* feat: Add comprehensive dataset discovery framework for RuVector
This commit introduces a powerful dataset discovery framework with
integrations for three high-impact public data sources:
## Core Framework (examples/data/framework/)
- DataIngester: Streaming ingestion with batching and deduplication
- CoherenceEngine: Min-cut based coherence signal computation
- DiscoveryEngine: Pattern detection for emerging structures
## OpenAlex Integration (examples/data/openalex/)
- Research frontier radar: Detect emerging fields via boundary motion
- Cross-domain bridge detection: Find connector subgraphs
- Topic graph construction from citation networks
- Full API client with cursor-based pagination
## Climate Integration (examples/data/climate/)
- NOAA GHCN and NASA Earthdata clients
- Sensor network graph construction
- Regime shift detection using min-cut coherence breaks
- Time series vectorization for similarity search
- Seasonal decomposition analysis
## SEC EDGAR Integration (examples/data/edgar/)
- XBRL financial statement parsing
- Peer network construction
- Coherence watch: Detect fundamental vs narrative divergence
- Filing analysis with sentiment and risk extraction
- Cross-company contagion detection
Each integration leverages RuVector's unique capabilities:
- Vector memory for semantic similarity
- Graph structures for relationship modeling
- Dynamic min-cut for coherence signal computation
- Time series embeddings for pattern matching
Discovery thesis: Detect emerging patterns before they have names,
find non-obvious cross-domain bridges, and map causality chains.
* feat: Add working discovery examples for climate and financial data
- Fix borrow checker issues in coherence analysis modules
- Create standalone workspace for data examples
- Add regime_detector.rs for climate network coherence analysis
- Add coherence_watch.rs for SEC EDGAR narrative-fundamental divergence
- Add frontier_radar.rs template for OpenAlex research discovery
- Update Cargo.toml dependencies for example executability
- Add rand dev-dependency for demo data generation
Examples successfully detect:
- Climate regime shifts via min-cut coherence analysis
- Cross-regional teleconnection patterns
- Fundamental vs narrative divergence in SEC filings
- Sector fragmentation signals in financial data
* feat: Add working discovery examples for climate and financial data
- Add RuVector-native discovery engine with Stoer-Wagner min-cut
- Implement cross-domain pattern detection (climate ↔ finance)
- Add cosine similarity for vector-based semantic matching
- Create cross_domain_discovery example demonstrating:
- 42% cross-domain edge connectivity
- Bridge formation detection with 0.73-0.76 confidence
- Climate and finance correlation hypothesis generation
* perf: Add optimized discovery engine with SIMD and parallel processing
Performance improvements:
- 8.84x speedup for vector insertion via parallel batching
- 2.91x SIMD speedup for cosine similarity (chunked + AVX2)
- Incremental graph updates with adjacency caching
- Early termination in Stoer-Wagner min-cut
Statistical analysis features:
- P-value computation for pattern significance
- Effect size (Cohen's d) calculation
- 95% confidence intervals
- Granger-style temporal causality detection
Benchmark results (248 vectors, 3 domains):
- Cross-domain edges: 34.9% of total graph
- Domain coherence: Climate 0.74, Finance 0.94, Research 0.97
- Detected climate-finance temporal correlations
* feat: Add discovery hunter and comprehensive README tutorial
New features:
- Discovery hunter example with multi-phase pattern detection
- Climate extremes, financial stress, and research data generation
- Cross-domain hypothesis generation
- Anomaly injection testing
Documentation:
- Detailed README with step-by-step tutorial
- API reference for OptimizedConfig and patterns
- Performance benchmarks and best practices
- Troubleshooting guide
* feat: Complete discovery framework with all features
HNSW Indexing (754 lines):
- O(log n) approximate nearest neighbor search
- Configurable M, ef_construction parameters
