ruvector/examples/data/framework/src/crossref_client.rs
rUv 38d93a6e8d feat: Add comprehensive dataset discovery framework for RuVector (#104)
* 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>
2026-01-04 14:36:41 -05:00

836 lines
27 KiB
Rust

//! CrossRef API Integration
//!
//! This module provides an async client for fetching scholarly publications from CrossRef.org,
//! converting responses to SemanticVector format for RuVector discovery.
//!
//! # CrossRef API Details
//! - Base URL: https://api.crossref.org
//! - Free access, no authentication required
//! - Returns JSON responses
//! - Rate limit: ~50 requests/second with polite pool
//! - Polite pool: Include email in User-Agent or Mailto header for better rate limits
//!
//! # Example
//! ```rust,ignore
//! use ruvector_data_framework::crossref_client::CrossRefClient;
//!
//! let client = CrossRefClient::new(Some("your-email@example.com".to_string()));
//!
//! // Search publications by keywords
//! let vectors = client.search_works("machine learning", 20).await?;
//!
//! // Get work by DOI
//! let work = client.get_work("10.1038/nature12373").await?;
//!
//! // Search by funder
//! let funded = client.search_by_funder("10.13039/100000001", 10).await?;
//!
//! // Find recent publications
//! let recent = client.search_recent("quantum computing", "2024-01-01").await?;
//! ```
use std::collections::HashMap;
use std::time::Duration;
use chrono::{DateTime, NaiveDate, Utc};
use reqwest::{Client, StatusCode};
use serde::Deserialize;
use tokio::time::sleep;
use crate::api_clients::SimpleEmbedder;
use crate::ruvector_native::{Domain, SemanticVector};
use crate::{FrameworkError, Result};
/// Rate limiting configuration for CrossRef API
const CROSSREF_RATE_LIMIT_MS: u64 = 1000; // 1 second between requests for safety (API allows ~50/sec)
const MAX_RETRIES: u32 = 3;
const RETRY_DELAY_MS: u64 = 2000;
const DEFAULT_EMBEDDING_DIM: usize = 384;
// ============================================================================
// CrossRef API Structures
// ============================================================================
/// CrossRef API response for works search
#[derive(Debug, Deserialize)]
struct CrossRefResponse {
#[serde(default)]
message: CrossRefMessage,
}
#[derive(Debug, Default, Deserialize)]
struct CrossRefMessage {
#[serde(default)]
items: Vec<CrossRefWork>,
#[serde(rename = "total-results", default)]
total_results: Option<u64>,
}
/// CrossRef work (publication)
#[derive(Debug, Deserialize)]
struct CrossRefWork {
#[serde(rename = "DOI")]
doi: String,
#[serde(default)]
title: Vec<String>,
#[serde(rename = "abstract", default)]
abstract_text: Option<String>,
#[serde(default)]
author: Vec<CrossRefAuthor>,
#[serde(rename = "published-print", default)]
published_print: Option<CrossRefDate>,
#[serde(rename = "published-online", default)]
published_online: Option<CrossRefDate>,
#[serde(rename = "container-title", default)]
container_title: Vec<String>,
#[serde(rename = "is-referenced-by-count", default)]
citation_count: Option<u64>,
#[serde(rename = "references-count", default)]
references_count: Option<u64>,
#[serde(default)]
subject: Vec<String>,
#[serde(default)]
funder: Vec<CrossRefFunder>,
#[serde(rename = "type", default)]
work_type: Option<String>,
#[serde(default)]
publisher: Option<String>,
}
#[derive(Debug, Deserialize)]
struct CrossRefAuthor {
#[serde(default)]
given: Option<String>,
#[serde(default)]
family: Option<String>,
#[serde(default)]
name: Option<String>,
#[serde(rename = "ORCID", default)]
orcid: Option<String>,
}
#[derive(Debug, Deserialize)]
struct CrossRefDate {
#[serde(rename = "date-parts", default)]
date_parts: Vec<Vec<i32>>,
}
#[derive(Debug, Deserialize)]
struct CrossRefFunder {
#[serde(default)]
name: Option<String>,
#[serde(rename = "DOI", default)]
doi: Option<String>,
}
// ============================================================================
// CrossRef Client
// ============================================================================
/// Client for CrossRef.org scholarly publication API
///
/// Provides methods to search for publications, filter by various criteria,
/// and convert results to SemanticVector format for RuVector analysis.
