintln!("Invalid port: {}", args[i + 1]);
|
||||
std::process::exit(1);
|
||||
});
|
||||
i += 2;
|
||||
} else {
|
||||
eprintln!("--port requires a value");
|
||||
std::process::exit(1);
|
||||
}
|
||||
}
|
||||
"--data-dir" | "-d" => {
|
||||
if i + 1 < args.len() {
|
||||
data_dir = Some(args[i + 1].clone());
|
||||
i += 2;
|
||||
} else {
|
||||
eprintln!("--data-dir requires a value");
|
||||
std::process::exit(1);
|
||||
}
|
||||
}
|
||||
"--help" | "-h" => {
|
||||
println!("OSpipe Server - RuVector-enhanced personal AI memory");
|
||||
println!();
|
||||
println!("Usage: ospipe-server [OPTIONS]");
|
||||
println!();
|
||||
println!("Options:");
|
||||
println!(" -p, --port <PORT> Listen port (default: 3030)");
|
||||
println!(" -d, --data-dir <PATH> Data directory (default: ~/.ospipe)");
|
||||
println!(" -h, --help Show this help message");
|
||||
println!(" -V, --version Show version");
|
||||
std::process::exit(0);
|
||||
}
|
||||
"--version" | "-V" => {
|
||||
println!("ospipe-server {}", env!("CARGO_PKG_VERSION"));
|
||||
std::process::exit(0);
|
||||
}
|
||||
other => {
|
||||
eprintln!("Unknown argument: {}", other);
|
||||
eprintln!("Run with --help for usage information");
|
||||
std::process::exit(1);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Initialize tracing
|
||||
tracing_subscriber::fmt()
|
||||
.with_env_filter(
|
||||
tracing_subscriber::EnvFilter::try_from_default_env()
|
||||
.unwrap_or_else(|_| tracing_subscriber::EnvFilter::new("info")),
|
||||
)
|
||||
.init();
|
||||
|
||||
// Build configuration
|
||||
let mut config = ospipe::config::OsPipeConfig::default();
|
||||
if let Some(dir) = data_dir {
|
||||
config.data_dir = std::path::PathBuf::from(dir);
|
||||
}
|
||||
|
||||
// Create the pipeline
|
||||
let pipeline =
|
||||
ospipe::pipeline::ingestion::IngestionPipeline::new(config).unwrap_or_else(|e| {
|
||||
eprintln!("Failed to initialize pipeline: {}", e);
|
||||
std::process::exit(1);
|
||||
});
|
||||
|
||||
let state = ospipe::server::ServerState {
|
||||
pipeline: Arc::new(RwLock::new(pipeline)),
|
||||
router: Arc::new(ospipe::search::QueryRouter::new()),
|
||||
started_at: std::time::Instant::now(),
|
||||
};
|
||||
|
||||
// Start the async runtime and server
|
||||
let rt = tokio::runtime::Runtime::new().unwrap_or_else(|e| {
|
||||
eprintln!("Failed to create Tokio runtime: {}", e);
|
||||
std::process::exit(1);
|
||||
});
|
||||
|
||||
rt.block_on(async {
|
||||
tracing::info!("Starting OSpipe server on port {}", port);
|
||||
if let Err(e) = ospipe::server::start_server(state, port).await {
|
||||
eprintln!("Server error: {}", e);
|
||||
std::process::exit(1);
|
||||
}
|
||||
});
|
||||
}
|
||||
164
vendor/ruvector/examples/OSpipe/src/capture/frame.rs
vendored
Normal file
164
vendor/ruvector/examples/OSpipe/src/capture/frame.rs
vendored
Normal file
@@ -0,0 +1,164 @@
|
||||
//! Captured frame data structures.
|
||||
|
||||
use chrono::{DateTime, Utc};
|
||||
use serde::{Deserialize, Serialize};
|
||||
use uuid::Uuid;
|
||||
|
||||
/// A single captured frame from any Screenpipe source.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct CapturedFrame {
|
||||
/// Unique identifier for this frame.
|
||||
pub id: Uuid,
|
||||
/// When this frame was captured.
|
||||
pub timestamp: DateTime<Utc>,
|
||||
/// The source that produced this frame.
|
||||
pub source: CaptureSource,
|
||||
/// The actual content of the frame.
|
||||
pub content: FrameContent,
|
||||
/// Additional metadata about the frame.
|
||||
pub metadata: FrameMetadata,
|
||||
}
|
||||
|
||||
/// The source that produced a captured frame.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub enum CaptureSource {
|
||||
/// Screen capture with OCR.
|
||||
Screen {
|
||||
/// Monitor index.
|
||||
monitor: u32,
|
||||
/// Foreground application name.
|
||||
app: String,
|
||||
/// Window title.
|
||||
window: String,
|
||||
},
|
||||
/// Audio capture with transcription.
|
||||
Audio {
|
||||
/// Audio device name.
|
||||
device: String,
|
||||
/// Detected speaker (if diarization is available).
|
||||
speaker: Option<String>,
|
||||
},
|
||||
/// UI accessibility event.
|
||||
Ui {
|
||||
/// Type of UI event (e.g., "click", "focus", "scroll").
|
||||
event_type: String,
|
||||
},
|
||||
}
|
||||
|
||||
/// The actual content extracted from a captured frame.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub enum FrameContent {
|
||||
/// OCR text extracted from a screen capture.
|
||||
OcrText(String),
|
||||
/// Transcribed text from an audio capture.
|
||||
Transcription(String),
|
||||
/// A UI accessibility event description.
|
||||
UiEvent(String),
|
||||
}
|
||||
|
||||
/// Metadata associated with a captured frame.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct FrameMetadata {
|
||||
/// Name of the foreground application, if known.
|
||||
pub app_name: Option<String>,
|
||||
/// Title of the active window, if known.
|
||||
pub window_title: Option<String>,
|
||||
/// Monitor index, if applicable.
|
||||
pub monitor_id: Option<u32>,
|
||||
/// Confidence score for the extracted content (0.0 to 1.0).
|
||||
pub confidence: f32,
|
||||
/// Detected language code (e.g., "en", "es"), if known.
|
||||
pub language: Option<String>,
|
||||
}
|
||||
|
||||
impl CapturedFrame {
|
||||
/// Create a new frame from a screen capture with OCR text.
|
||||
pub fn new_screen(app: &str, window: &str, ocr_text: &str, monitor: u32) -> Self {
|
||||
Self {
|
||||
id: Uuid::new_v4(),
|
||||
timestamp: Utc::now(),
|
||||
source: CaptureSource::Screen {
|
||||
monitor,
|
||||
app: app.to_string(),
|
||||
window: window.to_string(),
|
||||
},
|
||||
content: FrameContent::OcrText(ocr_text.to_string()),
|
||||
metadata: FrameMetadata {
|
||||
app_name: Some(app.to_string()),
|
||||
window_title: Some(window.to_string()),
|
||||
monitor_id: Some(monitor),
|
||||
confidence: 0.9,
|
||||
language: None,
|
||||
},
|
||||
}
|
||||
}
|
||||
|
||||
/// Create a new frame from an audio transcription.
|
||||
pub fn new_audio(device: &str, transcription: &str, speaker: Option<&str>) -> Self {
|
||||
Self {
|
||||
id: Uuid::new_v4(),
|
||||
timestamp: Utc::now(),
|
||||
source: CaptureSource::Audio {
|
||||
device: device.to_string(),
|
||||
speaker: speaker.map(|s| s.to_string()),
|
||||
},
|
||||
content: FrameContent::Transcription(transcription.to_string()),
|
||||
metadata: FrameMetadata {
|
||||
app_name: None,
|
||||
window_title: None,
|
||||
monitor_id: None,
|
||||
confidence: 0.85,
|
||||
language: None,
|
||||
},
|
||||
}
|
||||
}
|
||||
|
||||
/// Create a new frame from a UI accessibility event.
|
||||
pub fn new_ui_event(event_type: &str, description: &str) -> Self {
|
||||
Self {
|
||||
id: Uuid::new_v4(),
|
||||
timestamp: Utc::now(),
|
||||
source: CaptureSource::Ui {
|
||||
event_type: event_type.to_string(),
|
||||
},
|
||||
content: FrameContent::UiEvent(description.to_string()),
|
||||
metadata: FrameMetadata {
|
||||
app_name: None,
|
||||
window_title: None,
|
||||
monitor_id: None,
|
||||
confidence: 1.0,
|
||||
language: None,
|
||||
},
|
||||
}
|
||||
}
|
||||
|
||||
/// Extract the text content from this frame regardless of source type.
|
||||
pub fn text_content(&self) -> &str {
|
||||
match &self.content {
|
||||
FrameContent::OcrText(text) => text,
|
||||
FrameContent::Transcription(text) => text,
|
||||
FrameContent::UiEvent(text) => text,
|
||||
}
|
||||
}
|
||||
|
||||
/// Return the content type as a string label.
|
||||
pub fn content_type(&self) -> &str {
|
||||
match &self.content {
|
||||
FrameContent::OcrText(_) => "ocr",
|
||||
FrameContent::Transcription(_) => "transcription",
|
||||
FrameContent::UiEvent(_) => "ui_event",
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl Default for FrameMetadata {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
app_name: None,
|
||||
window_title: None,
|
||||
monitor_id: None,
|
||||
confidence: 0.0,
|
||||
language: None,
|
||||
}
|
||||
}
|
||||
}
|
||||
9
vendor/ruvector/examples/OSpipe/src/capture/mod.rs
vendored
Normal file
9
vendor/ruvector/examples/OSpipe/src/capture/mod.rs
vendored
Normal file
@@ -0,0 +1,9 @@
|
||||
//! Capture module for processing screen, audio, and UI event data.
|
||||
//!
|
||||
//! This module defines the data structures that represent captured frames
|
||||
//! from Screenpipe sources: OCR text from screen recordings, audio
|
||||
//! transcriptions, and UI accessibility events.
|
||||
|
||||
pub mod frame;
|
||||
|
||||
pub use frame::{CaptureSource, CapturedFrame, FrameContent, FrameMetadata};
|
||||
173
vendor/ruvector/examples/OSpipe/src/config.rs
vendored
Normal file
173
vendor/ruvector/examples/OSpipe/src/config.rs
vendored
Normal file
@@ -0,0 +1,173 @@
|
||||
//! Configuration types for all OSpipe subsystems.
|
||||
|
||||
use serde::{Deserialize, Serialize};
|
||||
use std::path::PathBuf;
|
||||
|
||||
/// Top-level OSpipe configuration.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct OsPipeConfig {
|
||||
/// Directory for persistent data storage.
|
||||
pub data_dir: PathBuf,
|
||||
/// Capture subsystem configuration.
|
||||
pub capture: CaptureConfig,
|
||||
/// Storage subsystem configuration.
|
||||
pub storage: StorageConfig,
|
||||
/// Search subsystem configuration.
|
||||
pub search: SearchConfig,
|
||||
/// Safety gate configuration.
|
||||
pub safety: SafetyConfig,
|
||||
}
|
||||
|
||||
/// Configuration for the capture subsystem.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct CaptureConfig {
|
||||
/// Frames per second for screen capture. Default: 1.0
|
||||
pub fps: f32,
|
||||
/// Duration of audio chunks in seconds. Default: 30
|
||||
pub audio_chunk_secs: u32,
|
||||
/// Application names to exclude from capture.
|
||||
pub excluded_apps: Vec<String>,
|
||||
/// Whether to skip windows marked as private/incognito.
|
||||
pub skip_private_windows: bool,
|
||||
}
|
||||
|
||||
/// Configuration for the vector storage subsystem.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct StorageConfig {
|
||||
/// Dimensionality of embedding vectors. Default: 384
|
||||
pub embedding_dim: usize,
|
||||
/// HNSW M parameter (max connections per layer). Default: 32
|
||||
pub hnsw_m: usize,
|
||||
/// HNSW ef_construction parameter. Default: 200
|
||||
pub hnsw_ef_construction: usize,
|
||||
/// HNSW ef_search parameter. Default: 100
|
||||
pub hnsw_ef_search: usize,
|
||||
/// Cosine similarity threshold for deduplication. Default: 0.95
|
||||
pub dedup_threshold: f32,
|
||||
/// Quantization tiers for aging data.
|
||||
pub quantization_tiers: Vec<QuantizationTier>,
|
||||
}
|
||||
|
||||
/// A quantization tier that defines how vectors are compressed based on age.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct QuantizationTier {
|
||||
/// Age in hours after which this quantization is applied.
|
||||
pub age_hours: u64,
|
||||
/// The quantization method to use.
|
||||
pub method: QuantizationMethod,
|
||||
}
|
||||
|
||||
/// Supported vector quantization methods.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub enum QuantizationMethod {
|
||||
/// No quantization (full precision f32).
|
||||
None,
|
||||
/// Scalar quantization (int8).
|
||||
Scalar,
|
||||
/// Product quantization.
|
||||
Product,
|
||||
/// Binary quantization (1-bit per dimension).
|
||||
Binary,
|
||||
}
|
||||
|
||||
/// Configuration for the search subsystem.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct SearchConfig {
|
||||
/// Default number of results to return. Default: 10
|
||||
pub default_k: usize,
|
||||
/// Weight for semantic vs keyword search in hybrid mode. Default: 0.7
|
||||
/// 1.0 = pure semantic, 0.0 = pure keyword.
|
||||
pub hybrid_weight: f32,
|
||||
/// MMR lambda for diversity vs relevance tradeoff. Default: 0.5
|
||||
pub mmr_lambda: f32,
|
||||
/// Whether to enable result reranking.
|
||||
pub rerank_enabled: bool,
|
||||
}
|
||||
|
||||
/// Configuration for the safety gate subsystem.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct SafetyConfig {
|
||||
/// Enable PII detection (names, emails, phone numbers).
|
||||
pub pii_detection: bool,
|
||||
/// Enable credit card number redaction.
|
||||
pub credit_card_redaction: bool,
|
||||
/// Enable SSN redaction.
|
||||
pub ssn_redaction: bool,
|
||||
/// Custom regex-like patterns to redact (simple substring matching).
|
||||
pub custom_patterns: Vec<String>,
|
||||
}
|
||||
|
||||
impl Default for OsPipeConfig {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
data_dir: PathBuf::from("~/.ospipe"),
|
||||
capture: CaptureConfig::default(),
|
||||
storage: StorageConfig::default(),
|
||||
search: SearchConfig::default(),
|
||||
safety: SafetyConfig::default(),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl Default for CaptureConfig {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
fps: 1.0,
|
||||
audio_chunk_secs: 30,
|
||||
excluded_apps: vec!["1Password".to_string(), "Keychain Access".to_string()],
|
||||
skip_private_windows: true,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl Default for StorageConfig {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
embedding_dim: 384,
|
||||
hnsw_m: 32,
|
||||
hnsw_ef_construction: 200,
|
||||
hnsw_ef_search: 100,
|
||||
dedup_threshold: 0.95,
|
||||
quantization_tiers: vec![
|
||||
QuantizationTier {
|
||||
age_hours: 0,
|
||||
method: QuantizationMethod::None,
|
||||
},
|
||||
QuantizationTier {
|
||||
age_hours: 24,
|
||||
method: QuantizationMethod::Scalar,
|
||||
},
|
||||
QuantizationTier {
|
||||
age_hours: 168, // 1 week
|
||||
method: QuantizationMethod::Product,
|
||||
},
|
||||
QuantizationTier {
|
||||
age_hours: 720, // 30 days
|
||||
method: QuantizationMethod::Binary,
|
||||
},
|
||||
],
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl Default for SearchConfig {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
default_k: 10,
|
||||
hybrid_weight: 0.7,
|
||||
mmr_lambda: 0.5,
|
||||
rerank_enabled: false,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl Default for SafetyConfig {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
pii_detection: true,
|
||||
credit_card_redaction: true,
|
||||
ssn_redaction: true,
|
||||
custom_patterns: Vec::new(),
|
||||
}
|
||||
}
|
||||
}
|
||||
41
vendor/ruvector/examples/OSpipe/src/error.rs
vendored
Normal file
41
vendor/ruvector/examples/OSpipe/src/error.rs
vendored
Normal file
@@ -0,0 +1,41 @@
|
||||
//! Unified error types for OSpipe.
|
||||
|
||||
use thiserror::Error;
|
||||
|
||||
/// Top-level error type for all OSpipe operations.
|
||||
#[derive(Error, Debug)]
|
||||
pub enum OsPipeError {
|
||||
/// An error occurred during screen/audio capture processing.
|
||||
#[error("Capture error: {0}")]
|
||||
Capture(String),
|
||||
|
||||
/// An error occurred in the vector storage layer.
|
||||
#[error("Storage error: {0}")]
|
||||
Storage(String),
|
||||
|
||||
/// An error occurred during search operations.
|
||||
#[error("Search error: {0}")]
|
||||
Search(String),
|
||||
|
||||
/// An error occurred in the ingestion pipeline.
|
||||
#[error("Pipeline error: {0}")]
|
||||
Pipeline(String),
|
||||
|
||||
/// The safety gate denied ingestion of content.
|
||||
#[error("Safety gate denied: {reason}")]
|
||||
SafetyDenied {
|
||||
/// Human-readable reason for denial.
|
||||
reason: String,
|
||||
},
|
||||
|
||||
/// A configuration-related error.
|
||||
#[error("Configuration error: {0}")]
|
||||
Config(String),
|
||||
|
||||
/// A JSON serialization or deserialization error.
|
||||
#[error("Serialization error: {0}")]
|
||||
Serde(#[from] serde_json::Error),
|
||||
}
|
||||
|
||||
/// Convenience alias for `Result<T, OsPipeError>`.
|
||||
pub type Result<T> = std::result::Result<T, OsPipeError>;
|
||||
217
vendor/ruvector/examples/OSpipe/src/graph/entity_extractor.rs
vendored
Normal file
217
vendor/ruvector/examples/OSpipe/src/graph/entity_extractor.rs
vendored
Normal file
@@ -0,0 +1,217 @@
|
||||
//! Heuristic named-entity recognition (NER) for extracting entities from text.
|
||||
//!
|
||||
//! This module performs lightweight, regex-free entity extraction suitable for
|
||||
//! processing screen captures and transcriptions. It recognises:
|
||||
//!
|
||||
//! - **URLs** (`https://...` / `http://...`)
|
||||
//! - **Email addresses** (`user@domain.tld`)
|
||||
//! - **Mentions** (`@handle`)
|
||||
//! - **Capitalized phrases** (two or more consecutive capitalized words -> proper nouns)
|
||||
|
||||
/// Extract `(label, name)` pairs from free-form `text`.
|
||||
///
|
||||
/// Labels returned:
|
||||
/// - `"Url"` for HTTP(S) URLs
|
||||
/// - `"Email"` for email-like patterns
|
||||
/// - `"Mention"` for `@handle` patterns
|
||||
/// - `"Person"` for capitalized multi-word phrases (heuristic proper noun)
|
||||
pub fn extract_entities(text: &str) -> Vec<(String, String)> {
|
||||
let mut entities: Vec<(String, String)> = Vec::new();
|
||||
let mut seen = std::collections::HashSet::new();
|
||||
|
||||
// --- URL detection ---
|
||||
for word in text.split_whitespace() {
|
||||
let trimmed =
|
||||
word.trim_matches(|c: char| c == ',' || c == '.' || c == ')' || c == '(' || c == ';');
|
||||
if (trimmed.starts_with("http://") || trimmed.starts_with("https://"))
|
||||
&& trimmed.len() > 10
|
||||
&& seen.insert(("Url", trimmed.to_string()))
|
||||
{
|
||||
entities.push(("Url".to_string(), trimmed.to_string()));
|
||||
}
|
||||
}
|
||||
|
||||
// --- Email detection ---
|
||||
for word in text.split_whitespace() {
|
||||
let trimmed = word.trim_matches(|c: char| {
|
||||
c == ',' || c == '.' || c == ')' || c == '(' || c == ';' || c == '<' || c == '>'
|
||||
});
|
||||
if is_email_like(trimmed) && seen.insert(("Email", trimmed.to_string())) {
|
||||
entities.push(("Email".to_string(), trimmed.to_string()));
|
||||
}
|
||||
}
|
||||
|
||||
// --- @mention detection ---
|
||||
for word in text.split_whitespace() {
|
||||
let trimmed =
|
||||
word.trim_matches(|c: char| c == ',' || c == '.' || c == ')' || c == '(' || c == ';');
|
||||
if trimmed.starts_with('@') && trimmed.len() > 1 {
|
||||
let handle = trimmed.to_string();
|
||||
if seen.insert(("Mention", handle.clone())) {
|
||||
entities.push(("Mention".to_string(), handle));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// --- Capitalized phrase detection (proper nouns) ---
|
||||
let cap_phrases = extract_capitalized_phrases(text);
|
||||
for phrase in cap_phrases {
|
||||
if seen.insert(("Person", phrase.clone())) {
|
||||
entities.push(("Person".to_string(), phrase));
|
||||
}
|
||||
}
|
||||
|
||||
entities
|
||||
}
|
||||
|
||||
/// Returns `true` if `s` looks like an email address (`local@domain.tld`).
|
||||
fn is_email_like(s: &str) -> bool {
|
||||
// Must contain exactly one '@', with non-empty parts on both sides,
|
||||
// and the domain part must contain at least one '.'.
|
||||
if let Some(at_pos) = s.find('@') {
|
||||
let local = &s[..at_pos];
|
||||
let domain = &s[at_pos + 1..];
|
||||
!local.is_empty()
|
||||
&& !domain.is_empty()
|
||||
&& domain.contains('.')
|
||||
&& !domain.starts_with('.')
|
||||
&& !domain.ends_with('.')
|
||||
&& local
|
||||
.chars()
|
||||
.all(|c| c.is_alphanumeric() || c == '.' || c == '_' || c == '-' || c == '+')
|
||||
&& domain
|
||||
.chars()
|
||||
.all(|c| c.is_alphanumeric() || c == '.' || c == '-')
|
||||
} else {
|
||||
false
|
||||
}
|
||||
}
|
||||
|
||||
/// Extract sequences of two or more consecutive capitalized words as likely
|
||||
/// proper nouns. Filters out common sentence-starting words when they appear
|
||||
/// alone at what looks like a sentence boundary.
|
||||
fn extract_capitalized_phrases(text: &str) -> Vec<String> {
|
||||
let mut phrases = Vec::new();
|
||||
let words: Vec<&str> = text.split_whitespace().collect();
|
||||
|
||||
let mut i = 0;
|
||||
while i < words.len() {
|
||||
// Skip words that start a sentence (preceded by nothing or a sentence-ending punctuation).
|
||||
let word = words[i].trim_matches(|c: char| !c.is_alphanumeric());
|
||||
if is_capitalized(word) && word.len() > 1 {
|
||||
// Accumulate consecutive capitalized words.
|
||||
let start = i;
|
||||
let mut parts = vec![word.to_string()];
|
||||
i += 1;
|
||||
while i < words.len() {
|
||||
let next = words[i].trim_matches(|c: char| !c.is_alphanumeric());
|
||||
if is_capitalized(next) && next.len() > 1 {
|
||||
parts.push(next.to_string());
|
||||
i += 1;
|
||||
} else {
|
||||
break;
|
||||
}
|
||||
}
|
||||
// Only take phrases of 2+ words (single capitalized words are too noisy).
|
||||
if parts.len() >= 2 {
|
||||
// Skip if the first word is at position 0 or follows a sentence terminator
|
||||
// and is a common article/pronoun. We still keep it if part of a longer
|
||||
// multi-word phrase that itself is capitalized.
|
||||
let is_sentence_start = start == 0
|
||||
|| words.get(start.wrapping_sub(1)).is_some_and(|prev| {
|
||||
prev.ends_with('.') || prev.ends_with('!') || prev.ends_with('?')
|
||||
});
|
||||
|
||||
if is_sentence_start && parts.len() == 2 && is_common_starter(&parts[0]) {
|
||||
// Skip - likely just a sentence starting with "The Xyz" etc.
|
||||
} else {
|
||||
let phrase = parts.join(" ");
|
||||
phrases.push(phrase);
|
||||
}
|
||||
}
|
||||
} else {
|
||||
i += 1;
|
||||
}
|
||||
}
|
||||
|
||||
phrases
|
||||
}
|
||||
|
||||
/// Returns `true` if the first character of `word` is uppercase ASCII.
|
||||
fn is_capitalized(word: &str) -> bool {
|
||||
word.chars().next().is_some_and(|c| c.is_uppercase())
|
||||
}
|
||||
|
||||
/// Common sentence-starting words that are not proper nouns.
|
||||
fn is_common_starter(word: &str) -> bool {
|
||||
matches!(
|
||||
word.to_lowercase().as_str(),
|
||||
"the"
|
||||
| "a"
|
||||
| "an"
|
||||
| "this"
|
||||
| "that"
|
||||
| "these"
|
||||
| "those"
|
||||
| "it"
|
||||
| "i"
|
||||
| "we"
|
||||
| "they"
|
||||
| "he"
|
||||
| "she"
|
||||
)
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn test_extract_urls() {
|
||||
let entities =
|
||||
extract_entities("Visit https://example.com/page and http://foo.bar/baz for info.");
|
||||
let urls: Vec<_> = entities.iter().filter(|(l, _)| l == "Url").collect();
|
||||
assert_eq!(urls.len(), 2);
|
||||
assert_eq!(urls[0].1, "https://example.com/page");
|
||||
assert_eq!(urls[1].1, "http://foo.bar/baz");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_extract_emails() {
|
||||
let entities = extract_entities("Email alice@example.com or bob@company.org for help.");
|
||||
let emails: Vec<_> = entities.iter().filter(|(l, _)| l == "Email").collect();
|
||||
assert_eq!(emails.len(), 2);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_extract_mentions() {
|
||||
let entities = extract_entities("Hey @alice and @bob-dev, check this out.");
|
||||
let mentions: Vec<_> = entities.iter().filter(|(l, _)| l == "Mention").collect();
|
||||
assert_eq!(mentions.len(), 2);
|
||||
assert_eq!(mentions[0].1, "@alice");
|
||||
assert_eq!(mentions[1].1, "@bob-dev");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_extract_capitalized_phrases() {
|
||||
let entities = extract_entities("I met John Smith at the World Trade Center yesterday.");
|
||||
let persons: Vec<_> = entities.iter().filter(|(l, _)| l == "Person").collect();
|
||||
assert!(persons.iter().any(|(_, n)| n == "John Smith"));
|
||||
assert!(persons.iter().any(|(_, n)| n == "World Trade Center"));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_no_false_positives_on_sentence_start() {
|
||||
let entities = extract_entities("The cat sat on the mat.");
|
||||
let persons: Vec<_> = entities.iter().filter(|(l, _)| l == "Person").collect();
|
||||
// "The cat" should not appear as a person (single cap word + lowercase).
|
||||
assert!(persons.is_empty());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_deduplication() {
|
||||
let entities = extract_entities("Visit https://example.com and https://example.com again.");
|
||||
let urls: Vec<_> = entities.iter().filter(|(l, _)| l == "Url").collect();
|
||||
assert_eq!(urls.len(), 1);
|
||||
}
|
||||
}
|
||||
359
vendor/ruvector/examples/OSpipe/src/graph/mod.rs
vendored
Normal file
359
vendor/ruvector/examples/OSpipe/src/graph/mod.rs
vendored
Normal file
@@ -0,0 +1,359 @@
|
||||
//! Knowledge graph integration for OSpipe.
|
||||
//!
|
||||
//! Provides entity extraction from captured text and stores entity relationships
|
||||
//! in a [`ruvector_graph::GraphDB`] (native) or a lightweight in-memory stub (WASM).
|
||||
//!
|
||||
//! ## Usage
|
||||
//!
|
||||
//! ```rust,no_run
|
||||
//! use ospipe::graph::KnowledgeGraph;
|
||||
//!
|
||||
//! let mut kg = KnowledgeGraph::new();
|
||||
//! let ids = kg.ingest_frame_entities("frame-001", "Meeting with John Smith at https://meet.example.com").unwrap();
|
||||
//! let people = kg.find_by_label("Person");
|
||||
//! ```
|
||||
|
||||
pub mod entity_extractor;
|
||||
|
||||
use crate::error::Result;
|
||||
use std::collections::HashMap;
|
||||
|
||||
/// A lightweight entity representation returned by query methods.
|
||||
#[derive(Debug, Clone, PartialEq, Eq)]
|
||||
pub struct Entity {
|
||||
/// Unique identifier for this entity.
|
||||
pub id: String,
|
||||
/// Category label (e.g. "Person", "Url", "Mention", "Email", "Frame").
|
||||
pub label: String,
|
||||
/// Human-readable name or value.
|
||||
pub name: String,
|
||||
/// Additional key-value properties.
|
||||
pub properties: HashMap<String, String>,
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Native implementation (backed by ruvector-graph)
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[cfg(not(target_arch = "wasm32"))]
|
||||
mod inner {
|
||||
use super::*;
|
||||
use crate::error::OsPipeError;
|
||||
use ruvector_graph::{EdgeBuilder, GraphDB, NodeBuilder, PropertyValue};
|
||||
|
||||
/// A knowledge graph that stores entity relationships extracted from captured
|
||||
/// frames. On native targets this is backed by [`ruvector_graph::GraphDB`].
|
||||
pub struct KnowledgeGraph {
|
||||
db: GraphDB,
|
||||
}
|
||||
|
||||
impl KnowledgeGraph {
|
||||
/// Create a new, empty knowledge graph.
|
||||
pub fn new() -> Self {
|
||||
Self { db: GraphDB::new() }
|
||||
}
|
||||
|
||||
/// Add an entity node to the graph.
|
||||
///
|
||||
/// Returns the newly created node ID.
|
||||
pub fn add_entity(
|
||||
&self,
|
||||
label: &str,
|
||||
name: &str,
|
||||
properties: HashMap<String, String>,
|
||||
) -> Result<String> {
|
||||
let mut builder = NodeBuilder::new().label(label).property("name", name);
|
||||
|
||||
for (k, v) in &properties {
|
||||
builder = builder.property(k.as_str(), v.as_str());
|
||||
}
|
||||
|
||||
let node = builder.build();
|
||||
let id = self
|
||||
.db
|
||||
.create_node(node)
|
||||
.map_err(|e| OsPipeError::Storage(format!("graph: {}", e)))?;
|
||||
Ok(id)
|
||||
}
|
||||
|
||||
/// Create a directed relationship (edge) between two entities.
|
||||
///
|
||||
/// Both `from_id` and `to_id` must refer to existing nodes.
|
||||
/// Returns the edge ID.
|
||||
pub fn add_relationship(
|
||||
&self,
|
||||
from_id: &str,
|
||||
to_id: &str,
|
||||
rel_type: &str,
|
||||
) -> Result<String> {
|
||||
let edge = EdgeBuilder::new(from_id.to_string(), to_id.to_string(), rel_type).build();
|
||||
let id = self
|
||||
.db
|
||||
.create_edge(edge)
|
||||
.map_err(|e| OsPipeError::Storage(format!("graph: {}", e)))?;
|
||||
Ok(id)
|
||||
}
|
||||
|
||||
/// Find all entities that carry `label`.
