mirror of
https://github.com/ruvnet/RuVector.git
synced 2026-05-31 05:13:39 +00:00
* 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>
770 lines
25 KiB
Rust
770 lines
25 KiB
Rust
//! Economic data API integrations for FRED, World Bank, and Alpha Vantage
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//!
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//! This module provides async clients for fetching economic indicators, global development data,
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//! and stock market information, converting responses to SemanticVector format for RuVector discovery.
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use std::collections::HashMap;
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use std::sync::Arc;
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use std::time::Duration;
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use chrono::{NaiveDate, Utc};
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use reqwest::{Client, StatusCode};
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use serde::Deserialize;
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use tokio::time::sleep;
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use crate::api_clients::SimpleEmbedder;
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use crate::ruvector_native::{Domain, SemanticVector};
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use crate::{FrameworkError, Result};
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/// Rate limiting configuration
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const FRED_RATE_LIMIT_MS: u64 = 100; // ~10 requests/second
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const WORLDBANK_RATE_LIMIT_MS: u64 = 100; // Conservative rate
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const ALPHAVANTAGE_RATE_LIMIT_MS: u64 = 12000; // 5 requests/minute for free tier
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const MAX_RETRIES: u32 = 3;
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const RETRY_DELAY_MS: u64 = 1000;
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// ============================================================================
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// FRED (Federal Reserve Economic Data) Client
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// ============================================================================
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/// FRED API observations response
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#[derive(Debug, Deserialize)]
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struct FredObservationsResponse {
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#[serde(default)]
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observations: Vec<FredObservation>,
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#[serde(default)]
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error_code: Option<i32>,
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#[serde(default)]
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error_message: Option<String>,
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}
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#[derive(Debug, Deserialize)]
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struct FredObservation {
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#[serde(default)]
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date: String,
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#[serde(default)]
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value: String,
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}
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/// FRED API series search response
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#[derive(Debug, Deserialize)]
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struct FredSeriesSearchResponse {
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seriess: Vec<FredSeries>,
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}
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#[derive(Debug, Deserialize)]
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struct FredSeries {
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id: String,
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title: String,
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#[serde(default)]
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units: String,
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#[serde(default)]
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frequency: String,
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#[serde(default)]
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seasonal_adjustment: String,
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#[serde(default)]
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notes: String,
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}
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/// Client for FRED (Federal Reserve Economic Data)
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///
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/// Provides access to 800,000+ US economic time series including:
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/// - GDP, unemployment, inflation, interest rates
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/// - Money supply, consumer spending, housing data
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/// - Regional economic indicators
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///
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/// # Example
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/// ```rust,ignore
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/// use ruvector_data_framework::FredClient;
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///
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/// let client = FredClient::new(None)?;
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/// let gdp_data = client.get_gdp().await?;
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/// let unemployment = client.get_unemployment().await?;
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/// let search_results = client.search_series("inflation").await?;
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/// ```
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pub struct FredClient {
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client: Client,
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base_url: String,
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api_key: Option<String>,
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rate_limit_delay: Duration,
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embedder: Arc<SimpleEmbedder>,
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}
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impl FredClient {
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/// Create a new FRED client
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///
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/// # Arguments
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/// * `api_key` - Optional FRED API key (get from https://fred.stlouisfed.org/docs/api/api_key.html)
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/// Basic access works without a key, but rate limits are more restrictive
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pub fn new(api_key: Option<String>) -> Result<Self> {
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let client = Client::builder()
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.timeout(Duration::from_secs(30))
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.build()
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.map_err(FrameworkError::Network)?;
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Ok(Self {
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client,
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base_url: "https://api.stlouisfed.org/fred".to_string(),
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api_key,
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rate_limit_delay: Duration::from_millis(FRED_RATE_LIMIT_MS),
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embedder: Arc::new(SimpleEmbedder::new(256)),
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})
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}
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/// Get observations for a specific FRED series
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///
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/// # Arguments
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/// * `series_id` - FRED series ID (e.g., "GDP", "UNRATE", "CPIAUCSL")
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/// * `limit` - Maximum number of observations to return (default: 100)
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///
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/// # Example
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/// ```rust,ignore
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/// let gdp = client.get_series("GDP", Some(50)).await?;
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/// ```
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pub async fn get_series(
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&self,
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series_id: &str,
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limit: Option<usize>,
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) -> Result<Vec<SemanticVector>> {
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// FRED API requires an API key as of 2025
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let api_key = self.api_key.as_ref().ok_or_else(|| {
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FrameworkError::Config(
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"FRED API key required. Get one at https://fred.stlouisfed.org/docs/api/api_key.html".to_string()
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)
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})?;
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let mut url = format!(
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"{}/series/observations?series_id={}&file_type=json&api_key={}",
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self.base_url, series_id, api_key
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);
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if let Some(lim) = limit {
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url.push_str(&format!("&limit={}", lim));
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}
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sleep(self.rate_limit_delay).await;
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let response = self.fetch_with_retry(&url).await?;
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let obs_response: FredObservationsResponse = response.json().await?;
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// Check for API error response
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if let Some(error_msg) = obs_response.error_message {
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return Err(FrameworkError::Ingestion(format!("FRED API error: {}", error_msg)));
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}
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let mut vectors = Vec::new();
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for obs in obs_response.observations {
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// Parse value, skip if invalid
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let value = match obs.value.parse::<f64>() {
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Ok(v) => v,
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Err(_) => continue, // Skip ".", missing values, etc.