///
/// # Rate Limiting
/// The client automatically enforces conservative rate limits (1 request/second).
/// Includes polite pool support via email configuration for better rate limits.
/// Includes retry logic for transient failures.
pub struct CrossRefClient {
client: Client,
embedder: SimpleEmbedder,
base_url: String,
polite_email: Option<String>,
}
impl CrossRefClient {
/// Create a new CrossRef API client
///
/// # Arguments
/// * `polite_email` - Email for polite pool access (optional but recommended for better rate limits)
///
/// # Example
/// ```rust,ignore
/// let client = CrossRefClient::new(Some("researcher@university.edu".to_string()));
/// ```
pub fn new(polite_email: Option<String>) -> Self {
Self::with_embedding_dim(polite_email, DEFAULT_EMBEDDING_DIM)
}
/// Create a new CrossRef API client with custom embedding dimension
///
/// # Arguments
/// * `polite_email` - Email for polite pool access
/// * `embedding_dim` - Dimension for text embeddings (default: 384)
pub fn with_embedding_dim(polite_email: Option<String>, embedding_dim: usize) -> Self {
let user_agent = if let Some(ref email) = polite_email {
format!("RuVector-Discovery/1.0 (mailto:{})", email)
} else {
"RuVector-Discovery/1.0".to_string()
};
Self {
client: Client::builder()
.user_agent(&user_agent)
.timeout(Duration::from_secs(30))
.build()
.expect("Failed to create HTTP client"),
embedder: SimpleEmbedder::new(embedding_dim),
base_url: "https://api.crossref.org".to_string(),
polite_email,
}
}
/// Search publications by keywords
///
/// # Arguments
/// * `query` - Search query (title, abstract, author, etc.)
/// * `limit` - Maximum number of results to return
///
/// # Example
/// ```rust,ignore
/// let vectors = client.search_works("climate change machine learning", 50).await?;
/// ```
pub async fn search_works(&self, query: &str, limit: usize) -> Result<Vec<SemanticVector>> {
let encoded_query = urlencoding::encode(query);
let mut url = format!(
"{}/works?query={}&rows={}",
self.base_url, encoded_query, limit
);
if let Some(email) = &self.polite_email {
url.push_str(&format!("&mailto={}", email));
}
self.fetch_and_parse(&url).await
}
/// Get a single work by DOI
///
/// # Arguments
/// * `doi` - Digital Object Identifier (e.g., "10.1038/nature12373")
///
/// # Example
/// ```rust,ignore
/// let work = client.get_work("10.1038/nature12373").await?;
/// ```
pub async fn get_work(&self, doi: &str) -> Result<Option<SemanticVector>> {
let normalized_doi = Self::normalize_doi(doi);
let mut url = format!("{}/works/{}", self.base_url, normalized_doi);
if let Some(email) = &self.polite_email {
url.push_str(&format!("?mailto={}", email));
}
sleep(Duration::from_millis(CROSSREF_RATE_LIMIT_MS)).await;
let response = self.fetch_with_retry(&url).await?;
let json_response: CrossRefResponse = response.json().await?;
if let Some(work) = json_response.message.items.into_iter().next() {
Ok(Some(self.work_to_vector(work)))
} else {
Ok(None)
}
}
/// Search publications funded by a specific organization
///
/// # Arguments
/// * `funder_id` - Funder DOI (e.g., "10.13039/100000001" for NSF)
/// * `limit` - Maximum number of results
///
/// # Example
/// ```rust,ignore
/// // Search NSF-funded research
/// let nsf_works = client.search_by_funder("10.13039/100000001", 20).await?;
/// ```
pub async fn search_by_funder(&self, funder_id: &str, limit: usize) -> Result<Vec<SemanticVector>> {
let mut url = format!(
"{}/funders/{}/works?rows={}",
self.base_url, funder_id, limit
);
if let Some(email) = &self.polite_email {
url.push_str(&format!("&mailto={}", email));
}
self.fetch_and_parse(&url).await
}
/// Search publications by subject area
