Update entity database

.gitattributes

@@ -62,3 +62,5 @@ entities-lite.db filter=lfs diff=lfs merge=lfs -text
 entities.db filter=lfs diff=lfs merge=lfs -text
 entities-v5-lite.db filter=lfs diff=lfs merge=lfs -text
 entities-v5.db filter=lfs diff=lfs merge=lfs -text
+entities-v6-lite.db filter=lfs diff=lfs merge=lfs -text
+entities-v6.db filter=lfs diff=lfs merge=lfs -text

README.md

@@ -15,35 +15,36 @@ tags:
   - vector-search
   - embeddings
 size_categories:
-  - 1M<n<10M
+  - 10M<n<100M
 pretty_name: Entity References Database
 configs:
   - config_name: full
     description: Full database with complete source metadata
   - config_name: lite
-    description: Core fields + embeddings only (recommended)
+    description: Core fields only, record content stripped (recommended)
 ---
 
 # Entity References Database
 
-A comprehensive entity database for organizations, people, roles, and locations with 768-dimensional embeddings for semantic matching. Built from authoritative sources (GLEIF, SEC, Companies House, Wikidata) for entity linking and named entity disambiguation.
+A comprehensive entity database for organizations, people, roles, and locations with embedding-based semantic search. Built from authoritative sources (GLEIF, SEC, Companies House, Wikidata) for entity linking and named entity disambiguation.
 
 ## Dataset Description
 
 - **Repository:** [Corp-o-Rate-Community/entity-references](https://huggingface.co/datasets/Corp-o-Rate-Community/entity-references)
-- **Paper:** N/A
+- **Source Code:** [corp-o-rate/corp-entity-db](https://github.com/corp-o-rate/corp-entity-db)
 - **Point of Contact:** Corp-o-Rate-Community
 
 ### Dataset Summary
 
-This dataset provides fast lookup and qualification of named entities using vector similarity search. It stores records from authoritative global sources with embeddings generated by `google/embeddinggemma-300m` (768 dimensions).
+This dataset provides fast lookup and qualification of named entities using vector similarity search. It stores records from authoritative global sources with embeddings generated by `google/embeddinggemma-300m` (768 dimensions), stored in USearch HNSW indexes for sub-millisecond approximate nearest neighbor search.
 
 **Key Features:**
-- **8M+ organization records** from GLEIF, SEC Edgar, Companies House, and Wikidata
-- **Notable people** including executives, politicians, athletes, artists, and more
-- **Roles and locations** with hierarchical relationships
-- **Vector embeddings** for semantic similarity search
+- **9.7M+ organization records** from GLEIF, SEC Edgar, Companies House, and Wikidata
+- **63M+ people records** including executives, politicians, athletes, artists, and more
+- **139K+ roles** and **25K+ locations** with hierarchical relationships
+- **USearch HNSW indexes** for sub-millisecond vector search (embeddings never stored in SQLite)
 - **Canonical linking** across sources (same entity from multiple sources linked)
+- **Three-tier people search** achieving 100% acc@1 on 280 queries across 12 person types
 
 ### Supported Tasks
 
@@ -57,9 +58,9 @@ English (en)
 
 ## Dataset Structure
 
-### Schema (v2 - Normalized)
+### Schema (v5)
 
-The database uses SQLite with the [sqlite-vec](https://github.com/asg017/sqlite-vec) extension for vector similarity search.
+The database uses SQLite with normalized FK references (INTEGER FKs replace TEXT enums). All embeddings exist only in USearch HNSW indexes, never in SQLite.
 