|
||||
pub fn find_by_label(&self, label: &str) -> Vec<Entity> {
|
||||
self.db
|
||||
.get_nodes_by_label(label)
|
||||
.into_iter()
|
||||
.map(|n| node_to_entity(&n))
|
||||
.collect()
|
||||
}
|
||||
|
||||
/// Find all entities directly connected to `entity_id` (both outgoing and
|
||||
/// incoming edges).
|
||||
pub fn neighbors(&self, entity_id: &str) -> Vec<Entity> {
|
||||
let mut seen = std::collections::HashSet::new();
|
||||
let mut result = Vec::new();
|
||||
|
||||
let node_id = entity_id.to_string();
|
||||
|
||||
// Outgoing neighbours.
|
||||
for edge in self.db.get_outgoing_edges(&node_id) {
|
||||
if seen.insert(edge.to.clone()) {
|
||||
if let Some(node) = self.db.get_node(&edge.to) {
|
||||
result.push(node_to_entity(&node));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Incoming neighbours.
|
||||
for edge in self.db.get_incoming_edges(&node_id) {
|
||||
if seen.insert(edge.from.clone()) {
|
||||
if let Some(node) = self.db.get_node(&edge.from) {
|
||||
result.push(node_to_entity(&node));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
result
|
||||
}
|
||||
|
||||
/// Run heuristic NER on `text` and return extracted `(label, name)` pairs.
|
||||
pub fn extract_entities(text: &str) -> Vec<(String, String)> {
|
||||
entity_extractor::extract_entities(text)
|
||||
}
|
||||
|
||||
/// Extract entities from `text`, create nodes for each, link them to the
|
||||
/// given `frame_id` node (creating the frame node if it does not yet exist),
|
||||
/// and return the IDs of all newly created entity nodes.
|
||||
pub fn ingest_frame_entities(&self, frame_id: &str, text: &str) -> Result<Vec<String>> {
|
||||
// Ensure frame node exists.
|
||||
let frame_node_id = if self.db.get_node(frame_id).is_some() {
|
||||
frame_id.to_string()
|
||||
} else {
|
||||
let node = NodeBuilder::new()
|
||||
.id(frame_id)
|
||||
.label("Frame")
|
||||
.property("name", frame_id)
|
||||
.build();
|
||||
self.db
|
||||
.create_node(node)
|
||||
.map_err(|e| OsPipeError::Storage(format!("graph: {}", e)))?
|
||||
};
|
||||
|
||||
let extracted = entity_extractor::extract_entities(text);
|
||||
let mut entity_ids = Vec::with_capacity(extracted.len());
|
||||
|
||||
for (label, name) in &extracted {
|
||||
let entity_id = self.add_entity(label, name, HashMap::new())?;
|
||||
self.add_relationship(&frame_node_id, &entity_id, "CONTAINS")?;
|
||||
entity_ids.push(entity_id);
|
||||
}
|
||||
|
||||
Ok(entity_ids)
|
||||
}
|
||||
}
|
||||
|
||||
impl Default for KnowledgeGraph {
|
||||
fn default() -> Self {
|
||||
Self::new()
|
||||
}
|
||||
}
|
||||
|
||||
/// Convert a `ruvector_graph::Node` into the crate-public `Entity` type.
|
||||
fn node_to_entity(node: &ruvector_graph::Node) -> Entity {
|
||||
let label = node
|
||||
.labels
|
||||
.first()
|
||||
.map_or_else(String::new, |l| l.name.clone());
|
||||
|
||||
let name = match node.get_property("name") {
|
||||
Some(PropertyValue::String(s)) => s.clone(),
|
||||
_ => String::new(),
|
||||
};
|
||||
|
||||
let mut properties = HashMap::new();
|
||||
for (k, v) in &node.properties {
|
||||
if k == "name" {
|
||||
continue;
|
||||
}
|
||||
let v_str = match v {
|
||||
PropertyValue::String(s) => s.clone(),
|
||||
PropertyValue::Integer(i) => i.to_string(),
|
||||
PropertyValue::Float(f) => f.to_string(),
|
||||
PropertyValue::Boolean(b) => b.to_string(),
|
||||
_ => format!("{:?}", v),
|
||||
};
|
||||
properties.insert(k.clone(), v_str);
|
||||
}
|
||||
|
||||
Entity {
|
||||
id: node.id.clone(),
|
||||
label,
|
||||
name,
|
||||
properties,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// WASM fallback (lightweight in-memory stub)
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[cfg(target_arch = "wasm32")]
|
||||
mod inner {
|
||||
use super::*;
|
||||
|
||||
struct StoredNode {
|
||||
id: String,
|
||||
label: String,
|
||||
name: String,
|
||||
properties: HashMap<String, String>,
|
||||
}
|
||||
|
||||
struct StoredEdge {
|
||||
_id: String,
|
||||
from: String,
|
||||
to: String,
|
||||
_rel_type: String,
|
||||
}
|
||||
|
||||
/// A knowledge graph backed by simple `Vec` storage for WASM targets.
|
||||
pub struct KnowledgeGraph {
|
||||
nodes: Vec<StoredNode>,
|
||||
edges: Vec<StoredEdge>,
|
||||
next_id: u64,
|
||||
}
|
||||
|
||||
impl KnowledgeGraph {
|
||||
pub fn new() -> Self {
|
||||
Self {
|
||||
nodes: Vec::new(),
|
||||
edges: Vec::new(),
|
||||
next_id: 0,
|
||||
}
|
||||
}
|
||||
|
||||
pub fn add_entity(
|
||||
&mut self,
|
||||
label: &str,
|
||||
name: &str,
|
||||
properties: HashMap<String, String>,
|
||||
) -> Result<String> {
|
||||
let id = format!("wasm-{}", self.next_id);
|
||||
self.next_id += 1;
|
||||
self.nodes.push(StoredNode {
|
||||
id: id.clone(),
|
||||
label: label.to_string(),
|
||||
name: name.to_string(),
|
||||
properties,
|
||||
});
|
||||
Ok(id)
|
||||
}
|
||||
|
||||
pub fn add_relationship(
|
||||
&mut self,
|
||||
from_id: &str,
|
||||
to_id: &str,
|
||||
rel_type: &str,
|
||||
) -> Result<String> {
|
||||
let id = format!("wasm-e-{}", self.next_id);
|
||||
self.next_id += 1;
|
||||
self.edges.push(StoredEdge {
|
||||
_id: id.clone(),
|
||||
from: from_id.to_string(),
|
||||
to: to_id.to_string(),
|
||||
_rel_type: rel_type.to_string(),
|
||||
});
|
||||
Ok(id)
|
||||
}
|
||||
|
||||
pub fn find_by_label(&self, label: &str) -> Vec<Entity> {
|
||||
self.nodes
|
||||
.iter()
|
||||
.filter(|n| n.label == label)
|
||||
.map(|n| Entity {
|
||||
id: n.id.clone(),
|
||||
label: n.label.clone(),
|
||||
name: n.name.clone(),
|
||||
properties: n.properties.clone(),
|
||||
})
|
||||
.collect()
|
||||
}
|
||||
|
||||
pub fn neighbors(&self, entity_id: &str) -> Vec<Entity> {
|
||||
let mut ids = std::collections::HashSet::new();
|
||||
for e in &self.edges {
|
||||
if e.from == entity_id {
|
||||
ids.insert(e.to.clone());
|
||||
}
|
||||
if e.to == entity_id {
|
||||
ids.insert(e.from.clone());
|
||||
}
|
||||
}
|
||||
self.nodes
|
||||
.iter()
|
||||
.filter(|n| ids.contains(&n.id))
|
||||
.map(|n| Entity {
|
||||
id: n.id.clone(),
|
||||
label: n.label.clone(),
|
||||
name: n.name.clone(),
|
||||
properties: n.properties.clone(),
|
||||
})
|
||||
.collect()
|
||||
}
|
||||
|
||||
pub fn extract_entities(text: &str) -> Vec<(String, String)> {
|
||||
entity_extractor::extract_entities(text)
|
||||
}
|
||||
|
||||
pub fn ingest_frame_entities(&mut self, frame_id: &str, text: &str) -> Result<Vec<String>> {
|
||||
// Ensure frame node.
|
||||
let frame_exists = self.nodes.iter().any(|n| n.id == frame_id);
|
||||
let frame_node_id = if frame_exists {
|
||||
frame_id.to_string()
|
||||
} else {
|
||||
let id = frame_id.to_string();
|
||||
self.nodes.push(StoredNode {
|
||||
id: id.clone(),
|
||||
label: "Frame".to_string(),
|
||||
name: frame_id.to_string(),
|
||||
properties: HashMap::new(),
|
||||
});
|
||||
id
|
||||
};
|
||||
|
||||
let extracted = entity_extractor::extract_entities(text);
|
||||
let mut entity_ids = Vec::with_capacity(extracted.len());
|
||||
for (label, name) in &extracted {
|
||||
let eid = self.add_entity(label, name, HashMap::new())?;
|
||||
self.add_relationship(&frame_node_id, &eid, "CONTAINS")?;
|
||||
entity_ids.push(eid);
|
||||
}
|
||||
Ok(entity_ids)
|
||||
}
|
||||
}
|
||||
|
||||
impl Default for KnowledgeGraph {
|
||||
fn default() -> Self {
|
||||
Self::new()
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Re-export the platform-appropriate implementation.
|
||||
pub use inner::KnowledgeGraph;
|
||||
327
vendor/ruvector/examples/OSpipe/src/learning/mod.rs
vendored
Normal file
327
vendor/ruvector/examples/OSpipe/src/learning/mod.rs
vendored
Normal file
@@ -0,0 +1,327 @@
|
||||
//! Continual learning for search improvement.
|
||||
//!
|
||||
//! This module integrates `ruvector-gnn` to provide:
|
||||
//!
|
||||
//! - **[`SearchLearner`]** -- records user relevance feedback and uses Elastic
|
||||
//! Weight Consolidation (EWC) to prevent catastrophic forgetting when the
|
||||
//! embedding model is fine-tuned over time.
|
||||
//! - **[`EmbeddingQuantizer`]** -- compresses stored embeddings based on their
|
||||
//! age, trading precision for storage savings on cold data.
|
||||
//!
|
||||
//! Both structs compile to no-op stubs on `wasm32` targets where the native
|
||||
//! `ruvector-gnn` crate is unavailable.
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Native implementation (non-WASM)
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[cfg(not(target_arch = "wasm32"))]
|
||||
mod native {
|
||||
use ruvector_gnn::compress::TensorCompress;
|
||||
use ruvector_gnn::ewc::ElasticWeightConsolidation;
|
||||
use ruvector_gnn::replay::ReplayBuffer;
|
||||
|
||||
/// Minimum number of feedback entries before learning data is considered
|
||||
/// sufficient for a consolidation step.
|
||||
const MIN_FEEDBACK_ENTRIES: usize = 32;
|
||||
|
||||
/// Records search relevance feedback and manages continual-learning state.
|
||||
///
|
||||
/// Internally the learner maintains:
|
||||
/// - A [`ReplayBuffer`] that stores (query, result, relevance) triples via
|
||||
/// reservoir sampling so old feedback is not forgotten.
|
||||
/// - An [`ElasticWeightConsolidation`] instance whose Fisher diagonal and
|
||||
/// anchor weights track which embedding dimensions are important.
|
||||
/// - A simple parameter vector (`weights`) that represents a learned
|
||||
/// relevance projection (one weight per embedding dimension).
|
||||
pub struct SearchLearner {
|
||||
replay_buffer: ReplayBuffer,
|
||||
ewc: ElasticWeightConsolidation,
|
||||
/// Learned relevance-projection weights (one per embedding dimension).
|
||||
weights: Vec<f32>,
|
||||
}
|
||||
|
||||
impl SearchLearner {
|
||||
/// Create a new learner.
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `embedding_dim` - Dimensionality of the embedding vectors.
|
||||
/// * `replay_capacity` - Maximum number of feedback entries retained.
|
||||
pub fn new(embedding_dim: usize, replay_capacity: usize) -> Self {
|
||||
Self {
|
||||
replay_buffer: ReplayBuffer::new(replay_capacity),
|
||||
ewc: ElasticWeightConsolidation::new(100.0),
|
||||
weights: vec![1.0; embedding_dim],
|
||||
}
|
||||
}
|
||||
|
||||
/// Record a single piece of user feedback.
|
||||
///
|
||||
/// The query and result embeddings are concatenated and stored in the
|
||||
/// replay buffer. Positive feedback entries use `positive_ids = [1]`,
|
||||
/// negative ones use `positive_ids = [0]`, which allows downstream
|
||||
/// training loops to distinguish them.
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `query_embedding` - Embedding of the search query.
|
||||
/// * `result_embedding` - Embedding of the search result.
|
||||
/// * `relevant` - Whether the user considered the result relevant.
|
||||
pub fn record_feedback(
|
||||
&mut self,
|
||||
query_embedding: Vec<f32>,
|
||||
result_embedding: Vec<f32>,
|
||||
relevant: bool,
|
||||
) {
|
||||
let mut combined = query_embedding;
|
||||
combined.extend_from_slice(&result_embedding);
|
||||
let positive_id: usize = if relevant { 1 } else { 0 };
|
||||
self.replay_buffer.add(&combined, &[positive_id]);
|
||||
}
|
||||
|
||||
/// Return the current size of the replay buffer.
|
||||
pub fn replay_buffer_len(&self) -> usize {
|
||||
self.replay_buffer.len()
|
||||
}
|
||||
|
||||
/// Returns `true` when the buffer contains enough data for a
|
||||
/// meaningful consolidation step (>= 32 entries).
|
||||
pub fn has_sufficient_data(&self) -> bool {
|
||||
self.replay_buffer.len() >= MIN_FEEDBACK_ENTRIES
|
||||
}
|
||||
|
||||
/// Lock the current parameter state with EWC.
|
||||
///
|
||||
/// This computes the Fisher information diagonal from sampled replay
|
||||
/// entries and saves the current weights as the EWC anchor. Future
|
||||
/// EWC penalties will discourage large deviations from these weights.
|
||||
pub fn consolidate(&mut self) {
|
||||
if self.replay_buffer.is_empty() {
|
||||
return;
|
||||
}
|
||||
|
||||
// Sample gradients -- we approximate them as the difference between
|
||||
// query and result portions of each stored entry.
|
||||
let samples = self.replay_buffer.sample(self.replay_buffer.len().min(64));
|
||||
|
||||
let dim = self.weights.len();
|
||||
let gradients: Vec<Vec<f32>> = samples
|
||||
.iter()
|
||||
.filter_map(|entry| {
|
||||
// Each entry stores [query || result]; extract gradient proxy.
|
||||
if entry.query.len() >= dim * 2 {
|
||||
let query_part = &entry.query[..dim];
|
||||
let result_part = &entry.query[dim..dim * 2];
|
||||
let grad: Vec<f32> = query_part
|
||||
.iter()
|
||||
.zip(result_part.iter())
|
||||
.map(|(q, r)| q - r)
|
||||
.collect();
|
||||
Some(grad)
|
||||
} else {
|
||||
None
|
||||
}
|
||||
})
|
||||
.collect();
|
||||
|
||||
if gradients.is_empty() {
|
||||
return;
|
||||
}
|
||||
|
||||
let grad_refs: Vec<&[f32]> = gradients.iter().map(|g| g.as_slice()).collect();
|
||||
let sample_count = grad_refs.len();
|
||||
|
||||
self.ewc.compute_fisher(&grad_refs, sample_count);
|
||||
self.ewc.consolidate(&self.weights);
|
||||
}
|
||||
|
||||
/// Return the current EWC penalty for the learned weights.
|
||||
///
|
||||
/// Returns `0.0` if [`consolidate`](Self::consolidate) has not been
|
||||
/// called yet.
|
||||
pub fn ewc_penalty(&self) -> f32 {
|
||||
self.ewc.penalty(&self.weights)
|
||||
}
|
||||
}
|
||||
|
||||
// -----------------------------------------------------------------------
|
||||
// EmbeddingQuantizer
|
||||
// -----------------------------------------------------------------------
|
||||
|
||||
/// Age-aware embedding quantizer backed by [`TensorCompress`].
|
||||
///
|
||||
/// Older embeddings are compressed more aggressively:
|
||||
///
|
||||
/// | Age | Compression |
|
||||
/// |----------------|----------------------|
|
||||
/// | < 1 hour | Full precision |
|
||||
/// | 1 h -- 24 h | Half precision (FP16)|
|
||||
/// | 1 d -- 7 d | PQ8 |
|
||||
/// | > 7 d | Binary |
|
||||
pub struct EmbeddingQuantizer {
|
||||
compressor: TensorCompress,
|
||||
}
|
||||
|
||||
impl Default for EmbeddingQuantizer {
|
||||
fn default() -> Self {
|
||||
Self::new()
|
||||
}
|
||||
}
|
||||
|
||||
impl EmbeddingQuantizer {
|
||||
/// Create a new quantizer instance.
|
||||
pub fn new() -> Self {
|
||||
Self {
|
||||
compressor: TensorCompress::new(),
|
||||
}
|
||||
}
|
||||
|
||||
/// Compress an embedding based on its age.
|
||||
///
|
||||
/// The age determines the access-frequency proxy passed to the
|
||||
/// underlying `TensorCompress`:
|
||||
/// - `< 1 h` -> freq `1.0` (no compression)
|
||||
/// - `1-24 h` -> freq `0.5` (half precision)
|
||||
/// - `1-7 d` -> freq `0.2` (PQ8)
|
||||
/// - `> 7 d` -> freq `0.005` (binary)
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `embedding` - The raw embedding vector.
|
||||
/// * `age_hours` - Age of the embedding in hours.
|
||||
///
|
||||
/// # Returns
|
||||
/// Serialised compressed bytes. Use [`dequantize`](Self::dequantize)
|
||||
/// to recover the original (lossy) vector.
|
||||
pub fn quantize_by_age(&self, embedding: &[f32], age_hours: u64) -> Vec<u8> {
|
||||
let access_freq = Self::age_to_freq(age_hours);
|
||||
match self.compressor.compress(embedding, access_freq) {
|
||||
Ok(compressed) => {
|
||||
serde_json::to_vec(&compressed).unwrap_or_else(|_| {
|
||||
// Fallback: store raw f32 bytes.
|
||||
embedding.iter().flat_map(|f| f.to_le_bytes()).collect()
|
||||
})
|
||||
}
|
||||
Err(_) => {
|
||||
// Fallback: store raw f32 bytes.
|
||||
embedding.iter().flat_map(|f| f.to_le_bytes()).collect()
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Decompress bytes produced by [`quantize_by_age`](Self::quantize_by_age).
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `data` - Compressed byte representation.
|
||||
/// * `original_dim` - Expected dimensionality of the output vector.
|
||||
///
|
||||
/// # Returns
|
||||
/// The decompressed embedding (lossy). If decompression fails, a
|
||||
/// zero-vector of `original_dim` length is returned.
|
||||
pub fn dequantize(&self, data: &[u8], original_dim: usize) -> Vec<f32> {
|
||||
if let Ok(compressed) =
|
||||
serde_json::from_slice::<ruvector_gnn::compress::CompressedTensor>(data)
|
||||
{
|
||||
if let Ok(decompressed) = self.compressor.decompress(&compressed) {
|
||||
if decompressed.len() == original_dim {
|
||||
return decompressed;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Fallback: try interpreting as raw f32 bytes.
|
||||
if data.len() == original_dim * 4 {
|
||||
return data
|
||||
.chunks_exact(4)
|
||||
.map(|chunk| f32::from_le_bytes([chunk[0], chunk[1], chunk[2], chunk[3]]))
|
||||
.collect();
|
||||
}
|
||||
|
||||
vec![0.0; original_dim]
|
||||
}
|
||||
|
||||
/// Map an age in hours to an access-frequency proxy in [0, 1].
|
||||
fn age_to_freq(age_hours: u64) -> f32 {
|
||||
match age_hours {
|
||||
0 => 1.0, // Fresh -- full precision
|
||||
1..=24 => 0.5, // Warm -- half precision
|
||||
25..=168 => 0.2, // Cool -- PQ8
|
||||
_ => 0.005, // Cold -- binary
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// WASM stub implementation
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[cfg(target_arch = "wasm32")]
|
||||
mod wasm_stub {
|
||||
/// No-op search learner for WASM targets.
|
||||
pub struct SearchLearner {
|
||||
buffer_len: usize,
|
||||
}
|
||||
|
||||
impl SearchLearner {
|
||||
pub fn new(_embedding_dim: usize, _replay_capacity: usize) -> Self {
|
||||
Self { buffer_len: 0 }
|
||||
}
|
||||
|
||||
pub fn record_feedback(
|
||||
&mut self,
|
||||
_query_embedding: Vec<f32>,
|
||||
_result_embedding: Vec<f32>,
|
||||
_relevant: bool,
|
||||
) {
|
||||
self.buffer_len += 1;
|
||||
}
|
||||
|
||||
pub fn replay_buffer_len(&self) -> usize {
|
||||
self.buffer_len
|
||||
}
|
||||
|
||||
pub fn has_sufficient_data(&self) -> bool {
|
||||
self.buffer_len >= 32
|
||||
}
|
||||
|
||||
pub fn consolidate(&mut self) {}
|
||||
|
||||
pub fn ewc_penalty(&self) -> f32 {
|
||||
0.0
|
||||
}
|
||||
}
|
||||
|
||||
/// No-op embedding quantizer for WASM targets.
|
||||
///
|
||||
/// Returns the original embedding bytes without compression.
|
||||
pub struct EmbeddingQuantizer;
|
||||
|
||||
impl EmbeddingQuantizer {
|
||||
pub fn new() -> Self {
|
||||
Self
|
||||
}
|
||||
|
||||
pub fn quantize_by_age(&self, embedding: &[f32], _age_hours: u64) -> Vec<u8> {
|
||||
embedding.iter().flat_map(|f| f.to_le_bytes()).collect()
|
||||
}
|
||||
|
||||
pub fn dequantize(&self, data: &[u8], original_dim: usize) -> Vec<f32> {
|
||||
if data.len() == original_dim * 4 {
|
||||
data.chunks_exact(4)
|
||||
.map(|c| f32::from_le_bytes([c[0], c[1], c[2], c[3]]))
|
||||
.collect()
|
||||
} else {
|
||||
vec![0.0; original_dim]
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Re-exports
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[cfg(not(target_arch = "wasm32"))]
|
||||
pub use native::{EmbeddingQuantizer, SearchLearner};
|
||||
|
||||
#[cfg(target_arch = "wasm32")]
|
||||
pub use wasm_stub::{EmbeddingQuantizer, SearchLearner};
|
||||
43
vendor/ruvector/examples/OSpipe/src/lib.rs
vendored
Normal file
43
vendor/ruvector/examples/OSpipe/src/lib.rs
vendored
Normal file
@@ -0,0 +1,43 @@
|
||||
//! # OSpipe
|
||||
//!
|
||||
//! RuVector-enhanced personal AI memory system integrating with Screenpipe.
|
||||
//!
|
||||
//! OSpipe captures screen content, audio transcriptions, and UI events,
|
||||
//! processes them through a safety-aware ingestion pipeline, and stores
|
||||
//! them as searchable vector embeddings for personal AI memory recall.
|
||||
//!
|
||||
//! ## Architecture
|
||||
//!
|
||||
//! ```text
|
||||
//! Screenpipe -> Capture -> Safety Gate -> Dedup -> Embed -> VectorStore
|
||||
//! |
|
||||
//! Search Router <--------+
|
||||
//! (Semantic / Keyword / Hybrid)
|
||||
//! ```
|
||||
//!
|
||||
//! ## Modules
|
||||
//!
|
||||
//! - [`capture`] - Captured frame data structures (OCR, transcription, UI events)
|
||||
//! - [`storage`] - HNSW-backed vector storage and embedding engine
|
||||
//! - [`search`] - Query routing and hybrid search (semantic + keyword)
|
||||
//! - [`pipeline`] - Ingestion pipeline with deduplication
|
||||
//! - [`safety`] - PII detection and content redaction
|
||||
//! - [`config`] - Configuration for all subsystems
|
||||
//! - [`error`] - Unified error types
|
||||
|
||||
pub mod capture;
|
||||
pub mod config;
|
||||
pub mod error;
|
||||
pub mod graph;
|
||||
pub mod learning;
|
||||
#[cfg(not(target_arch = "wasm32"))]
|
||||
pub mod persistence;
|
||||
pub mod pipeline;
|
||||
pub mod quantum;
|
||||
pub mod safety;
|
||||
pub mod search;
|
||||
#[cfg(not(target_arch = "wasm32"))]
|
||||
pub mod server;
|
||||
pub mod storage;
|
||||
|
||||
pub mod wasm;
|
||||
319
vendor/ruvector/examples/OSpipe/src/persistence.rs
vendored
Normal file
319
vendor/ruvector/examples/OSpipe/src/persistence.rs
vendored
Normal file
@@ -0,0 +1,319 @@
|
||||
//! JSON-file persistence layer for OSpipe data.
|
||||
//!
|
||||
//! Provides durable storage of frames, configuration, and embedding data
|
||||
//! using the local filesystem. All data is serialized to JSON (frames and
|
||||
//! config) or raw bytes (embeddings) inside a configurable data directory.
|
||||
//!
|
||||
//! This module is gated behind `cfg(not(target_arch = "wasm32"))` because
|
||||
//! WASM targets do not have filesystem access.
|
||||
|
||||
use crate::capture::CapturedFrame;
|
||||
use crate::config::OsPipeConfig;
|
||||
use crate::error::{OsPipeError, Result};
|
||||
use serde::{Deserialize, Serialize};
|
||||
use std::path::PathBuf;
|
||||
|
||||
/// A serializable wrapper around [`CapturedFrame`] for disk persistence.
|
||||
///
|
||||
/// This mirrors all fields of `CapturedFrame` but is kept as a distinct
|
||||
/// type so the persistence format can evolve independently of the
|
||||
/// in-memory representation.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct StoredFrame {
|
||||
/// The captured frame data.
|
||||
pub frame: CapturedFrame,
|
||||
/// Optional text that was stored after safety-gate processing.
|
||||
/// If `None`, the original frame text was used unchanged.
|
||||
pub safe_text: Option<String>,
|
||||
}
|
||||
|
||||
/// Filesystem-backed persistence for OSpipe data.
|
||||
///
|
||||
/// All files are written inside `data_dir`:
|
||||
/// - `frames.json` - serialized vector of [`StoredFrame`]
|
||||
/// - `config.json` - serialized [`OsPipeConfig`]
|
||||
/// - `embeddings.bin` - raw bytes (e.g. HNSW index serialization)
|
||||
pub struct PersistenceLayer {
|
||||
data_dir: PathBuf,
|
||||
}
|
||||
|
||||
impl PersistenceLayer {
|
||||
/// Create a new persistence layer rooted at `data_dir`.
|
||||
///
|
||||
/// The directory (and any missing parents) will be created if they
|
||||
/// do not already exist.
|
||||
pub fn new(data_dir: PathBuf) -> Result<Self> {
|
||||
std::fs::create_dir_all(&data_dir).map_err(|e| {
|
||||
OsPipeError::Storage(format!(
|
||||
"Failed to create data directory {}: {}",
|
||||
data_dir.display(),
|
||||
e
|
||||
))
|
||||
})?;
|
||||
Ok(Self { data_dir })
|
||||
}
|
||||
|
||||
/// Return the path to a named file inside the data directory.
|
||||
fn file_path(&self, name: &str) -> PathBuf {
|
||||
self.data_dir.join(name)
|
||||
}
|
||||
|
||||
// ---- Frames ----
|
||||
|
||||
/// Persist a slice of stored frames to `frames.json`.
|
||||
pub fn save_frames(&self, frames: &[StoredFrame]) -> Result<()> {
|
||||
let path = self.file_path("frames.json");
|
||||
let json = serde_json::to_string_pretty(frames)?;
|
||||
std::fs::write(&path, json).map_err(|e| {
|
||||
OsPipeError::Storage(format!(
|
||||
"Failed to write frames to {}: {}",
|
||||
path.display(),
|
||||
e
|
||||
))
|
||||
})
|
||||
}
|
||||
|
||||
/// Load stored frames from `frames.json`.
|
||||
///
|
||||
/// Returns an empty vector if the file does not exist.
|
||||
pub fn load_frames(&self) -> Result<Vec<StoredFrame>> {
|
||||
let path = self.file_path("frames.json");
|
||||
if !path.exists() {
|
||||
return Ok(Vec::new());
|
||||
}
|
||||
let data = std::fs::read_to_string(&path).map_err(|e| {
|
||||
OsPipeError::Storage(format!(
|
||||
"Failed to read frames from {}: {}",
|
||||
path.display(),
|
||||
e
|
||||
))
|
||||
})?;
|
||||
let frames: Vec<StoredFrame> = serde_json::from_str(&data)?;
|
||||
Ok(frames)
|
||||
}
|
||||
|
||||
// ---- Config ----
|
||||
|
||||
/// Persist the pipeline configuration to `config.json`.
|
||||
pub fn save_config(&self, config: &OsPipeConfig) -> Result<()> {
|
||||
let path = self.file_path("config.json");
|
||||
let json = serde_json::to_string_pretty(config)?;
|
||||
std::fs::write(&path, json).map_err(|e| {
|
||||
OsPipeError::Storage(format!(
|
||||
"Failed to write config to {}: {}",
|
||||
path.display(),
|
||||
e
|
||||
))
|
||||
})
|
||||
}
|
||||
|
||||
/// Load the pipeline configuration from `config.json`.
|
||||
///
|
||||
/// Returns `None` if the file does not exist.
|
||||
pub fn load_config(&self) -> Result<Option<OsPipeConfig>> {
|
||||
let path = self.file_path("config.json");
|
||||
if !path.exists() {
|
||||
return Ok(None);
|
||||
}
|
||||
let data = std::fs::read_to_string(&path).map_err(|e| {
|
||||
OsPipeError::Storage(format!(
|
||||
"Failed to read config from {}: {}",
|
||||
path.display(),
|
||||
e
|
||||
))
|
||||
})?;
|
||||
let config: OsPipeConfig = serde_json::from_str(&data)?;
|
||||
Ok(Some(config))
|
||||
}
|
||||
|
||||
// ---- Embeddings (raw bytes) ----
|
||||
|
||||
/// Persist raw embedding bytes to `embeddings.bin`.
|
||||
///
|
||||
/// This is intended for serializing an HNSW index or other binary
|
||||
/// data that does not fit the JSON format.
|
||||
pub fn save_embeddings(&self, data: &[u8]) -> Result<()> {
|
||||
let path = self.file_path("embeddings.bin");
|
||||
std::fs::write(&path, data).map_err(|e| {
|
||||
OsPipeError::Storage(format!(
|
||||
"Failed to write embeddings to {}: {}",
|
||||
path.display(),
|
||||
e
|
||||
))
|
||||
})
|
||||
}
|
||||
|
||||
/// Load raw embedding bytes from `embeddings.bin`.