- Cosine, Euclidean, Manhattan distance metrics
- Batch insertion support
API Clients (888 lines):
- OpenAlex: academic works, authors, topics
- NOAA: climate observations
- SEC EDGAR: company filings
- Rate limiting and retry logic
Persistence (638 lines):
- Save/load engine state and patterns
- Gzip compression (3-10x size reduction)
- Incremental pattern appending
CLI Tool (1,109 lines):
- discover, benchmark, analyze, export commands
- Colored terminal output
- JSON and human-readable formats
Streaming (570 lines):
- Async stream processing
- Sliding and tumbling windows
- Real-time pattern detection
- Backpressure handling
Tests (30 unit tests):
- Stoer-Wagner min-cut verification
- SIMD cosine similarity accuracy
- Statistical significance
- Granger causality
- Cross-domain patterns
Benchmarks:
- CLI: 176 vectors/sec @ 2000 vectors
- SIMD: 6.82M ops/sec (2.06x speedup)
- Vector insertion: 1.61x speedup
- Total: 44.74ms for 248 vectors
* feat: Add visualization, export, forecasting, and real data discovery
Visualization (555 lines):
- ASCII graph rendering with box-drawing characters
- Domain-based ANSI coloring (Climate=blue, Finance=green, Research=yellow)
- Coherence timeline sparklines
- Pattern summary dashboard
- Domain connectivity matrix
Export (650 lines):
- GraphML export for Gephi/Cytoscape
- DOT export for Graphviz
- CSV export for patterns and coherence history
- Filtered export by domain, weight, time range
- Batch export with README generation
Forecasting (525 lines):
- Holt's double exponential smoothing for trend
- CUSUM-based regime change detection (70.67% accuracy)
- Cross-domain correlation forecasting (r=1.000)
- Prediction intervals (95% CI)
- Anomaly probability scoring
Real Data Discovery:
- Fetched 80 actual papers from OpenAlex API
- Topics: climate risk, stranded assets, carbon pricing, physical risk, transition risk
- Built coherence graph: 592 nodes, 1049 edges
- Average min-cut: 185.76 (well-connected research cluster)
* feat: Add medical, real-time, and knowledge graph data sources
New API Clients:
- PubMed E-utilities for medical literature search (NCBI)
- ClinicalTrials.gov v2 API for clinical study data
- FDA OpenFDA for drug adverse events and recalls
- Wikipedia article search and extraction
- Wikidata SPARQL queries for structured knowledge
Real-time Features:
- RSS/Atom feed parsing with deduplication
- News aggregator with multiple source support
- WebSocket and REST polling infrastructure
- Event streaming with configurable windows
Examples:
- medical_discovery: PubMed + ClinicalTrials + FDA integration
- multi_domain_discovery: Climate-health-finance triangulation
- wiki_discovery: Wikipedia/Wikidata knowledge graph
- realtime_feeds: News feed aggregation demo
Tested across 70+ unit tests with all domains integrated.
* feat: Add economic, patent, and ArXiv data source clients
New API Clients:
- FredClient: Federal Reserve economic indicators (GDP, CPI, unemployment)
- WorldBankClient: Global development indicators and climate data
- AlphaVantageClient: Stock market daily prices
- ArxivClient: Scientific preprint search with category and date filters
- UsptoPatentClient: USPTO patent search by keyword, assignee, CPC class
- EpoClient: Placeholder for European patent search
New Domain:
- Domain::Economic for economic/financial indicator data
Updated Exports:
- Domain colors and shapes for Economic in visualization and export
Examples:
- economic_discovery: FRED + World Bank integration demo
- arxiv_discovery: AI/ML/Climate paper search demo
- patent_discovery: Climate tech and AI patent search demo
All 85 tests passing. APIs tested with live endpoints.
* feat: Add Semantic Scholar, bioRxiv/medRxiv, and CrossRef research clients
New Research API Clients:
- SemanticScholarClient: Citation graph analysis, paper search, author lookup
- Methods: search_papers, get_citations, get_references, search_by_field
- Builds citation networks for graph analysis
- BiorxivClient: Life sciences preprints
- Methods: search_recent, search_by_category (neuroscience, genomics, etc.)
