Create README.md

README.md

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+---
+license: mit
+task_categories:
+  - text-classification
+  - feature-extraction
+language:
+  - en
+tags:
+  - entity-resolution
+  - named-entity-recognition
+  - company-data
+  - embeddings
+  - sqlite
+  - vector-search
+size_categories:
+  - 1M<n<10M
+---
+
+# Entity References Database
+
+A pre-built SQLite database with vector embeddings for organization/entity resolution. Used by the [corp-extractor](https://pypi.org/project/corp-extractor/) library for fast entity qualification via embedding similarity search.
+
+## Overview
+
+This database contains organization records from multiple authoritative sources, each with:
+- Organization name (canonical)
+- Source identifier (LEI, CIK, UK Company Number, Wikidata QID)
+- Entity type classification (business, nonprofit, government, etc.)
+- Vector embeddings for semantic search (768-dim, using [embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m))
+
+## Data Sources
+
+| Source | Records | Identifier | Description |
+|--------|---------|------------|-------------|
+| GLEIF | ~3.2M | LEI (Legal Entity Identifier) | Global legal entities from the LEI system |
+| SEC Edgar | ~100K+ | CIK (Central Index Key) | US SEC-registered filers |
+| Companies House | ~5M | UK Company Number | UK registered companies |
+| Wikidata | Variable | Wikidata QID | Notable organizations from Wikidata |
+
+## Entity Types
+
+Organizations are classified into the following types:
+
+| Category | Types |
+|----------|-------|
+| Business | `business`, `fund`, `branch` |
+| Non-profit | `nonprofit`, `ngo`, `foundation`, `trade_union` |
+| Government | `government`, `international_org`, `political_party` |
+| Other | `educational`, `research`, `healthcare`, `media`, `sports`, `religious`, `unknown` |
+
+## Database Variants
+
+| File | Description | Use Case |
+|------|-------------|----------|
+| `entities.db` | Full database with complete source record metadata | When you need full record details |
+| `entities-lite.db` | Lite version without record data | Default - faster download, smaller size |
+| `entities.db.gz` | Compressed full database | When bandwidth is limited |
+| `entities-lite.db.gz` | Compressed lite database | Smallest download size |
+
+## Schema
+
+### organizations table
+```sql
+CREATE TABLE organizations (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    name TEXT NOT NULL,
+    name_normalized TEXT NOT NULL,
+    source TEXT NOT NULL,  -- 'gleif', 'sec_edgar', 'companies_house', 'wikipedia'
+    source_id TEXT NOT NULL,
+    region TEXT NOT NULL DEFAULT '',
+    entity_type TEXT NOT NULL DEFAULT 'unknown',
+    record TEXT NOT NULL,  -- JSON with full source record (empty in lite version)
+    UNIQUE(source, source_id)
+);
+```
+
+### organization_embeddings table (sqlite-vec)
+```sql
+CREATE VIRTUAL TABLE organization_embeddings USING vec0(
+    org_id INTEGER PRIMARY KEY,
+    embedding float[768]
+);
+```
+
+## Usage with corp-extractor
+
+```bash
+# Install
+pip install corp-extractor
+
+# Download the database (lite version by default)
+corp-extractor db download
+
+# Download full version
+corp-extractor db download --full
+
+# Search for an organization
+corp-extractor db search "Microsoft"
+
+# Check database status
+corp-extractor db status
+```
+
+### Python API
+
+```python
+from statement_extractor.database import OrganizationDatabase, CompanyEmbedder
+
+# Load database
+database = OrganizationDatabase()
+embedder = CompanyEmbedder()
+
+# Search by embedding similarity
+query_embedding = embedder.embed("Microsoft Corporation")
+results = database.search(query_embedding, top_k=5)
+
+for record, similarity in results:
+    print(f"{record.name} ({record.source}:{record.source_id}) - {similarity:.3f}")
+```
+
+## Building Your Own Database
+
+```bash
+# Import from authoritative sources
+corp-extractor db import-gleif --download
+corp-extractor db import-sec --download
+corp-extractor db import-companies-house --download
+corp-extractor db import-wikidata --limit 50000
+
+# Upload to HuggingFace
+export HF_TOKEN="hf_..."
+corp-extractor db upload
+```
+
+## License
+
+MIT License - the database structure and embedding generation code are MIT licensed.
+
+Individual data sources have their own licenses:
+- GLEIF: Open license for LEI data
+- SEC Edgar: Public domain (US government)
+- Companies House: Open Government Licence
+- Wikidata: CC0 (public domain)
+
+## Links
+
+- [Corp-Extractor on PyPI](https://pypi.org/project/corp-extractor/)
+- [Corp-Extractor GitHub](https://github.com/corp-o-rate/statement-extractor)
+- [Statement Extractor Model](https://huggingface.co/Corp-o-Rate-Community/statement-extractor)