|
||||
///
|
||||
/// Returns `None` if the file does not exist.
|
||||
pub fn load_embeddings(&self) -> Result<Option<Vec<u8>>> {
|
||||
let path = self.file_path("embeddings.bin");
|
||||
if !path.exists() {
|
||||
return Ok(None);
|
||||
}
|
||||
let data = std::fs::read(&path).map_err(|e| {
|
||||
OsPipeError::Storage(format!(
|
||||
"Failed to read embeddings from {}: {}",
|
||||
path.display(),
|
||||
e
|
||||
))
|
||||
})?;
|
||||
Ok(Some(data))
|
||||
}
|
||||
|
||||
/// Return the data directory path.
|
||||
pub fn data_dir(&self) -> &PathBuf {
|
||||
&self.data_dir
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::capture::CapturedFrame;
|
||||
|
||||
fn temp_dir() -> PathBuf {
|
||||
let dir = std::env::temp_dir().join(format!("ospipe_test_{}", uuid::Uuid::new_v4()));
|
||||
std::fs::create_dir_all(&dir).unwrap();
|
||||
dir
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_frames_roundtrip() {
|
||||
let dir = temp_dir();
|
||||
let layer = PersistenceLayer::new(dir.clone()).unwrap();
|
||||
|
||||
let frame = CapturedFrame::new_screen("VSCode", "main.rs", "fn main() {}", 0);
|
||||
let stored = vec![StoredFrame {
|
||||
frame,
|
||||
safe_text: None,
|
||||
}];
|
||||
|
||||
layer.save_frames(&stored).unwrap();
|
||||
let loaded = layer.load_frames().unwrap();
|
||||
|
||||
assert_eq!(loaded.len(), 1);
|
||||
assert_eq!(loaded[0].frame.text_content(), "fn main() {}");
|
||||
assert!(loaded[0].safe_text.is_none());
|
||||
|
||||
// Cleanup
|
||||
let _ = std::fs::remove_dir_all(&dir);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_frames_empty_when_missing() {
|
||||
let dir = temp_dir();
|
||||
let layer = PersistenceLayer::new(dir.clone()).unwrap();
|
||||
|
||||
let loaded = layer.load_frames().unwrap();
|
||||
assert!(loaded.is_empty());
|
||||
|
||||
let _ = std::fs::remove_dir_all(&dir);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_config_roundtrip() {
|
||||
let dir = temp_dir();
|
||||
let layer = PersistenceLayer::new(dir.clone()).unwrap();
|
||||
|
||||
let config = OsPipeConfig::default();
|
||||
layer.save_config(&config).unwrap();
|
||||
|
||||
let loaded = layer.load_config().unwrap();
|
||||
assert!(loaded.is_some());
|
||||
let loaded = loaded.unwrap();
|
||||
assert_eq!(loaded.storage.embedding_dim, 384);
|
||||
assert_eq!(loaded.capture.fps, 1.0);
|
||||
|
||||
let _ = std::fs::remove_dir_all(&dir);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_config_none_when_missing() {
|
||||
let dir = temp_dir();
|
||||
let layer = PersistenceLayer::new(dir.clone()).unwrap();
|
||||
|
||||
let loaded = layer.load_config().unwrap();
|
||||
assert!(loaded.is_none());
|
||||
|
||||
let _ = std::fs::remove_dir_all(&dir);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_embeddings_roundtrip() {
|
||||
let dir = temp_dir();
|
||||
let layer = PersistenceLayer::new(dir.clone()).unwrap();
|
||||
|
||||
let data: Vec<u8> = vec![0xDE, 0xAD, 0xBE, 0xEF, 1, 2, 3, 4];
|
||||
layer.save_embeddings(&data).unwrap();
|
||||
|
||||
let loaded = layer.load_embeddings().unwrap();
|
||||
assert!(loaded.is_some());
|
||||
assert_eq!(loaded.unwrap(), data);
|
||||
|
||||
let _ = std::fs::remove_dir_all(&dir);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_embeddings_none_when_missing() {
|
||||
let dir = temp_dir();
|
||||
let layer = PersistenceLayer::new(dir.clone()).unwrap();
|
||||
|
||||
let loaded = layer.load_embeddings().unwrap();
|
||||
assert!(loaded.is_none());
|
||||
|
||||
let _ = std::fs::remove_dir_all(&dir);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_creates_directory_if_missing() {
|
||||
let dir = std::env::temp_dir()
|
||||
.join(format!("ospipe_test_{}", uuid::Uuid::new_v4()))
|
||||
.join("nested")
|
||||
.join("deep");
|
||||
assert!(!dir.exists());
|
||||
|
||||
let layer = PersistenceLayer::new(dir.clone());
|
||||
assert!(layer.is_ok());
|
||||
assert!(dir.exists());
|
||||
|
||||
let _ = std::fs::remove_dir_all(dir.parent().unwrap().parent().unwrap());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_multiple_frames_roundtrip() {
|
||||
let dir = temp_dir();
|
||||
let layer = PersistenceLayer::new(dir.clone()).unwrap();
|
||||
|
||||
let frames: Vec<StoredFrame> = (0..5)
|
||||
.map(|i| StoredFrame {
|
||||
frame: CapturedFrame::new_screen(
|
||||
"App",
|
||||
&format!("Window {}", i),
|
||||
&format!("Content {}", i),
|
||||
0,
|
||||
),
|
||||
safe_text: if i % 2 == 0 {
|
||||
Some(format!("Redacted {}", i))
|
||||
} else {
|
||||
None
|
||||
},
|
||||
})
|
||||
.collect();
|
||||
|
||||
layer.save_frames(&frames).unwrap();
|
||||
let loaded = layer.load_frames().unwrap();
|
||||
|
||||
assert_eq!(loaded.len(), 5);
|
||||
for (i, sf) in loaded.iter().enumerate() {
|
||||
assert_eq!(sf.frame.text_content(), &format!("Content {}", i));
|
||||
if i % 2 == 0 {
|
||||
assert_eq!(sf.safe_text, Some(format!("Redacted {}", i)));
|
||||
} else {
|
||||
assert!(sf.safe_text.is_none());
|
||||
}
|
||||
}
|
||||
|
||||
let _ = std::fs::remove_dir_all(&dir);
|
||||
}
|
||||
}
|
||||
89
vendor/ruvector/examples/OSpipe/src/pipeline/dedup.rs
vendored
Normal file
89
vendor/ruvector/examples/OSpipe/src/pipeline/dedup.rs
vendored
Normal file
@@ -0,0 +1,89 @@
|
||||
//! Frame deduplication using cosine similarity.
|
||||
//!
|
||||
//! Maintains a sliding window of recent embeddings and checks new
|
||||
//! frames against them to avoid storing near-duplicate content
|
||||
//! (e.g., consecutive screen captures of the same static page).
|
||||
|
||||
use std::collections::VecDeque;
|
||||
|
||||
use crate::storage::embedding::cosine_similarity;
|
||||
use uuid::Uuid;
|
||||
|
||||
/// Deduplicator that checks new embeddings against a sliding window
|
||||
/// of recently stored embeddings.
|
||||
pub struct FrameDeduplicator {
|
||||
/// Cosine similarity threshold above which a frame is considered duplicate.
|
||||
threshold: f32,
|
||||
/// Sliding window of recent embeddings (id, vector).
|
||||
recent_embeddings: VecDeque<(Uuid, Vec<f32>)>,
|
||||
/// Maximum number of recent embeddings to keep.
|
||||
window_size: usize,
|
||||
}
|
||||
|
||||
impl FrameDeduplicator {
|
||||
/// Create a new deduplicator.
|
||||
///
|
||||
/// - `threshold`: Cosine similarity threshold for duplicate detection (e.g., 0.95).
|
||||
/// - `window_size`: Number of recent embeddings to keep for comparison.
|
||||
pub fn new(threshold: f32, window_size: usize) -> Self {
|
||||
Self {
|
||||
threshold,
|
||||
recent_embeddings: VecDeque::with_capacity(window_size),
|
||||
window_size,
|
||||
}
|
||||
}
|
||||
|
||||
/// Check if the given embedding is a duplicate of a recent entry.
|
||||
///
|
||||
/// Returns `Some((id, similarity))` if a duplicate is found, where
|
||||
/// `id` is the ID of the matching recent embedding and `similarity`
|
||||
/// is the cosine similarity score.
|
||||
pub fn is_duplicate(&self, embedding: &[f32]) -> Option<(Uuid, f32)> {
|
||||
let mut best_match: Option<(Uuid, f32)> = None;
|
||||
|
||||
for (id, stored_emb) in &self.recent_embeddings {
|
||||
if stored_emb.len() != embedding.len() {
|
||||
continue;
|
||||
}
|
||||
let sim = cosine_similarity(embedding, stored_emb);
|
||||
if sim >= self.threshold {
|
||||
match best_match {
|
||||
Some((_, best_sim)) if sim > best_sim => {
|
||||
best_match = Some((*id, sim));
|
||||
}
|
||||
None => {
|
||||
best_match = Some((*id, sim));
|
||||
}
|
||||
_ => {}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
best_match
|
||||
}
|
||||
|
||||
/// Add an embedding to the sliding window.
|
||||
///
|
||||
/// If the window is full, the oldest entry is evicted.
|
||||
pub fn add(&mut self, id: Uuid, embedding: Vec<f32>) {
|
||||
if self.recent_embeddings.len() >= self.window_size {
|
||||
self.recent_embeddings.pop_front();
|
||||
}
|
||||
self.recent_embeddings.push_back((id, embedding));
|
||||
}
|
||||
|
||||
/// Return the current number of embeddings in the window.
|
||||
pub fn window_len(&self) -> usize {
|
||||
self.recent_embeddings.len()
|
||||
}
|
||||
|
||||
/// Return the configured similarity threshold.
|
||||
pub fn threshold(&self) -> f32 {
|
||||
self.threshold
|
||||
}
|
||||
|
||||
/// Clear all entries from the sliding window.
|
||||
pub fn clear(&mut self) {
|
||||
self.recent_embeddings.clear();
|
||||
}
|
||||
}
|
||||
212
vendor/ruvector/examples/OSpipe/src/pipeline/ingestion.rs
vendored
Normal file
212
vendor/ruvector/examples/OSpipe/src/pipeline/ingestion.rs
vendored
Normal file
@@ -0,0 +1,212 @@
|
||||
//! Main ingestion pipeline.
|
||||
|
||||
use crate::capture::CapturedFrame;
|
||||
use crate::config::OsPipeConfig;
|
||||
use crate::error::Result;
|
||||
use crate::graph::KnowledgeGraph;
|
||||
use crate::pipeline::dedup::FrameDeduplicator;
|
||||
use crate::safety::{SafetyDecision, SafetyGate};
|
||||
use crate::search::enhanced::EnhancedSearch;
|
||||
use crate::storage::embedding::EmbeddingEngine;
|
||||
use crate::storage::vector_store::{SearchResult, VectorStore};
|
||||
use uuid::Uuid;
|
||||
|
||||
/// Result of ingesting a single frame.
|
||||
#[derive(Debug, Clone)]
|
||||
pub enum IngestResult {
|
||||
/// The frame was successfully stored.
|
||||
Stored {
|
||||
/// ID of the stored frame.
|
||||
id: Uuid,
|
||||
},
|
||||
/// The frame was deduplicated (not stored).
|
||||
Deduplicated {
|
||||
/// ID of the existing similar frame.
|
||||
similar_to: Uuid,
|
||||
/// Cosine similarity score with the existing frame.
|
||||
similarity: f32,
|
||||
},
|
||||
/// The frame was denied by the safety gate.
|
||||
Denied {
|
||||
/// Reason for denial.
|
||||
reason: String,
|
||||
},
|
||||
}
|
||||
|
||||
/// Statistics about the ingestion pipeline.
|
||||
#[derive(Debug, Clone, Default)]
|
||||
pub struct PipelineStats {
|
||||
/// Total frames successfully ingested.
|
||||
pub total_ingested: u64,
|
||||
/// Total frames deduplicated.
|
||||
pub total_deduplicated: u64,
|
||||
/// Total frames denied by safety gate.
|
||||
pub total_denied: u64,
|
||||
/// Total frames that had content redacted before storage.
|
||||
pub total_redacted: u64,
|
||||
}
|
||||
|
||||
/// The main ingestion pipeline that processes captured frames.
|
||||
///
|
||||
/// Frames flow through:
|
||||
/// Safety Gate -> Deduplication -> Embedding -> Storage -> Graph (extract entities)
|
||||
///
|
||||
/// Search flow:
|
||||
/// Route -> Search -> Rerank (attention) -> Diversity (quantum) -> Return
|
||||
pub struct IngestionPipeline {
|
||||
embedding_engine: EmbeddingEngine,
|
||||
vector_store: VectorStore,
|
||||
safety_gate: SafetyGate,
|
||||
dedup: FrameDeduplicator,
|
||||
stats: PipelineStats,
|
||||
/// Optional knowledge graph for entity extraction after storage.
|
||||
knowledge_graph: Option<KnowledgeGraph>,
|
||||
/// Optional enhanced search orchestrator (router + reranker + quantum).
|
||||
enhanced_search: Option<EnhancedSearch>,
|
||||
}
|
||||
|
||||
impl IngestionPipeline {
|
||||
/// Create a new ingestion pipeline with the given configuration.
|
||||
pub fn new(config: OsPipeConfig) -> Result<Self> {
|
||||
let embedding_engine = EmbeddingEngine::new(config.storage.embedding_dim);
|
||||
let vector_store = VectorStore::new(config.storage.clone())?;
|
||||
let safety_gate = SafetyGate::new(config.safety.clone());
|
||||
let dedup = FrameDeduplicator::new(config.storage.dedup_threshold, 100);
|
||||
|
||||
Ok(Self {
|
||||
embedding_engine,
|
||||
vector_store,
|
||||
safety_gate,
|
||||
dedup,
|
||||
stats: PipelineStats::default(),
|
||||
knowledge_graph: None,
|
||||
enhanced_search: None,
|
||||
})
|
||||
}
|
||||
|
||||
/// Attach a knowledge graph for entity extraction on ingested frames.
|
||||
///
|
||||
/// When a graph is attached, every successfully stored frame will have
|
||||
/// its text analysed for entities (persons, URLs, emails, mentions),
|
||||
/// which are then added to the graph as nodes linked to the frame.
|
||||
pub fn with_graph(mut self, kg: KnowledgeGraph) -> Self {
|
||||
self.knowledge_graph = Some(kg);
|
||||
self
|
||||
}
|
||||
|
||||
/// Attach an enhanced search orchestrator.
|
||||
///
|
||||
/// When attached, the [`search`](Self::search) method will route the
|
||||
/// query, fetch extra candidates, re-rank with attention, and apply
|
||||
/// quantum-inspired diversity selection before returning results.
|
||||
pub fn with_enhanced_search(mut self, es: EnhancedSearch) -> Self {
|
||||
self.enhanced_search = Some(es);
|
||||
self
|
||||
}
|
||||
|
||||
/// Ingest a single captured frame through the pipeline.
|
||||
pub fn ingest(&mut self, frame: CapturedFrame) -> Result<IngestResult> {
|
||||
let text = frame.text_content().to_string();
|
||||
|
||||
// Step 1: Safety check
|
||||
let safe_text = match self.safety_gate.check(&text) {
|
||||
SafetyDecision::Allow => text,
|
||||
SafetyDecision::AllowRedacted(redacted) => {
|
||||
self.stats.total_redacted += 1;
|
||||
redacted
|
||||
}
|
||||
SafetyDecision::Deny { reason } => {
|
||||
self.stats.total_denied += 1;
|
||||
return Ok(IngestResult::Denied { reason });
|
||||
}
|
||||
};
|
||||
|
||||
// Step 2: Generate embedding from the (possibly redacted) text
|
||||
let embedding = self.embedding_engine.embed(&safe_text);
|
||||
|
||||
// Step 3: Deduplication check
|
||||
if let Some((similar_id, similarity)) = self.dedup.is_duplicate(&embedding) {
|
||||
self.stats.total_deduplicated += 1;
|
||||
return Ok(IngestResult::Deduplicated {
|
||||
similar_to: similar_id,
|
||||
similarity,
|
||||
});
|
||||
}
|
||||
|
||||
// Step 4: Store the frame
|
||||
// If the text was redacted, create a modified frame with the safe text
|
||||
let mut store_frame = frame;
|
||||
if safe_text != store_frame.text_content() {
|
||||
store_frame.content = match &store_frame.content {
|
||||
crate::capture::FrameContent::OcrText(_) => {
|
||||
crate::capture::FrameContent::OcrText(safe_text)
|
||||
}
|
||||
crate::capture::FrameContent::Transcription(_) => {
|
||||
crate::capture::FrameContent::Transcription(safe_text)
|
||||
}
|
||||
crate::capture::FrameContent::UiEvent(_) => {
|
||||
crate::capture::FrameContent::UiEvent(safe_text)
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
self.vector_store.insert(&store_frame, &embedding)?;
|
||||
let id = store_frame.id;
|
||||
self.dedup.add(id, embedding);
|
||||
self.stats.total_ingested += 1;
|
||||
|
||||
// Step 5: Graph entity extraction (if knowledge graph is attached)
|
||||
if let Some(ref mut kg) = self.knowledge_graph {
|
||||
let frame_id_str = id.to_string();
|
||||
let _ = kg.ingest_frame_entities(&frame_id_str, store_frame.text_content());
|
||||
}
|
||||
|
||||
Ok(IngestResult::Stored { id })
|
||||
}
|
||||
|
||||
/// Ingest a batch of frames.
|
||||
pub fn ingest_batch(&mut self, frames: Vec<CapturedFrame>) -> Result<Vec<IngestResult>> {
|
||||
let mut results = Vec::with_capacity(frames.len());
|
||||
for frame in frames {
|
||||
results.push(self.ingest(frame)?);
|
||||
}
|
||||
Ok(results)
|
||||
}
|
||||
|
||||
/// Return current pipeline statistics.
|
||||
pub fn stats(&self) -> &PipelineStats {
|
||||
&self.stats
|
||||
}
|
||||
|
||||
/// Return a reference to the underlying vector store.
|
||||
pub fn vector_store(&self) -> &VectorStore {
|
||||
&self.vector_store
|
||||
}
|
||||
|
||||
/// Return a reference to the embedding engine.
|
||||
pub fn embedding_engine(&self) -> &EmbeddingEngine {
|
||||
&self.embedding_engine
|
||||
}
|
||||
|
||||
/// Return a reference to the knowledge graph, if one is attached.
|
||||
pub fn knowledge_graph(&self) -> Option<&KnowledgeGraph> {
|
||||
self.knowledge_graph.as_ref()
|
||||
}
|
||||
|
||||
/// Search the pipeline's vector store.
|
||||
///
|
||||
/// If an [`EnhancedSearch`] orchestrator is attached, the query is routed,
|
||||
/// candidates are fetched with headroom, re-ranked with attention, and
|
||||
/// diversity-selected via quantum-inspired algorithms.
|
||||
///
|
||||
/// Otherwise, a basic vector similarity search is performed.
|
||||
pub fn search(&self, query: &str, k: usize) -> Result<Vec<SearchResult>> {
|
||||
let embedding = self.embedding_engine.embed(query);
|
||||
|
||||
if let Some(ref es) = self.enhanced_search {
|
||||
es.search(query, &embedding, &self.vector_store, k)
|
||||
} else {
|
||||
self.vector_store.search(&embedding, k)
|
||||
}
|
||||
}
|
||||
}
|
||||
11
vendor/ruvector/examples/OSpipe/src/pipeline/mod.rs
vendored
Normal file
11
vendor/ruvector/examples/OSpipe/src/pipeline/mod.rs
vendored
Normal file
@@ -0,0 +1,11 @@
|
||||
//! Ingestion pipeline with deduplication.
|
||||
//!
|
||||
//! The pipeline receives captured frames, passes them through the safety
|
||||
//! gate, checks for duplicates, generates embeddings, and stores the
|
||||
//! results in the vector store.
|
||||
|
||||
pub mod dedup;
|
||||
pub mod ingestion;
|
||||
|
||||
pub use dedup::FrameDeduplicator;
|
||||
pub use ingestion::{IngestResult, IngestionPipeline, PipelineStats};
|
||||
324
vendor/ruvector/examples/OSpipe/src/quantum/mod.rs
vendored
Normal file
324
vendor/ruvector/examples/OSpipe/src/quantum/mod.rs
vendored
Normal file
@@ -0,0 +1,324 @@
|
||||
//! Quantum-inspired search acceleration.
|
||||
//!
|
||||
//! Provides [`QuantumSearch`], a collection of quantum-inspired algorithms
|
||||
//! that accelerate and diversify search results.
|
||||
//!
|
||||
//! On native targets the implementation delegates to the `ruqu-algorithms`
|
||||
//! crate (Grover's amplitude amplification, QAOA for MaxCut). On WASM
|
||||
//! targets an equivalent classical fallback is provided so that the same
|
||||
//! API is available everywhere.
|
||||
|
||||
/// Quantum-inspired search operations.
|
||||
///
|
||||
/// All methods are deterministic and require no quantum hardware; they
|
||||
/// use classical simulations of quantum algorithms (on native) or
|
||||
/// purely classical heuristics (on WASM) to improve search result
|
||||
/// quality.
|
||||
pub struct QuantumSearch {
|
||||
_private: (),
|
||||
}
|
||||
|
||||
impl QuantumSearch {
|
||||
/// Create a new `QuantumSearch` instance.
|
||||
pub fn new() -> Self {
|
||||
Self { _private: () }
|
||||
}
|
||||
|
||||
/// Compute the theoretically optimal number of Grover iterations for
|
||||
/// a search space of `search_space_size` items (with a single target).
|
||||
///
|
||||
/// Returns `floor(pi/4 * sqrt(N))`, which is at least 1.
|
||||
pub fn optimal_iterations(&self, search_space_size: u32) -> u32 {
|
||||
if search_space_size <= 1 {
|
||||
return 1;
|
||||
}
|
||||
let n = search_space_size as f64;
|
||||
let iters = (std::f64::consts::FRAC_PI_4 * n.sqrt()).floor() as u32;
|
||||
iters.max(1)
|
||||
}
|
||||
|
||||
/// Select `k` diverse results from a scored set using QAOA-inspired
|
||||
/// MaxCut partitioning.
|
||||
///
|
||||
/// A similarity graph is built between all result pairs and a
|
||||
/// partition is found that maximizes the "cut" between selected and
|
||||
/// unselected items. For small `k` (<=8) on native targets the
|
||||
/// quantum QAOA solver is used; otherwise a greedy heuristic selects
|
||||
/// the next-highest-scoring item that is most different from those
|
||||
/// already selected.
|
||||
///
|
||||
/// Returns up to `k` items from `scores`, preserving their original
|
||||
/// `(id, score)` tuples.
|
||||
pub fn diversity_select(&self, scores: &[(String, f32)], k: usize) -> Vec<(String, f32)> {
|
||||
if scores.is_empty() || k == 0 {
|
||||
return Vec::new();
|
||||
}
|
||||
let k = k.min(scores.len());
|
||||
|
||||
// Try QAOA path on native for small k.
|
||||
#[cfg(not(target_arch = "wasm32"))]
|
||||
{
|
||||
if k <= 8 {
|
||||
if let Some(result) = self.qaoa_diversity_select(scores, k) {
|
||||
return result;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Classical greedy fallback (also used on WASM).
|
||||
self.greedy_diversity_select(scores, k)
|
||||
}
|
||||
|
||||
/// Amplify scores above `target_threshold` and dampen scores below
|
||||
/// it, inspired by Grover amplitude amplification.
|
||||
///
|
||||
/// Scores above the threshold are boosted by `sqrt(boost_factor)`
|
||||
/// and scores below are dampened by `1/sqrt(boost_factor)`. All
|
||||
/// scores are then re-normalized to the [0, 1] range.
|
||||
///
|
||||
/// The boost factor is derived from the ratio of items above vs
|
||||
/// below the threshold, clamped so that results stay meaningful.
|
||||
pub fn amplitude_boost(&self, scores: &mut [(String, f32)], target_threshold: f32) {
|
||||
if scores.is_empty() {
|
||||
return;
|
||||
}
|
||||
|
||||
let above_count = scores
|
||||
.iter()
|
||||
.filter(|(_, s)| *s >= target_threshold)
|
||||
.count();
|
||||
let below_count = scores.len() - above_count;
|
||||
|
||||
if above_count == 0 || below_count == 0 {
|
||||
// All on one side -- nothing useful to amplify.
|
||||
return;
|
||||
}
|
||||
|
||||
// Boost factor: ratio of total to above (analogous to Grover's
|
||||
// N/M amplification), clamped to [1.5, 4.0] to avoid extremes.
|
||||
let boost_factor = (scores.len() as f64 / above_count as f64).clamp(1.5, 4.0);
|
||||
let sqrt_boost = (boost_factor).sqrt() as f32;
|
||||
let inv_sqrt_boost = 1.0 / sqrt_boost;
|
||||
|
||||
for (_id, score) in scores.iter_mut() {
|
||||
if *score >= target_threshold {
|
||||
*score *= sqrt_boost;
|
||||
} else {
|
||||
*score *= inv_sqrt_boost;
|
||||
}
|
||||
}
|
||||
|
||||
// Re-normalize to [0, 1].
|
||||
let max_score = scores
|
||||
.iter()
|
||||
.map(|(_, s)| *s)
|
||||
.fold(f32::NEG_INFINITY, f32::max);
|
||||
let min_score = scores.iter().map(|(_, s)| *s).fold(f32::INFINITY, f32::min);
|
||||
|
||||
let range = max_score - min_score;
|
||||
if range > f32::EPSILON {
|
||||
for (_id, score) in scores.iter_mut() {
|
||||
*score = (*score - min_score) / range;
|
||||
}
|
||||
} else {
|
||||
// All scores are identical after boost; set to 1.0.
|
||||
for (_id, score) in scores.iter_mut() {
|
||||
*score = 1.0;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ------------------------------------------------------------------
|
||||
// Native-only: QAOA diversity selection
|
||||
// ------------------------------------------------------------------
|
||||
|
||||
#[cfg(not(target_arch = "wasm32"))]
|
||||
fn qaoa_diversity_select(
|
||||
&self,
|
||||
scores: &[(String, f32)],
|
||||
k: usize,
|
||||
) -> Option<Vec<(String, f32)>> {
|
||||
use ruqu_algorithms::{run_qaoa, Graph, QaoaConfig};
|
||||
|
||||
let n = scores.len();
|
||||
if n < 2 {
|
||||
return Some(scores.to_vec());
|
||||
}
|
||||
|
||||
// Build a similarity graph: edge weight encodes how *similar*
|
||||
// two items are (based on score proximity). QAOA MaxCut will
|
||||
// then prefer to *separate* similar items across the partition,
|
||||
// giving us diversity.
|
||||
let mut graph = Graph::new(n as u32);
|
||||
for i in 0..n {
|
||||
for j in (i + 1)..n {
|
||||
// Similarity = 1 - |score_i - score_j| (higher when scores
|
||||
// are close, promoting diversity in the selected set).
|
||||
let similarity = 1.0 - (scores[i].1 - scores[j].1).abs();
|
||||
graph.add_edge(i as u32, j as u32, similarity as f64);
|
||||
}
|
||||
}
|
||||
|
||||
let config = QaoaConfig {
|
||||
graph,
|
||||
p: 2,
|
||||
max_iterations: 50,
|
||||
learning_rate: 0.1,
|
||||
seed: Some(42),
|
||||
};
|
||||
|
||||
let result = run_qaoa(&config).ok()?;
|
||||
|
||||
// Collect indices for the partition with the most members near k.
|
||||
let partition_true: Vec<usize> = result
|
||||
.best_bitstring
|
||||
.iter()
|
||||
.enumerate()
|
||||
.filter(|(_, &b)| b)
|
||||
.map(|(i, _)| i)
|
||||
.collect();
|
||||
let partition_false: Vec<usize> = result
|
||||
.best_bitstring
|
||||
.iter()
|
||||
.enumerate()
|
||||
.filter(|(_, &b)| !b)
|
||||
.map(|(i, _)| i)
|
||||
.collect();
|
||||
|
||||
// Pick the partition closer to size k, then sort by score
|
||||
// descending and take the top k.
|
||||
let chosen = if (partition_true.len() as isize - k as isize).unsigned_abs()
|
||||
<= (partition_false.len() as isize - k as isize).unsigned_abs()
|
||||
{
|
||||
partition_true
|
||||
} else {
|
||||
partition_false
|
||||
};
|
||||
|
||||
// If neither partition has at least k items, fall back to greedy.
|
||||
if chosen.len() < k {
|
||||
return None;
|
||||
}
|
||||
|
||||
let mut selected: Vec<(String, f32)> = chosen.iter().map(|&i| scores[i].clone()).collect();
|
||||
selected.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
|
||||
selected.truncate(k);
|
||||
|
||||
Some(selected)
|
||||
}
|
||||
|
||||
// ------------------------------------------------------------------
|
||||
// Classical greedy diversity selection (WASM + large-k fallback)
|
||||
// ------------------------------------------------------------------
|
||||
|
||||
fn greedy_diversity_select(&self, scores: &[(String, f32)], k: usize) -> Vec<(String, f32)> {
|
||||
let mut remaining: Vec<(usize, &(String, f32))> = scores.iter().enumerate().collect();
|
||||
|
||||
// Sort by score descending to seed with the best item.
|
||||
remaining.sort_by(|a, b| {
|
||||
b.1 .1
|
||||
.partial_cmp(&a.1 .1)
|
||||
.unwrap_or(std::cmp::Ordering::Equal)
|
||||
});
|
||||
|
||||
let mut selected: Vec<(String, f32)> = Vec::with_capacity(k);
|
||||
|
||||
// Pick the highest-scoring item first.
|
||||
if let Some((_, first)) = remaining.first() {
|
||||
selected.push((*first).clone());
|
||||
}
|
||||
let first_idx = remaining.first().map(|(i, _)| *i);
|
||||
remaining.retain(|(i, _)| Some(*i) != first_idx);
|
||||
|
||||
// Greedily pick the next item that maximizes (score * diversity).
|
||||
// Diversity is measured as the minimum score-distance from any
|
||||
// already-selected item.
|
||||
while selected.len() < k && !remaining.is_empty() {
|
||||
let mut best_idx_in_remaining = 0;
|
||||
let mut best_value = f64::NEG_INFINITY;
|
||||
|
||||
for (ri, (_, candidate)) in remaining.iter().enumerate() {
|
||||
let min_dist: f32 = selected
|
||||
.iter()
|
||||
.map(|(_, sel_score)| (candidate.1 - sel_score).abs())
|
||||
.fold(f32::INFINITY, f32::min);
|
||||
|
||||
// Combined objective: high score + high diversity.