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};
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// Parse date
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let date = NaiveDate::parse_from_str(&obs.date, "%Y-%m-%d")
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.ok()
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.and_then(|d| d.and_hms_opt(0, 0, 0))
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.map(|dt| dt.and_utc())
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.unwrap_or_else(Utc::now);
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// Create text for embedding
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let text = format!("{} on {}: {}", series_id, obs.date, value);
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let embedding = self.embedder.embed_text(&text);
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let mut metadata = HashMap::new();
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metadata.insert("series_id".to_string(), series_id.to_string());
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metadata.insert("date".to_string(), obs.date.clone());
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metadata.insert("value".to_string(), value.to_string());
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metadata.insert("source".to_string(), "fred".to_string());
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vectors.push(SemanticVector {
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id: format!("FRED:{}:{}", series_id, obs.date),
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embedding,
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domain: Domain::Economic,
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timestamp: date,
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metadata,
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});
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}
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Ok(vectors)
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}
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/// Search for FRED series by keywords
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///
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/// # Arguments
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/// * `keywords` - Search terms (e.g., "unemployment rate", "consumer price index")
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///
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/// # Example
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/// ```rust,ignore
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/// let inflation_series = client.search_series("inflation").await?;
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/// ```
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pub async fn search_series(&self, keywords: &str) -> Result<Vec<SemanticVector>> {
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let mut url = format!(
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"{}/series/search?search_text={}&file_type=json&limit=50",
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self.base_url,
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urlencoding::encode(keywords)
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);
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if let Some(key) = &self.api_key {
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url.push_str(&format!("&api_key={}", key));
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}
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sleep(self.rate_limit_delay).await;
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let response = self.fetch_with_retry(&url).await?;
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let search_response: FredSeriesSearchResponse = response.json().await?;
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let mut vectors = Vec::new();
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for series in search_response.seriess {
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// Create text for embedding
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let text = format!(
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"{} {} {} {}",
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series.title, series.units, series.frequency, series.notes
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);
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let embedding = self.embedder.embed_text(&text);