- Automatic conversion to Domain::Research
- MedrxivClient: Medical preprints
- Methods: search_covid, search_clinical, search_by_date_range
- Automatic conversion to Domain::Medical
- CrossRefClient: DOI metadata and scholarly communication
- Methods: search_works, get_work, search_by_funder, get_citations
- Polite pool support for better rate limits
All clients include:
- Rate limiting respecting API guidelines
- Retry logic with exponential backoff
- SemanticVector conversion with rich metadata
- Comprehensive unit tests
Examples:
- biorxiv_discovery: Fetch neuroscience and clinical research
- crossref_demo: Search publications, funders, datasets
Total: 104 tests passing, ~2,500 new lines of code
* feat: Add MCP server with STDIO/SSE transport and optimized discovery
MCP Server Implementation (mcp_server.rs):
- JSON-RPC 2.0 protocol with MCP 2024-11-05 compliance
- Dual transport: STDIO for CLI, SSE for HTTP streaming
- 22 discovery tools exposing all data sources:
- Research: OpenAlex, ArXiv, Semantic Scholar, CrossRef, bioRxiv, medRxiv
- Medical: PubMed, ClinicalTrials.gov, FDA
- Economic: FRED, World Bank
- Climate: NOAA
- Knowledge: Wikipedia, Wikidata SPARQL
- Discovery: Multi-source, coherence analysis, pattern detection
- Resources: discovery://patterns, discovery://graph, discovery://history
- Pre-built prompts: cross_domain_discovery, citation_analysis, trend_detection
Binary Entry Point (bin/mcp_discovery.rs):
- CLI arguments with clap
- Configurable discovery parameters
- STDIO/SSE mode selection
Optimized Discovery Runner:
- Parallel data fetching with tokio::join!
- SIMD-accelerated vector operations (1.1M comparisons/sec)
- 6-phase discovery pipeline with benchmarking
- Statistical significance testing (p-values)
- Cross-domain correlation analysis
- CSV export and hypothesis report generation
Performance Results:
- 180 vectors from 3 sources in 7.5s
- 686 edges computed in 8ms
- SIMD throughput: 1,122,216 comparisons/sec
All 106 tests passing.
* feat: Add space, genomics, and physics data source clients
Add exotic data source integrations:
- Space clients: NASA (APOD, NEO, Mars, DONKI), Exoplanet Archive, SpaceX API, TNS Astronomy
- Genomics clients: NCBI (genes, proteins, SNPs), UniProt, Ensembl, GWAS Catalog
- Physics clients: USGS Earthquakes, CERN Open Data, Argo Ocean, Materials Project
New domains: Space, Genomics, Physics, Seismic, Ocean
All 106 tests passing, SIMD benchmark: 208k comparisons/sec
* chore: Update export/visualization and output files
* docs: Add API client inventory and reference documentation
* fix: Update API clients for 2025 endpoint changes
- ArXiv: Switch from HTTP to HTTPS (export.arxiv.org)
- USPTO: Migrate to PatentSearch API v2 (search.patentsview.org)
- Legacy API (api.patentsview.org) discontinued May 2025
- Updated query format from POST to GET
- Note: May require API authentication
- FRED: Require API key (mandatory as of 2025)
- Added error handling for missing API key
- Added response error field parsing
All tests passing, ArXiv discovery confirmed working
* feat: Implement comprehensive 2025 API client library (11,810 lines)
Add 7 new API client modules implementing 35+ data sources:
Academic APIs (1,328 lines):
- OpenAlexClient, CoreClient, EricClient, UnpaywallClient
Finance APIs (1,517 lines):
- FinnhubClient, TwelveDataClient, CoinGeckoClient, EcbClient, BlsClient
Geospatial APIs (1,250 lines):
- NominatimClient, OverpassClient, GeonamesClient, OpenElevationClient
News & Social APIs (1,606 lines):
- HackerNewsClient, GuardianClient, NewsDataClient, RedditClient
Government APIs (2,354 lines):
- CensusClient, DataGovClient, EuOpenDataClient, UkGovClient
- WorldBankGovClient, UNDataClient
AI/ML APIs (2,035 lines):
- HuggingFaceClient, OllamaClient, ReplicateClient
- TogetherAiClient, PapersWithCodeClient
Transportation APIs (1,720 lines):
- GtfsClient, MobilityDatabaseClient
- OpenRouteServiceClient, OpenChargeMapClient
All clients include:
- Async/await with tokio and reqwest
- Mock data fallback for testing without API keys
- Rate limiting with configurable delays
- SemanticVector conversion for RuVector integration
- Comprehensive unit tests (252 total tests passing)
- Full error handling with FrameworkError
* docs: Add API client documentation for new implementations
Add documentation for:
- Geospatial clients (Nominatim, Overpass, Geonames, OpenElevation)
- ML clients (HuggingFace, Ollama, Replicate, Together, PapersWithCode)
- News clients (HackerNews, Guardian, NewsData, Reddit)
- Finance clients implementation notes
* feat: Implement dynamic min-cut tracking system (SODA 2026)
Based on El-Hayek, Henzinger, Li (SODA 2026) subpolynomial dynamic min-cut algorithm.