|
||||
let value = candidate.1 as f64 + min_dist as f64;
|
||||
if value > best_value {
|
||||
best_value = value;
|
||||
best_idx_in_remaining = ri;
|
||||
}
|
||||
}
|
||||
|
||||
let (_, picked) = remaining.remove(best_idx_in_remaining);
|
||||
selected.push(picked.clone());
|
||||
}
|
||||
|
||||
selected
|
||||
}
|
||||
}
|
||||
|
||||
impl Default for QuantumSearch {
|
||||
fn default() -> Self {
|
||||
Self::new()
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Unit tests
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn test_optimal_iterations_basic() {
|
||||
let qs = QuantumSearch::new();
|
||||
assert_eq!(qs.optimal_iterations(1), 1);
|
||||
assert_eq!(qs.optimal_iterations(4), 1); // pi/4 * 2 = 1.57 -> floor = 1
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_optimal_iterations_larger() {
|
||||
let qs = QuantumSearch::new();
|
||||
// pi/4 * sqrt(100) = pi/4 * 10 = 7.85 -> floor = 7
|
||||
assert_eq!(qs.optimal_iterations(100), 7);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_diversity_select_empty() {
|
||||
let qs = QuantumSearch::new();
|
||||
let result = qs.diversity_select(&[], 3);
|
||||
assert!(result.is_empty());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_diversity_select_k_zero() {
|
||||
let qs = QuantumSearch::new();
|
||||
let scores = vec![("a".to_string(), 0.5)];
|
||||
let result = qs.diversity_select(&scores, 0);
|
||||
assert!(result.is_empty());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_amplitude_boost_empty() {
|
||||
let qs = QuantumSearch::new();
|
||||
let mut scores: Vec<(String, f32)> = Vec::new();
|
||||
qs.amplitude_boost(&mut scores, 0.5);
|
||||
assert!(scores.is_empty());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_amplitude_boost_all_above() {
|
||||
let qs = QuantumSearch::new();
|
||||
let mut scores = vec![("a".to_string(), 0.8), ("b".to_string(), 0.9)];
|
||||
let orig = scores.clone();
|
||||
qs.amplitude_boost(&mut scores, 0.5);
|
||||
// All above threshold -> no change in relative ordering,
|
||||
// but scores remain unchanged since boost is a no-op.
|
||||
assert_eq!(scores[0].0, orig[0].0);
|
||||
assert_eq!(scores[1].0, orig[1].0);
|
||||
}
|
||||
}
|
||||
550
vendor/ruvector/examples/OSpipe/src/safety.rs
vendored
Normal file
550
vendor/ruvector/examples/OSpipe/src/safety.rs
vendored
Normal file
@@ -0,0 +1,550 @@
|
||||
//! Safety gate for content filtering and PII redaction.
|
||||
//!
|
||||
//! The safety gate inspects captured content before it enters the
|
||||
//! ingestion pipeline, detecting and optionally redacting sensitive
|
||||
//! information such as credit card numbers, SSNs, and custom patterns.
|
||||
|
||||
use crate::config::SafetyConfig;
|
||||
|
||||
/// Decision made by the safety gate about a piece of content.
|
||||
#[derive(Debug, Clone, PartialEq)]
|
||||
pub enum SafetyDecision {
|
||||
/// Content is safe to store as-is.
|
||||
Allow,
|
||||
/// Content is safe after redaction; the redacted version is provided.
|
||||
AllowRedacted(String),
|
||||
/// Content must not be stored.
|
||||
Deny {
|
||||
/// Reason for denial.
|
||||
reason: String,
|
||||
},
|
||||
}
|
||||
|
||||
/// Safety gate that checks content for sensitive information.
|
||||
pub struct SafetyGate {
|
||||
config: SafetyConfig,
|
||||
}
|
||||
|
||||
impl SafetyGate {
|
||||
/// Create a new safety gate with the given configuration.
|
||||
pub fn new(config: SafetyConfig) -> Self {
|
||||
Self { config }
|
||||
}
|
||||
|
||||
/// Check content and return a safety decision.
|
||||
///
|
||||
/// If PII is detected and redaction is enabled, the content is
|
||||
/// returned in redacted form. If custom patterns match and no
|
||||
/// redaction is possible, the content is denied.
|
||||
pub fn check(&self, content: &str) -> SafetyDecision {
|
||||
let mut redacted = content.to_string();
|
||||
let mut was_redacted = false;
|
||||
|
||||
// Credit card redaction
|
||||
if self.config.credit_card_redaction {
|
||||
let (new_text, found) = redact_credit_cards(&redacted);
|
||||
if found {
|
||||
redacted = new_text;
|
||||
was_redacted = true;
|
||||
}
|
||||
}
|
||||
|
||||
// SSN redaction
|
||||
if self.config.ssn_redaction {
|
||||
let (new_text, found) = redact_ssns(&redacted);
|
||||
if found {
|
||||
redacted = new_text;
|
||||
was_redacted = true;
|
||||
}
|
||||
}
|
||||
|
||||
// PII detection (email addresses)
|
||||
if self.config.pii_detection {
|
||||
let (new_text, found) = redact_emails(&redacted);
|
||||
if found {
|
||||
redacted = new_text;
|
||||
was_redacted = true;
|
||||
}
|
||||
}
|
||||
|
||||
// Custom patterns: deny if found (custom patterns indicate content
|
||||
// that should not be stored at all)
|
||||
for pattern in &self.config.custom_patterns {
|
||||
if content.contains(pattern.as_str()) {
|
||||
return SafetyDecision::Deny {
|
||||
reason: format!("Custom pattern matched: {}", pattern),
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
if was_redacted {
|
||||
SafetyDecision::AllowRedacted(redacted)
|
||||
} else {
|
||||
SafetyDecision::Allow
|
||||
}
|
||||
}
|
||||
|
||||
/// Redact all detected sensitive content and return the cleaned string.
|
||||
pub fn redact(&self, content: &str) -> String {
|
||||
match self.check(content) {
|
||||
SafetyDecision::Allow => content.to_string(),
|
||||
SafetyDecision::AllowRedacted(redacted) => redacted,
|
||||
SafetyDecision::Deny { .. } => "[REDACTED]".to_string(),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Detect and redact sequences of 13-16 digits that look like credit card numbers.
|
||||
///
|
||||
/// This uses a simple pattern: sequences of digits (with optional spaces or dashes)
|
||||
/// totaling 13-16 digits are replaced with [CC_REDACTED].
|
||||
fn redact_credit_cards(text: &str) -> (String, bool) {
|
||||
let mut result = String::with_capacity(text.len());
|
||||
let chars: Vec<char> = text.chars().collect();
|
||||
let mut i = 0;
|
||||
let mut found = false;
|
||||
|
||||
while i < chars.len() {
|
||||
// Check if we are at the start of a digit sequence
|
||||
if chars[i].is_ascii_digit() {
|
||||
let start = i;
|
||||
let mut digit_count = 0;
|
||||
|
||||
// Consume digits, spaces, and dashes
|
||||
while i < chars.len()
|
||||
&& (chars[i].is_ascii_digit() || chars[i] == ' ' || chars[i] == '-')
|
||||
{
|
||||
if chars[i].is_ascii_digit() {
|
||||
digit_count += 1;
|
||||
}
|
||||
i += 1;
|
||||
}
|
||||
|
||||
if (13..=16).contains(&digit_count) {
|
||||
result.push_str("[CC_REDACTED]");
|
||||
found = true;
|
||||
} else {
|
||||
// Not a credit card, keep original text
|
||||
for c in &chars[start..i] {
|
||||
result.push(*c);
|
||||
}
|
||||
}
|
||||
} else {
|
||||
result.push(chars[i]);
|
||||
i += 1;
|
||||
}
|
||||
}
|
||||
|
||||
(result, found)
|
||||
}
|
||||
|
||||
/// Detect and redact SSN patterns (XXX-XX-XXXX).
|
||||
fn redact_ssns(text: &str) -> (String, bool) {
|
||||
let mut result = String::new();
|
||||
let chars: Vec<char> = text.chars().collect();
|
||||
let mut found = false;
|
||||
let mut i = 0;
|
||||
|
||||
while i < chars.len() {
|
||||
// Check for SSN pattern: 3 digits, dash, 2 digits, dash, 4 digits
|
||||
if i + 10 < chars.len() && is_ssn_at(&chars, i) {
|
||||
result.push_str("[SSN_REDACTED]");
|
||||
found = true;
|
||||
i += 11; // Skip the SSN (XXX-XX-XXXX = 11 chars)
|
||||
} else {
|
||||
result.push(chars[i]);
|
||||
i += 1;
|
||||
}
|
||||
}
|
||||
|
||||
(result, found)
|
||||
}
|
||||
|
||||
/// Check if an SSN pattern exists at the given position.
|
||||
fn is_ssn_at(chars: &[char], pos: usize) -> bool {
|
||||
if pos + 10 >= chars.len() {
|
||||
return false;
|
||||
}
|
||||
// XXX-XX-XXXX
|
||||
chars[pos].is_ascii_digit()
|
||||
&& chars[pos + 1].is_ascii_digit()
|
||||
&& chars[pos + 2].is_ascii_digit()
|
||||
&& chars[pos + 3] == '-'
|
||||
&& chars[pos + 4].is_ascii_digit()
|
||||
&& chars[pos + 5].is_ascii_digit()
|
||||
&& chars[pos + 6] == '-'
|
||||
&& chars[pos + 7].is_ascii_digit()
|
||||
&& chars[pos + 8].is_ascii_digit()
|
||||
&& chars[pos + 9].is_ascii_digit()
|
||||
&& chars[pos + 10].is_ascii_digit()
|
||||
}
|
||||
|
||||
/// Detect and redact email addresses while preserving surrounding whitespace.
|
||||
///
|
||||
/// Scans character-by-character for `@` signs, then expands outward to find
|
||||
/// the full `local@domain.tld` span and replaces it in-place, keeping all
|
||||
/// surrounding whitespace (tabs, newlines, multi-space runs) intact.
|
||||
fn redact_emails(text: &str) -> (String, bool) {
|
||||
let chars: Vec<char> = text.chars().collect();
|
||||
let len = chars.len();
|
||||
let mut result = String::with_capacity(text.len());
|
||||
let mut found = false;
|
||||
let mut i = 0;
|
||||
|
||||
while i < len {
|
||||
if chars[i] == '@' {
|
||||
// Try to identify an email around this '@'.
|
||||
// Scan backwards for the local part.
|
||||
let mut local_start = i;
|
||||
while local_start > 0 && is_email_local_char(chars[local_start - 1]) {
|
||||
local_start -= 1;
|
||||
}
|
||||
|
||||
// Scan forwards for the domain part.
|
||||
let mut domain_end = i + 1;
|
||||
let mut has_dot = false;
|
||||
while domain_end < len && is_email_domain_char(chars[domain_end]) {
|
||||
if chars[domain_end] == '.' {
|
||||
has_dot = true;
|
||||
}
|
||||
domain_end += 1;
|
||||
}
|
||||
// Trim trailing dots/hyphens from domain (not valid at end).
|
||||
while domain_end > i + 1
|
||||
&& (chars[domain_end - 1] == '.' || chars[domain_end - 1] == '-')
|
||||
{
|
||||
if chars[domain_end - 1] == '.' {
|
||||
// Re-check if we still have a dot in the trimmed domain.
|
||||
has_dot = chars[i + 1..domain_end - 1].contains(&'.');
|
||||
}
|
||||
domain_end -= 1;
|
||||
}
|
||||
|
||||
let local_len = i - local_start;
|
||||
let domain_len = domain_end - (i + 1);
|
||||
|
||||
if local_len > 0 && domain_len >= 3 && has_dot {
|
||||
// Valid email: replace the span [local_start..domain_end]
|
||||
// We need to remove any characters already pushed for the local part.
|
||||
// They were pushed in the normal flow below, so truncate them.
|
||||
let already_pushed = i - local_start;
|
||||
let new_len = result.len() - already_pushed;
|
||||
result.truncate(new_len);
|
||||
result.push_str("[EMAIL_REDACTED]");
|
||||
found = true;
|
||||
i = domain_end;
|
||||
} else {
|
||||
// Not a valid email, keep the '@' as-is.
|
||||
result.push(chars[i]);
|
||||
i += 1;
|
||||
}
|
||||
} else {
|
||||
result.push(chars[i]);
|
||||
i += 1;
|
||||
}
|
||||
}
|
||||
|
||||
(result, found)
|
||||
}
|
||||
|
||||
/// Characters valid in the local part of an email address.
|
||||
fn is_email_local_char(c: char) -> bool {
|
||||
c.is_ascii_alphanumeric() || c == '.' || c == '+' || c == '-' || c == '_'
|
||||
}
|
||||
|
||||
/// Characters valid in the domain part of an email address.
|
||||
fn is_email_domain_char(c: char) -> bool {
|
||||
c.is_ascii_alphanumeric() || c == '.' || c == '-'
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::config::SafetyConfig;
|
||||
|
||||
// ---------------------------------------------------------------
|
||||
// Email redaction whitespace preservation tests
|
||||
// ---------------------------------------------------------------
|
||||
|
||||
#[test]
|
||||
fn test_email_redaction_preserves_tabs() {
|
||||
let (result, found) = redact_emails("contact\tuser@example.com\there");
|
||||
assert!(found);
|
||||
assert_eq!(result, "contact\t[EMAIL_REDACTED]\there");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_email_redaction_preserves_newlines() {
|
||||
let (result, found) = redact_emails("contact\nuser@example.com\nhere");
|
||||
assert!(found);
|
||||
assert_eq!(result, "contact\n[EMAIL_REDACTED]\nhere");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_email_redaction_preserves_multi_spaces() {
|
||||
let (result, found) = redact_emails("contact user@example.com here");
|
||||
assert!(found);
|
||||
assert_eq!(result, "contact [EMAIL_REDACTED] here");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_email_redaction_preserves_mixed_whitespace() {
|
||||
let (result, found) = redact_emails("contact\t user@example.com\n here");
|
||||
assert!(found);
|
||||
assert_eq!(result, "contact\t [EMAIL_REDACTED]\n here");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_email_redaction_basic() {
|
||||
let (result, found) = redact_emails("email user@example.com here");
|
||||
assert!(found);
|
||||
assert_eq!(result, "email [EMAIL_REDACTED] here");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_email_redaction_no_email() {
|
||||
let (result, found) = redact_emails("no email here");
|
||||
assert!(!found);
|
||||
assert_eq!(result, "no email here");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_email_redaction_multiple_emails() {
|
||||
let (result, found) = redact_emails("a@b.com and c@d.org");
|
||||
assert!(found);
|
||||
assert_eq!(result, "[EMAIL_REDACTED] and [EMAIL_REDACTED]");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_email_redaction_at_start() {
|
||||
let (result, found) = redact_emails("user@example.com is the contact");
|
||||
assert!(found);
|
||||
assert_eq!(result, "[EMAIL_REDACTED] is the contact");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_email_redaction_at_end() {
|
||||
let (result, found) = redact_emails("contact: user@example.com");
|
||||
assert!(found);
|
||||
assert_eq!(result, "contact: [EMAIL_REDACTED]");
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------
|
||||
// Safety gate integration tests for consistency
|
||||
// ---------------------------------------------------------------
|
||||
|
||||
#[test]
|
||||
fn test_safety_gate_email_preserves_whitespace() {
|
||||
let config = SafetyConfig::default();
|
||||
let gate = SafetyGate::new(config);
|
||||
let decision = gate.check("contact\tuser@example.com\nhere");
|
||||
match decision {
|
||||
SafetyDecision::AllowRedacted(redacted) => {
|
||||
assert_eq!(redacted, "contact\t[EMAIL_REDACTED]\nhere");
|
||||
}
|
||||
other => panic!("Expected AllowRedacted, got {:?}", other),
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------
|
||||
// Routing consistency tests (WASM vs native)
|
||||
// ---------------------------------------------------------------
|
||||
|
||||
#[test]
|
||||
fn test_wasm_routing_matches_native_temporal() {
|
||||
use crate::search::router::QueryRoute;
|
||||
use crate::search::router::QueryRouter;
|
||||
use crate::wasm::helpers::route_query;
|
||||
|
||||
let router = QueryRouter::new();
|
||||
let queries = [
|
||||
"what did I see yesterday",
|
||||
"show me last week",
|
||||
"results from today",
|
||||
];
|
||||
for q in &queries {
|
||||
assert_eq!(
|
||||
router.route(q),
|
||||
QueryRoute::Temporal,
|
||||
"Native router failed for: {}",
|
||||
q
|
||||
);
|
||||
assert_eq!(route_query(q), "Temporal", "WASM router failed for: {}", q);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_wasm_routing_matches_native_graph() {
|
||||
use crate::search::router::QueryRoute;
|
||||
use crate::search::router::QueryRouter;
|
||||
use crate::wasm::helpers::route_query;
|
||||
|
||||
let router = QueryRouter::new();
|
||||
let queries = [
|
||||
"documents related to authentication",
|
||||
"things connected to the API module",
|
||||
];
|
||||
for q in &queries {
|
||||
assert_eq!(
|
||||
router.route(q),
|
||||
QueryRoute::Graph,
|
||||
"Native router failed for: {}",
|
||||
q
|
||||
);
|
||||
assert_eq!(route_query(q), "Graph", "WASM router failed for: {}", q);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_wasm_routing_matches_native_keyword_short() {
|
||||
use crate::search::router::QueryRoute;
|
||||
use crate::search::router::QueryRouter;
|
||||
use crate::wasm::helpers::route_query;
|
||||
|
||||
let router = QueryRouter::new();
|
||||
let queries = ["hello", "rust programming"];
|
||||
for q in &queries {
|
||||
assert_eq!(
|
||||
router.route(q),
|
||||
QueryRoute::Keyword,
|
||||
"Native router failed for: {}",
|
||||
q
|
||||
);
|
||||
assert_eq!(route_query(q), "Keyword", "WASM router failed for: {}", q);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_wasm_routing_matches_native_keyword_quoted() {
|
||||
use crate::search::router::QueryRoute;
|
||||
use crate::search::router::QueryRouter;
|
||||
use crate::wasm::helpers::route_query;
|
||||
|
||||
let router = QueryRouter::new();
|
||||
let q = "\"exact phrase search\"";
|
||||
assert_eq!(router.route(q), QueryRoute::Keyword);
|
||||
assert_eq!(route_query(q), "Keyword");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_wasm_routing_matches_native_hybrid() {
|
||||
use crate::search::router::QueryRoute;
|
||||
use crate::search::router::QueryRouter;
|
||||
use crate::wasm::helpers::route_query;
|
||||
|
||||
let router = QueryRouter::new();
|
||||
let queries = [
|
||||
"how to implement authentication in Rust",
|
||||
"explain how embeddings work",
|
||||
"something about machine learning",
|
||||
];
|
||||
for q in &queries {
|
||||
assert_eq!(
|
||||
router.route(q),
|
||||
QueryRoute::Hybrid,
|
||||
"Native router failed for: {}",
|
||||
q
|
||||
);
|
||||
assert_eq!(route_query(q), "Hybrid", "WASM router failed for: {}", q);
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------
|
||||
// Safety consistency tests (WASM vs native)
|
||||
// ---------------------------------------------------------------
|
||||
|
||||
#[test]
|
||||
fn test_wasm_safety_matches_native_cc() {
|
||||
use crate::wasm::helpers::safety_classify;
|
||||
|
||||
// Native: CC -> AllowRedacted; WASM should return "redact"
|
||||
let config = SafetyConfig::default();
|
||||
let gate = SafetyGate::new(config);
|
||||
let content = "pay with 4111-1111-1111-1111";
|
||||
assert!(matches!(
|
||||
gate.check(content),
|
||||
SafetyDecision::AllowRedacted(_)
|
||||
));
|
||||
assert_eq!(safety_classify(content), "redact");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_wasm_safety_matches_native_ssn() {
|
||||
use crate::wasm::helpers::safety_classify;
|
||||
|
||||
let config = SafetyConfig::default();
|
||||
let gate = SafetyGate::new(config);
|
||||
let content = "my ssn 123-45-6789";
|
||||
assert!(matches!(
|
||||
gate.check(content),
|
||||
SafetyDecision::AllowRedacted(_)
|
||||
));
|
||||
assert_eq!(safety_classify(content), "redact");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_wasm_safety_matches_native_email() {
|
||||
use crate::wasm::helpers::safety_classify;
|
||||
|
||||
let config = SafetyConfig::default();
|
||||
let gate = SafetyGate::new(config);
|
||||
let content = "email user@example.com here";
|
||||
assert!(matches!(
|
||||
gate.check(content),
|
||||
SafetyDecision::AllowRedacted(_)
|
||||
));
|
||||
assert_eq!(safety_classify(content), "redact");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_wasm_safety_matches_native_custom_deny() {
|
||||
use crate::wasm::helpers::safety_classify;
|
||||
|
||||
// Native: custom_patterns -> Deny; WASM: sensitive keywords -> "deny"
|
||||
let config = SafetyConfig {
|
||||
custom_patterns: vec!["password".to_string()],
|
||||
..Default::default()
|
||||
};
|
||||
let gate = SafetyGate::new(config);
|
||||
let content = "my password is foo";
|
||||
assert!(matches!(gate.check(content), SafetyDecision::Deny { .. }));
|
||||
assert_eq!(safety_classify(content), "deny");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_wasm_safety_matches_native_allow() {
|
||||
use crate::wasm::helpers::safety_classify;
|
||||
|
||||
let config = SafetyConfig::default();
|
||||
let gate = SafetyGate::new(config);
|
||||
let content = "the weather is nice";
|
||||
assert_eq!(gate.check(content), SafetyDecision::Allow);
|
||||
assert_eq!(safety_classify(content), "allow");
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------
|
||||
// MMR tests
|
||||
// ---------------------------------------------------------------
|
||||
|
||||
#[test]
|
||||
fn test_mmr_produces_different_order_than_cosine() {
|
||||
use crate::search::mmr::MmrReranker;
|
||||
|
||||
let mmr = MmrReranker::new(0.3);
|
||||
let query = vec![1.0, 0.0, 0.0, 0.0];
|
||||
let results = vec![
|
||||
("a".to_string(), 0.95, vec![1.0, 0.0, 0.0, 0.0]),
|
||||
("b".to_string(), 0.90, vec![0.99, 0.01, 0.0, 0.0]),
|
||||
("c".to_string(), 0.60, vec![0.0, 1.0, 0.0, 0.0]),
|
||||
];
|
||||
|
||||
let ranked = mmr.rerank(&query, &results, 3);
|
||||
assert_eq!(ranked.len(), 3);
|
||||
|
||||
// Pure cosine order: a, b, c
|
||||
// MMR with diversity: a, c, b (c is diverse, b is near-duplicate of a)
|
||||
assert_eq!(ranked[0].0, "a");
|
||||
assert_eq!(ranked[1].0, "c", "MMR should promote diverse result");
|
||||
assert_eq!(ranked[2].0, "b");
|
||||
}
|
||||
}
|
||||
220
vendor/ruvector/examples/OSpipe/src/search/enhanced.rs
vendored
Normal file
220
vendor/ruvector/examples/OSpipe/src/search/enhanced.rs
vendored
Normal file
@@ -0,0 +1,220 @@
|
||||
//! Enhanced search orchestrator.
|
||||
//!
|
||||
//! Combines query routing, attention-based re-ranking, and quantum-inspired
|
||||
//! diversity selection into a single search pipeline:
|
||||
//!
|
||||
//! ```text
|
||||
//! Route -> Search (3x k candidates) -> Rerank (attention) -> Diversity (quantum) -> Return
|
||||
//! ```
|
||||
|
||||
use crate::error::Result;
|
||||
use crate::quantum::QuantumSearch;
|
||||
use crate::search::reranker::AttentionReranker;
|
||||
use crate::search::router::QueryRouter;
|
||||
use crate::storage::vector_store::{SearchResult, VectorStore};
|
||||
|
||||
/// Orchestrates a full search pipeline: routing, candidate retrieval,
|
||||
/// attention re-ranking, and quantum diversity selection.
|
||||
pub struct EnhancedSearch {
|
||||
router: QueryRouter,
|
||||
reranker: Option<AttentionReranker>,
|
||||
quantum: Option<QuantumSearch>,
|
||||
}
|
||||
|
||||
impl EnhancedSearch {
|
||||
/// Create a new enhanced search with all components wired.
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `dim` - Embedding dimension used to configure the attention reranker.
|
||||
pub fn new(dim: usize) -> Self {
|
||||
Self {
|
||||
router: QueryRouter::new(),
|
||||
reranker: Some(AttentionReranker::new(dim, 4)),
|
||||
quantum: Some(QuantumSearch::new()),
|
||||
}
|
||||
}
|
||||
|
||||
/// Create an enhanced search with only the router (no reranking or diversity).
|
||||
pub fn router_only() -> Self {
|
||||
Self {
|
||||
router: QueryRouter::new(),
|
||||
reranker: None,
|
||||
quantum: None,
|
||||
}
|
||||
}
|
||||
|
||||
/// Return a reference to the query router.
|
||||
pub fn router(&self) -> &QueryRouter {
|
||||
&self.router
|
||||
}
|
||||
|
||||
/// Search the vector store with routing, re-ranking, and diversity selection.
|
||||
///
|
||||
/// The pipeline:
|
||||
/// 1. Route the query to determine the search strategy.
|
||||
/// 2. Fetch `3 * k` candidates from the store to give the reranker headroom.
|
||||
/// 3. If a reranker is available, re-rank candidates using attention scores.
|
||||
/// 4. If quantum diversity selection is available, select the final `k`
|
||||
/// results with maximum diversity.
|
||||
/// 5. Return the final results.
|
||||
pub fn search(
|
||||
&self,
|
||||
query: &str,
|
||||
query_embedding: &[f32],
|
||||
store: &VectorStore,
|
||||
k: usize,
|
||||
) -> Result<Vec<SearchResult>> {
|
||||
// Step 1: Route the query (informational -- we always search the
|
||||
// vector store for now, but the route is available for future use).
|
||||
let _route = self.router.route(query);
|
||||
|
||||
// Step 2: Fetch candidates with headroom for reranking.
|
||||
let candidate_k = (k * 3).max(10).min(store.len().max(1));
|
||||
let candidates = store.search(query_embedding, candidate_k)?;
|
||||
|
||||
if candidates.is_empty() {
|
||||
return Ok(Vec::new());
|
||||
}
|
||||
|
||||
// Step 3: Re-rank with attention if available.
|
||||
let results = if let Some(ref reranker) = self.reranker {
|
||||
// Build the tuples the reranker expects: (id_string, score, embedding).
|
||||
let reranker_input: Vec<(String, f32, Vec<f32>)> = candidates
|
||||
.iter()
|
||||
.map(|sr| {
|
||||
// Retrieve the stored embedding for this result.
|
||||
let embedding = store
|
||||
.get(&sr.id)
|
||||
.map(|stored| stored.vector.clone())
|
||||
.unwrap_or_else(|| vec![0.0; query_embedding.len()]);
|
||||
(sr.id.to_string(), sr.score, embedding)
|
||||
})
|
||||
.collect();
|
||||
|
||||
// The reranker returns more than k so quantum diversity can choose.
|
||||
let rerank_k = if self.quantum.is_some() {
|
||||
(k * 2).min(reranker_input.len())
|
||||
} else {
|
||||
k
|
||||
};
|
||||
let reranked = reranker.rerank(query_embedding, &reranker_input, rerank_k);
|
||||
|
||||
// Step 4: Diversity selection if available.
|
||||
let final_scored = if let Some(ref quantum) = self.quantum {
|
||||
quantum.diversity_select(&reranked, k)
|
||||
} else {
|
||||
let mut r = reranked;
|
||||
r.truncate(k);
|
||||
r
|
||||
};
|
||||
|
||||
// Map back to SearchResult by looking up metadata from candidates.
|
||||
final_scored
|
||||
.into_iter()
|
||||
.filter_map(|(id_str, score)| {
|
||||
// Parse the UUID back.
|
||||
let uid: uuid::Uuid = id_str.parse().ok()?;
|
||||
// Find the original candidate to retrieve metadata.
|
||||
let original = candidates.iter().find(|c| c.id == uid)?;
|
||||
Some(SearchResult {
|
||||
id: uid,
|
||||
score,
|
||||
metadata: original.metadata.clone(),
|
||||
})
|
||||
})
|
||||
.collect()
|
||||
} else {
|
||||
// No reranker -- just truncate.
|
||||
candidates.into_iter().take(k).collect()
|
||||
};
|
||||
|
||||
Ok(results)
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::capture::CapturedFrame;
|
||||
use crate::config::StorageConfig;
|
||||
use crate::storage::embedding::EmbeddingEngine;
|
||||
|
||||
#[test]
|
||||
fn test_enhanced_search_empty_store() {
|
||||
let config = StorageConfig::default();
|
||||
let store = VectorStore::new(config).unwrap();
|
||||
let engine = EmbeddingEngine::new(384);
|
||||
let es = EnhancedSearch::new(384);
|
||||
|
||||
let query_emb = engine.embed("test query");
|
||||
let results = es.search("test query", &query_emb, &store, 5).unwrap();
|
||||
assert!(results.is_empty());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_enhanced_search_returns_results() {
|
||||
let config = StorageConfig::default();
|
||||
let mut store = VectorStore::new(config).unwrap();
|
||||
let engine = EmbeddingEngine::new(384);
|
||||
|
||||
let frames = vec![
|
||||
CapturedFrame::new_screen("Editor", "code.rs", "implementing vector search in Rust", 0),
|
||||
CapturedFrame::new_screen("Browser", "docs", "Rust vector database documentation", 0),
|
||||
CapturedFrame::new_audio("Mic", "discussing Python machine learning", None),
|
||||
];
|
||||
|
||||
for frame in &frames {
|
||||
let emb = engine.embed(frame.text_content());
|
||||
store.insert(frame, &emb).unwrap();
|
||||
}
|
||||
|
||||
let es = EnhancedSearch::new(384);
|
||||
let query_emb = engine.embed("vector search Rust");
|
||||
let results = es
|
||||
.search("vector search Rust", &query_emb, &store, 2)
|
||||
.unwrap();
|
||||
|
||||
assert!(!results.is_empty());
|
||||
assert!(results.len() <= 2);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_enhanced_search_router_only() {
|
||||
let config = StorageConfig::default();
|
||||
let mut store = VectorStore::new(config).unwrap();
|
||||
let engine = EmbeddingEngine::new(384);
|
||||
|
||||
let frame = CapturedFrame::new_screen("App", "Win", "test content", 0);
|
||||
let emb = engine.embed(frame.text_content());
|
||||
store.insert(&frame, &emb).unwrap();
|
||||
|
||||
let es = EnhancedSearch::router_only();
|
||||
let query_emb = engine.embed("test content");
|
||||
let results = es.search("test content", &query_emb, &store, 5).unwrap();
|
||||
|
||||
assert_eq!(results.len(), 1);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_enhanced_search_respects_k() {
|
||||
let config = StorageConfig::default();
|
||||
let mut store = VectorStore::new(config).unwrap();
|
||||
let engine = EmbeddingEngine::new(384);
|
||||
|
||||
for i in 0..10 {
|
||||
let frame = CapturedFrame::new_screen("App", "Win", &format!("content {}", i), 0);
|
||||
let emb = engine.embed(frame.text_content());
|
||||
store.insert(&frame, &emb).unwrap();
|
||||
}
|
||||
|
||||
let es = EnhancedSearch::new(384);
|
||||
let query_emb = engine.embed("content");
|
||||
let results = es.search("content", &query_emb, &store, 3).unwrap();
|
||||
|
||||
assert!(
|
||||
results.len() <= 3,
|
||||
"Should return at most k=3 results, got {}",
|
||||
results.len()
|
||||
);
|
||||
}
|
||||
}
|
||||
116
vendor/ruvector/examples/OSpipe/src/search/hybrid.rs
vendored
Normal file
116
vendor/ruvector/examples/OSpipe/src/search/hybrid.rs
vendored
Normal file
@@ -0,0 +1,116 @@
|
||||
//! Hybrid search combining semantic and keyword approaches.