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let mut metadata = HashMap::new();
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metadata.insert("series_id".to_string(), series.id.clone());
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metadata.insert("title".to_string(), series.title.clone());
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metadata.insert("units".to_string(), series.units);
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metadata.insert("frequency".to_string(), series.frequency);
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metadata.insert("seasonal_adjustment".to_string(), series.seasonal_adjustment);
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metadata.insert("source".to_string(), "fred_search".to_string());
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vectors.push(SemanticVector {
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id: format!("FRED_SERIES:{}", series.id),
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embedding,
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domain: Domain::Economic,
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timestamp: Utc::now(),
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metadata,
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});
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}
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Ok(vectors)
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}
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/// Get US GDP data (Gross Domestic Product)
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///
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/// # Example
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/// ```rust,ignore
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/// let gdp = client.get_gdp().await?;
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/// ```
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pub async fn get_gdp(&self) -> Result<Vec<SemanticVector>> {
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self.get_series("GDP", Some(100)).await
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}
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/// Get US unemployment rate
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///
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/// # Example
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/// ```rust,ignore
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/// let unemployment = client.get_unemployment().await?;
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/// ```
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pub async fn get_unemployment(&self) -> Result<Vec<SemanticVector>> {
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self.get_series("UNRATE", Some(100)).await
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}
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/// Get US Consumer Price Index (CPI) - inflation indicator
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///
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/// # Example
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/// ```rust,ignore
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/// let cpi = client.get_cpi().await?;
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/// ```
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pub async fn get_cpi(&self) -> Result<Vec<SemanticVector>> {
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self.get_series("CPIAUCSL", Some(100)).await
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}
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/// Get US Federal Funds Rate
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///
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/// # Example
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/// ```rust,ignore
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/// let interest_rates = client.get_interest_rate().await?;
|
|
/// ```
|
|
pub async fn get_interest_rate(&self) -> Result<Vec<SemanticVector>> {
|
|
self.get_series("DFF", Some(100)).await
|
|
}
|
|
|
|
/// Get US M2 Money Supply
|
|
///
|
|
/// # Example
|
|
/// ```rust,ignore
|
|
/// let money_supply = client.get_money_supply().await?;
|
|
/// ```
|
|
pub async fn get_money_supply(&self) -> Result<Vec<SemanticVector>> {
|
|
self.get_series("M2SL", Some(100)).await
|
|
}
|
|
|
|
/// Fetch with retry logic