 #### Organizations Table
 
@@ -68,7 +69,7 @@ The database uses SQLite with the [sqlite-vec](https://github.com/asg017/sqlite-
 | `id` | INTEGER | Primary key |
 | `qid` | INTEGER | Wikidata QID as integer (e.g., 2283 for Q2283) |
 | `name` | TEXT | Organization name |
-| `name_normalized` | TEXT | Lowercased, normalized name |
+| `name_normalized` | TEXT | Normalized name (via corp-names) |
 | `source_id` | INTEGER FK | Reference to source_types |
 | `source_identifier` | TEXT | LEI, CIK, Company Number, etc. |
 | `region_id` | INTEGER FK | Reference to locations |
@@ -77,7 +78,7 @@ The database uses SQLite with the [sqlite-vec](https://github.com/asg017/sqlite-
 | `to_date` | TEXT | Dissolution date (ISO format) |
 | `canon_id` | INTEGER | ID of canonical record |
 | `canon_size` | INTEGER | Size of canonical group |
-| `record` | JSON | Full source record (omitted in lite) |
+| `record` | JSON | Full source record (stripped in lite) |
 
 #### People Table
 
@@ -86,7 +87,7 @@ The database uses SQLite with the [sqlite-vec](https://github.com/asg017/sqlite-
 | `id` | INTEGER | Primary key |
 | `qid` | INTEGER | Wikidata QID as integer |
 | `name` | TEXT | Display name |
-| `name_normalized` | TEXT | Lowercased, normalized name |
+| `name_normalized` | TEXT | Normalized name (via corp-names) |
 | `source_id` | INTEGER FK | Reference to source_types |
 | `source_identifier` | TEXT | QID, Owner CIK, Person number |
 | `country_id` | INTEGER FK | Reference to locations |
@@ -98,7 +99,9 @@ The database uses SQLite with the [sqlite-vec](https://github.com/asg017/sqlite-
 | `to_date` | TEXT | Role end date (ISO format) |
 | `birth_date` | TEXT | Date of birth (ISO format) |
 | `death_date` | TEXT | Date of death (ISO format) |
-| `record` | JSON | Full source record (omitted in lite) |
+| `canon_id` | INTEGER | ID of canonical record |
+| `canon_size` | INTEGER | Size of canonical group |
+| `record` | JSON | Full source record (stripped in lite) |
 
 #### Roles Table
 
@@ -124,34 +127,46 @@ The database uses SQLite with the [sqlite-vec](https://github.com/asg017/sqlite-
 | `parent_ids` | TEXT JSON | Parent location IDs in hierarchy |
 | `location_type_id` | INTEGER FK | Reference to location_types |
 
-#### Embedding Tables (sqlite-vec)
+#### USearch HNSW Indexes
+
+All embeddings are generated on-the-fly during index building and stored only in versioned USearch HNSW index files:
+
+| File | Dimensions | Description |
+|------|-----------|-------------|
+| `organizations_usearch_v5.bin` | 768 | Organization embeddings |
+| `people_usearch_v5.bin` | 768 | Composite person embeddings (name\|role\|org as 3×256-dim segments) |
+| `people_identity_usearch_v5.bin` | 256 | Name-only person embeddings (Matryoshka-truncated) |
+
+The composite people index only includes people with org associations. Name, role, and org are embedded separately, independently L2-normalized, weighted (name=8, role=1, org=4), and concatenated into a 768-dim vector for AND-style matching.
+
+#### Metadata Table
 
 | Table | Columns |
 |-------|---------|
-| `organization_embeddings` | org_id INTEGER, embedding FLOAT[768] |
-| `organization_embeddings_scalar` | org_id INTEGER, embedding INT8[768] |
-| `person_embeddings` | person_id INTEGER, embedding FLOAT[768] |
-| `person_embeddings_scalar` | person_id INTEGER, embedding INT8[768] |
+| `db_info` | key TEXT PRIMARY KEY, value TEXT NOT NULL |
 
-**Scalar (int8) embeddings** provide 75% storage reduction with ~92% recall at top-100.
+Contains `schema_version = 5` for version detection.
 
