ruvector/crates/ruvector-postgres/scripts/download_models.rs
rUv 9cf95ff6ae
feat(rvlite): Add multi-query language support (SPARQL, SQL, Cypher) (#69)
* fix(rvlite): Resolve getrandom WASM conflict with hnsw_rs patch

Resolves the getrandom version conflict that prevented rvlite from
compiling to WASM. The issue was caused by hnsw_rs 0.3.3 using
rand 0.9 -> getrandom 0.3, while the workspace uses rand 0.8 ->
getrandom 0.2.

Changes:
- Add [patch.crates-io] to workspace Cargo.toml for hnsw_rs
- Include patched hnsw_rs 0.3.3 with rand 0.8 dependency
- Modify hnsw_rs/Cargo.toml: rand = "0.8" (was "0.9")

Note: This patch is applied but not actively used since rvlite
disables the HNSW feature via default-features = false. The patch
ensures compatibility if HNSW is enabled in the future.

Build Status:
 WASM compiles successfully
 Bundle size: 96 KB gzipped (with ruvector-core)
 Full vector operations working
 No getrandom conflicts

Related:
- rvlite uses ruvector-core with memory-only feature
- Avoids hnsw_rs dependency via default-features = false
- Target-specific getrandom dependency enables "js" feature

🤖 Generated with Claude Code

* feat(rvlite): Add multi-query language support (SPARQL, SQL, Cypher)

This comprehensive update adds support for three query languages to rvlite,
making it a versatile WASM-powered vector database with knowledge graph
capabilities. The implementation includes full parsers, AST representations,
and executors for each language.

## SPARQL Implementation
- W3C SPARQL 1.1 compliant query parser
- Triple pattern matching with subject/predicate/object
- SELECT, CONSTRUCT, ASK, and DESCRIBE query forms
- FILTER expressions with comparison and logical operators
- OPTIONAL patterns and UNION support
- ORDER BY, LIMIT, OFFSET modifiers
- Built-in RDF triple store with in-memory indexing

## SQL Implementation
- Standard SQL SELECT with projections and aliases
- WHERE clause with complex boolean expressions
- JOIN support (INNER, LEFT, RIGHT, FULL, CROSS)
- Aggregate functions (COUNT, SUM, AVG, MIN, MAX)
- GROUP BY and HAVING clauses
- ORDER BY with ASC/DESC, LIMIT/OFFSET
- Subqueries and nested expressions
- Vector similarity search via special syntax

## Cypher Implementation
- Neo4j-compatible Cypher query language
- MATCH patterns with node and relationship traversal
- CREATE, MERGE, SET, DELETE operations
- WHERE clause filtering
- RETURN with aliases and expressions
- ORDER BY, SKIP, LIMIT modifiers
- Variable-length path patterns
- Property graph store with adjacency indexing

## Additional Changes
- Interactive React dashboard with visualization
- Supply chain simulation demo
- Graph visualization components
- IndexedDB persistence layer for browser storage
- WASM getrandom conflict resolution for hnsw_rs
- SONA time compatibility for cross-platform builds
- NPM package for rvlite distribution
- Documentation for all query implementations

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2025-12-11 13:52:23 -05:00

48 lines
1.7 KiB
Rust

//! Pre-download ONNX embedding models for Docker build
//!
//! This script downloads the default embedding model during Docker build
//! so it's available immediately at runtime without network access.
use fastembed::{TextEmbedding, InitOptions, EmbeddingModel};
fn main() {
println!("=== Downloading Embedding Models ===");
// Download the default model (all-MiniLM-L6-v2)
println!("Downloading all-MiniLM-L6-v2...");
let options = InitOptions::new(EmbeddingModel::AllMiniLML6V2)
.with_show_download_progress(true);
match TextEmbedding::try_new(options) {
Ok(mut model) => {
// Test the model works
let result = model.embed(vec!["test"], None);
match result {
Ok(embeddings) => {
println!("✓ Model loaded successfully");
println!(" Embedding dimensions: {}", embeddings[0].len());
}
Err(e) => {
eprintln!("✗ Model test failed: {}", e);
std::process::exit(1);
}
}
}
Err(e) => {
eprintln!("✗ Failed to download model: {}", e);
std::process::exit(1);
}
}
// Optionally download BGE-small for better quality
println!("\nDownloading BAAI/bge-small-en-v1.5...");
let options = InitOptions::new(EmbeddingModel::BGESmallENV15)
.with_show_download_progress(true);
match TextEmbedding::try_new(options) {
Ok(_) => println!("✓ BGE-small model loaded successfully"),
Err(e) => println!("⚠ BGE-small download failed (optional): {}", e),
}
println!("\n=== Model Download Complete ===");
}