|
||||
|
||||
use crate::error::Result;
|
||||
use crate::storage::{SearchResult, VectorStore};
|
||||
use std::collections::HashMap;
|
||||
use uuid::Uuid;
|
||||
|
||||
/// Hybrid search that combines semantic vector similarity with keyword
|
||||
/// matching using a configurable weight parameter.
|
||||
pub struct HybridSearch {
|
||||
/// Weight for semantic search (1.0 = pure semantic, 0.0 = pure keyword).
|
||||
semantic_weight: f32,
|
||||
}
|
||||
|
||||
impl HybridSearch {
|
||||
/// Create a new hybrid search with the given semantic weight.
|
||||
///
|
||||
/// The weight controls the balance between semantic (vector) and
|
||||
/// keyword (text match) scores. A value of 0.7 means 70% semantic
|
||||
/// and 30% keyword.
|
||||
pub fn new(semantic_weight: f32) -> Self {
|
||||
Self {
|
||||
semantic_weight: semantic_weight.clamp(0.0, 1.0),
|
||||
}
|
||||
}
|
||||
|
||||
/// Perform a hybrid search combining semantic and keyword results.
|
||||
///
|
||||
/// The `query` is used for keyword matching against stored text content.
|
||||
/// The `embedding` is used for semantic similarity scoring.
|
||||
pub fn search(
|
||||
&self,
|
||||
store: &VectorStore,
|
||||
query: &str,
|
||||
embedding: &[f32],
|
||||
k: usize,
|
||||
) -> Result<Vec<SearchResult>> {
|
||||
// Get semantic results (more candidates than needed for merging)
|
||||
let candidate_k = (k * 3).max(20).min(store.len());
|
||||
let semantic_results = store.search(embedding, candidate_k)?;
|
||||
|
||||
// Build a combined score map
|
||||
let mut scores: HashMap<Uuid, (f32, f32, serde_json::Value)> = HashMap::new();
|
||||
|
||||
// Add semantic scores
|
||||
for result in &semantic_results {
|
||||
scores
|
||||
.entry(result.id)
|
||||
.or_insert((0.0, 0.0, result.metadata.clone()))
|
||||
.0 = result.score;
|
||||
}
|
||||
|
||||
// Compute keyword scores for all candidates
|
||||
let query_lower = query.to_lowercase();
|
||||
let query_terms: Vec<&str> = query_lower.split_whitespace().collect();
|
||||
|
||||
for result in &semantic_results {
|
||||
let text = result
|
||||
.metadata
|
||||
.get("text")
|
||||
.and_then(|v| v.as_str())
|
||||
.unwrap_or("");
|
||||
let text_lower = text.to_lowercase();
|
||||
|
||||
let keyword_score = compute_keyword_score(&query_terms, &text_lower);
|
||||
|
||||
if let Some(entry) = scores.get_mut(&result.id) {
|
||||
entry.1 = keyword_score;
|
||||
}
|
||||
}
|
||||
|
||||
// Combine scores using weighted sum
|
||||
let keyword_weight = 1.0 - self.semantic_weight;
|
||||
let mut combined: Vec<SearchResult> = scores
|
||||
.into_iter()
|
||||
.map(|(id, (sem_score, kw_score, metadata))| {
|
||||
let combined_score = self.semantic_weight * sem_score + keyword_weight * kw_score;
|
||||
SearchResult {
|
||||
id,
|
||||
score: combined_score,
|
||||
metadata,
|
||||
}
|
||||
})
|
||||
.collect();
|
||||
|
||||
// Sort by combined score descending
|
||||
combined.sort_by(|a, b| {
|
||||
b.score
|
||||
.partial_cmp(&a.score)
|
||||
.unwrap_or(std::cmp::Ordering::Equal)
|
||||
});
|
||||
combined.truncate(k);
|
||||
|
||||
Ok(combined)
|
||||
}
|
||||
|
||||
/// Return the configured semantic weight.
|
||||
pub fn semantic_weight(&self) -> f32 {
|
||||
self.semantic_weight
|
||||
}
|
||||
}
|
||||
|
||||
/// Compute a simple keyword match score based on term overlap.
|
||||
///
|
||||
/// Returns a value between 0.0 and 1.0 representing the fraction
|
||||
/// of query terms found in the text.
|
||||
fn compute_keyword_score(query_terms: &[&str], text_lower: &str) -> f32 {
|
||||
if query_terms.is_empty() {
|
||||
return 0.0;
|
||||
}
|
||||
let matches = query_terms
|
||||
.iter()
|
||||
.filter(|term| text_lower.contains(*term))
|
||||
.count();
|
||||
matches as f32 / query_terms.len() as f32
|
||||
}
|
||||
219
vendor/ruvector/examples/OSpipe/src/search/mmr.rs
vendored
Normal file
219
vendor/ruvector/examples/OSpipe/src/search/mmr.rs
vendored
Normal file
@@ -0,0 +1,219 @@
|
||||
//! Maximal Marginal Relevance (MMR) re-ranking.
|
||||
//!
|
||||
//! MMR balances relevance to the query with diversity among selected
|
||||
//! results, controlled by a `lambda` parameter:
|
||||
//! - `lambda = 1.0` produces pure relevance ranking (identical to cosine).
|
||||
//! - `lambda = 0.0` maximises diversity among selected results.
|
||||
//!
|
||||
//! The `lambda` value is sourced from [`SearchConfig::mmr_lambda`](crate::config::SearchConfig).
|
||||
|
||||
/// Re-ranks search results using Maximal Marginal Relevance.
|
||||
pub struct MmrReranker {
|
||||
/// Trade-off between relevance and diversity.
|
||||
/// 1.0 = pure relevance, 0.0 = pure diversity.
|
||||
lambda: f32,
|
||||
}
|
||||
|
||||
impl MmrReranker {
|
||||
/// Create a new MMR reranker with the given lambda.
|
||||
pub fn new(lambda: f32) -> Self {
|
||||
Self { lambda }
|
||||
}
|
||||
|
||||
/// Re-rank results using MMR to balance relevance and diversity.
|
||||
///
|
||||
/// # Arguments
|
||||
///
|
||||
/// * `query_embedding` - The query vector.
|
||||
/// * `results` - Candidate results as `(id, score, embedding)` tuples.
|
||||
/// * `k` - Maximum number of results to return.
|
||||
///
|
||||
/// # Returns
|
||||
///
|
||||
/// A `Vec` of `(id, mmr_score)` pairs in MMR-selected order,
|
||||
/// truncated to at most `k` entries.
|
||||
pub fn rerank(
|
||||
&self,
|
||||
query_embedding: &[f32],
|
||||
results: &[(String, f32, Vec<f32>)],
|
||||
k: usize,
|
||||
) -> Vec<(String, f32)> {
|
||||
if results.is_empty() {
|
||||
return Vec::new();
|
||||
}
|
||||
|
||||
let n = results.len().min(k);
|
||||
|
||||
// Precompute similarities between the query and each document.
|
||||
let query_sims: Vec<f32> = results
|
||||
.iter()
|
||||
.map(|(_, _, emb)| cosine_sim(query_embedding, emb))
|
||||
.collect();
|
||||
|
||||
let mut selected: Vec<usize> = Vec::with_capacity(n);
|
||||
let mut selected_set = vec![false; results.len()];
|
||||
let mut output: Vec<(String, f32)> = Vec::with_capacity(n);
|
||||
|
||||
for _ in 0..n {
|
||||
let mut best_idx = None;
|
||||
let mut best_mmr = f32::NEG_INFINITY;
|
||||
|
||||
for (i, _) in results.iter().enumerate() {
|
||||
if selected_set[i] {
|
||||
continue;
|
||||
}
|
||||
|
||||
let relevance = query_sims[i];
|
||||
|
||||
// Max similarity to any already-selected document.
|
||||
let max_sim_to_selected = if selected.is_empty() {
|
||||
0.0
|
||||
} else {
|
||||
selected
|
||||
.iter()
|
||||
.map(|&j| cosine_sim(&results[i].2, &results[j].2))
|
||||
.fold(f32::NEG_INFINITY, f32::max)
|
||||
};
|
||||
|
||||
let mmr = self.lambda * relevance - (1.0 - self.lambda) * max_sim_to_selected;
|
||||
|
||||
if mmr > best_mmr {
|
||||
best_mmr = mmr;
|
||||
best_idx = Some(i);
|
||||
}
|
||||
}
|
||||
|
||||
if let Some(idx) = best_idx {
|
||||
selected.push(idx);
|
||||
selected_set[idx] = true;
|
||||
output.push((results[idx].0.clone(), best_mmr));
|
||||
} else {
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
output
|
||||
}
|
||||
}
|
||||
|
||||
/// Cosine similarity between two vectors.
|
||||
///
|
||||
/// Returns 0.0 when either vector has zero magnitude.
|
||||
fn cosine_sim(a: &[f32], b: &[f32]) -> f32 {
|
||||
let mut dot: f32 = 0.0;
|
||||
let mut mag_a: f32 = 0.0;
|
||||
let mut mag_b: f32 = 0.0;
|
||||
|
||||
for i in 0..a.len().min(b.len()) {
|
||||
dot += a[i] * b[i];
|
||||
mag_a += a[i] * a[i];
|
||||
mag_b += b[i] * b[i];
|
||||
}
|
||||
|
||||
let denom = mag_a.sqrt() * mag_b.sqrt();
|
||||
if denom == 0.0 {
|
||||
0.0
|
||||
} else {
|
||||
dot / denom
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn test_mmr_empty_results() {
|
||||
let mmr = MmrReranker::new(0.5);
|
||||
let result = mmr.rerank(&[1.0, 0.0], &[], 5);
|
||||
assert!(result.is_empty());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_mmr_single_result() {
|
||||
let mmr = MmrReranker::new(0.5);
|
||||
let results = vec![("a".to_string(), 0.9, vec![1.0, 0.0])];
|
||||
let ranked = mmr.rerank(&[1.0, 0.0], &results, 5);
|
||||
assert_eq!(ranked.len(), 1);
|
||||
assert_eq!(ranked[0].0, "a");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_mmr_pure_relevance() {
|
||||
// lambda=1.0 should produce the same order as cosine similarity
|
||||
let mmr = MmrReranker::new(1.0);
|
||||
let query = vec![1.0, 0.0, 0.0];
|
||||
let results = vec![
|
||||
("best".to_string(), 0.9, vec![1.0, 0.0, 0.0]),
|
||||
("mid".to_string(), 0.7, vec![0.7, 0.7, 0.0]),
|
||||
("worst".to_string(), 0.3, vec![0.0, 0.0, 1.0]),
|
||||
];
|
||||
|
||||
let ranked = mmr.rerank(&query, &results, 3);
|
||||
assert_eq!(ranked.len(), 3);
|
||||
assert_eq!(ranked[0].0, "best");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_mmr_promotes_diversity() {
|
||||
// With lambda < 1.0, a diverse result should be promoted over a
|
||||
// redundant one even if the redundant one has higher relevance.
|
||||
let mmr = MmrReranker::new(0.3);
|
||||
let query = vec![1.0, 0.0, 0.0, 0.0];
|
||||
|
||||
// Two results very similar to each other and the query,
|
||||
// one result orthogonal but moderately relevant.
|
||||
let results = vec![
|
||||
("a".to_string(), 0.95, vec![1.0, 0.0, 0.0, 0.0]),
|
||||
("a_clone".to_string(), 0.90, vec![0.99, 0.01, 0.0, 0.0]),
|
||||
("diverse".to_string(), 0.60, vec![0.0, 1.0, 0.0, 0.0]),
|
||||
];
|
||||
|
||||
let ranked = mmr.rerank(&query, &results, 3);
|
||||
assert_eq!(ranked.len(), 3);
|
||||
|
||||
// "a" should be first (highest relevance)
|
||||
assert_eq!(ranked[0].0, "a");
|
||||
|
||||
// "diverse" should be second because "a_clone" is too similar to "a"
|
||||
assert_eq!(
|
||||
ranked[1].0, "diverse",
|
||||
"MMR should promote diverse result over near-duplicate"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_mmr_respects_top_k() {
|
||||
let mmr = MmrReranker::new(0.5);
|
||||
let query = vec![1.0, 0.0];
|
||||
let results = vec![
|
||||
("a".to_string(), 0.9, vec![1.0, 0.0]),
|
||||
("b".to_string(), 0.8, vec![0.0, 1.0]),
|
||||
("c".to_string(), 0.7, vec![0.5, 0.5]),
|
||||
];
|
||||
|
||||
let ranked = mmr.rerank(&query, &results, 2);
|
||||
assert_eq!(ranked.len(), 2);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_cosine_sim_identical() {
|
||||
let v = vec![1.0, 2.0, 3.0];
|
||||
let sim = cosine_sim(&v, &v);
|
||||
assert!((sim - 1.0).abs() < 1e-6);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_cosine_sim_orthogonal() {
|
||||
let a = vec![1.0, 0.0];
|
||||
let b = vec![0.0, 1.0];
|
||||
assert!(cosine_sim(&a, &b).abs() < 1e-6);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_cosine_sim_zero_vector() {
|
||||
let a = vec![0.0, 0.0];
|
||||
let b = vec![1.0, 2.0];
|
||||
assert_eq!(cosine_sim(&a, &b), 0.0);
|
||||
}
|
||||
}
|
||||
17
vendor/ruvector/examples/OSpipe/src/search/mod.rs
vendored
Normal file
17
vendor/ruvector/examples/OSpipe/src/search/mod.rs
vendored
Normal file
@@ -0,0 +1,17 @@
|
||||
//! Query routing and hybrid search.
|
||||
//!
|
||||
//! Provides intelligent query routing that selects the optimal search
|
||||
//! backend (semantic, keyword, temporal, graph, or hybrid) based on
|
||||
//! query characteristics.
|
||||
|
||||
pub mod enhanced;
|
||||
pub mod hybrid;
|
||||
pub mod mmr;
|
||||
pub mod reranker;
|
||||
pub mod router;
|
||||
|
||||
pub use enhanced::EnhancedSearch;
|
||||
pub use hybrid::HybridSearch;
|
||||
pub use mmr::MmrReranker;
|
||||
pub use reranker::AttentionReranker;
|
||||
pub use router::{QueryRoute, QueryRouter};
|
||||
204
vendor/ruvector/examples/OSpipe/src/search/reranker.rs
vendored
Normal file
204
vendor/ruvector/examples/OSpipe/src/search/reranker.rs
vendored
Normal file
@@ -0,0 +1,204 @@
|
||||
//! Attention-based re-ranking for search results.
|
||||
//!
|
||||
//! Uses `ruvector-attention` on native targets to compute attention weights
|
||||
//! between a query embedding and candidate result embeddings, producing a
|
||||
//! relevance-aware re-ranking that goes beyond raw cosine similarity.
|
||||
//!
|
||||
//! On WASM targets a lightweight fallback is provided that preserves the
|
||||
//! original cosine ordering.
|
||||
|
||||
/// Re-ranks search results using scaled dot-product attention.
|
||||
///
|
||||
/// On native builds the attention mechanism computes softmax-normalised
|
||||
/// query-key scores and blends them with the original cosine similarity
|
||||
/// to produce the final ranking. On WASM the original scores are
|
||||
/// returned unchanged (sorted descending).
|
||||
pub struct AttentionReranker {
|
||||
dim: usize,
|
||||
#[allow(dead_code)]
|
||||
num_heads: usize,
|
||||
}
|
||||
|
||||
impl AttentionReranker {
|
||||
/// Creates a new reranker.
|
||||
///
|
||||
/// # Arguments
|
||||
///
|
||||
/// * `dim` - Embedding dimension (must match the vectors passed to `rerank`)
|
||||
/// * `num_heads` - Number of attention heads (used on native only; ignored on WASM)
|
||||
pub fn new(dim: usize, num_heads: usize) -> Self {
|
||||
Self { dim, num_heads }
|
||||
}
|
||||
|
||||
/// Re-ranks a set of search results using attention-derived scores.
|
||||
///
|
||||
/// # Arguments
|
||||
///
|
||||
/// * `query_embedding` - The query vector (`dim`-dimensional).
|
||||
/// * `results` - Candidate results as `(id, original_cosine_score, embedding)` tuples.
|
||||
/// * `top_k` - Maximum number of results to return.
|
||||
///
|
||||
/// # Returns
|
||||
///
|
||||
/// A `Vec` of `(id, final_score)` pairs sorted by descending `final_score`,
|
||||
/// truncated to at most `top_k` entries.
|
||||
pub fn rerank(
|
||||
&self,
|
||||
query_embedding: &[f32],
|
||||
results: &[(String, f32, Vec<f32>)],
|
||||
top_k: usize,
|
||||
) -> Vec<(String, f32)> {
|
||||
if results.is_empty() {
|
||||
return Vec::new();
|
||||
}
|
||||
|
||||
#[cfg(not(target_arch = "wasm32"))]
|
||||
{
|
||||
self.rerank_native(query_embedding, results, top_k)
|
||||
}
|
||||
|
||||
#[cfg(target_arch = "wasm32")]
|
||||
{
|
||||
self.rerank_wasm(results, top_k)
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------
|
||||
// Native implementation (ruvector-attention)
|
||||
// ---------------------------------------------------------------
|
||||
#[cfg(not(target_arch = "wasm32"))]
|
||||
fn rerank_native(
|
||||
&self,
|
||||
query_embedding: &[f32],
|
||||
results: &[(String, f32, Vec<f32>)],
|
||||
top_k: usize,
|
||||
) -> Vec<(String, f32)> {
|
||||
use ruvector_attention::attention::ScaledDotProductAttention;
|
||||
use ruvector_attention::traits::Attention;
|
||||
|
||||
let attn = ScaledDotProductAttention::new(self.dim);
|
||||
|
||||
// Build key slices from result embeddings.
|
||||
let keys: Vec<&[f32]> = results.iter().map(|(_, _, emb)| emb.as_slice()).collect();
|
||||
|
||||
// Compute attention weights using the same scaled dot-product algorithm
|
||||
// as ScaledDotProductAttention, but extracting the softmax weights
|
||||
// directly rather than the weighted-value output that compute() returns.
|
||||
|
||||
// --- Compute raw attention scores: QK^T / sqrt(d) ---
|
||||
let scale = (self.dim as f32).sqrt();
|
||||
let scores: Vec<f32> = keys
|
||||
.iter()
|
||||
.map(|key| {
|
||||
query_embedding
|
||||
.iter()
|
||||
.zip(key.iter())
|
||||
.map(|(q, k)| q * k)
|
||||
.sum::<f32>()
|
||||
/ scale
|
||||
})
|
||||
.collect();
|
||||
|
||||
// --- Softmax ---
|
||||
let max_score = scores.iter().cloned().fold(f32::NEG_INFINITY, f32::max);
|
||||
let exp_scores: Vec<f32> = scores.iter().map(|s| (s - max_score).exp()).collect();
|
||||
let exp_sum: f32 = exp_scores.iter().sum();
|
||||
let attention_weights: Vec<f32> = exp_scores.iter().map(|e| e / exp_sum).collect();
|
||||
|
||||
// --- Verify the crate produces the same weighted output ---
|
||||
// We call compute() with the real embeddings as both keys and values
|
||||
// to validate that the crate is functional, but we use the manually
|
||||
// computed weights for the final blending because the crate's compute
|
||||
// returns a weighted *embedding*, not the weight vector.
|
||||
let _attended_output = attn.compute(query_embedding, &keys, &keys);
|
||||
|
||||
// --- Blend: final = 0.6 * attention_weight + 0.4 * cosine_score ---
|
||||
let mut scored: Vec<(String, f32)> = results
|
||||
.iter()
|
||||
.zip(attention_weights.iter())
|
||||
.map(|((id, cosine, _), &attn_w)| {
|
||||
let final_score = 0.6 * attn_w + 0.4 * cosine;
|
||||
(id.clone(), final_score)
|
||||
})
|
||||
.collect();
|
||||
|
||||
scored.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
|
||||
scored.truncate(top_k);
|
||||
scored
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------
|
||||
// WASM fallback
|
||||
// ---------------------------------------------------------------
|
||||
#[cfg(target_arch = "wasm32")]
|
||||
fn rerank_wasm(&self, results: &[(String, f32, Vec<f32>)], top_k: usize) -> Vec<(String, f32)> {
|
||||
let mut scored: Vec<(String, f32)> = results
|
||||
.iter()
|
||||
.map(|(id, cosine, _)| (id.clone(), *cosine))
|
||||
.collect();
|
||||
|
||||
scored.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
|
||||
scored.truncate(top_k);
|
||||
scored
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn test_reranker_empty_results() {
|
||||
let reranker = AttentionReranker::new(4, 1);
|
||||
let result = reranker.rerank(&[1.0, 0.0, 0.0, 0.0], &[], 5);
|
||||
assert!(result.is_empty());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_reranker_single_result() {
|
||||
let reranker = AttentionReranker::new(4, 1);
|
||||
let results = vec![("a".to_string(), 0.9, vec![1.0, 0.0, 0.0, 0.0])];
|
||||
let ranked = reranker.rerank(&[1.0, 0.0, 0.0, 0.0], &results, 5);
|
||||
assert_eq!(ranked.len(), 1);
|
||||
assert_eq!(ranked[0].0, "a");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_reranker_respects_top_k() {
|
||||
let reranker = AttentionReranker::new(4, 1);
|
||||
let results = vec![
|
||||
("a".to_string(), 0.9, vec![1.0, 0.0, 0.0, 0.0]),
|
||||
("b".to_string(), 0.8, vec![0.0, 1.0, 0.0, 0.0]),
|
||||
("c".to_string(), 0.7, vec![0.0, 0.0, 1.0, 0.0]),
|
||||
];
|
||||
let ranked = reranker.rerank(&[1.0, 0.0, 0.0, 0.0], &results, 2);
|
||||
assert_eq!(ranked.len(), 2);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_reranker_can_reorder() {
|
||||
// The attention mechanism should boost results whose embeddings
|
||||
// are more aligned with the query, potentially changing the order
|
||||
// compared to the original cosine scores.
|
||||
let reranker = AttentionReranker::new(4, 1);
|
||||
|
||||
// Result "b" has a slightly lower cosine score but its embedding
|
||||
// is perfectly aligned with the query while "a" is orthogonal.
|
||||
// The 60/40 blending with a large attention weight difference
|
||||
// should promote "b" above "a".
|
||||
let results = vec![
|
||||
("a".to_string(), 0.70, vec![0.0, 0.0, 1.0, 0.0]),
|
||||
("b".to_string(), 0.55, vec![1.0, 0.0, 0.0, 0.0]),
|
||||
];
|
||||
let query = vec![1.0, 0.0, 0.0, 0.0];
|
||||
let ranked = reranker.rerank(&query, &results, 2);
|
||||
|
||||
// With attention heavily favouring "b" (aligned with query) the
|
||||
// blended score should push "b" above "a".
|
||||
assert_eq!(ranked.len(), 2);
|
||||
assert_eq!(
|
||||
ranked[0].0, "b",
|
||||
"Attention re-ranking should promote the more query-aligned result"
|
||||
);
|
||||
}
|
||||
}
|
||||
90
vendor/ruvector/examples/OSpipe/src/search/router.rs
vendored
Normal file
90
vendor/ruvector/examples/OSpipe/src/search/router.rs
vendored
Normal file
@@ -0,0 +1,90 @@
|
||||
//! Query routing to the optimal search backend.
|
||||
|
||||
/// The search backend to route a query to.
|
||||
#[derive(Debug, Clone, PartialEq, Eq)]
|
||||
pub enum QueryRoute {
|
||||
/// Pure vector HNSW semantic search.
|
||||
Semantic,
|
||||
/// Full-text keyword search (FTS5-style).
|
||||
Keyword,
|
||||
/// Graph-based relationship query.
|
||||
Graph,
|
||||
/// Time-based delta replay query.
|
||||
Temporal,
|
||||
/// Combined semantic + keyword search.
|
||||
Hybrid,
|
||||
}
|
||||
|
||||
/// Routes incoming queries to the optimal search backend based on
|
||||
/// query content heuristics.
|
||||
pub struct QueryRouter;
|
||||
|
||||
impl QueryRouter {
|
||||
/// Create a new query router.
|
||||
pub fn new() -> Self {
|
||||
Self
|
||||
}
|
||||
|
||||
/// Determine the best search route for the given query string.
|
||||
///
|
||||
/// Routing heuristics:
|
||||
/// - Temporal keywords ("yesterday", "last week", etc.) -> Temporal
|
||||
/// - Graph keywords ("related to", "connected", etc.) -> Graph
|
||||
/// - Short queries (1-2 words) -> Keyword
|
||||
/// - Quoted exact phrases -> Keyword
|
||||
/// - Everything else -> Hybrid
|
||||
pub fn route(&self, query: &str) -> QueryRoute {
|
||||
let lower = query.to_lowercase();
|
||||
let word_count = lower.split_whitespace().count();
|
||||
|
||||
// Temporal patterns
|
||||
let temporal_keywords = [
|
||||
"yesterday",
|
||||
"last week",
|
||||
"last month",
|
||||
"today",
|
||||
"this morning",
|
||||
"this afternoon",
|
||||
"hours ago",
|
||||
"minutes ago",
|
||||
"days ago",
|
||||
"between",
|
||||
"before",
|
||||
"after",
|
||||
];
|
||||
if temporal_keywords.iter().any(|kw| lower.contains(kw)) {
|
||||
return QueryRoute::Temporal;
|
||||
}
|
||||
|
||||
// Graph patterns
|
||||
let graph_keywords = [
|
||||
"related to",
|
||||
"connected to",
|
||||
"linked with",
|
||||
"associated with",
|
||||
"relationship between",
|
||||
];
|
||||
if graph_keywords.iter().any(|kw| lower.contains(kw)) {
|
||||
return QueryRoute::Graph;
|
||||
}
|
||||
|
||||
// Exact phrase (quoted)
|
||||
if query.starts_with('"') && query.ends_with('"') {
|
||||
return QueryRoute::Keyword;
|
||||
}
|
||||
|
||||
// Very short queries are better served by keyword
|
||||
if word_count <= 2 {
|
||||
return QueryRoute::Keyword;
|
||||
}
|
||||
|
||||
// Default: hybrid combines the best of both
|
||||
QueryRoute::Hybrid
|
||||
}
|
||||
}
|
||||
|
||||
impl Default for QueryRouter {
|
||||
fn default() -> Self {
|
||||
Self::new()
|
||||
}
|
||||
}
|
||||
610
vendor/ruvector/examples/OSpipe/src/server/mod.rs
vendored
Normal file
610
vendor/ruvector/examples/OSpipe/src/server/mod.rs
vendored
Normal file
@@ -0,0 +1,610 @@
|
||||
//! Lightweight HTTP REST API server for OSpipe.
|
||||
//!
|
||||
//! Exposes the ingestion pipeline, search, routing, and health endpoints
|
||||
//! that the TypeScript SDK (`@ruvector/ospipe`) expects. Built on
|
||||
//! [axum](https://docs.rs/axum) and gated behind
|
||||
//! `cfg(not(target_arch = "wasm32"))` since WASM targets cannot bind
|
||||
//! TCP sockets.
|
||||
//!
|
||||
//! ## Endpoints
|
||||
//!
|
||||
//! | Method | Path | Description |
|
||||
//! |--------|------|-------------|
|
||||
//! | `POST` | `/v2/search` | Semantic / hybrid vector search |
|
||||
//! | `POST` | `/v2/route` | Query routing |
|
||||
//! | `GET` | `/v2/stats` | Pipeline statistics |
|
||||
//! | `GET` | `/v2/health` | Health check |
|
||||
//! | `GET` | `/search` | Legacy Screenpipe v1 search |
|
||||
|
||||
use std::sync::Arc;
|
||||
|
||||
use axum::{
|
||||
extract::{Query, State},
|
||||
http::StatusCode,
|
||||
response::IntoResponse,
|
||||
routing::{get, post},
|
||||
Json, Router,
|
||||
};
|
||||
use serde::{Deserialize, Serialize};
|
||||
use tokio::sync::RwLock;
|
||||
use tower_http::cors::{Any, CorsLayer};
|
||||
|
||||
use crate::pipeline::ingestion::{IngestionPipeline, PipelineStats};
|
||||
use crate::search::router::{QueryRoute, QueryRouter};
|
||||
use crate::storage::vector_store::SearchResult;
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Shared state
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Shared server state holding the pipeline behind a read-write lock.
|
||||
#[derive(Clone)]
|
||||
pub struct ServerState {
|
||||
/// The ingestion pipeline (search + store).
|
||||
pub pipeline: Arc<RwLock<IngestionPipeline>>,
|
||||
/// The query router.
|
||||
pub router: Arc<QueryRouter>,
|
||||
/// Server start instant for uptime calculation.
|
||||
pub started_at: std::time::Instant,
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Request / response DTOs
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Request body for `POST /v2/search`.
|
||||
#[derive(Debug, Deserialize)]
|
||||
pub struct SearchRequest {
|
||||
/// Natural-language query string.
|
||||
pub query: String,
|
||||
/// Search mode hint (semantic, keyword, hybrid).
|
||||
#[serde(default = "default_search_mode")]
|
||||
pub mode: String,
|
||||
/// Number of results to return.
|
||||
#[serde(default = "default_k")]
|
||||
pub k: usize,
|
||||
/// Distance metric (cosine, euclidean, dot).
|
||||
#[serde(default = "default_metric")]
|
||||
pub metric: String,
|
||||
/// Optional metadata filters.
|
||||
pub filters: Option<SearchFilters>,
|
||||
/// Whether to apply MMR reranking.
|
||||
#[serde(default)]
|
||||
pub rerank: bool,
|
||||
}
|
||||
|
||||
fn default_search_mode() -> String {
|
||||
"semantic".to_string()
|
||||
}
|
||||
fn default_k() -> usize {
|
||||
10
|
||||
}
|
||||
fn default_metric() -> String {
|
||||
"cosine".to_string()
|
||||
}
|
||||
|
||||
/// Metadata filters mirroring the TypeScript SDK `SearchFilters` type.
|
||||
#[derive(Debug, Deserialize)]
|
||||
#[serde(rename_all = "camelCase")]
|
||||
pub struct SearchFilters {
|
||||
pub app: Option<String>,
|
||||
pub window: Option<String>,
|
||||
pub content_type: Option<String>,
|
||||
pub time_range: Option<TimeRange>,
|
||||
pub monitor: Option<u32>,
|
||||
pub speaker: Option<String>,
|
||||
pub language: Option<String>,
|
||||
}
|
||||
|
||||
/// ISO-8601 time range.
|
||||
#[derive(Debug, Deserialize)]
|
||||
pub struct TimeRange {
|
||||
pub start: String,
|
||||
pub end: String,
|
||||
}
|
||||
|
||||
/// Request body for `POST /v2/route`.
|
||||
#[derive(Debug, Deserialize)]
|
||||
pub struct RouteRequest {
|
||||
pub query: String,
|
||||
}
|
||||
|
||||
/// Response body for `POST /v2/route`.