 #### Enum Lookup Tables
 
 | Table | Values |
 |-------|--------|
-| `source_types` | gleif, sec_edgar, companies_house, wikidata |
-| `people_types` | executive, politician, government, military, legal, professional, academic, artist, media, athlete, entrepreneur, journalist, activist, scientist, unknown |
+| `source_types` | gleif, sec_edgar, companies_house, wikidata, pycountry |
+| `people_types` | executive, politician, government, military, legal, professional, academic, artist, media, athlete, journalist, activist, unknown |
 | `organization_types` | business, fund, branch, nonprofit, ngo, foundation, government, international_org, political_party, trade_union, educational, research, healthcare, media, sports, religious, unknown |
-| `simplified_location_types` | continent, country, subdivision, city, district, other |
+| `simplified_location_types` | continent, country, subdivision, city, district, historic, other |
 
 ### Data Splits
 
-| Config | Size | Contents |
-|--------|------|----------|
-| `entities-lite.db` | ~50GB | Core fields + embeddings only |
-| `entities.db` | ~74GB | Full records with source metadata |
+| Config | Contents |
+|--------|----------|
+| `entities-v5-lite.db` | Core fields, record content stripped, `name_normalized` kept |
+| `entities-v5.db` | Full records with source metadata |
+| `organizations_usearch_v5.bin` | USearch HNSW index for organization search |
+| `people_usearch_v5.bin` | Composite USearch HNSW index for people search |
+| `people_identity_usearch_v5.bin` | Identity USearch HNSW index for people name-only search |
 
-The lite version is recommended for most use cases.
+The lite version + USearch indexes is recommended for most use cases.
 
 ## Dataset Creation
 
@@ -161,25 +176,24 @@ The lite version is recommended for most use cases.
 
 | Source | Records | Identifier | Coverage |
 |--------|---------|------------|----------|
-| [GLEIF](https://www.gleif.org/) | ~3.2M | LEI (Legal Entity Identifier) | Global companies with LEI |
-| [SEC Edgar](https://www.sec.gov/) | ~100K+ | CIK (Central Index Key) | All SEC filers |
-| [Companies House](https://www.gov.uk/government/organisations/companies-house) | ~5M | Company Number | UK registered companies |
-| [Wikidata](https://www.wikidata.org/) | Variable | QID | Notable companies worldwide |
+| [Companies House](https://www.gov.uk/government/organisations/companies-house) | 5.5M | Company Number | UK registered companies |
+| [GLEIF](https://www.gleif.org/) | 2.6M | LEI (Legal Entity Identifier) | Global companies with LEI |
+| [Wikidata](https://www.wikidata.org/) | 1.5M | QID | Notable companies worldwide |
+| [SEC Edgar](https://www.sec.gov/) | 73K | CIK (Central Index Key) | All SEC filers |
 
 #### People
 
 | Source | Records | Identifier | Coverage |
 |--------|---------|------------|----------|
-| [Wikidata](https://www.wikidata.org/) | Variable | QID | Notable people with English Wikipedia |
-| [SEC Form 4](https://www.sec.gov/) | ~280K/year | Owner CIK | US public company insiders |
-| [Companies House](https://www.gov.uk/government/organisations/companies-house) | ~15M+ | Person number | UK company officers |
+| [Companies House](https://www.gov.uk/government/organisations/companies-house) | 27.5M | Person number | UK company officers |
+| [Wikidata](https://www.wikidata.org/) | 36M | QID | Notable people with English Wikipedia |
 
 ### Embedding Model
 
 | Property | Value |
 |----------|-------|
 | Model | `google/embeddinggemma-300m` |
-| Dimensions | 768 |
+| Dimensions | 768 (256 with Matryoshka truncation) |
 | Framework | sentence-transformers |
 | Size | ~300M parameters |
 
@@ -198,87 +212,81 @@ Records are linked across sources based on:
 2. Same normalized name + same organization
 3. Same normalized name + overlapping date ranges
 