///
/// # Arguments
/// * `subject` - Subject area or field
/// * `limit` - Maximum number of results
///
/// # Example
/// ```rust,ignore
/// let biology_works = client.search_by_subject("molecular biology", 30).await?;
/// ```
pub async fn search_by_subject(&self, subject: &str, limit: usize) -> Result<Vec<SemanticVector>> {
let encoded_subject = urlencoding::encode(subject);
let mut url = format!(
"{}/works?filter=has-subject:true&query.subject={}&rows={}",
self.base_url, encoded_subject, limit
);
if let Some(email) = &self.polite_email {
url.push_str(&format!("&mailto={}", email));
}
self.fetch_and_parse(&url).await
}
/// Get publications that cite a specific DOI
///
/// # Arguments
/// * `doi` - DOI of the work to find citations for
/// * `limit` - Maximum number of results
///
/// # Example
/// ```rust,ignore
/// let citing_works = client.get_citations("10.1038/nature12373", 15).await?;
/// ```
pub async fn get_citations(&self, doi: &str, limit: usize) -> Result<Vec<SemanticVector>> {
let normalized_doi = Self::normalize_doi(doi);
let mut url = format!(
"{}/works?filter=references:{}&rows={}",
self.base_url, normalized_doi, limit
);
if let Some(email) = &self.polite_email {
url.push_str(&format!("&mailto={}", email));
}
self.fetch_and_parse(&url).await
}
/// Search recent publications since a specific date
///
/// # Arguments
/// * `query` - Search query
/// * `from_date` - Start date in YYYY-MM-DD format
/// * `limit` - Maximum number of results
///
/// # Example
/// ```rust,ignore
/// let recent = client.search_recent("artificial intelligence", "2024-01-01", 25).await?;
/// ```
pub async fn search_recent(&self, query: &str, from_date: &str, limit: usize) -> Result<Vec<SemanticVector>> {
let encoded_query = urlencoding::encode(query);
let mut url = format!(
"{}/works?query={}&filter=from-pub-date:{}&rows={}",
self.base_url, encoded_query, from_date, limit
);
if let Some(email) = &self.polite_email {
url.push_str(&format!("&mailto={}", email));
}
self.fetch_and_parse(&url).await
}
/// Search publications by type
///
/// # Arguments
/// * `work_type` - Type of publication (e.g., "journal-article", "book-chapter", "proceedings-article", "dataset")
/// * `query` - Optional search query
/// * `limit` - Maximum number of results
///
/// # Supported Types
/// - `journal-article` - Journal articles
/// - `book-chapter` - Book chapters
/// - `proceedings-article` - Conference proceedings
/// - `dataset` - Research datasets
/// - `monograph` - Monographs
/// - `report` - Technical reports
///
/// # Example
/// ```rust,ignore
/// let datasets = client.search_by_type("dataset", Some("climate"), 10).await?;
/// let articles = client.search_by_type("journal-article", None, 20).await?;
/// ```
pub async fn search_by_type(
&self,
work_type: &str,
query: Option<&str>,
limit: usize,
) -> Result<Vec<SemanticVector>> {
let mut url = format!(
"{}/works?filter=type:{}&rows={}",
self.base_url, work_type, limit
);
if let Some(q) = query {
let encoded_query = urlencoding::encode(q);
url.push_str(&format!("&query={}", encoded_query));
}
if let Some(email) = &self.polite_email {
url.push_str(&format!("&mailto={}", email));
}
self.fetch_and_parse(&url).await
}
/// Fetch and parse CrossRef API response
async fn fetch_and_parse(&self, url: &str) -> Result<Vec<SemanticVector>> {
// Rate limiting
sleep(Duration::from_millis(CROSSREF_RATE_LIMIT_MS)).await;
let response = self.fetch_with_retry(url).await?;
let crossref_response: CrossRefResponse = response.json().await?;
// Convert works to SemanticVectors
let vectors = crossref_response
.message
.items
.into_iter()
.map(|work| self.work_to_vector(work))