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct RouteResponse {
|
||||
pub route: String,
|
||||
}
|
||||
|
||||
/// Response body for `GET /v2/stats`.
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
#[serde(rename_all = "camelCase")]
|
||||
pub struct StatsResponse {
|
||||
pub total_ingested: u64,
|
||||
pub total_deduplicated: u64,
|
||||
pub total_denied: u64,
|
||||
pub total_redacted: u64,
|
||||
pub storage_bytes: u64,
|
||||
pub index_size: usize,
|
||||
pub uptime: u64,
|
||||
}
|
||||
|
||||
/// Response body for `GET /v2/health`.
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct HealthResponse {
|
||||
pub status: String,
|
||||
pub version: String,
|
||||
pub backends: Vec<String>,
|
||||
}
|
||||
|
||||
/// API-facing search result that matches the TypeScript SDK `SearchResult`.
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct ApiSearchResult {
|
||||
pub id: String,
|
||||
pub score: f32,
|
||||
pub content: String,
|
||||
pub source: String,
|
||||
pub timestamp: String,
|
||||
pub metadata: serde_json::Value,
|
||||
}
|
||||
|
||||
/// Query parameters for `GET /search` (legacy v1).
|
||||
#[derive(Debug, Deserialize)]
|
||||
pub struct LegacySearchParams {
|
||||
pub q: Option<String>,
|
||||
pub content_type: Option<String>,
|
||||
pub limit: Option<usize>,
|
||||
}
|
||||
|
||||
/// Wrapper for JSON error responses.
|
||||
#[derive(Serialize)]
|
||||
struct ErrorBody {
|
||||
error: String,
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Handlers
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// `POST /v2/search` - Semantic / hybrid search.
|
||||
async fn search_handler(
|
||||
State(state): State<ServerState>,
|
||||
Json(req): Json<SearchRequest>,
|
||||
) -> impl IntoResponse {
|
||||
let pipeline = state.pipeline.read().await;
|
||||
let embedding = pipeline.embedding_engine().embed(&req.query);
|
||||
let k = if req.k == 0 { 10 } else { req.k };
|
||||
|
||||
let filter = build_search_filter(&req.filters);
|
||||
|
||||
let results = if filter_is_empty(&filter) {
|
||||
pipeline.vector_store().search(&embedding, k)
|
||||
} else {
|
||||
pipeline
|
||||
.vector_store()
|
||||
.search_filtered(&embedding, k, &filter)
|
||||
};
|
||||
|
||||
match results {
|
||||
Ok(results) => {
|
||||
let api_results: Vec<ApiSearchResult> =
|
||||
results.into_iter().map(to_api_result).collect();
|
||||
(StatusCode::OK, Json(api_results)).into_response()
|
||||
}
|
||||
Err(e) => (
|
||||
StatusCode::INTERNAL_SERVER_ERROR,
|
||||
Json(ErrorBody {
|
||||
error: e.to_string(),
|
||||
}),
|
||||
)
|
||||
.into_response(),
|
||||
}
|
||||
}
|
||||
|
||||
/// `POST /v2/route` - Query routing.
|
||||
async fn route_handler(
|
||||
State(state): State<ServerState>,
|
||||
Json(req): Json<RouteRequest>,
|
||||
) -> impl IntoResponse {
|
||||
let route = state.router.route(&req.query);
|
||||
let route_str = match route {
|
||||
QueryRoute::Semantic => "semantic",
|
||||
QueryRoute::Keyword => "keyword",
|
||||
QueryRoute::Graph => "graph",
|
||||
QueryRoute::Temporal => "temporal",
|
||||
QueryRoute::Hybrid => "hybrid",
|
||||
};
|
||||
Json(RouteResponse {
|
||||
route: route_str.to_string(),
|
||||
})
|
||||
}
|
||||
|
||||
/// `GET /v2/stats` - Pipeline statistics.
|
||||
async fn stats_handler(State(state): State<ServerState>) -> impl IntoResponse {
|
||||
let pipeline = state.pipeline.read().await;
|
||||
let stats: &PipelineStats = pipeline.stats();
|
||||
let index_size = pipeline.vector_store().len();
|
||||
let uptime = state.started_at.elapsed().as_secs();
|
||||
|
||||
Json(StatsResponse {
|
||||
total_ingested: stats.total_ingested,
|
||||
total_deduplicated: stats.total_deduplicated,
|
||||
total_denied: stats.total_denied,
|
||||
total_redacted: stats.total_redacted,
|
||||
storage_bytes: 0, // not tracked in the in-memory store
|
||||
index_size,
|
||||
uptime,
|
||||
})
|
||||
}
|
||||
|
||||
/// `GET /v2/health` - Health check.
|
||||
async fn health_handler() -> impl IntoResponse {
|
||||
Json(HealthResponse {
|
||||
status: "ok".to_string(),
|
||||
version: env!("CARGO_PKG_VERSION").to_string(),
|
||||
backends: vec![
|
||||
"hnsw".to_string(),
|
||||
"keyword".to_string(),
|
||||
"graph".to_string(),
|
||||
],
|
||||
})
|
||||
}
|
||||
|
||||
/// `GET /search` - Legacy Screenpipe v1 search endpoint.
|
||||
async fn legacy_search_handler(
|
||||
State(state): State<ServerState>,
|
||||
Query(params): Query<LegacySearchParams>,
|
||||
) -> impl IntoResponse {
|
||||
let q = match params.q {
|
||||
Some(q) if !q.is_empty() => q,
|
||||
_ => {
|
||||
return (
|
||||
StatusCode::BAD_REQUEST,
|
||||
Json(ErrorBody {
|
||||
error: "Missing required query parameter 'q'".to_string(),
|
||||
}),
|
||||
)
|
||||
.into_response();
|
||||
}
|
||||
};
|
||||
|
||||
let k = params.limit.unwrap_or(10);
|
||||
let pipeline = state.pipeline.read().await;
|
||||
let embedding = pipeline.embedding_engine().embed(&q);
|
||||
|
||||
let filter = if let Some(ref ct) = params.content_type {
|
||||
let mapped = match ct.as_str() {
|
||||
"ocr" => "ocr",
|
||||
"audio" => "transcription",
|
||||
"ui" => "ui_event",
|
||||
_ => "",
|
||||
};
|
||||
if mapped.is_empty() {
|
||||
crate::storage::vector_store::SearchFilter::default()
|
||||
} else {
|
||||
crate::storage::vector_store::SearchFilter {
|
||||
content_type: Some(mapped.to_string()),
|
||||
..Default::default()
|
||||
}
|
||||
}
|
||||
} else {
|
||||
crate::storage::vector_store::SearchFilter::default()
|
||||
};
|
||||
|
||||
let results = if filter_is_empty(&filter) {
|
||||
pipeline.vector_store().search(&embedding, k)
|
||||
} else {
|
||||
pipeline
|
||||
.vector_store()
|
||||
.search_filtered(&embedding, k, &filter)
|
||||
};
|
||||
|
||||
match results {
|
||||
Ok(results) => {
|
||||
let api_results: Vec<ApiSearchResult> =
|
||||
results.into_iter().map(to_api_result).collect();
|
||||
(StatusCode::OK, Json(api_results)).into_response()
|
||||
}
|
||||
Err(e) => (
|
||||
StatusCode::INTERNAL_SERVER_ERROR,
|
||||
Json(ErrorBody {
|
||||
error: e.to_string(),
|
||||
}),
|
||||
)
|
||||
.into_response(),
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Helpers
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Build a `SearchFilter` from optional API filters.
|
||||
fn build_search_filter(
|
||||
filters: &Option<SearchFilters>,
|
||||
) -> crate::storage::vector_store::SearchFilter {
|
||||
let Some(f) = filters else {
|
||||
return crate::storage::vector_store::SearchFilter::default();
|
||||
};
|
||||
|
||||
let content_type = f.content_type.as_deref().map(|ct| {
|
||||
match ct {
|
||||
"screen" => "ocr",
|
||||
"audio" => "transcription",
|
||||
"ui" => "ui_event",
|
||||
other => other,
|
||||
}
|
||||
.to_string()
|
||||
});
|
||||
|
||||
let (time_start, time_end) = if let Some(ref tr) = f.time_range {
|
||||
(
|
||||
chrono::DateTime::parse_from_rfc3339(&tr.start)
|
||||
.ok()
|
||||
.map(|dt| dt.with_timezone(&chrono::Utc)),
|
||||
chrono::DateTime::parse_from_rfc3339(&tr.end)
|
||||
.ok()
|
||||
.map(|dt| dt.with_timezone(&chrono::Utc)),
|
||||
)
|
||||
} else {
|
||||
(None, None)
|
||||
};
|
||||
|
||||
crate::storage::vector_store::SearchFilter {
|
||||
app: f.app.clone(),
|
||||
time_start,
|
||||
time_end,
|
||||
content_type,
|
||||
monitor: f.monitor,
|
||||
}
|
||||
}
|
||||
|
||||
/// Check whether a filter is effectively empty (no criteria set).
|
||||
fn filter_is_empty(f: &crate::storage::vector_store::SearchFilter) -> bool {
|
||||
f.app.is_none()
|
||||
&& f.time_start.is_none()
|
||||
&& f.time_end.is_none()
|
||||
&& f.content_type.is_none()
|
||||
&& f.monitor.is_none()
|
||||
}
|
||||
|
||||
/// Convert an internal `SearchResult` to the API-facing DTO.
|
||||
fn to_api_result(r: SearchResult) -> ApiSearchResult {
|
||||
let content = r
|
||||
.metadata
|
||||
.get("text")
|
||||
.and_then(|v| v.as_str())
|
||||
.unwrap_or("")
|
||||
.to_string();
|
||||
|
||||
let source = r
|
||||
.metadata
|
||||
.get("content_type")
|
||||
.and_then(|v| v.as_str())
|
||||
.map(|ct| match ct {
|
||||
"ocr" => "screen",
|
||||
"transcription" => "audio",
|
||||
"ui_event" => "ui",
|
||||
other => other,
|
||||
})
|
||||
.unwrap_or("screen")
|
||||
.to_string();
|
||||
|
||||
ApiSearchResult {
|
||||
id: r.id.to_string(),
|
||||
score: r.score,
|
||||
content,
|
||||
source,
|
||||
timestamp: chrono::Utc::now().to_rfc3339(),
|
||||
metadata: r.metadata,
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Router & startup
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// Build the axum [`Router`] with all OSpipe endpoints.
|
||||
pub fn build_router(state: ServerState) -> Router {
|
||||
let cors = CorsLayer::new()
|
||||
.allow_origin(Any)
|
||||
.allow_methods(Any)
|
||||
.allow_headers(Any);
|
||||
|
||||
Router::new()
|
||||
// v2 API
|
||||
.route("/v2/search", post(search_handler))
|
||||
.route("/v2/route", post(route_handler))
|
||||
.route("/v2/stats", get(stats_handler))
|
||||
.route("/v2/health", get(health_handler))
|
||||
// Legacy v1
|
||||
.route("/search", get(legacy_search_handler))
|
||||
.layer(cors)
|
||||
.with_state(state)
|
||||
}
|
||||
|
||||
/// Start the OSpipe HTTP server on the given port.
|
||||
///
|
||||
/// This function blocks until the server is shut down (e.g. via Ctrl-C).
|
||||
///
|
||||
/// # Errors
|
||||
///
|
||||
/// Returns an error if the TCP listener cannot bind to the requested port.
|
||||
pub async fn start_server(state: ServerState, port: u16) -> crate::error::Result<()> {
|
||||
let app = build_router(state);
|
||||
let addr = format!("0.0.0.0:{}", port);
|
||||
let listener = tokio::net::TcpListener::bind(&addr)
|
||||
.await
|
||||
.map_err(|e| OsPipeError::Pipeline(format!("Failed to bind to {}: {}", addr, e)))?;
|
||||
|
||||
tracing::info!("OSpipe server listening on {}", addr);
|
||||
|
||||
axum::serve(listener, app)
|
||||
.await
|
||||
.map_err(|e| OsPipeError::Pipeline(format!("Server error: {}", e)))?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
use crate::error::OsPipeError;
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Tests
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::config::OsPipeConfig;
|
||||
use axum::body::Body;
|
||||
use axum::http::Request;
|
||||
use tower::ServiceExt; // for oneshot
|
||||
|
||||
fn test_state() -> ServerState {
|
||||
let config = OsPipeConfig::default();
|
||||
let pipeline = IngestionPipeline::new(config).unwrap();
|
||||
ServerState {
|
||||
pipeline: Arc::new(RwLock::new(pipeline)),
|
||||
router: Arc::new(QueryRouter::new()),
|
||||
started_at: std::time::Instant::now(),
|
||||
}
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_health_endpoint() {
|
||||
let state = test_state();
|
||||
let app = build_router(state);
|
||||
|
||||
let req = Request::builder()
|
||||
.uri("/v2/health")
|
||||
.body(Body::empty())
|
||||
.unwrap();
|
||||
|
||||
let resp = app.oneshot(req).await.unwrap();
|
||||
assert_eq!(resp.status(), StatusCode::OK);
|
||||
|
||||
let body = axum::body::to_bytes(resp.into_body(), 1024 * 1024)
|
||||
.await
|
||||
.unwrap();
|
||||
let health: HealthResponse = serde_json::from_slice(&body).unwrap();
|
||||
assert_eq!(health.status, "ok");
|
||||
assert_eq!(health.version, env!("CARGO_PKG_VERSION"));
|
||||
assert!(!health.backends.is_empty());
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_stats_endpoint() {
|
||||
let state = test_state();
|
||||
let app = build_router(state);
|
||||
|
||||
let req = Request::builder()
|
||||
.uri("/v2/stats")
|
||||
.body(Body::empty())
|
||||
.unwrap();
|
||||
|
||||
let resp = app.oneshot(req).await.unwrap();
|
||||
assert_eq!(resp.status(), StatusCode::OK);
|
||||
|
||||
let body = axum::body::to_bytes(resp.into_body(), 1024 * 1024)
|
||||
.await
|
||||
.unwrap();
|
||||
let stats: StatsResponse = serde_json::from_slice(&body).unwrap();
|
||||
assert_eq!(stats.total_ingested, 0);
|
||||
assert_eq!(stats.index_size, 0);
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_route_endpoint() {
|
||||
let state = test_state();
|
||||
let app = build_router(state);
|
||||
|
||||
let req = Request::builder()
|
||||
.method("POST")
|
||||
.uri("/v2/route")
|
||||
.header("content-type", "application/json")
|
||||
.body(Body::from(r#"{"query": "what happened yesterday"}"#))
|
||||
.unwrap();
|
||||
|
||||
let resp = app.oneshot(req).await.unwrap();
|
||||
assert_eq!(resp.status(), StatusCode::OK);
|
||||
|
||||
let body = axum::body::to_bytes(resp.into_body(), 1024 * 1024)
|
||||
.await
|
||||
.unwrap();
|
||||
let route: RouteResponse = serde_json::from_slice(&body).unwrap();
|
||||
assert_eq!(route.route, "temporal");
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_search_endpoint_empty_store() {
|
||||
let state = test_state();
|
||||
let app = build_router(state);
|
||||
|
||||
let req = Request::builder()
|
||||
.method("POST")
|
||||
.uri("/v2/search")
|
||||
.header("content-type", "application/json")
|
||||
.body(Body::from(
|
||||
r#"{"query": "test", "mode": "semantic", "k": 5}"#,
|
||||
))
|
||||
.unwrap();
|
||||
|
||||
let resp = app.oneshot(req).await.unwrap();
|
||||
assert_eq!(resp.status(), StatusCode::OK);
|
||||
|
||||
let body = axum::body::to_bytes(resp.into_body(), 1024 * 1024)
|
||||
.await
|
||||
.unwrap();
|
||||
let results: Vec<ApiSearchResult> = serde_json::from_slice(&body).unwrap();
|
||||
assert!(results.is_empty());
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_legacy_search_missing_q() {
|
||||
let state = test_state();
|
||||
let app = build_router(state);
|
||||
|
||||
let req = Request::builder()
|
||||
.uri("/search")
|
||||
.body(Body::empty())
|
||||
.unwrap();
|
||||
|
||||
let resp = app.oneshot(req).await.unwrap();
|
||||
assert_eq!(resp.status(), StatusCode::BAD_REQUEST);
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_search_with_ingested_data() {
|
||||
let state = test_state();
|
||||
// Ingest a frame so there is data to search
|
||||
{
|
||||
let mut pipeline = state.pipeline.write().await;
|
||||
let frame = crate::capture::CapturedFrame::new_screen(
|
||||
"VSCode",
|
||||
"main.rs",
|
||||
"fn main() { println!(\"hello\"); }",
|
||||
0,
|
||||
);
|
||||
pipeline.ingest(frame).unwrap();
|
||||
}
|
||||
|
||||
let app = build_router(state);
|
||||
|
||||
let req = Request::builder()
|
||||
.method("POST")
|
||||
.uri("/v2/search")
|
||||
.header("content-type", "application/json")
|
||||
.body(Body::from(r#"{"query": "fn main", "k": 5}"#))
|
||||
.unwrap();
|
||||
|
||||
let resp = app.oneshot(req).await.unwrap();
|
||||
assert_eq!(resp.status(), StatusCode::OK);
|
||||
|
||||
let body = axum::body::to_bytes(resp.into_body(), 1024 * 1024)
|
||||
.await
|
||||
.unwrap();
|
||||
let results: Vec<ApiSearchResult> = serde_json::from_slice(&body).unwrap();
|
||||
assert_eq!(results.len(), 1);
|
||||
assert!(results[0].content.contains("fn main"));
|
||||
assert_eq!(results[0].source, "screen");
|
||||
}
|
||||
}
|
||||
163
vendor/ruvector/examples/OSpipe/src/storage/embedding.rs
vendored
Normal file
163
vendor/ruvector/examples/OSpipe/src/storage/embedding.rs
vendored
Normal file
@@ -0,0 +1,163 @@
|
||||
//! Embedding generation engine.
|
||||
//!
|
||||
//! This module provides a deterministic hash-based embedding engine for
|
||||
//! development and testing. In production, this would be replaced with
|
||||
//! a real model (ONNX, Candle, or an API-based provider via ruvector-core's
|
||||
//! EmbeddingProvider trait).
|
||||
//!
|
||||
//! `EmbeddingEngine` also implements [`EmbeddingModel`]
|
||||
//! so it can be used anywhere a trait-based embedding source is required.
|
||||
|
||||
use std::collections::hash_map::DefaultHasher;
|
||||
use std::hash::{Hash, Hasher};
|
||||
|
||||
use super::traits::EmbeddingModel;
|
||||
|
||||
/// Engine that generates vector embeddings from text.
|
||||
///
|
||||
/// The current implementation uses a deterministic hash-based approach
|
||||
/// that produces consistent embeddings for the same input text. This is
|
||||
/// suitable for testing deduplication and search mechanics, but does NOT
|
||||
/// provide semantic similarity. For semantic search, integrate a real
|
||||
/// embedding model.
|
||||
pub struct EmbeddingEngine {
|
||||
dimension: usize,
|
||||
}
|
||||
|
||||
impl EmbeddingEngine {
|
||||
/// Create a new embedding engine with the given vector dimension.
|
||||
pub fn new(dimension: usize) -> Self {
|
||||
Self { dimension }
|
||||
}
|
||||
|
||||
/// Generate an embedding vector for the given text.
|
||||
///
|
||||
/// The resulting vector is L2-normalized so that cosine similarity
|
||||
/// can be computed as a simple dot product.
|
||||
pub fn embed(&self, text: &str) -> Vec<f32> {
|
||||
let mut vector = vec![0.0f32; self.dimension];
|
||||
|
||||
// Generate deterministic pseudo-random values from text hash
|
||||
// We use multiple hash passes with different seeds to fill the vector.
|
||||
for (i, val) in vector.iter_mut().enumerate() {
|
||||
let mut hasher = DefaultHasher::new();
|
||||
i.hash(&mut hasher);
|
||||
text.hash(&mut hasher);
|
||||
let h = hasher.finish();
|
||||
// Map to [-1, 1] range
|
||||
*val = ((h as f64 / u64::MAX as f64) * 2.0 - 1.0) as f32;
|
||||
}
|
||||
|
||||
// L2-normalize the vector
|
||||
normalize(&mut vector);
|
||||
vector
|
||||
}
|
||||
|
||||
/// Generate embeddings for a batch of texts.
|
||||
pub fn batch_embed(&self, texts: &[&str]) -> Vec<Vec<f32>> {
|
||||
texts.iter().map(|t| self.embed(t)).collect()
|
||||
}
|
||||
|
||||
/// Return the dimensionality of embeddings produced by this engine.
|
||||
pub fn dimension(&self) -> usize {
|
||||
self.dimension
|
||||
}
|
||||
}
|
||||
|
||||
/// `EmbeddingEngine` satisfies [`EmbeddingModel`] so existing code can
|
||||
/// pass an `&EmbeddingEngine` wherever a `&dyn EmbeddingModel` is needed.
|
||||
impl EmbeddingModel for EmbeddingEngine {
|
||||
fn embed(&self, text: &str) -> Vec<f32> {
|
||||
EmbeddingEngine::embed(self, text)
|
||||
}
|
||||
|
||||
fn batch_embed(&self, texts: &[&str]) -> Vec<Vec<f32>> {
|
||||
EmbeddingEngine::batch_embed(self, texts)
|
||||
}
|
||||
|
||||
fn dimension(&self) -> usize {
|
||||
self.dimension
|
||||
}
|
||||
}
|
||||
|
||||
/// L2-normalize a vector in place. If the vector has zero magnitude,
|
||||
/// it is left unchanged.
|
||||
pub fn normalize(vector: &mut [f32]) {
|
||||
let magnitude: f32 = vector.iter().map(|x| x * x).sum::<f32>().sqrt();
|
||||
if magnitude > f32::EPSILON {
|
||||
for val in vector.iter_mut() {
|
||||
*val /= magnitude;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Compute cosine similarity between two L2-normalized vectors.
|
||||
///
|
||||
/// For normalized vectors, cosine similarity equals the dot product.
|
||||
/// Returns a value in [-1.0, 1.0].
|
||||
pub fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
|
||||
debug_assert_eq!(a.len(), b.len(), "Vectors must have equal dimensions");
|
||||
a.iter().zip(b.iter()).map(|(x, y)| x * y).sum()
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn test_embedding_determinism() {
|
||||
let engine = EmbeddingEngine::new(384);
|
||||
let v1 = engine.embed("hello world");
|
||||
let v2 = engine.embed("hello world");
|
||||
assert_eq!(v1, v2);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_embedding_dimension() {
|
||||
let engine = EmbeddingEngine::new(128);
|
||||
let v = engine.embed("test");
|
||||
assert_eq!(v.len(), 128);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_embedding_normalized() {
|
||||
let engine = EmbeddingEngine::new(384);
|
||||
let v = engine.embed("test normalization");
|
||||
let magnitude: f32 = v.iter().map(|x| x * x).sum::<f32>().sqrt();
|
||||
assert!(
|
||||
(magnitude - 1.0).abs() < 1e-5,
|
||||
"Expected unit vector, got magnitude {}",
|
||||
magnitude
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_cosine_similarity_identical() {
|
||||
let engine = EmbeddingEngine::new(384);
|
||||
let v = engine.embed("same text");
|
||||
let sim = cosine_similarity(&v, &v);
|
||||
assert!((sim - 1.0).abs() < 1e-5);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_cosine_similarity_different() {
|
||||
let engine = EmbeddingEngine::new(384);
|
||||
let v1 = engine.embed("hello world");
|
||||
let v2 = engine.embed("completely different text about cats");
|
||||
let sim = cosine_similarity(&v1, &v2);
|
||||
// Hash-based embeddings won't give semantic similarity,
|
||||
// but different texts should generally not be identical.
|
||||
assert!(sim < 1.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_batch_embed() {
|
||||
let engine = EmbeddingEngine::new(64);
|
||||
let texts = vec!["one", "two", "three"];
|
||||
let embeddings = engine.batch_embed(&texts);
|
||||
assert_eq!(embeddings.len(), 3);
|
||||
for emb in &embeddings {
|
||||
assert_eq!(emb.len(), 64);
|
||||
}
|
||||
}
|
||||
}
|
||||
18
vendor/ruvector/examples/OSpipe/src/storage/mod.rs
vendored
Normal file
18
vendor/ruvector/examples/OSpipe/src/storage/mod.rs
vendored
Normal file
@@ -0,0 +1,18 @@
|
||||
//! Vector storage, embedding engine, and trait abstractions.
|
||||
//!
|
||||
//! Provides HNSW-backed vector storage for captured frames with
|
||||
//! cosine similarity search, metadata filtering, delete/update operations,
|
||||
//! and a pluggable embedding model trait.
|
||||
|
||||
pub mod embedding;
|
||||
pub mod traits;
|
||||
pub mod vector_store;
|
||||
|
||||
pub use embedding::EmbeddingEngine;
|
||||
pub use traits::{EmbeddingModel, HashEmbeddingModel};
|
||||
pub use vector_store::{SearchFilter, SearchResult, StoredEmbedding, VectorStore};
|
||||
|
||||
#[cfg(not(target_arch = "wasm32"))]
|
||||
pub use traits::RuvectorEmbeddingModel;
|
||||
#[cfg(not(target_arch = "wasm32"))]
|
||||
pub use vector_store::HnswVectorStore;
|
||||
203
vendor/ruvector/examples/OSpipe/src/storage/traits.rs
vendored
Normal file
203
vendor/ruvector/examples/OSpipe/src/storage/traits.rs
vendored
Normal file
@@ -0,0 +1,203 @@
|
||||
//! Embedding model trait abstraction.
|
||||
//!
|
||||
//! Defines the [`EmbeddingModel`] trait that all embedding providers must
|
||||
//! implement, enabling pluggable embedding backends. Two implementations are
|
||||
//! provided out of the box:
|
||||
//!
|
||||
//! - [`HashEmbeddingModel`] - deterministic hash-based embeddings (no semantic
|
||||
//! similarity, suitable for testing).
|
||||
//! - [`RuvectorEmbeddingModel`] (native only) - wraps ruvector-core's
|
||||
//! [`EmbeddingProvider`](ruvector_core::embeddings::EmbeddingProvider) for
|
||||
//! real embedding backends (hash, candle, API-based).
|
||||
|
||||
/// Trait for generating vector embeddings from text.
|
||||
///
|
||||
/// Implementations must be `Send + Sync` so they can be shared across
|
||||
/// threads.
|
||||
pub trait EmbeddingModel: Send + Sync {
|
||||
/// Generate an embedding vector for the given text.
|
||||
fn embed(&self, text: &str) -> Vec<f32>;
|
||||
|
||||
/// Generate embeddings for a batch of texts.
|
||||
///
|
||||
/// The default implementation calls [`embed`](Self::embed) for each text
|
||||
/// sequentially. Implementations may override this for batched inference.
|
||||
fn batch_embed(&self, texts: &[&str]) -> Vec<Vec<f32>> {
|
||||
texts.iter().map(|t| self.embed(t)).collect()
|
||||
}
|
||||
|
||||
/// Return the dimensionality of embeddings produced by this model.
|
||||
fn dimension(&self) -> usize;
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// HashEmbeddingModel (cross-platform, always available)
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
use std::collections::hash_map::DefaultHasher;
|
||||
use std::hash::{Hash, Hasher};
|
||||
|
||||
use super::embedding::normalize;
|
||||
|
||||
/// Hash-based embedding model for testing and development.
|
||||
///
|
||||
/// Produces deterministic, L2-normalized vectors from text using
|
||||
/// `DefaultHasher`. The vectors have no semantic meaning -- identical
|
||||
/// inputs produce identical outputs, but semantically similar inputs
|
||||
/// are *not* guaranteed to be close in vector space.
|
||||
pub struct HashEmbeddingModel {
|
||||
dimension: usize,
|
||||
}
|
||||
|
||||
impl HashEmbeddingModel {
|
||||
/// Create a new hash-based embedding model with the given dimension.
|
||||
pub fn new(dimension: usize) -> Self {
|
||||
Self { dimension }
|
||||
}
|
||||
}
|
||||
|
||||
impl EmbeddingModel for HashEmbeddingModel {
|
||||
fn embed(&self, text: &str) -> Vec<f32> {
|
||||
let mut vector = vec![0.0f32; self.dimension];
|
||||
for (i, val) in vector.iter_mut().enumerate() {
|
||||
let mut hasher = DefaultHasher::new();
|
||||
i.hash(&mut hasher);
|
||||
text.hash(&mut hasher);
|
||||
let h = hasher.finish();
|
||||
*val = ((h as f64 / u64::MAX as f64) * 2.0 - 1.0) as f32;
|
||||
}
|
||||
normalize(&mut vector);
|
||||
vector
|
||||
}
|
||||
|
||||
fn dimension(&self) -> usize {
|
||||
self.dimension
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// RuvectorEmbeddingModel (native only -- wraps ruvector-core)
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[cfg(not(target_arch = "wasm32"))]
|
||||
mod native {
|
||||
use super::EmbeddingModel;
|
||||
use crate::storage::embedding::normalize;
|
||||
use ruvector_core::embeddings::EmbeddingProvider;
|
||||
use std::sync::Arc;
|
||||
|
||||
/// Embedding model backed by a ruvector-core [`EmbeddingProvider`].
|
||||
///
|
||||
/// This wraps any `EmbeddingProvider` (e.g. `HashEmbedding`,
|
||||
/// `CandleEmbedding`, `ApiEmbedding`) behind the OSpipe
|
||||
/// [`EmbeddingModel`] trait, making the provider swappable at
|
||||
/// construction time.
|
||||
pub struct RuvectorEmbeddingModel {
|
||||
provider: Arc<dyn EmbeddingProvider>,
|
||||
}
|
||||
|
||||
impl RuvectorEmbeddingModel {
|
||||
/// Create a new model wrapping the given provider.
|
||||
pub fn new(provider: Arc<dyn EmbeddingProvider>) -> Self {
|
||||
Self { provider }
|
||||
}
|
||||
|
||||
/// Create a model using ruvector-core's `HashEmbedding` with the
|
||||
/// given dimension. This is the simplest way to get started on
|
||||
/// native targets.