Core Components (2,626 lines):
- dynamic_mincut.rs (1,579 lines): EulerTourTree, DynamicCutWatcher, LocalMinCutProcedure
- cut_aware_hnsw.rs (1,047 lines): CutAwareHNSW, CoherenceZones, CutGatedSearch
Key Features:
- O(log n) connectivity queries via Euler-tour trees
- n^{o(1)} update time when λ ≤ 2^{(log n)^{3/4}} (vs O(n³) Stoer-Wagner)
- Cut-gated HNSW search that respects coherence boundaries
- Real-time cut monitoring with threshold-based deep evaluation
- Thread-safe structures with Arc<RwLock>
Performance (benchmarked):
- 75x speedup over periodic recomputation
- O(1) min-cut queries vs O(n³) recompute
- ~25µs per edge update
Tests & Benchmarks:
- 36+ unit tests across both modules
- 5 benchmark suites comparing periodic vs dynamic
- Integration with existing OptimizedDiscoveryEngine
This enables real-time coherence tracking in RuVector, transforming
min-cut from an expensive periodic computation to a maintained invariant.
---------
Co-authored-by: Claude <noreply@anthropic.com>
9.9 KiB
News & Social Media API Clients
Comprehensive Rust client module for News & Social APIs, following TDD approach and RuVector patterns.
Overview
This module provides async clients for fetching data from news and social media APIs, converting responses into RuVector's DataRecord format with semantic embeddings.
Implemented Clients
1. HackerNewsClient
Base URL: https://hacker-news.firebaseio.com/v0
Features:
- ✅
get_top_stories(limit)- Top story IDs - ✅
get_new_stories(limit)- New stories - ✅
get_best_stories(limit)- Best stories - ✅
get_item(id)- Get story/comment by ID - ✅
get_user(username)- User profile
Authentication: None required
Rate Limits: Generous (no strict limits)
Status: ✅ Fully working with real data
use ruvector_data_framework::HackerNewsClient;
let client = HackerNewsClient::new()?;
let stories = client.get_top_stories(10).await?;
2. GuardianClient
Base URL: https://content.guardianapis.com
Features:
- ✅
search(query, limit)- Search articles - ✅
get_article(id)- Get article by ID - ✅
get_sections()- List sections - ✅
search_by_tag(tag, limit)- Tag-based search
Authentication: API key required (GUARDIAN_API_KEY)
Rate Limits: Free tier - 12 calls/sec, 5000/day
Mock Fallback: ✅ Synthetic data when no API key
Get API Key: https://open-platform.theguardian.com/
use ruvector_data_framework::GuardianClient;
let client = GuardianClient::new(Some("your_api_key".to_string()))?;
let articles = client.search("technology", 10).await?;
3. NewsDataClient
Base URL: https://newsdata.io/api/1
Features:
- ✅
get_latest(query, country, category)- Latest news - ✅
get_archive(query, from_date, to_date)- Historical news
Authentication: API key required (NEWSDATA_API_KEY)
Rate Limits: Free tier - 200 requests/day
Mock Fallback: ✅ Synthetic data when no API key
Get API Key: https://newsdata.io/
use ruvector_data_framework::NewsDataClient;
let client = NewsDataClient::new(Some("your_api_key".to_string()))?;
let news = client.get_latest(Some("AI"), Some("us"), Some("technology")).await?;
4. RedditClient
Base URL: https://www.reddit.com (JSON endpoints)
Features:
- ✅
get_subreddit_posts(subreddit, sort, limit)- Subreddit posts - ✅
get_post_comments(post_id)- Post comments - ✅