-**Source priority:** gleif > sec_edgar > companies_house > wikidata
+**Source priority:** wikidata > sec_edgar > companies_house
 
 ## Usage
 
 ### Installation
 
 ```bash
-pip install corp-extractor
+pip install corp-entity-db
 ```
 
 ### Download
 
 ```bash
-# Download lite version (recommended)
-corp-extractor db download
+# Download lite version + USearch indexes (recommended)
+corp-entity-db download
 
-# Download full version
-corp-extractor db download --full
+# Download full version + USearch indexes
+corp-entity-db download --full
 ```
 
-**Storage location:** `~/.cache/corp-extractor/entities-v2.db`
+**Storage location:** `~/.cache/corp-extractor/entities-v5.db` (or `entities-v5-lite.db` for lite)
 
 ### Search
 
 ```bash
 # Search organizations
-corp-extractor db search "Microsoft"
+corp-entity-db search "Microsoft"
 
-# Search people
-corp-extractor db search-people "Tim Cook"
+# Search people (composite embedding + name fallback + identity fallback)
+corp-entity-db search-people "Tim Cook" --role CEO --org Apple
 
 # Search roles
-corp-extractor db search-roles "CEO"
+corp-entity-db search-roles "CEO"
 
 # Search locations
-corp-extractor db search-locations "California"
+corp-entity-db search-locations "California"
 
 # Check database status
-corp-extractor db status
+corp-entity-db status
 ```
 
 ### Python API
 
 ```python
-from statement_extractor.database import OrganizationDatabase, PersonDatabase
+from corp_entity_db import OrganizationDatabase, CompanyEmbedder, get_database_path
 
 # Search organizations
-org_db = OrganizationDatabase()
-matches = org_db.search_by_name("Microsoft Corporation", top_k=5)
-for match in matches:
-    print(f"{match.company.name} ({match.company.source}:{match.company.source_id})")
-    print(f"  Similarity: {match.similarity_score:.3f}")
-
-# Search people
-person_db = PersonDatabase()
-matches = person_db.search_by_name("Tim Cook", top_k=5)
-for match in matches:
-    print(f"{match.person.name} - {match.person.known_for_role} at {match.person.known_for_org}")
-```
-
-### Use in Pipeline
-
-```python
-from statement_extractor.pipeline import ExtractionPipeline
-
-pipeline = ExtractionPipeline()
-ctx = pipeline.process("Microsoft CEO Satya Nadella announced new AI features.")
-
-for stmt in ctx.labeled_statements:
-    print(f"{stmt.subject_fqn} --[{stmt.statement.predicate}]--> {stmt.object_fqn}")
+db = OrganizationDatabase(get_database_path())
+embedder = CompanyEmbedder()
+matches = db.search(embedder.embed("Microsoft"), top_k=10)
+for record, score in matches:
+    print(f"{record.name} ({record.entity_type}) - score: {score:.3f}")
+
+# Search people (composite embeddings + name fallback + identity fallback)
+from corp_entity_db import PersonDatabase, get_person_database
+person_db = get_person_database()
+query_emb = embedder.embed_composite_person("Tim Cook", role="CEO", org="Apple")
+matches = person_db.search(
+    query_emb, top_k=5, query_name="Tim Cook",
+    embedder=embedder, query_role="CEO", query_org="Apple",
+)
 ```
 
 ## Technical Details
 
 ### Vector Search Performance
 
+Using USearch HNSW indexes with int8 quantization:
+
 | Database Size | Search Time | Memory |
 |---------------|-------------|--------|
-| 100K records | ~50ms | ~500MB |
-| 1M records | ~200ms | ~3GB |
-| 8M records | ~500ms | ~20GB |
+| 100K records | <1ms | ~200MB |
+| 1M records | <5ms | ~1GB |
+| 10M records | <10ms | ~8GB |
+| 50M records | <20ms | ~40GB |
 