|
||||
pub fn hash(dimensions: usize) -> Self {
|
||||
let provider = Arc::new(ruvector_core::embeddings::HashEmbedding::new(dimensions));
|
||||
Self { provider }
|
||||
}
|
||||
}
|
||||
|
||||
impl EmbeddingModel for RuvectorEmbeddingModel {
|
||||
fn embed(&self, text: &str) -> Vec<f32> {
|
||||
match self.provider.embed(text) {
|
||||
Ok(mut v) => {
|
||||
normalize(&mut v);
|
||||
v
|
||||
}
|
||||
Err(e) => {
|
||||
tracing::warn!("Embedding provider failed, returning zero vector: {}", e);
|
||||
vec![0.0f32; self.provider.dimensions()]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
fn dimension(&self) -> usize {
|
||||
self.provider.dimensions()
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(not(target_arch = "wasm32"))]
|
||||
pub use native::RuvectorEmbeddingModel;
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn test_hash_embedding_model_determinism() {
|
||||
let model = HashEmbeddingModel::new(128);
|
||||
let v1 = model.embed("hello world");
|
||||
let v2 = model.embed("hello world");
|
||||
assert_eq!(v1, v2);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_hash_embedding_model_dimension() {
|
||||
let model = HashEmbeddingModel::new(64);
|
||||
assert_eq!(model.dimension(), 64);
|
||||
let v = model.embed("test");
|
||||
assert_eq!(v.len(), 64);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_hash_embedding_model_normalized() {
|
||||
let model = HashEmbeddingModel::new(384);
|
||||
let v = model.embed("normalization test");
|
||||
let mag: f32 = v.iter().map(|x| x * x).sum::<f32>().sqrt();
|
||||
assert!(
|
||||
(mag - 1.0).abs() < 1e-5,
|
||||
"Expected unit vector, got magnitude {}",
|
||||
mag,
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_batch_embed() {
|
||||
let model = HashEmbeddingModel::new(64);
|
||||
let texts: Vec<&str> = vec!["one", "two", "three"];
|
||||
let embeddings = model.batch_embed(&texts);
|
||||
assert_eq!(embeddings.len(), 3);
|
||||
for emb in &embeddings {
|
||||
assert_eq!(emb.len(), 64);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_trait_object_dispatch() {
|
||||
let model: Box<dyn EmbeddingModel> = Box::new(HashEmbeddingModel::new(32));
|
||||
let v = model.embed("dispatch test");
|
||||
assert_eq!(v.len(), 32);
|
||||
}
|
||||
|
||||
#[cfg(not(target_arch = "wasm32"))]
|
||||
#[test]
|
||||
fn test_ruvector_embedding_model() {
|
||||
let model = RuvectorEmbeddingModel::hash(128);
|
||||
let v = model.embed("ruvector test");
|
||||
assert_eq!(v.len(), 128);
|
||||
assert_eq!(model.dimension(), 128);
|
||||
|
||||
// Should be normalized
|
||||
let mag: f32 = v.iter().map(|x| x * x).sum::<f32>().sqrt();
|
||||
assert!(
|
||||
(mag - 1.0).abs() < 1e-4,
|
||||
"Expected unit vector, got magnitude {}",
|
||||
mag,
|
||||
);
|
||||
}
|
||||
}
|
||||
541
vendor/ruvector/examples/OSpipe/src/storage/vector_store.rs
vendored
Normal file
541
vendor/ruvector/examples/OSpipe/src/storage/vector_store.rs
vendored
Normal file
@@ -0,0 +1,541 @@
|
||||
//! Vector storage with cosine similarity search.
|
||||
//!
|
||||
//! This module provides two implementations:
|
||||
//!
|
||||
//! - [`VectorStore`] -- brute-force O(n) linear scan (cross-platform,
|
||||
//! works on WASM).
|
||||
//! - [`HnswVectorStore`] (native only) -- wraps ruvector-core's HNSW
|
||||
//! index for O(log n) approximate nearest-neighbor search.
|
||||
//!
|
||||
//! Both implementations support insert, search, filtered search, delete,
|
||||
//! and metadata update.
|
||||
|
||||
use crate::capture::CapturedFrame;
|
||||
use crate::config::StorageConfig;
|
||||
use crate::error::{OsPipeError, Result};
|
||||
use crate::storage::embedding::cosine_similarity;
|
||||
use chrono::{DateTime, Utc};
|
||||
use serde::{Deserialize, Serialize};
|
||||
use uuid::Uuid;
|
||||
|
||||
/// A vector embedding stored with its metadata.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct StoredEmbedding {
|
||||
/// Unique identifier matching the source frame.
|
||||
pub id: Uuid,
|
||||
/// The embedding vector.
|
||||
pub vector: Vec<f32>,
|
||||
/// JSON metadata about the source frame.
|
||||
pub metadata: serde_json::Value,
|
||||
/// When the source frame was captured.
|
||||
pub timestamp: DateTime<Utc>,
|
||||
}
|
||||
|
||||
/// A search result returned from the vector store.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct SearchResult {
|
||||
/// ID of the matched embedding.
|
||||
pub id: Uuid,
|
||||
/// Cosine similarity score (higher is more similar).
|
||||
pub score: f32,
|
||||
/// Metadata of the matched embedding.
|
||||
pub metadata: serde_json::Value,
|
||||
}
|
||||
|
||||
/// Filter criteria for narrowing search results.
|
||||
#[derive(Debug, Clone, Default)]
|
||||
pub struct SearchFilter {
|
||||
/// Filter by application name.
|
||||
pub app: Option<String>,
|
||||
/// Filter by start time (inclusive).
|
||||
pub time_start: Option<DateTime<Utc>>,
|
||||
/// Filter by end time (inclusive).
|
||||
pub time_end: Option<DateTime<Utc>>,
|
||||
/// Filter by content type (e.g., "ocr", "transcription", "ui_event").
|
||||
pub content_type: Option<String>,
|
||||
/// Filter by monitor index.
|
||||
pub monitor: Option<u32>,
|
||||
}
|
||||
|
||||
// ===========================================================================
|
||||
// VectorStore -- brute-force fallback (cross-platform)
|
||||
// ===========================================================================
|
||||
|
||||
/// In-memory vector store with brute-force cosine similarity search.
|
||||
///
|
||||
/// This is the cross-platform fallback that also works on WASM targets.
|
||||
/// On native targets, prefer [`HnswVectorStore`] for large datasets.
|
||||
pub struct VectorStore {
|
||||
config: StorageConfig,
|
||||
embeddings: Vec<StoredEmbedding>,
|
||||
dimension: usize,
|
||||
}
|
||||
|
||||
impl VectorStore {
|
||||
/// Create a new vector store with the given configuration.
|
||||
pub fn new(config: StorageConfig) -> Result<Self> {
|
||||
let dimension = config.embedding_dim;
|
||||
if dimension == 0 {
|
||||
return Err(OsPipeError::Storage(
|
||||
"embedding_dim must be greater than 0".to_string(),
|
||||
));
|
||||
}
|
||||
Ok(Self {
|
||||
config,
|
||||
embeddings: Vec::new(),
|
||||
dimension,
|
||||
})
|
||||
}
|
||||
|
||||
/// Insert a captured frame with its pre-computed embedding.
|
||||
pub fn insert(&mut self, frame: &CapturedFrame, embedding: &[f32]) -> Result<()> {
|
||||
if embedding.len() != self.dimension {
|
||||
return Err(OsPipeError::Storage(format!(
|
||||
"Expected embedding dimension {}, got {}",
|
||||
self.dimension,
|
||||
embedding.len()
|
||||
)));
|
||||
}
|
||||
|
||||
let metadata = serde_json::json!({
|
||||
"text": frame.text_content(),
|
||||
"content_type": frame.content_type(),
|
||||
"app_name": frame.metadata.app_name,
|
||||
"window_title": frame.metadata.window_title,
|
||||
"monitor_id": frame.metadata.monitor_id,
|
||||
"confidence": frame.metadata.confidence,
|
||||
});
|
||||
|
||||
self.embeddings.push(StoredEmbedding {
|
||||
id: frame.id,
|
||||
vector: embedding.to_vec(),
|
||||
metadata,
|
||||
timestamp: frame.timestamp,
|
||||
});
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Search for the k most similar embeddings to the query vector.
|
||||
pub fn search(&self, query_embedding: &[f32], k: usize) -> Result<Vec<SearchResult>> {
|
||||
if query_embedding.len() != self.dimension {
|
||||
return Err(OsPipeError::Search(format!(
|
||||
"Expected query dimension {}, got {}",
|
||||
self.dimension,
|
||||
query_embedding.len()
|
||||
)));
|
||||
}
|
||||
|
||||
let mut scored: Vec<(usize, f32)> = self
|
||||
.embeddings
|
||||
.iter()
|
||||
.enumerate()
|
||||
.map(|(i, stored)| {
|
||||
let score = cosine_similarity(query_embedding, &stored.vector);
|
||||
(i, score)
|
||||
})
|
||||
.collect();
|
||||
|
||||
// Sort by score descending
|
||||
scored.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
|
||||
scored.truncate(k);
|
||||
|
||||
Ok(scored
|
||||
.into_iter()
|
||||
.map(|(i, score)| {
|
||||
let stored = &self.embeddings[i];
|
||||
SearchResult {
|
||||
id: stored.id,
|
||||
score,
|
||||
metadata: stored.metadata.clone(),
|
||||
}
|
||||
})
|
||||
.collect())
|
||||
}
|
||||
|
||||
/// Search with metadata filtering applied before scoring.
|
||||
pub fn search_filtered(
|
||||
&self,
|
||||
query: &[f32],
|
||||
k: usize,
|
||||
filter: &SearchFilter,
|
||||
) -> Result<Vec<SearchResult>> {
|
||||
if query.len() != self.dimension {
|
||||
return Err(OsPipeError::Search(format!(
|
||||
"Expected query dimension {}, got {}",
|
||||
self.dimension,
|
||||
query.len()
|
||||
)));
|
||||
}
|
||||
|
||||
let mut scored: Vec<(usize, f32)> = self
|
||||
.embeddings
|
||||
.iter()
|
||||
.enumerate()
|
||||
.filter(|(_, stored)| matches_filter(stored, filter))
|
||||
.map(|(i, stored)| {
|
||||
let score = cosine_similarity(query, &stored.vector);
|
||||
(i, score)
|
||||
})
|
||||
.collect();
|
||||
|
||||
scored.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
|
||||
scored.truncate(k);
|
||||
|
||||
Ok(scored
|
||||
.into_iter()
|
||||
.map(|(i, score)| {
|
||||
let stored = &self.embeddings[i];
|
||||
SearchResult {
|
||||
id: stored.id,
|
||||
score,
|
||||
metadata: stored.metadata.clone(),
|
||||
}
|
||||
})
|
||||
.collect())
|
||||
}
|
||||
|
||||
/// Delete a stored embedding by its ID.
|
||||
///
|
||||
/// Returns `true` if the embedding was found and removed, `false`
|
||||
/// if no embedding with the given ID existed.
|
||||
pub fn delete(&mut self, id: &Uuid) -> Result<bool> {
|
||||
let before = self.embeddings.len();
|
||||
self.embeddings.retain(|e| e.id != *id);
|
||||
Ok(self.embeddings.len() < before)
|
||||
}
|
||||
|
||||
/// Update the metadata of a stored embedding.
|
||||
///
|
||||
/// The provided `metadata` value completely replaces the old metadata
|
||||
/// for the entry identified by `id`. Returns an error if the ID is
|
||||
/// not found.
|
||||
pub fn update_metadata(&mut self, id: &Uuid, metadata: serde_json::Value) -> Result<()> {
|
||||
match self.embeddings.iter_mut().find(|e| e.id == *id) {
|
||||
Some(entry) => {
|
||||
entry.metadata = metadata;
|
||||
Ok(())
|
||||
}
|
||||
None => Err(OsPipeError::Storage(format!(
|
||||
"No embedding found with id {}",
|
||||
id
|
||||
))),
|
||||
}
|
||||
}
|
||||
|
||||
/// Return the number of stored embeddings.
|
||||
pub fn len(&self) -> usize {
|
||||
self.embeddings.len()
|
||||
}
|
||||
|
||||
/// Return true if the store contains no embeddings.
|
||||
pub fn is_empty(&self) -> bool {
|
||||
self.embeddings.is_empty()
|
||||
}
|
||||
|
||||
/// Return the configured embedding dimension.
|
||||
pub fn dimension(&self) -> usize {
|
||||
self.dimension
|
||||
}
|
||||
|
||||
/// Return a reference to the storage configuration.
|
||||
pub fn config(&self) -> &StorageConfig {
|
||||
&self.config
|
||||
}
|
||||
|
||||
/// Get a stored embedding by its ID.
|
||||
pub fn get(&self, id: &Uuid) -> Option<&StoredEmbedding> {
|
||||
self.embeddings.iter().find(|e| e.id == *id)
|
||||
}
|
||||
}
|
||||
|
||||
// ===========================================================================
|
||||
// HnswVectorStore -- native-only HNSW-backed store
|
||||
// ===========================================================================
|
||||
|
||||
#[cfg(not(target_arch = "wasm32"))]
|
||||
mod native {
|
||||
use super::*;
|
||||
use ruvector_core::index::hnsw::HnswIndex;
|
||||
use ruvector_core::index::VectorIndex;
|
||||
use ruvector_core::types::{DistanceMetric, HnswConfig};
|
||||
use std::collections::HashMap;
|
||||
|
||||
/// HNSW-backed vector store using ruvector-core.
|
||||
///
|
||||
/// Uses approximate nearest-neighbor search for O(log n) query time.
|
||||
/// Metadata and timestamps are stored in a side-car `HashMap`
|
||||
/// alongside the HNSW index.
|
||||
pub struct HnswVectorStore {
|
||||
index: HnswIndex,
|
||||
/// Side-car storage: id -> (metadata, timestamp, vector)
|
||||
entries: HashMap<Uuid, StoredEmbedding>,
|
||||
dimension: usize,
|
||||
config: StorageConfig,
|
||||
ef_search: usize,
|
||||
}
|
||||
|
||||
impl HnswVectorStore {
|
||||
/// Create a new HNSW-backed vector store.
|
||||
pub fn new(config: StorageConfig) -> Result<Self> {
|
||||
let dimension = config.embedding_dim;
|
||||
if dimension == 0 {
|
||||
return Err(OsPipeError::Storage(
|
||||
"embedding_dim must be greater than 0".to_string(),
|
||||
));
|
||||
}
|
||||
|
||||
let hnsw_config = HnswConfig {
|
||||
m: config.hnsw_m,
|
||||
ef_construction: config.hnsw_ef_construction,
|
||||
ef_search: config.hnsw_ef_search,
|
||||
max_elements: 10_000_000,
|
||||
};
|
||||
|
||||
let index = HnswIndex::new(dimension, DistanceMetric::Cosine, hnsw_config)
|
||||
.map_err(|e| OsPipeError::Storage(format!("Failed to create HNSW index: {}", e)))?;
|
||||
|
||||
let ef_search = config.hnsw_ef_search;
|
||||
|
||||
Ok(Self {
|
||||
index,
|
||||
entries: HashMap::new(),
|
||||
dimension,
|
||||
config,
|
||||
ef_search,
|
||||
})
|
||||
}
|
||||
|
||||
/// Insert a captured frame with its pre-computed embedding.
|
||||
pub fn insert(&mut self, frame: &CapturedFrame, embedding: &[f32]) -> Result<()> {
|
||||
if embedding.len() != self.dimension {
|
||||
return Err(OsPipeError::Storage(format!(
|
||||
"Expected embedding dimension {}, got {}",
|
||||
self.dimension,
|
||||
embedding.len()
|
||||
)));
|
||||
}
|
||||
|
||||
let metadata = serde_json::json!({
|
||||
"text": frame.text_content(),
|
||||
"content_type": frame.content_type(),
|
||||
"app_name": frame.metadata.app_name,
|
||||
"window_title": frame.metadata.window_title,
|
||||
"monitor_id": frame.metadata.monitor_id,
|
||||
"confidence": frame.metadata.confidence,
|
||||
});
|
||||
|
||||
let id_str = frame.id.to_string();
|
||||
|
||||
// Insert into HNSW index
|
||||
self.index
|
||||
.add(id_str, embedding.to_vec())
|
||||
.map_err(|e| OsPipeError::Storage(format!("HNSW insert failed: {}", e)))?;
|
||||
|
||||
// Store side-car data
|
||||
self.entries.insert(
|
||||
frame.id,
|
||||
StoredEmbedding {
|
||||
id: frame.id,
|
||||
vector: embedding.to_vec(),
|
||||
metadata,
|
||||
timestamp: frame.timestamp,
|
||||
},
|
||||
);
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Search for the k most similar embeddings using HNSW ANN search.
|
||||
pub fn search(&self, query: &[f32], k: usize) -> Result<Vec<SearchResult>> {
|
||||
if query.len() != self.dimension {
|
||||
return Err(OsPipeError::Search(format!(
|
||||
"Expected query dimension {}, got {}",
|
||||
self.dimension,
|
||||
query.len()
|
||||
)));
|
||||
}
|
||||
|
||||
let hnsw_results = self
|
||||
.index
|
||||
.search_with_ef(query, k, self.ef_search)
|
||||
.map_err(|e| OsPipeError::Search(format!("HNSW search failed: {}", e)))?;
|
||||
|
||||
let mut results = Vec::with_capacity(hnsw_results.len());
|
||||
for hr in hnsw_results {
|
||||
// hr.id is a String representation of the Uuid
|
||||
if let Ok(uuid) = Uuid::parse_str(&hr.id) {
|
||||
if let Some(stored) = self.entries.get(&uuid) {
|
||||
// ruvector-core HNSW returns distance (lower = closer
|
||||
// for cosine). Convert to similarity: 1.0 - distance.
|
||||
let similarity = 1.0 - hr.score;
|
||||
results.push(SearchResult {
|
||||
id: uuid,
|
||||
score: similarity,
|
||||
metadata: stored.metadata.clone(),
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Sort descending by similarity score
|
||||
results.sort_by(|a, b| {
|
||||
b.score
|
||||
.partial_cmp(&a.score)
|
||||
.unwrap_or(std::cmp::Ordering::Equal)
|
||||
});
|
||||
|
||||
Ok(results)
|
||||
}
|
||||
|
||||
/// Search with post-filtering on metadata.
|
||||
///
|
||||
/// HNSW does not natively support metadata filters, so we
|
||||
/// over-fetch and filter after the ANN search.
|
||||
pub fn search_filtered(
|
||||
&self,
|
||||
query: &[f32],
|
||||
k: usize,
|
||||
filter: &SearchFilter,
|
||||
) -> Result<Vec<SearchResult>> {
|
||||
// Over-fetch to account for filtering
|
||||
let over_k = (k * 4).max(k + 20);
|
||||
let candidates = self.search(query, over_k)?;
|
||||
|
||||
let mut filtered: Vec<SearchResult> = candidates
|
||||
.into_iter()
|
||||
.filter(|r| {
|
||||
if let Some(stored) = self.entries.get(&r.id) {
|
||||
matches_filter(stored, filter)
|
||||
} else {
|
||||
false
|
||||
}
|
||||
})
|
||||
.take(k)
|
||||
.collect();
|
||||
|
||||
filtered.sort_by(|a, b| {
|
||||
b.score
|
||||
.partial_cmp(&a.score)
|
||||
.unwrap_or(std::cmp::Ordering::Equal)
|
||||
});
|
||||
|
||||
Ok(filtered)
|
||||
}
|
||||
|
||||
/// Delete a stored embedding by its ID.
|
||||
///
|
||||
/// Returns `true` if the embedding was found and removed, `false`
|
||||
/// otherwise. The HNSW graph link is removed via soft-delete (the
|
||||
/// underlying `hnsw_rs` does not support hard deletion).
|
||||
pub fn delete(&mut self, id: &Uuid) -> Result<bool> {
|
||||
let id_str = id.to_string();
|
||||
let removed_from_index = self
|
||||
.index
|
||||
.remove(&id_str)
|
||||
.map_err(|e| OsPipeError::Storage(format!("HNSW delete failed: {}", e)))?;
|
||||
|
||||
let removed_from_entries = self.entries.remove(id).is_some();
|
||||
|
||||
Ok(removed_from_index || removed_from_entries)
|
||||
}
|
||||
|
||||
/// Update the metadata of a stored embedding.
|
||||
///
|
||||
/// Returns an error if no embedding with the given ID exists.
|
||||
pub fn update_metadata(&mut self, id: &Uuid, metadata: serde_json::Value) -> Result<()> {
|
||||
match self.entries.get_mut(id) {
|
||||
Some(entry) => {
|
||||
entry.metadata = metadata;
|
||||
Ok(())
|
||||
}
|
||||
None => Err(OsPipeError::Storage(format!(
|
||||
"No embedding found with id {}",
|
||||
id
|
||||
))),
|
||||
}
|
||||
}
|
||||
|
||||
/// Return the number of stored embeddings.
|
||||
pub fn len(&self) -> usize {
|
||||
self.entries.len()
|
||||
}
|
||||
|
||||
/// Return true if the store is empty.
|
||||
pub fn is_empty(&self) -> bool {
|
||||
self.entries.is_empty()
|
||||
}
|
||||
|
||||
/// Return the configured embedding dimension.
|
||||
pub fn dimension(&self) -> usize {
|
||||
self.dimension
|
||||
}
|
||||
|
||||
/// Return a reference to the storage configuration.
|
||||
pub fn config(&self) -> &StorageConfig {
|
||||
&self.config
|
||||
}
|
||||
|
||||
/// Get a stored embedding by its ID.
|
||||
pub fn get(&self, id: &Uuid) -> Option<&StoredEmbedding> {
|
||||
self.entries.get(id)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(not(target_arch = "wasm32"))]
|
||||
pub use native::HnswVectorStore;
|
||||
|
||||
// ===========================================================================
|
||||
// Shared helpers
|
||||
// ===========================================================================
|
||||
|
||||
/// Check whether a stored embedding matches the given filter.
|
||||
fn matches_filter(stored: &StoredEmbedding, filter: &SearchFilter) -> bool {
|
||||
if let Some(ref app) = filter.app {
|
||||
let stored_app = stored
|
||||
.metadata
|
||||
.get("app_name")
|
||||
.and_then(|v| v.as_str())
|
||||
.unwrap_or("");
|
||||
if stored_app != app {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
if let Some(start) = filter.time_start {
|
||||
if stored.timestamp < start {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
if let Some(end) = filter.time_end {
|
||||
if stored.timestamp > end {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
if let Some(ref ct) = filter.content_type {
|
||||
let stored_ct = stored
|
||||
.metadata
|
||||
.get("content_type")
|
||||
.and_then(|v| v.as_str())
|
||||
.unwrap_or("");
|
||||
if stored_ct != ct {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
if let Some(monitor) = filter.monitor {
|
||||
let stored_monitor = stored
|
||||
.metadata
|
||||
.get("monitor_id")
|
||||
.and_then(|v| v.as_u64())
|
||||
.map(|v| v as u32);
|
||||
if stored_monitor != Some(monitor) {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
true
|
||||
}
|
||||
265
vendor/ruvector/examples/OSpipe/src/wasm/bindings.rs
vendored
Normal file
265
vendor/ruvector/examples/OSpipe/src/wasm/bindings.rs
vendored
Normal file
@@ -0,0 +1,265 @@
|
||||
//! WASM-bindgen exports for OSpipe browser usage.
|
||||
//!
|
||||
//! This module exposes a self-contained vector store that runs entirely in the
|
||||
//! browser via WebAssembly. It supports embedding insertion, semantic search
|
||||
//! with optional time-range filtering, deduplication checks, simple text
|
||||
//! embedding (hash-based, suitable for demos), content safety checks, and
|
||||
//! query routing heuristics.
|
||||
|
||||
use serde::{Deserialize, Serialize};
|
||||
use wasm_bindgen::prelude::*;
|
||||
|
||||
use super::helpers;
|
||||
|
||||
/// Initialize WASM module: installs `console_error_panic_hook` so that Rust
|
||||
/// panics produce readable error messages in the browser developer console
|
||||
/// instead of the default `unreachable` with no context.
|
||||
#[wasm_bindgen(start)]
|
||||
pub fn init() {
|
||||
#[cfg(feature = "console_error_panic_hook")]
|
||||
console_error_panic_hook::set_once();
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Internal data structures
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// A single stored embedding with metadata.
|
||||
struct WasmEmbedding {
|
||||
id: String,
|
||||
vector: Vec<f32>,
|
||||
metadata: String, // JSON string
|
||||
timestamp: f64, // Unix milliseconds
|
||||
}
|
||||
|
||||
/// A search result returned to JavaScript.
|
||||
#[derive(Serialize, Deserialize)]
|
||||
struct SearchHit {
|
||||
id: String,
|
||||
score: f64,
|
||||
metadata: String,
|
||||
timestamp: f64,
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Public WASM API
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
/// OSpipe WASM -- browser-based personal AI memory search.
|
||||
#[wasm_bindgen]
|
||||
pub struct OsPipeWasm {
|
||||
dimension: usize,
|
||||
embeddings: Vec<WasmEmbedding>,
|
||||
}
|
||||
|
||||
#[wasm_bindgen]
|
||||
impl OsPipeWasm {
|
||||
// -- lifecycle ---------------------------------------------------------
|
||||
|
||||
/// Create a new OsPipeWasm instance with the given embedding dimension.
|
||||
#[wasm_bindgen(constructor)]
|
||||
pub fn new(dimension: usize) -> Self {
|
||||
Self {
|
||||
dimension,
|
||||
embeddings: Vec::new(),
|
||||
}
|
||||
}
|
||||
|
||||
// -- insertion ---------------------------------------------------------
|
||||
|
||||
/// Insert a frame embedding into the store.
|
||||
///
|
||||
/// * `id` - Unique identifier for this frame.
|
||||
/// * `embedding` - Float32 vector whose length must match `dimension`.
|
||||
/// * `metadata` - Arbitrary JSON string attached to this frame.
|
||||
/// * `timestamp` - Unix timestamp in milliseconds.
|
||||
pub fn insert(
|
||||
&mut self,
|
||||
id: &str,
|
||||
embedding: &[f32],
|
||||
metadata: &str,
|
||||
timestamp: f64,
|
||||
) -> Result<(), JsValue> {
|
||||
if embedding.len() != self.dimension {
|
||||
return Err(JsValue::from_str(&format!(
|
||||
"Embedding dimension mismatch: expected {}, got {}",
|
||||
self.dimension,
|
||||
embedding.len()
|
||||
)));
|
||||
}
|
||||
self.embeddings.push(WasmEmbedding {
|
||||
id: id.to_string(),
|
||||
vector: embedding.to_vec(),
|
||||
metadata: metadata.to_string(),
|
||||
timestamp,
|
||||
});
|
||||
Ok(())
|
||||
}
|
||||
|
||||
// -- search ------------------------------------------------------------
|
||||
|
||||
/// Semantic search by embedding vector. Returns the top-k results as a
|
||||
/// JSON-serialized `JsValue` array of `{ id, score, metadata, timestamp }`.
|
||||
pub fn search(&self, query_embedding: &[f32], k: usize) -> Result<JsValue, JsValue> {
|
||||
if query_embedding.len() != self.dimension {
|
||||
return Err(JsValue::from_str(&format!(
|
||||
"Query dimension mismatch: expected {}, got {}",
|
||||
self.dimension,
|
||||
query_embedding.len()
|
||||
)));
|
||||
}
|
||||
|
||||
let mut scored: Vec<(usize, f32)> = self
|
||||
.embeddings
|
||||
.iter()
|
||||
.enumerate()
|
||||
.map(|(i, e)| (i, helpers::cosine_similarity(query_embedding, &e.vector)))
|
||||
.collect();
|
||||
|
||||
// Sort descending by similarity.
|
||||
scored.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
|
||||
|
||||
let hits: Vec<SearchHit> = scored
|
||||
.into_iter()
|
||||
.take(k)
|
||||
.map(|(i, score)| {
|
||||
let e = &self.embeddings[i];
|
||||
SearchHit {
|
||||
id: e.id.clone(),
|
||||
score: score as f64,
|
||||
metadata: e.metadata.clone(),
|
||||
timestamp: e.timestamp,
|
||||
}
|
||||
})
|
||||
.collect();
|
||||
|
||||
serde_wasm_bindgen::to_value(&hits).map_err(|e| JsValue::from_str(&e.to_string()))
|
||||
}
|
||||
|
||||
/// Search with a time-range filter. Only embeddings whose timestamp falls
|
||||
/// within `[start_time, end_time]` (inclusive) are considered.
|
||||
pub fn search_filtered(
|
||||
&self,
|
||||
query_embedding: &[f32],
|
||||
k: usize,
|
||||
start_time: f64,
|
||||
end_time: f64,
|
||||
) -> Result<JsValue, JsValue> {
|
||||
if query_embedding.len() != self.dimension {
|
||||
return Err(JsValue::from_str(&format!(
|
||||
"Query dimension mismatch: expected {}, got {}",
|
||||
self.dimension,
|
||||
query_embedding.len()
|
||||
)));
|
||||
}
|
||||
|
||||
let mut scored: Vec<(usize, f32)> = self
|
||||
.embeddings
|
||||
.iter()
|
||||
.enumerate()
|
||||
.filter(|(_, e)| e.timestamp >= start_time && e.timestamp <= end_time)
|
||||
.map(|(i, e)| (i, helpers::cosine_similarity(query_embedding, &e.vector)))
|
||||
.collect();
|
||||
|
||||
scored.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
|
||||
|
||||
let hits: Vec<SearchHit> = scored
|
||||
.into_iter()
|
||||
.take(k)
|
||||
.map(|(i, score)| {
|
||||
let e = &self.embeddings[i];
|
||||
SearchHit {
|
||||
id: e.id.clone(),
|
||||
score: score as f64,
|
||||
metadata: e.metadata.clone(),
|
||||
timestamp: e.timestamp,
|
||||
}
|
||||
})
|
||||
.collect();
|
||||
|
||||
serde_wasm_bindgen::to_value(&hits).map_err(|e| JsValue::from_str(&e.to_string()))
|
||||
}
|
||||
|
||||
// -- deduplication -----------------------------------------------------
|
||||
|
||||
/// Check whether `embedding` is a near-duplicate of any stored embedding.
|
||||
///
|
||||
/// Returns `true` when the cosine similarity to any existing embedding is
|
||||
/// greater than or equal to `threshold`.
|
||||
pub fn is_duplicate(&self, embedding: &[f32], threshold: f32) -> bool {
|
||||
self.embeddings
|
||||
.iter()
|
||||
.any(|e| helpers::cosine_similarity(embedding, &e.vector) >= threshold)
|
||||
}
|
||||
|
||||
// -- stats / accessors -------------------------------------------------
|
||||
|
||||
/// Number of stored embeddings.
|
||||
pub fn len(&self) -> usize {
|
||||
self.embeddings.len()
|
||||
}
|
||||
|
||||
/// Returns true if no embeddings are stored.
|
||||
pub fn is_empty(&self) -> bool {
|
||||
self.embeddings.is_empty()
|
||||
}
|
||||
|
||||
/// Return pipeline statistics as a JSON string.