.collect();
Ok(vectors)
}
/// Convert CrossRef work to SemanticVector
fn work_to_vector(&self, work: CrossRefWork) -> SemanticVector {
// Extract title
let title = work
.title
.first()
.cloned()
.unwrap_or_else(|| "Untitled".to_string());
// Extract abstract
let abstract_text = work.abstract_text.unwrap_or_default();
// Parse publication date (prefer print, fallback to online)
let timestamp = work
.published_print
.or(work.published_online)
.and_then(|date| Self::parse_crossref_date(&date))
.unwrap_or_else(Utc::now);
// Generate embedding from title + abstract
let combined_text = if abstract_text.is_empty() {
title.clone()
} else {
format!("{} {}", title, abstract_text)
};
let embedding = self.embedder.embed_text(&combined_text);
// Extract authors
let authors = work
.author
.iter()
.map(|a| Self::format_author_name(a))
.collect::<Vec<_>>()
.join("; ");
// Extract journal/container
let journal = work
.container_title
.first()
.cloned()
.unwrap_or_default();
// Extract subjects
let subjects = work.subject.join(", ");
// Extract funders
let funders = work
.funder
.iter()
.filter_map(|f| f.name.clone())
.collect::<Vec<_>>()
.join(", ");
// Build metadata
let mut metadata = HashMap::new();
metadata.insert("doi".to_string(), work.doi.clone());
metadata.insert("title".to_string(), title);
metadata.insert("abstract".to_string(), abstract_text);
metadata.insert("authors".to_string(), authors);
metadata.insert("journal".to_string(), journal);
metadata.insert("subjects".to_string(), subjects);
metadata.insert(
"citation_count".to_string(),
work.citation_count.unwrap_or(0).to_string(),
);
metadata.insert(
"references_count".to_string(),
work.references_count.unwrap_or(0).to_string(),
);
metadata.insert("funders".to_string(), funders);
metadata.insert(
"type".to_string(),
work.work_type.unwrap_or_else(|| "unknown".to_string()),
);
if let Some(publisher) = work.publisher {
metadata.insert("publisher".to_string(), publisher);
}
metadata.insert("source".to_string(), "crossref".to_string());
SemanticVector {
id: format!("doi:{}", work.doi),
embedding,
domain: Domain::Research,
timestamp,
metadata,
}
}
/// Parse CrossRef date format
fn parse_crossref_date(date: &CrossRefDate) -> Option<DateTime<Utc>> {
if let Some(parts) = date.date_parts.first() {
if parts.is_empty() {
return None;
}
let year = parts[0];
let month = parts.get(1).copied().unwrap_or(1).max(1).min(12);
let day = parts.get(2).copied().unwrap_or(1).max(1).min(31);
NaiveDate::from_ymd_opt(year, month as u32, day as u32)
.and_then(|d| d.and_hms_opt(0, 0, 0))
.map(|dt| dt.and_utc())
} else {
None
}
}
/// Format author name from CrossRef author structure
fn format_author_name(author: &CrossRefAuthor) -> String {
if let Some(name) = &author.name {
name.clone()
} else {
let given = author.given.as_deref().unwrap_or("");
let family = author.family.as_deref().unwrap_or("");
format!("{} {}", given, family).trim().to_string()
}
}
/// Normalize DOI (remove http://, https://, doi.org/ prefixes)
fn normalize_doi(doi: &str) -> String {
doi.trim()
.trim_start_matches("http://")
.trim_start_matches("https://")
.trim_start_matches("doi.org/")
.trim_start_matches("dx.doi.org/")
.to_string()
}
/// 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;
tracing::warn!(
"Rate limited by CrossRef, retrying in {}ms",
RETRY_DELAY_MS * retries as u64
);
sleep(Duration::from_millis(RETRY_DELAY_MS * retries as u64)).await;
continue;
}
if !response.status().is_success() {
return Err(FrameworkError::Network(
reqwest::Error::from(response.error_for_status().unwrap_err()),
));
}
return Ok(response);
}
Err(_) if retries < MAX_RETRIES => {
retries += 1;