 ### Similarity Thresholds
 
@@ -289,51 +297,46 @@ for stmt in ctx.labeled_statements:
 | 0.55 - 0.70 | Moderate match (may need verification) |
 | < 0.55 | Weak match (likely different entity) |
 
-### Canonical ID Format
-
-| Source | Prefix | Example |
-|--------|--------|---------|
-| GLEIF | `LEI` | `LEI:INR2EJN1ERAN0W5ZP974` |
-| SEC Edgar | `SEC-CIK` | `SEC-CIK:0000789019` |
-| Companies House | `UK-CH` | `UK-CH:00445790` |
-| Wikidata | `WIKIDATA` | `WIKIDATA:Q2283` |
-
 ## Building from Source
 
 ```bash
 # Import data 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 100000
-corp-extractor db import-people --all --limit 50000
+corp-entity-db import-gleif --download
+corp-entity-db import-sec --download
+corp-entity-db import-companies-house
+corp-entity-db import-people --all
+corp-entity-db import-wikidata-dump --download --limit 50000
 
-# Link equivalent records
-corp-extractor db canonicalize
+# Post-import: build USearch indexes, VACUUM
+corp-entity-db post-import
 
-# Generate scalar embeddings (75% smaller)
-corp-extractor db backfill-scalar
+# Link equivalent records
+corp-entity-db canonicalize
 
 # Create lite version for deployment
-corp-extractor db create-lite ~/.cache/corp-extractor/entities.db
+corp-entity-db create-lite ~/.cache/corp-extractor/entities-v5.db
 ```
 
 ### Wikidata Dump Import (Recommended for Large Imports)
 
 ```bash
 # Download and import from Wikidata dump (~100GB)
-corp-extractor db import-wikidata-dump --download --limit 50000
+corp-entity-db import-wikidata-dump --download --limit 50000
 
 # Import only people
-corp-extractor db import-wikidata-dump --download --people --no-orgs
+corp-entity-db import-wikidata-dump --download --people --no-orgs
 
 # Import only locations
-corp-extractor db import-wikidata-dump --dump dump.json.bz2 --locations --no-people --no-orgs
+corp-entity-db import-wikidata-dump --dump dump.json.bz2 --locations --no-people --no-orgs
+```
 
-# Resume interrupted import
-corp-extractor db import-wikidata-dump --dump dump.bz2 --resume
+**Build extras** (optional):
+```bash
+pip install "corp-entity-db[build]"  # Adds orjson + indexed_bzip2
 ```
 
+The dump import uses a 3-thread parallel pipeline (reader → embedder → writer) and creates multiple records per person (one per position+org combination). Canonicalization runs automatically at the end. Supports `.bz2`, `.zst`, and `.gz` compressed dumps.
+
 ## Considerations for Using the Data
 
 ### Social Impact
@@ -351,7 +354,6 @@ This dataset enables entity linking for NLP applications. Users should be aware
 
 ### Limitations
 
-- The database does not automatically deduplicate across sources
 - Embedding similarity is not perfect for entity disambiguation
 - Updates require re-importing from source data
 
@@ -364,10 +366,10 @@ Apache 2.0
 If you use this dataset, please cite:
 
 ```bibtex
-@dataset{entity_references_2024,
+@dataset{entity_references_2025,
   title = {Entity References Database},
   author = {Corp-o-Rate-Community},
-  year = {2024},
+  year = {2025},
   publisher = {Hugging Face},
   url = {https://huggingface.co/datasets/Corp-o-Rate-Community/entity-references}
 }
@@ -379,4 +381,4 @@ Corp-o-Rate-Community
 
 ## Dataset Card Contact
 
-Open an issue on the [GitHub repository](https://github.com/corp-o-rate/statement-extractor) for questions or feedback.
+Open an issue on the [GitHub repository](https://github.com/corp-o-rate/corp-entity-db) for questions or feedback.

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+}