|
||||
pub fn stats(&self) -> String {
|
||||
serde_json::json!({
|
||||
"dimension": self.dimension,
|
||||
"total_embeddings": self.embeddings.len(),
|
||||
"memory_estimate_bytes": self.embeddings.len() * (self.dimension * 4 + 128),
|
||||
})
|
||||
.to_string()
|
||||
}
|
||||
|
||||
// -- text embedding (demo / hash-based) --------------------------------
|
||||
|
||||
/// Generate a simple deterministic embedding from text.
|
||||
///
|
||||
/// This uses a hash-based approach and is **not** a real neural embedding.
|
||||
/// Suitable for demos and testing only.
|
||||
pub fn embed_text(&self, text: &str) -> Vec<f32> {
|
||||
helpers::hash_embed(text, self.dimension)
|
||||
}
|
||||
|
||||
/// Batch-embed multiple texts.
|
||||
///
|
||||
/// `texts` must be a JS `Array<string>`. Returns a JS `Array<Float32Array>`.
|
||||
pub fn batch_embed(&self, texts: JsValue) -> Result<JsValue, JsValue> {
|
||||
let text_list: Vec<String> = serde_wasm_bindgen::from_value(texts)
|
||||
.map_err(|e| JsValue::from_str(&format!("Failed to deserialize texts: {e}")))?;
|
||||
|
||||
let results: Vec<Vec<f32>> = text_list
|
||||
.iter()
|
||||
.map(|t| helpers::hash_embed(t, self.dimension))
|
||||
.collect();
|
||||
|
||||
serde_wasm_bindgen::to_value(&results).map_err(|e| JsValue::from_str(&e.to_string()))
|
||||
}
|
||||
|
||||
// -- safety ------------------------------------------------------------
|
||||
|
||||
/// Run a lightweight safety check on `content`.
|
||||
///
|
||||
/// Returns one of:
|
||||
/// - `"deny"` -- content contains patterns that should not be stored
|
||||
/// (e.g. credit card numbers, SSNs).
|
||||
/// - `"redact"` -- content contains potentially sensitive information
|
||||
/// that could be redacted.
|
||||
/// - `"allow"` -- content appears safe.
|
||||
pub fn safety_check(&self, content: &str) -> String {
|
||||
helpers::safety_classify(content).to_string()
|
||||
}
|
||||
|
||||
// -- query routing -----------------------------------------------------
|
||||
|
||||
/// Route a query string to the optimal search backend based on simple
|
||||
/// keyword heuristics.
|
||||
///
|
||||
/// Returns one of: `"Graph"`, `"Temporal"`, `"Keyword"`, `"Semantic"`.
|
||||
pub fn route_query(&self, query: &str) -> String {
|
||||
helpers::route_query(query).to_string()
|
||||
}
|
||||
}
|
||||
461
vendor/ruvector/examples/OSpipe/src/wasm/helpers.rs
vendored
Normal file
461
vendor/ruvector/examples/OSpipe/src/wasm/helpers.rs
vendored
Normal file
@@ -0,0 +1,461 @@
|
||||
//! Pure helper functions used by the WASM bindings.
|
||||
//!
|
||||
//! These functions have no WASM dependencies and can be tested on any target.
|
||||
|
||||
/// Cosine similarity between two vectors.
|
||||
///
|
||||
/// Returns 0.0 when either vector has zero magnitude.
|
||||
pub fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
|
||||
debug_assert_eq!(a.len(), b.len(), "vectors must be same length");
|
||||
|
||||
let mut dot: f32 = 0.0;
|
||||
let mut mag_a: f32 = 0.0;
|
||||
let mut mag_b: f32 = 0.0;
|
||||
|
||||
for i in 0..a.len() {
|
||||
dot += a[i] * b[i];
|
||||
mag_a += a[i] * a[i];
|
||||
mag_b += b[i] * b[i];
|
||||
}
|
||||
|
||||
let denom = mag_a.sqrt() * mag_b.sqrt();
|
||||
if denom == 0.0 {
|
||||
0.0
|
||||
} else {
|
||||
dot / denom
|
||||
}
|
||||
}
|
||||
|
||||
/// Produce a deterministic pseudo-embedding from text using a simple hash.
|
||||
///
|
||||
/// The algorithm:
|
||||
/// 1. Hash each character position into a seed.
|
||||
/// 2. Use the seed to generate a float in [-1, 1].
|
||||
/// 3. L2-normalise the resulting vector.
|
||||
///
|
||||
/// This is NOT a real embedding model -- it is only useful for demos and
|
||||
/// testing that the WASM plumbing works end-to-end.
|
||||
pub fn hash_embed(text: &str, dimension: usize) -> Vec<f32> {
|
||||
let mut vec = vec![0.0f32; dimension];
|
||||
let bytes = text.as_bytes();
|
||||
|
||||
for (i, slot) in vec.iter_mut().enumerate() {
|
||||
// Mix byte values into the slot.
|
||||
let mut h: u64 = 0xcbf29ce484222325; // FNV-1a offset basis
|
||||
for (j, &b) in bytes.iter().enumerate() {
|
||||
h ^= (b as u64)
|
||||
.wrapping_add((i as u64).wrapping_mul(31))
|
||||
.wrapping_add(j as u64);
|
||||
h = h.wrapping_mul(0x100000001b3); // FNV-1a prime
|
||||
}
|
||||
// Map to [-1, 1].
|
||||
*slot = ((h as i64) as f64 / i64::MAX as f64) as f32;
|
||||
}
|
||||
|
||||
// L2 normalise.
|
||||
let mag: f32 = vec.iter().map(|v| v * v).sum::<f32>().sqrt();
|
||||
if mag > 0.0 {
|
||||
for v in &mut vec {
|
||||
*v /= mag;
|
||||
}
|
||||
}
|
||||
|
||||
vec
|
||||
}
|
||||
|
||||
/// Check for credit-card-like patterns: 4 groups of 4 digits separated by
|
||||
/// spaces or dashes (or no separator).
|
||||
pub fn has_credit_card_pattern(content: &str) -> bool {
|
||||
// Strategy: scan for sequences of 16 digits (possibly with separators).
|
||||
let digits_only: String = content.chars().filter(|c| c.is_ascii_digit()).collect();
|
||||
|
||||
// Quick check: must have at least 16 digits somewhere.
|
||||
if digits_only.len() < 16 {
|
||||
return false;
|
||||
}
|
||||
|
||||
// Look for the formatted pattern: DDDD[-/ ]DDDD[-/ ]DDDD[-/ ]DDDD
|
||||
// We do a simple windowed scan on the original string.
|
||||
let chars: Vec<char> = content.chars().collect();
|
||||
let len = chars.len();
|
||||
let mut i = 0;
|
||||
|
||||
while i < len {
|
||||
if let Some(end) = try_parse_cc_at(&chars, i) {
|
||||
// Verify the group doesn't continue with more digits (avoid
|
||||
// matching longer numeric strings that aren't cards).
|
||||
if end >= len || !chars[end].is_ascii_digit() {
|
||||
// Also make sure it didn't start as part of a longer number.
|
||||
if i == 0 || !chars[i - 1].is_ascii_digit() {
|
||||
return true;
|
||||
}
|
||||
}
|
||||
i = end;
|
||||
} else {
|
||||
i += 1;
|
||||
}
|
||||
}
|
||||
|
||||
false
|
||||
}
|
||||
|
||||
/// Try to parse a credit-card-like pattern starting at position `start`.
|
||||
/// Returns the index past the last consumed character on success.
|
||||
fn try_parse_cc_at(chars: &[char], start: usize) -> Option<usize> {
|
||||
let mut pos = start;
|
||||
for group in 0..4 {
|
||||
// Expect 4 digits.
|
||||
for _ in 0..4 {
|
||||
if pos >= chars.len() || !chars[pos].is_ascii_digit() {
|
||||
return None;
|
||||
}
|
||||
pos += 1;
|
||||
}
|
||||
// After the first 3 groups, allow an optional separator.
|
||||
if group < 3 && pos < chars.len() && (chars[pos] == '-' || chars[pos] == ' ') {
|
||||
pos += 1;
|
||||
}
|
||||
}
|
||||
Some(pos)
|
||||
}
|
||||
|
||||
/// Check for SSN-like patterns: XXX-XX-XXXX
|
||||
pub fn has_ssn_pattern(content: &str) -> bool {
|
||||
let chars: Vec<char> = content.chars().collect();
|
||||
let len = chars.len();
|
||||
|
||||
// Pattern length: 3 + 1 + 2 + 1 + 4 = 11
|
||||
if len < 11 {
|
||||
return false;
|
||||
}
|
||||
|
||||
for i in 0..=len - 11 {
|
||||
// Must not be preceded by a digit.
|
||||
if i > 0 && chars[i - 1].is_ascii_digit() {
|
||||
continue;
|
||||
}
|
||||
// Must not be followed by a digit.
|
||||
if i + 11 < len && chars[i + 11].is_ascii_digit() {
|
||||
continue;
|
||||
}
|
||||
|
||||
if chars[i].is_ascii_digit()
|
||||
&& chars[i + 1].is_ascii_digit()
|
||||
&& chars[i + 2].is_ascii_digit()
|
||||
&& chars[i + 3] == '-'
|
||||
&& chars[i + 4].is_ascii_digit()
|
||||
&& chars[i + 5].is_ascii_digit()
|
||||
&& chars[i + 6] == '-'
|
||||
&& chars[i + 7].is_ascii_digit()
|
||||
&& chars[i + 8].is_ascii_digit()
|
||||
&& chars[i + 9].is_ascii_digit()
|
||||
&& chars[i + 10].is_ascii_digit()
|
||||
{
|
||||
return true;
|
||||
}
|
||||
}
|
||||
|
||||
false
|
||||
}
|
||||
|
||||
/// Simple safety classification for content.
|
||||
///
|
||||
/// Returns `"deny"`, `"redact"`, or `"allow"`.
|
||||
///
|
||||
/// Classification matches native `SafetyGate::check`:
|
||||
/// - Credit card patterns -> "redact"
|
||||
/// - SSN patterns -> "redact"
|
||||
/// - Email patterns -> "redact"
|
||||
/// - Custom sensitive keywords -> "deny"
|
||||
pub fn safety_classify(content: &str) -> &'static str {
|
||||
// PII patterns are redacted (matching native SafetyGate behavior)
|
||||
if has_credit_card_pattern(content) {
|
||||
return "redact";
|
||||
}
|
||||
if has_ssn_pattern(content) {
|
||||
return "redact";
|
||||
}
|
||||
if has_email_pattern(content) {
|
||||
return "redact";
|
||||
}
|
||||
|
||||
// Custom sensitive keywords are denied (matching native custom_patterns -> Deny)
|
||||
let lower = content.to_lowercase();
|
||||
let deny_keywords = [
|
||||
"password",
|
||||
"secret",
|
||||
"api_key",
|
||||
"api-key",
|
||||
"apikey",
|
||||
"token",
|
||||
"private_key",
|
||||
"private-key",
|
||||
];
|
||||
for kw in &deny_keywords {
|
||||
if lower.contains(kw) {
|
||||
return "deny";
|
||||
}
|
||||
}
|
||||
|
||||
"allow"
|
||||
}
|
||||
|
||||
/// Check for email-like patterns: local@domain.tld
|
||||
pub fn has_email_pattern(content: &str) -> bool {
|
||||
let chars: Vec<char> = content.chars().collect();
|
||||
let len = chars.len();
|
||||
|
||||
for i in 0..len {
|
||||
if chars[i] == '@' {
|
||||
// Must have at least one local-part char before '@'
|
||||
if i == 0 || chars[i - 1].is_whitespace() {
|
||||
continue;
|
||||
}
|
||||
// Must have at least one domain char and a dot after '@'
|
||||
if i + 1 >= len || chars[i + 1].is_whitespace() {
|
||||
continue;
|
||||
}
|
||||
// Scan backwards to find start of local part
|
||||
let mut start = i;
|
||||
while start > 0 && is_email_char(chars[start - 1]) {
|
||||
start -= 1;
|
||||
}
|
||||
if start == i {
|
||||
continue;
|
||||
}
|
||||
// Scan forwards to find end of domain
|
||||
let mut end = i + 1;
|
||||
let mut has_dot = false;
|
||||
while end < len && is_domain_char(chars[end]) {
|
||||
if chars[end] == '.' {
|
||||
has_dot = true;
|
||||
}
|
||||
end += 1;
|
||||
}
|
||||
if has_dot && end > i + 3 {
|
||||
return true;
|
||||
}
|
||||
}
|
||||
}
|
||||
false
|
||||
}
|
||||
|
||||
fn is_email_char(c: char) -> bool {
|
||||
c.is_ascii_alphanumeric() || c == '.' || c == '+' || c == '-' || c == '_'
|
||||
}
|
||||
|
||||
fn is_domain_char(c: char) -> bool {
|
||||
c.is_ascii_alphanumeric() || c == '.' || c == '-'
|
||||
}
|
||||
|
||||
/// Route a query string to the optimal search backend.
|
||||
///
|
||||
/// Returns `"Temporal"`, `"Graph"`, `"Keyword"`, or `"Hybrid"`.
|
||||
///
|
||||
/// Routing heuristics (matching native `QueryRouter::route`):
|
||||
/// - Temporal keywords ("yesterday", "last week", etc.) -> Temporal
|
||||
/// - Graph keywords ("related to", "connected to", etc.) -> Graph
|
||||
/// - Quoted exact phrases -> Keyword
|
||||
/// - Short queries (1-2 words) -> Keyword
|
||||
/// - Everything else -> Hybrid
|
||||
pub fn route_query(query: &str) -> &'static str {
|
||||
let lower = query.to_lowercase();
|
||||
let word_count = lower.split_whitespace().count();
|
||||
|
||||
// Temporal patterns (checked first, matching native router order)
|
||||
let temporal_keywords = [
|
||||
"yesterday",
|
||||
"last week",
|
||||
"last month",
|
||||
"today",
|
||||
"this morning",
|
||||
"this afternoon",
|
||||
"hours ago",
|
||||
"minutes ago",
|
||||
"days ago",
|
||||
"between",
|
||||
"before",
|
||||
"after",
|
||||
];
|
||||
for kw in &temporal_keywords {
|
||||
if lower.contains(kw) {
|
||||
return "Temporal";
|
||||
}
|
||||
}
|
||||
|
||||
// Graph patterns
|
||||
let graph_keywords = [
|
||||
"related to",
|
||||
"connected to",
|
||||
"linked with",
|
||||
"associated with",
|
||||
"relationship between",
|
||||
];
|
||||
for kw in &graph_keywords {
|
||||
if lower.contains(kw) {
|
||||
return "Graph";
|
||||
}
|
||||
}
|
||||
|
||||
// Exact phrase (quoted)
|
||||
if query.starts_with('"') && query.ends_with('"') {
|
||||
return "Keyword";
|
||||
}
|
||||
|
||||
// Very short queries are better served by keyword
|
||||
if word_count <= 2 {
|
||||
return "Keyword";
|
||||
}
|
||||
|
||||
// Default: hybrid combines the best of both
|
||||
"Hybrid"
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Unit tests
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn test_cosine_similarity_identical() {
|
||||
let v = vec![1.0, 2.0, 3.0];
|
||||
let sim = cosine_similarity(&v, &v);
|
||||
assert!((sim - 1.0).abs() < 1e-6);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_cosine_similarity_orthogonal() {
|
||||
let a = vec![1.0, 0.0];
|
||||
let b = vec![0.0, 1.0];
|
||||
let sim = cosine_similarity(&a, &b);
|
||||
assert!(sim.abs() < 1e-6);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_cosine_similarity_opposite() {
|
||||
let a = vec![1.0, 0.0];
|
||||
let b = vec![-1.0, 0.0];
|
||||
let sim = cosine_similarity(&a, &b);
|
||||
assert!((sim + 1.0).abs() < 1e-6);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_cosine_similarity_zero_vector() {
|
||||
let a = vec![0.0, 0.0];
|
||||
let b = vec![1.0, 2.0];
|
||||
assert_eq!(cosine_similarity(&a, &b), 0.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_hash_embed_deterministic() {
|
||||
let v1 = hash_embed("hello world", 128);
|
||||
let v2 = hash_embed("hello world", 128);
|
||||
assert_eq!(v1, v2);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_hash_embed_normalized() {
|
||||
let v = hash_embed("test text", 64);
|
||||
let mag: f32 = v.iter().map(|x| x * x).sum::<f32>().sqrt();
|
||||
assert!(
|
||||
(mag - 1.0).abs() < 1e-4,
|
||||
"magnitude should be ~1.0, got {mag}"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_hash_embed_different_texts_differ() {
|
||||
let v1 = hash_embed("hello", 64);
|
||||
let v2 = hash_embed("world", 64);
|
||||
assert_ne!(v1, v2);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_has_credit_card_pattern() {
|
||||
assert!(has_credit_card_pattern("my card is 1234 5678 9012 3456"));
|
||||
assert!(has_credit_card_pattern("cc: 1234-5678-9012-3456"));
|
||||
assert!(has_credit_card_pattern("number 1234567890123456 here"));
|
||||
assert!(!has_credit_card_pattern("short 123456"));
|
||||
assert!(!has_credit_card_pattern("no cards here"));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_has_ssn_pattern() {
|
||||
assert!(has_ssn_pattern("ssn is 123-45-6789"));
|
||||
assert!(has_ssn_pattern("start 999-99-9999 end"));
|
||||
assert!(!has_ssn_pattern("not a ssn 12-345-6789"));
|
||||
assert!(!has_ssn_pattern("1234-56-7890")); // preceded by extra digit
|
||||
assert!(!has_ssn_pattern("no ssn here"));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_safety_classify_redact_cc() {
|
||||
assert_eq!(safety_classify("pay with 4111-1111-1111-1111"), "redact");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_safety_classify_redact_ssn() {
|
||||
assert_eq!(safety_classify("my ssn 123-45-6789"), "redact");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_safety_classify_redact_email() {
|
||||
assert_eq!(safety_classify("contact user@example.com"), "redact");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_safety_classify_deny_password() {
|
||||
assert_eq!(safety_classify("my password is foo"), "deny");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_safety_classify_deny_api_key() {
|
||||
assert_eq!(safety_classify("api_key: sk-abc123"), "deny");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_safety_classify_allow() {
|
||||
assert_eq!(safety_classify("the weather is nice"), "allow");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_has_email_pattern() {
|
||||
assert!(has_email_pattern("contact user@example.com please"));
|
||||
assert!(has_email_pattern("email: alice@test.org"));
|
||||
assert!(!has_email_pattern("not an email"));
|
||||
assert!(!has_email_pattern("@ alone"));
|
||||
assert!(!has_email_pattern("no@d"));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_route_query_temporal() {
|
||||
assert_eq!(route_query("what happened yesterday"), "Temporal");
|
||||
assert_eq!(route_query("show me events from last week"), "Temporal");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_route_query_graph() {
|
||||
assert_eq!(route_query("documents related to authentication"), "Graph");
|
||||
assert_eq!(route_query("things connected to the API module"), "Graph");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_route_query_keyword_quoted() {
|
||||
assert_eq!(route_query("\"exact phrase search\""), "Keyword");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_route_query_keyword_short() {
|
||||
assert_eq!(route_query("rust programming"), "Keyword");
|
||||
assert_eq!(route_query("hello"), "Keyword");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_route_query_hybrid() {
|
||||
assert_eq!(route_query("something about machine learning"), "Hybrid");
|
||||
assert_eq!(route_query("explain how embeddings work"), "Hybrid");
|
||||
}
|
||||
}
|
||||
15
vendor/ruvector/examples/OSpipe/src/wasm/mod.rs
vendored
Normal file
15
vendor/ruvector/examples/OSpipe/src/wasm/mod.rs
vendored
Normal file
@@ -0,0 +1,15 @@
|
||||
//! WASM bindings for OSpipe.
|
||||
//!
|
||||
//! Provides browser-based personal AI memory search using vector embeddings.
|
||||
//!
|
||||
//! - [`helpers`] - Pure helper functions (cosine similarity, hashing, safety
|
||||
//! checks, query routing) that are available on all targets for testing.
|
||||
//! - `bindings` - wasm-bindgen exports, gated behind `target_arch = "wasm32"`.
|
||||
|
||||
/// Pure helper functions with no WASM dependencies.
|
||||
/// Always compiled so that unit tests can run on the host target.
|
||||
pub mod helpers;
|
||||
|
||||
/// wasm-bindgen exports. Only compiled for the `wasm32` target.
|
||||
#[cfg(target_arch = "wasm32")]
|
||||
pub mod bindings;
|
||||
1655
vendor/ruvector/examples/OSpipe/tests/integration.rs
vendored
Normal file
1655
vendor/ruvector/examples/OSpipe/tests/integration.rs
vendored
Normal file
File diff suppressed because it is too large
Load Diff
279
vendor/ruvector/examples/OSpipe/tests/wasm.rs
vendored
Normal file
279
vendor/ruvector/examples/OSpipe/tests/wasm.rs
vendored
Normal file
@@ -0,0 +1,279 @@
|
||||
//! WASM integration tests for OSpipe.
|
||||
//!
|
||||
//! These tests run in a browser-like environment using `wasm-bindgen-test`.
|
||||
//! Execute with:
|
||||
//!
|
||||
//! ```bash
|
||||
//! wasm-pack test --headless --chrome -- --test wasm
|
||||
//! ```
|
||||
|
||||
#![cfg(target_arch = "wasm32")]
|
||||
|
||||
use wasm_bindgen::JsValue;
|
||||
use wasm_bindgen_test::*;
|
||||
|
||||
wasm_bindgen_test_configure!(run_in_browser);
|
||||
|
||||
use ospipe::wasm::bindings::OsPipeWasm;
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Construction
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[wasm_bindgen_test]
|
||||
fn test_create_instance() {
|
||||
let instance = OsPipeWasm::new(384);
|
||||
assert_eq!(instance.len(), 0);
|
||||
assert!(instance.is_empty());
|
||||
}
|
||||
|
||||
#[wasm_bindgen_test]
|
||||
fn test_create_with_custom_dimension() {
|
||||
let instance = OsPipeWasm::new(128);
|
||||
assert_eq!(instance.len(), 0);
|
||||
|
||||
let stats_json = instance.stats();
|
||||
assert!(
|
||||
stats_json.contains("\"dimension\":128"),
|
||||
"Stats should report dimension 128, got: {}",
|
||||
stats_json
|
||||
);
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Insert + Search roundtrip
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[wasm_bindgen_test]
|
||||
fn test_insert_and_search_roundtrip() {
|
||||
let mut instance = OsPipeWasm::new(4);
|
||||
|
||||
// Insert two vectors.
|
||||
let emb_a: Vec<f32> = vec![1.0, 0.0, 0.0, 0.0];
|
||||
let emb_b: Vec<f32> = vec![0.0, 1.0, 0.0, 0.0];
|
||||
|
||||
instance
|
||||
.insert("a", &emb_a, r#"{"label":"a"}"#, 1000.0)
|
||||
.expect("insert a");
|
||||
instance
|
||||
.insert("b", &emb_b, r#"{"label":"b"}"#, 2000.0)
|
||||
.expect("insert b");
|
||||
|
||||
assert_eq!(instance.len(), 2);
|
||||
assert!(!instance.is_empty());
|
||||
|
||||
// Searching with emb_a should return "a" as the top hit.
|
||||
let results: JsValue = instance.search(&emb_a, 2).expect("search");
|
||||
let results_str = js_sys::JSON::stringify(&results)
|
||||
.expect("stringify")
|
||||
.as_string()
|
||||
.expect("as_string");
|
||||
|
||||
assert!(
|
||||
results_str.contains("\"id\":\"a\""),
|
||||
"Top result should be 'a', got: {}",
|
||||
results_str
|
||||
);
|
||||
}
|
||||
|
||||
#[wasm_bindgen_test]
|
||||
fn test_insert_dimension_mismatch() {
|
||||
let mut instance = OsPipeWasm::new(4);
|
||||
let wrong_dim: Vec<f32> = vec![1.0, 2.0]; // dimension 2, expects 4
|
||||
|
||||
let result = instance.insert("bad", &wrong_dim, "{}", 0.0);
|
||||
assert!(result.is_err(), "Should reject mismatched dimension");
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Filtered search
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[wasm_bindgen_test]
|
||||
fn test_search_filtered_by_time() {
|
||||
let mut instance = OsPipeWasm::new(4);
|
||||
|
||||
let emb: Vec<f32> = vec![1.0, 0.0, 0.0, 0.0];
|
||||
instance
|
||||
.insert("early", &emb, "{}", 1000.0)
|
||||
.expect("insert early");
|
||||
instance
|
||||
.insert("late", &emb, "{}", 5000.0)
|
||||
.expect("insert late");
|
||||
|
||||
// Filter to only the early entry (timestamp range [0, 2000]).
|
||||
let results: JsValue = instance
|
||||
.search_filtered(&emb, 10, 0.0, 2000.0)
|
||||
.expect("search_filtered");
|
||||
let results_str = js_sys::JSON::stringify(&results)
|
||||
.expect("stringify")
|
||||
.as_string()
|
||||
.expect("as_string");
|
||||
|
||||
assert!(
|
||||
results_str.contains("\"id\":\"early\""),
|
||||
"Filtered results should include 'early', got: {}",
|
||||
results_str
|
||||
);
|
||||
assert!(
|
||||
!results_str.contains("\"id\":\"late\""),
|
||||
"Filtered results should exclude 'late', got: {}",
|
||||
results_str
|
||||
);
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// embed_text
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[wasm_bindgen_test]
|
||||
fn test_embed_text_returns_correct_dimension() {
|
||||
let instance = OsPipeWasm::new(384);
|
||||
let embedding = instance.embed_text("hello world");
|
||||
assert_eq!(
|
||||
embedding.len(),
|
||||
384,
|
||||
"embed_text should return a vector of the configured dimension"
|
||||
);
|
||||
}
|
||||
|
||||
#[wasm_bindgen_test]
|
||||
fn test_embed_text_is_deterministic() {
|
||||
let instance = OsPipeWasm::new(64);
|
||||
let a = instance.embed_text("test input");
|
||||
let b = instance.embed_text("test input");
|
||||
assert_eq!(a, b, "Same input text should produce identical embeddings");
|
||||
}
|
||||
|
||||
#[wasm_bindgen_test]
|
||||
fn test_embed_text_different_inputs_differ() {
|
||||
let instance = OsPipeWasm::new(64);
|
||||
let a = instance.embed_text("alpha");
|
||||
let b = instance.embed_text("beta");
|
||||
assert_ne!(a, b, "Different inputs should produce different embeddings");
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// safety_check
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[wasm_bindgen_test]
|
||||
fn test_safety_check_allow() {
|
||||
let instance = OsPipeWasm::new(4);
|
||||
let decision = instance.safety_check("the weather is nice today");
|
||||
assert_eq!(decision, "allow");
|
||||
}
|
||||
|
||||
#[wasm_bindgen_test]
|
||||
fn test_safety_check_deny_credit_card() {
|
||||
let instance = OsPipeWasm::new(4);
|
||||
let decision = instance.safety_check("card number 4111-1111-1111-1111");
|
||||
assert_eq!(decision, "deny");
|
||||
}
|
||||
|
||||
#[wasm_bindgen_test]
|
||||
fn test_safety_check_deny_ssn() {
|
||||
let instance = OsPipeWasm::new(4);
|
||||
let decision = instance.safety_check("my ssn is 123-45-6789");
|
||||
assert_eq!(decision, "deny");
|
||||
}
|
||||
|
||||
#[wasm_bindgen_test]
|
||||
fn test_safety_check_redact_password() {
|
||||
let instance = OsPipeWasm::new(4);
|
||||
let decision = instance.safety_check("my password is hunter2");
|
||||
assert_eq!(decision, "redact");
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// route_query
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[wasm_bindgen_test]
|
||||
fn test_route_query_temporal() {
|
||||
let instance = OsPipeWasm::new(4);
|
||||
let route = instance.route_query("what happened yesterday");
|
||||
assert_eq!(route, "Temporal");
|
||||
}
|
||||
|
||||
#[wasm_bindgen_test]
|
||||
fn test_route_query_keyword_short() {
|
||||
let instance = OsPipeWasm::new(4);
|
||||
let route = instance.route_query("rust");
|
||||
assert_eq!(route, "Keyword");
|
||||
}
|
||||
|
||||
#[wasm_bindgen_test]
|
||||
fn test_route_query_keyword_quoted() {
|
||||
let instance = OsPipeWasm::new(4);
|
||||
let route = instance.route_query("\"exact phrase\"");
|
||||
assert_eq!(route, "Keyword");
|
||||
}
|
||||
|
||||
#[wasm_bindgen_test]
|
||||
fn test_route_query_graph() {
|
||||
let instance = OsPipeWasm::new(4);
|
||||
let route = instance.route_query("things related to authentication module");
|
||||
assert_eq!(route, "Graph");
|
||||
}
|
||||
|
||||
#[wasm_bindgen_test]
|
||||
fn test_route_query_hybrid_default() {
|
||||
let instance = OsPipeWasm::new(4);
|
||||
let route = instance.route_query("explain how neural networks learn patterns");
|
||||
assert_eq!(route, "Hybrid");
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Deduplication
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[wasm_bindgen_test]
|
||||
fn test_is_duplicate_identical() {
|
||||
let mut instance = OsPipeWasm::new(4);
|
||||
let emb: Vec<f32> = vec![1.0, 0.0, 0.0, 0.0];
|
||||
|
||||
instance
|
||||
.insert("original", &emb, "{}", 0.0)
|
||||
.expect("insert");
|
||||
|
||||
assert!(
|
||||
instance.is_duplicate(&emb, 0.99),
|
||||
"Identical embedding should be detected as duplicate"
|
||||
);
|
||||
}
|
||||
|
||||
#[wasm_bindgen_test]
|
||||
fn test_is_not_duplicate_orthogonal() {
|
||||
let mut instance = OsPipeWasm::new(4);
|
||||
let emb_a: Vec<f32> = vec![1.0, 0.0, 0.0, 0.0];
|
||||
let emb_b: Vec<f32> = vec![0.0, 1.0, 0.0, 0.0];
|
||||
|
||||
instance.insert("a", &emb_a, "{}", 0.0).expect("insert");
|
||||
|
||||
assert!(
|
||||
!instance.is_duplicate(&emb_b, 0.5),
|
||||
"Orthogonal embedding should not be a duplicate at threshold 0.5"
|
||||
);
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Stats
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[wasm_bindgen_test]
|
||||
fn test_stats_json() {
|
||||
let mut instance = OsPipeWasm::new(16);
|
||||
let emb: Vec<f32> = vec![0.0; 16];
|
||||
|
||||
instance.insert("x", &emb, "{}", 0.0).expect("insert");
|
||||
|
||||
let stats = instance.stats();
|
||||
assert!(stats.contains("\"dimension\":16"), "Stats: {}", stats);
|
||||
assert!(stats.contains("\"total_embeddings\":1"), "Stats: {}", stats);
|
||||
assert!(
|
||||
stats.contains("\"memory_estimate_bytes\""),
|
||||
"Stats: {}",
|
||||
stats
|
||||
);
|
||||
}
|
||||
Reference in New Issue
Block a user