|
|
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 < MAX_RETRIES {
|
|
retries += 1;
|
|
sleep(Duration::from_millis(RETRY_DELAY_MS * retries as u64)).await;
|
|
continue;
|
|
}
|
|
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)),
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// ============================================================================
|
|
// World Bank Open Data Client
|
|
// ============================================================================
|
|
|
|
/// World Bank API response (v2)
|
|
#[derive(Debug, Deserialize)]
|
|
struct WorldBankResponse {
|
|
#[serde(default)]
|
|
page: u32,
|
|
#[serde(default)]
|
|
pages: u32,
|
|
#[serde(default)]
|
|
per_page: u32,
|
|
#[serde(default)]
|
|
total: u32,
|
|
}
|
|
|
|
/// World Bank indicator data point
|
|
#[derive(Debug, Deserialize)]
|
|
struct WorldBankIndicator {
|
|
indicator: WorldBankIndicatorInfo,
|
|
country: WorldBankCountryInfo,
|
|
#[serde(default)]
|
|
countryiso3code: String,
|
|
#[serde(default)]
|
|
date: String,
|
|
#[serde(default)]
|
|
value: Option<f64>,
|
|
#[serde(default)]
|
|
unit: String,
|
|
#[serde(default)]
|
|
obs_status: String,
|
|
}
|
|
|
|
#[derive(Debug, Deserialize)]
|
|
struct WorldBankIndicatorInfo {
|
|
id: String,
|
|
value: String,
|
|
}
|
|
|
|
#[derive(Debug, Deserialize)]
|
|
struct WorldBankCountryInfo {
|
|
id: String,
|
|
value: String,
|
|
}
|
|
|
|
/// Client for World Bank Open Data API
|
|
///
|
|
/// Provides access to global development indicators including:
|
|
/// - GDP per capita, population, poverty rates
|
|
/// - Health expenditure, life expectancy
|
|
/// - CO2 emissions, renewable energy
|
|
/// - Education, infrastructure metrics
|
|
///
|
|
/// # Example
|
|
/// ```rust,ignore
|
|
/// use ruvector_data_framework::WorldBankClient;
|
|
///
|
|
/// let client = WorldBankClient::new()?;
|
|
/// let gdp_global = client.get_gdp_global().await?;
|
|
/// let climate = client.get_climate_indicators().await?;
|
|
/// let health = client.get_indicator("USA", "SH.XPD.CHEX.GD.ZS").await?;
|
|
/// ```
|
|
pub struct WorldBankClient {
|
|
client: Client,
|
|
base_url: String,
|
|
rate_limit_delay: Duration,
|
|
embedder: Arc<SimpleEmbedder>,
|
|
}
|
|
|
|
impl WorldBankClient {
|
|
/// Create a new World Bank client
|
|
pub fn new() -> Result<Self> {
|
|
let client = Client::builder()
|
|
.timeout(Duration::from_secs(30))
|
|
.build()
|
|
.map_err(FrameworkError::Network)?;
|
|
|
|
Ok(Self {
|
|
client,
|
|
base_url: "https://api.worldbank.org/v2".to_string(),
|
|
rate_limit_delay: Duration::from_millis(WORLDBANK_RATE_LIMIT_MS),
|
|
embedder: Arc::new(SimpleEmbedder::new(256)),
|
|
})
|
|
}
|
|
|
|
/// Get indicator data for a specific country
|
|
///
|
|
/// # Arguments
|
|
/// * `country` - ISO 3-letter country code (e.g., "USA", "CHN", "GBR") or "all"
|
|
/// * `indicator` - World Bank indicator code (e.g., "NY.GDP.PCAP.CD" for GDP per capita)
|
|
///
|
|
/// # Example
|
|
/// ```rust,ignore
|
|
/// // Get US GDP per capita
|
|
/// let us_gdp = client.get_indicator("USA", "NY.GDP.PCAP.CD").await?;
|
|
/// ```
|
|
pub async fn get_indicator(
|
|
&self,
|
|
country: &str,
|
|
indicator: &str,
|
|
) -> Result<Vec<SemanticVector>> {
|
|
let url = format!(
|
|
"{}/country/{}/indicator/{}?format=json&per_page=100",
|
|
self.base_url, country, indicator
|
|
);
|
|
|
|
sleep(self.rate_limit_delay).await;
|
|
let response = self.fetch_with_retry(&url).await?;
|
|
let text = response.text().await?;
|
|
|
|
// World Bank API returns [metadata, data]
|
|
let json_values: Vec<serde_json::Value> = serde_json::from_str(&text)?;
|
|
|
|
if json_values.len() < 2 {
|
|
return Ok(Vec::new());
|
|
}
|
|
|
|
let indicators: Vec<WorldBankIndicator> = serde_json::from_value(json_values[1].clone())?;
|
|
|
|
let mut vectors = Vec::new();
|
|
for ind in indicators {
|
|
// Skip null values
|
|
let value = match ind.value {
|
|
Some(v) => v,
|
|
None => continue,
|
|
};
|
|
|
|
// Parse date
|
|
let year = ind.date.parse::<i32>().unwrap_or(2020);
|
|
let date = NaiveDate::from_ymd_opt(year, 1, 1)
|
|
.and_then(|d| d.and_hms_opt(0, 0, 0))
|
|
.map(|dt| dt.and_utc())
|
|
.unwrap_or_else(Utc::now);
|
|
|
|
// Create text for embedding
|
|
let text = format!(
|
|
"{} {} in {}: {}",
|
|
ind.country.value, ind.indicator.value, ind.date, value
|
|
);
|
|
let embedding = self.embedder.embed_text(&text);
|
|
|
|
let mut metadata = HashMap::new();
|
|
metadata.insert("country".to_string(), ind.country.value);
|
|