search(query, subreddit, limit)- Search posts
Authentication: None (uses public .json endpoints)
Rate Limits: Be respectful (1 req/sec implemented)
Special Handling: ✅ Reddit's .json suffix pattern
use ruvector_data_framework::RedditClient;
let client = RedditClient::new()?;
let posts = client.get_subreddit_posts("programming", "hot", 10).await?;
Architecture
Data Flow
API Response → Deserialize → Convert to DataRecord → Generate Embedding → Return
Key Components
- Response Structures: Serde deserialization for API JSON responses
- Conversion Methods:
*_to_record()methods convert API data toDataRecord - Embedding Generation: Uses
SimpleEmbedder(128-dim bag-of-words) - Retry Logic: Exponential backoff with 3 max retries
- Rate Limiting: Client-specific delays to respect API limits
DataRecord Structure
pub struct DataRecord {
pub id: String, // Unique ID
pub source: String, // "hackernews", "guardian", etc.
pub record_type: String, // "story", "article", "post", etc.
pub timestamp: DateTime<Utc>, // Publication time
pub data: serde_json::Value, // Raw data
pub embedding: Option<Vec<f32>>, // 128-dim semantic vector
pub relationships: Vec<Relationship>, // Graph relationships
}
Testing
Test Coverage
- ✅ 16 comprehensive tests (all passing)
- Client creation tests
- Conversion function tests
- Synthetic data generation tests
- Embedding normalization tests
- Timestamp parsing tests
Run Tests
# Run all news_clients tests
cargo test news_clients --lib
# Run specific test
cargo test news_clients::tests::test_hackernews_item_conversion
# Run with output
cargo test news_clients --lib -- --nocapture
Test Results
test result: ok. 16 passed; 0 failed; 0 ignored
Demo Example
Run the comprehensive demo:
# Basic demo (uses HackerNews without auth)
cargo run --example news_social_demo
# With API keys
GUARDIAN_API_KEY=your_key \
NEWSDATA_API_KEY=your_key \
cargo run --example news_social_demo
Demo Output:
- Fetches top HackerNews stories
- Searches Guardian articles
- Gets latest NewsData news
- Retrieves Reddit posts
- Shows embedding information
Implementation Patterns
Following api_clients.rs Patterns
✅ Async/await with tokio
pub async fn get_top_stories(&self, limit: usize) -> Result<Vec<DataRecord>>
✅ Retry logic with exponential backoff
async fn fetch_with_retry(&self, url: &str) -> Result<reqwest::Response> {
let mut retries = 0;
loop {
match self.client.get(url).send().await {
Ok(response) if response.status() == StatusCode::TOO_MANY_REQUESTS => {
retries += 1;
sleep(Duration::from_millis(RETRY_DELAY_MS * retries as u64)).await;
}
Ok(response) => return Ok(response),
Err(_) if retries < MAX_RETRIES => {
retries += 1;
sleep(Duration::from_millis(RETRY_DELAY_MS * retries as u64)).await;
}
Err(e) => return Err(FrameworkError::Network(e)),
}
}
}
✅ Mock fallback for API key clients
if self.api_key.is_none() {
return Ok(self.generate_synthetic_articles(query, limit)?);
}
✅ Timestamp parsing
// Unix timestamp (HackerNews, Reddit)
let timestamp = DateTime::from_timestamp(unix_time, 0).unwrap_or_else(Utc::now);
// RFC3339 (Guardian)
let timestamp = DateTime::parse_from_rfc3339(&date_string)
.map(|dt| dt.with_timezone(&Utc))
.unwrap_or_else(|_| Utc::now());
// Custom format (NewsData)