tracing::warn!("Request failed, retrying ({}/{})", retries, MAX_RETRIES);
sleep(Duration::from_millis(RETRY_DELAY_MS * retries as u64)).await;
}
Err(e) => return Err(FrameworkError::Network(e)),
}
}
}
}
impl Default for CrossRefClient {
fn default() -> Self {
Self::new(None)
}
}
// ============================================================================
// Tests
// ============================================================================
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_crossref_client_creation() {
let client = CrossRefClient::new(Some("test@example.com".to_string()));
assert_eq!(client.base_url, "https://api.crossref.org");
assert_eq!(client.polite_email, Some("test@example.com".to_string()));
}
#[test]
fn test_crossref_client_without_email() {
let client = CrossRefClient::new(None);
assert_eq!(client.base_url, "https://api.crossref.org");
assert_eq!(client.polite_email, None);
}
#[test]
fn test_custom_embedding_dim() {
let client = CrossRefClient::with_embedding_dim(None, 512);
let embedding = client.embedder.embed_text("test");
assert_eq!(embedding.len(), 512);
}
#[test]
fn test_normalize_doi() {
assert_eq!(
CrossRefClient::normalize_doi("10.1038/nature12373"),
"10.1038/nature12373"
);
assert_eq!(
CrossRefClient::normalize_doi("http://doi.org/10.1038/nature12373"),
"10.1038/nature12373"
);
assert_eq!(
CrossRefClient::normalize_doi("https://dx.doi.org/10.1038/nature12373"),
"10.1038/nature12373"
);
assert_eq!(
CrossRefClient::normalize_doi(" 10.1038/nature12373 "),
"10.1038/nature12373"
);
}
#[test]
fn test_parse_crossref_date() {
// Full date
let date1 = CrossRefDate {
date_parts: vec![vec![2024, 3, 15]],
};
let parsed1 = CrossRefClient::parse_crossref_date(&date1);
assert!(parsed1.is_some());
let dt1 = parsed1.unwrap();
assert_eq!(dt1.format("%Y-%m-%d").to_string(), "2024-03-15");
// Year and month only
let date2 = CrossRefDate {
date_parts: vec![vec![2024, 3]],
};
let parsed2 = CrossRefClient::parse_crossref_date(&date2);
assert!(parsed2.is_some());
// Year only
let date3 = CrossRefDate {
date_parts: vec![vec![2024]],
};
let parsed3 = CrossRefClient::parse_crossref_date(&date3);
assert!(parsed3.is_some());
// Empty date parts
let date4 = CrossRefDate {
date_parts: vec![vec![]],
};
let parsed4 = CrossRefClient::parse_crossref_date(&date4);
assert!(parsed4.is_none());
}
#[test]
fn test_format_author_name() {
// Full name
let author1 = CrossRefAuthor {
given: Some("John".to_string()),
family: Some("Doe".to_string()),
name: None,
orcid: None,
};
assert_eq!(
CrossRefClient::format_author_name(&author1),
"John Doe"
);
// Name field only
let author2 = CrossRefAuthor {
given: None,
family: None,
name: Some("Jane Smith".to_string()),
orcid: None,
};
assert_eq!(
CrossRefClient::format_author_name(&author2),
"Jane Smith"
);
// Family name only
let author3 = CrossRefAuthor {
given: None,
family: Some("Einstein".to_string()),
name: None,
orcid: None,
};
assert_eq!(
CrossRefClient::format_author_name(&author3),
"Einstein"
);
}
#[test]
fn test_work_to_vector() {
let client = CrossRefClient::new(None);
let work = CrossRefWork {
doi: "10.1234/example.2024".to_string(),
title: vec!["Deep Learning for Climate Science".to_string()],
abstract_text: Some("We propose a novel approach to climate modeling...".to_string()),
author: vec![
CrossRefAuthor {
given: Some("Alice".to_string()),
family: Some("Johnson".to_string()),
name: None,
orcid: Some("0000-0001-2345-6789".to_string()),
},
CrossRefAuthor {
given: Some("Bob".to_string()),
family: Some("Smith".to_string()),
name: None,
orcid: None,
},
],
published_print: Some(CrossRefDate {
date_parts: vec![vec![2024, 6, 15]],
}),
published_online: None,