metadata.insert("country_code".to_string(), ind.countryiso3code.clone());
|
|
metadata.insert("indicator_id".to_string(), ind.indicator.id.clone());
|
|
metadata.insert("indicator_name".to_string(), ind.indicator.value);
|
|
metadata.insert("date".to_string(), ind.date.clone());
|
|
metadata.insert("value".to_string(), value.to_string());
|
|
metadata.insert("source".to_string(), "worldbank".to_string());
|
|
|
|
vectors.push(SemanticVector {
|
|
id: format!("WB:{}:{}:{}", ind.countryiso3code, ind.indicator.id, ind.date),
|
|
embedding,
|
|
domain: Domain::Economic,
|
|
timestamp: date,
|
|
metadata,
|
|
});
|
|
}
|
|
|
|
Ok(vectors)
|
|
}
|
|
|
|
/// Get global GDP per capita data
|
|
///
|
|
/// # Example
|
|
/// ```rust,ignore
|
|
/// let gdp_global = client.get_gdp_global().await?;
|
|
/// ```
|
|
pub async fn get_gdp_global(&self) -> Result<Vec<SemanticVector>> {
|
|
// Get GDP per capita for major economies
|
|
self.get_indicator("all", "NY.GDP.PCAP.CD").await
|
|
}
|
|
|
|
/// Get climate change indicators (CO2 emissions, renewable energy)
|
|
///
|
|
/// # Example
|
|
/// ```rust,ignore
|
|
/// let climate = client.get_climate_indicators().await?;
|
|
/// ```
|
|
pub async fn get_climate_indicators(&self) -> Result<Vec<SemanticVector>> {
|
|
// CO2 emissions (metric tons per capita)
|
|
let mut vectors = self.get_indicator("all", "EN.ATM.CO2E.PC").await?;
|
|
|
|
// Renewable energy consumption (% of total)
|
|
sleep(self.rate_limit_delay).await;
|
|
let renewable = self.get_indicator("all", "EG.FEC.RNEW.ZS").await?;
|
|
vectors.extend(renewable);
|
|
|
|
Ok(vectors)
|
|
}
|
|
|
|
/// Get health expenditure indicators
|
|
///
|
|
/// # Example
|
|
/// ```rust,ignore
|
|
/// let health = client.get_health_indicators().await?;
|
|
/// ```
|
|
pub async fn get_health_indicators(&self) -> Result<Vec<SemanticVector>> {
|
|
// Health expenditure as % of GDP
|
|
let mut vectors = self.get_indicator("all", "SH.XPD.CHEX.GD.ZS").await?;
|
|
|
|
// Life expectancy at birth
|
|
sleep(self.rate_limit_delay).await;
|
|
let life_exp = self.get_indicator("all", "SP.DYN.LE00.IN").await?;
|
|
vectors.extend(life_exp);
|
|
|
|
Ok(vectors)
|
|
}
|
|
|
|
/// Get population data
|
|
///
|
|
/// # Example
|
|
/// ```rust,ignore
|
|
/// let population = client.get_population().await?;
|
|
/// ```
|
|
pub async fn get_population(&self) -> Result<Vec<SemanticVector>> {
|
|
self.get_indicator("all", "SP.POP.TOTL").await
|
|
}
|
|
|
|
/// Fetch with retry logic
|
|
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 < MAX_RETRIES {
|
|
retries += 1;
|
|
sleep(Duration::from_millis(RETRY_DELAY_MS * retries as u64)).await;
|
|
continue;
|
|
}
|
|
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)),
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
impl Default for WorldBankClient {
|
|
fn default() -> Self {
|
|
Self::new().expect("Failed to create WorldBank client")
|
|
}
|
|
}
|
|
|
|
// ============================================================================
|
|
// Alpha Vantage Client (Optional - Stock Market Data)
|
|
// ============================================================================
|
|
|
|
/// Alpha Vantage time series data
|
|
#[derive(Debug, Deserialize)]
|
|
struct AlphaVantageTimeSeriesResponse {
|
|
#[serde(rename = "Meta Data", default)]
|
|
meta_data: Option<serde_json::Value>,
|
|
#[serde(rename = "Time Series (Daily)", default)]
|
|
time_series: Option<HashMap<String, AlphaVantageDailyData>>,
|
|
}
|
|
|
|
#[derive(Debug, Deserialize)]
|
|
struct AlphaVantageDailyData {
|
|
#[serde(rename = "1. open")]
|
|
open: String,
|
|
#[serde(rename = "2. high")]
|
|
high: String,
|
|
#[serde(rename = "3. low")]
|
|
low: String,
|
|
#[serde(rename = "4. close")]
|
|
close: String,
|
|
#[serde(rename = "5. volume")]
|
|
volume: String,
|
|
}
|
|
|
|
/// Client for Alpha Vantage API (stock market data)
|
|
///
|
|
/// Provides access to:
|
|
/// - Daily stock prices
|
|
/// - Sector performance
|
|
/// - Technical indicators
|
|
///
|
|
/// **Note**: Free tier limited to 5 requests per minute, 500 per day
|
|
///
|
|
/// # Example
|
|
/// ```rust,ignore
|
|
/// use ruvector_data_framework::AlphaVantageClient;
|
|
///
|
|
/// let client = AlphaVantageClient::new("YOUR_API_KEY".to_string())?;
|
|
/// let aapl = client.get_daily_stock("AAPL").await?;
|
|
/// ```
|
|
pub struct AlphaVantageClient {
|
|
client: Client,
|
|
base_url: String,
|
|
api_key: String,
|
|
rate_limit_delay: Duration,
|
|
embedder: Arc<SimpleEmbedder>,
|
|