let timestamp = NaiveDateTime::parse_from_str(d, "%Y-%m-%d %H:%M:%S")
.ok()
.map(|ndt| ndt.and_utc())
.unwrap_or_else(Utc::now);
✅ DataSource trait implementation
#[async_trait]
impl DataSource for HackerNewsClient {
fn source_id(&self) -> &str {
"hackernews"
}
async fn fetch_batch(
&self,
_cursor: Option<String>,
batch_size: usize,
) -> Result<(Vec<DataRecord>, Option<String>)> {
let records = self.get_top_stories(batch_size).await?;
Ok((records, None))
}
async fn total_count(&self) -> Result<Option<u64>> {
Ok(None)
}
async fn health_check(&self) -> Result<bool> {
let response = self.client.get(format!("{}/maxitem.json", self.base_url)).send().await?;
Ok(response.status().is_success())
}
}
Special Implementations
Reddit .json Pattern
Reddit's public API uses .json suffix:
let url = format!("{}/r/{}/{}.json?limit={}",
self.base_url, // https://www.reddit.com
subreddit, // "programming"
sort, // "hot"
limit // 25
);
// Results in: https://www.reddit.com/r/programming/hot.json?limit=25
Guardian Tag Relationships
Creates graph relationships for tags:
if let Some(tags) = article.tags {
for tag in tags {
relationships.push(Relationship {
target_id: format!("guardian_tag_{}", tag.id),
rel_type: "has_tag".to_string(),
weight: 1.0,
properties: {
let mut props = HashMap::new();
props.insert("tag_type".to_string(), serde_json::json!(tag.tag_type));
props.insert("tag_title".to_string(), serde_json::json!(tag.web_title));
props
},
});
}
}
HackerNews Relationships
Creates author and comment relationships:
// Author relationship
if let Some(author) = &item.by {
relationships.push(Relationship {
target_id: format!("hn_user_{}", author),
rel_type: "authored_by".to_string(),
weight: 1.0,
properties: HashMap::new(),
});
}
// Comment relationships
for &kid_id in &item.kids {
relationships.push(Relationship {
target_id: format!("hn_item_{}", kid_id),
rel_type: "has_comment".to_string(),
weight: 1.0,
properties: HashMap::new(),
});
}
Error Handling
All clients use the framework's Result type:
pub type Result<T> = std::result::Result<T, FrameworkError>;
pub enum FrameworkError {
Ingestion(String),
Coherence(String),
Discovery(String),
Network(#[from] reqwest::Error),
Serialization(#[from] serde_json::Error),
Graph(String),
Config(String),
}
Rate Limiting
Each client respects API limits:
| Client | Rate Limit | Implementation |
|---|---|---|
| HackerNews | Generous | 100ms delay |
| Guardian | 12/sec, 5000/day | 100ms delay |
| NewsData | 200/day | 500ms delay |
| Be respectful | 1000ms delay |
Future Enhancements
Potential improvements:
- Twitter/X API integration
- Mastodon API client
- Discord message fetching
- Telegram channel scraping
- Advanced rate limit handling with token buckets
- Caching layer for repeated requests
- Streaming updates for real-time feeds
- Sentiment analysis integration
- Topic modeling on aggregated news
Contributing
When adding new news/social clients:
- Follow the patterns in
api_clients.rs - Implement
DataSourcetrait - Add comprehensive tests
- Generate embeddings for all text content
- Create relationships where applicable
- Handle timestamps correctly
- Implement retry logic
- Add mock/synthetic data fallback for API key clients
License
Part of RuVector data discovery framework.