container_title: vec!["Nature Climate Change".to_string()],
citation_count: Some(42),
references_count: Some(35),
subject: vec!["Climate Science".to_string(), "Machine Learning".to_string()],
funder: vec![CrossRefFunder {
name: Some("National Science Foundation".to_string()),
doi: Some("10.13039/100000001".to_string()),
}],
work_type: Some("journal-article".to_string()),
publisher: Some("Nature Publishing Group".to_string()),
};
let vector = client.work_to_vector(work);
assert_eq!(vector.id, "doi:10.1234/example.2024");
assert_eq!(vector.domain, Domain::Research);
assert_eq!(
vector.metadata.get("doi").unwrap(),
"10.1234/example.2024"
);
assert_eq!(
vector.metadata.get("title").unwrap(),
"Deep Learning for Climate Science"
);
assert_eq!(
vector.metadata.get("authors").unwrap(),
"Alice Johnson; Bob Smith"
);
assert_eq!(
vector.metadata.get("journal").unwrap(),
"Nature Climate Change"
);
assert_eq!(vector.metadata.get("citation_count").unwrap(), "42");
assert_eq!(
vector.metadata.get("subjects").unwrap(),
"Climate Science, Machine Learning"
);
assert_eq!(
vector.metadata.get("funders").unwrap(),
"National Science Foundation"
);
assert_eq!(vector.metadata.get("type").unwrap(), "journal-article");
assert_eq!(
vector.metadata.get("publisher").unwrap(),
"Nature Publishing Group"
);
assert_eq!(vector.embedding.len(), DEFAULT_EMBEDDING_DIM);
}
#[tokio::test]
#[ignore] // Ignore by default to avoid hitting CrossRef API in tests
async fn test_search_works_integration() {
let client = CrossRefClient::new(Some("test@example.com".to_string()));
let results = client.search_works("machine learning", 5).await;
assert!(results.is_ok());
let vectors = results.unwrap();
assert!(vectors.len() <= 5);
if !vectors.is_empty() {
let first = &vectors[0];
assert!(first.id.starts_with("doi:"));
assert_eq!(first.domain, Domain::Research);
assert!(first.metadata.contains_key("title"));
assert!(first.metadata.contains_key("doi"));
}
}
#[tokio::test]
#[ignore] // Ignore by default to avoid hitting CrossRef API in tests
async fn test_get_work_integration() {
let client = CrossRefClient::new(Some("test@example.com".to_string()));
// Try to fetch a known work (Nature paper on AlphaFold)
let result = client.get_work("10.1038/s41586-021-03819-2").await;
assert!(result.is_ok());
let work = result.unwrap();
assert!(work.is_some());
let vector = work.unwrap();
assert_eq!(vector.id, "doi:10.1038/s41586-021-03819-2");
assert_eq!(vector.domain, Domain::Research);
}
#[tokio::test]
#[ignore] // Ignore by default to avoid hitting CrossRef API in tests
async fn test_search_by_funder_integration() {
let client = CrossRefClient::new(Some("test@example.com".to_string()));
// Search NSF-funded works
let results = client.search_by_funder("10.13039/100000001", 3).await;
assert!(results.is_ok());
let vectors = results.unwrap();
assert!(vectors.len() <= 3);
}
#[tokio::test]
#[ignore] // Ignore by default to avoid hitting CrossRef API in tests
async fn test_search_by_type_integration() {
let client = CrossRefClient::new(Some("test@example.com".to_string()));
// Search for datasets
let results = client.search_by_type("dataset", Some("climate"), 5).await;
assert!(results.is_ok());
let vectors = results.unwrap();
assert!(vectors.len() <= 5);
}
#[tokio::test]
#[ignore] // Ignore by default to avoid hitting CrossRef API in tests
async fn test_search_recent_integration() {
let client = CrossRefClient::new(Some("test@example.com".to_string()));
// Search recent papers
let results = client
.search_recent("quantum computing", "2024-01-01", 5)
.await;
assert!(results.is_ok());
let vectors = results.unwrap();
assert!(vectors.len() <= 5);
}
}