}
|
|
|
|
impl AlphaVantageClient {
|
|
/// Create a new Alpha Vantage client
|
|
///
|
|
/// # Arguments
|
|
/// * `api_key` - Alpha Vantage API key (get free key from https://www.alphavantage.co/support/#api-key)
|
|
pub fn new(api_key: String) -> Result<Self> {
|
|
let client = Client::builder()
|
|
.timeout(Duration::from_secs(30))
|
|
.build()
|
|
.map_err(FrameworkError::Network)?;
|
|
|
|
Ok(Self {
|
|
client,
|
|
base_url: "https://www.alphavantage.co/query".to_string(),
|
|
api_key,
|
|
rate_limit_delay: Duration::from_millis(ALPHAVANTAGE_RATE_LIMIT_MS),
|
|
embedder: Arc::new(SimpleEmbedder::new(256)),
|
|
})
|
|
}
|
|
|
|
/// Get daily stock price data
|
|
///
|
|
/// # Arguments
|
|
/// * `symbol` - Stock ticker symbol (e.g., "AAPL", "MSFT", "TSLA")
|
|
///
|
|
/// # Example
|
|
/// ```rust,ignore
|
|
/// let aapl = client.get_daily_stock("AAPL").await?;
|
|
/// ```
|
|
pub async fn get_daily_stock(&self, symbol: &str) -> Result<Vec<SemanticVector>> {
|
|
let url = format!(
|
|
"{}?function=TIME_SERIES_DAILY&symbol={}&apikey={}",
|
|
self.base_url, symbol, self.api_key
|
|
);
|
|
|
|
sleep(self.rate_limit_delay).await;
|
|
let response = self.fetch_with_retry(&url).await?;
|
|
let ts_response: AlphaVantageTimeSeriesResponse = response.json().await?;
|
|
|
|
let time_series = match ts_response.time_series {
|
|
Some(ts) => ts,
|
|
None => return Ok(Vec::new()),
|
|
};
|
|
|
|
let mut vectors = Vec::new();
|
|
for (date_str, data) in time_series.iter().take(100) {
|
|
// Parse values
|
|
let close = data.close.parse::<f64>().unwrap_or(0.0);
|
|
let volume = data.volume.parse::<f64>().unwrap_or(0.0);
|
|
|
|
// Parse date
|
|
let date = NaiveDate::parse_from_str(date_str, "%Y-%m-%d")
|
|
.ok()
|
|
.and_then(|d| d.and_hms_opt(0, 0, 0))
|
|
.map(|dt| dt.and_utc())
|
|
.unwrap_or_else(Utc::now);
|
|
|
|
// Create text for embedding
|
|
let text = format!(
|
|
"{} stock on {}: close ${}, volume {}",
|
|
symbol, date_str, close, volume
|
|
);
|
|
let embedding = self.embedder.embed_text(&text);
|
|
|
|
let mut metadata = HashMap::new();
|
|
metadata.insert("symbol".to_string(), symbol.to_string());
|
|
metadata.insert("date".to_string(), date_str.clone());
|
|
metadata.insert("open".to_string(), data.open.clone());
|
|
metadata.insert("high".to_string(), data.high.clone());
|
|
metadata.insert("low".to_string(), data.low.clone());
|
|
metadata.insert("close".to_string(), data.close.clone());
|
|
metadata.insert("volume".to_string(), data.volume.clone());
|
|
metadata.insert("source".to_string(), "alphavantage".to_string());
|
|
|
|
vectors.push(SemanticVector {
|
|
id: format!("AV:{}:{}", symbol, date_str),
|
|
embedding,
|
|
domain: Domain::Finance,
|
|
timestamp: date,
|
|
metadata,
|
|
});
|
|
}
|
|
|
|
Ok(vectors)
|
|
}
|
|
|
|
/// Fetch with retry logic
|
|
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 < MAX_RETRIES {
|
|
retries += 1;
|
|
sleep(Duration::from_millis(RETRY_DELAY_MS * retries as u64)).await;
|
|
continue;
|
|
}
|
|
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)),
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// ============================================================================
|
|
// Tests
|
|
// ============================================================================
|
|
|
|
#[cfg(test)]
|
|
mod tests {
|
|
use super::*;
|
|
|
|
#[tokio::test]
|
|
async fn test_fred_client_creation() {
|
|
let client = FredClient::new(None);
|
|
assert!(client.is_ok());
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_fred_client_with_key() {
|
|
let client = FredClient::new(Some("test_key".to_string()));
|
|
assert!(client.is_ok());
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_worldbank_client_creation() {
|
|
let client = WorldBankClient::new();
|
|
assert!(client.is_ok());
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_alphavantage_client_creation() {
|
|
let client = AlphaVantageClient::new("test_key".to_string());
|
|
assert!(client.is_ok());
|
|
}
|
|
|
|
#[test]
|
|
fn test_rate_limiting() {
|
|
// Verify rate limits are set correctly
|
|
let fred = FredClient::new(None).unwrap();
|
|
assert_eq!(fred.rate_limit_delay, Duration::from_millis(FRED_RATE_LIMIT_MS));
|
|
|
|
let wb = WorldBankClient::new().unwrap();
|
|
assert_eq!(wb.rate_limit_delay, Duration::from_millis(WORLDBANK_RATE_LIMIT_MS));
|
|
|
|
let av = AlphaVantageClient::new("test".to_string()).unwrap();
|
|
assert_eq!(av.rate_limit_delay, Duration::from_millis(ALPHAVANTAGE_RATE_LIMIT_MS));
|
|
}
|
|
}
|