feat: sync wrapper for ONNX embeddings function

[vkozyura]

🆕 Add Local VECTOR_EMBEDDING Support via ONNX Runtime

This PR introduces local semantic search capabilities by implementing a VECTOR_EMBEDDING function powered by the ONNX runtime and the Xenova/all-MiniLM-L6-v2 model, enabling embedding generation without external service dependencies.

What Changed

New Files:

• lib/vector_embedding/index.js — Exports a synchronous vector_embedding(text, text_type, model_and_version) function. Auto-initializes the ONNX model on import and returns a JSON-stringified 384-dimensional float array. Returns a zero-filled vector for null/empty input.

• lib/vector_embedding/embedding.js — Core embedding logic: WordPiece tokenization, overlapping chunked processing for long texts, mean pooling, and L2 normalization using the ONNX model.

• lib/vector_embedding/InferenceSession.js — A synchronous wrapper around onnxruntime-common's InferenceSession, adapted to bypass async worker threads so it is compatible with SQLite's synchronous user-defined function API.

• lib/vector_embedding/model-utils.js — Utilities for file checks, on-demand model downloading from Hugging Face (~10MB, cached locally), vocabulary loading, pre-tokenization, and WordPiece tokenization.

Modified Files:

• package.json — Adds onnxruntime-node ^1.20.1 as a runtime dependency.
• CHANGELOG.md — Documents the new VECTOR_EMBEDDING function under Version 1.2.0.
• README.md — Adds documentation for the exported vector_embedding API, including function signature, parameters, return value, and error handling.

New Tests:

• tests/vector.test.js — Covers embedding dimensions (384), determinism, semantic similarity, edge cases (null/empty input), and multi-model support.

Note: Produces 384-dimensional vectors (vs. 768 in SAP HANA), optimized for local development scenarios. Model versions SAP_GXY.20250407 and SAP_GXY.20240715 are supported.

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• 🔄 Regenerate and Update Summary

PR Bot Information

Version: 1.29.18

• Summary Prompt: Default Prompt
• File Content Strategy: Full file content
• Event Trigger: pull_request.edited
• Output Template: Repository PR Template
• LLM: anthropic--claude-4.6-sonnet
• Correlation ID: 22c18060-90d0-11f1-9a98-bc3a3f408703

[hyperspace-pr-bot]

The PR introduces a useful SQLite VECTOR_EMBEDDING shim for local development, but has several substantive correctness issues: a race condition in prototype patching, repeated model initialization per connection, missing null-guards before synchronous inference, a chunking off-by-one that silently drops tokens, and unsafe file download handling that can leave corrupt cached models. Please address the comments above (including those from the prior review round) before merging.

PR Bot Information

Version: 1.29.9

• Correlation ID: adee7d50-8cc5-11f1-8581-44e300807992
• Event Trigger: pull_request.ready_for_review
• Experiment Variant: control
• File Content Strategy: Full file content
• LLM: anthropic--claude-4.6-sonnet

[sjvans]

adds almost 300 MB to build even though not needed in production