diff --git a/PROVENANCE.md b/PROVENANCE.md index 0b8be4b48339fc4dd3291e1b66ab703faa62be02..e4a431b9dbfc3f4ce4bc9591397e2c47d9d106ec 100644 --- a/PROVENANCE.md +++ b/PROVENANCE.md @@ -1,145 +1,131 @@ # Data Provenance -This document describes the origin, processing method, and lineage of every table in the Epstein Document Archive. +Per-table documentation of data sources, extraction methods, and quality notes. -## Overview +## documents -The dataset combines two OCR pipelines and multiple community-curated layers: +**1,424,673 rows.** One row per PDF file from the DOJ Epstein release. -| Source | Files | Method | Quality | -|--------|-------|--------|---------| -| Gemini OCR | 848,228 | Gemini 2.5 Flash Lite | High (structured JSON output) | -| Community Tesseract | 537,622 | Tesseract OCR via community repos | Variable | -| Community curation | -- | Manual + automated analysis | Curated | -| ML recovery | 39,588 pages | Redaction recovery model | Experimental | +- **856,028 files** processed with **Gemini 2.5 Flash Lite** ($0.10/$0.40 per 1M tokens). Full structured extraction: document type classification, date parsing, entity extraction, handwriting/stamp detection, photo description. +- **531,279 files** (DataSet 9) imported from the [rhowardstone/Epstein-research-data](https://github.com/rhowardstone/Epstein-research-data) community project using **Tesseract OCR**. Raw text only — no entity extraction or document classification. These have `ocr_source = 'tesseract-community'`. +- **1,377 files** originally Tesseract, upgraded to Gemini in v2 (604 from DS10, 773 from other datasets). +- **37,369 files** have `is_photo = true` (photos, stamps, blank pages). +- `email_fields` column (new in v2): JSON-encoded email metadata (from, to, cc, subject, date) for email-type documents. -**Success rate: 99.97%** (1,385,850 / 1,386,322 attempted). +Distinguish OCR source: `ocr_source IS NULL` = Gemini, `ocr_source = 'tesseract-community'` = community. -## Per-table provenance +## entities -### `documents` (1,413,765 rows) +**10,629,198 rows.** Named entities extracted from documents. -| Subset | Rows | Source | Method | -|--------|------|--------|--------| -| `ocr_source IS NULL` | 848,228 | DOJ FOIA PDFs | Gemini 2.5 Flash Lite (`gemini-2.5-flash-lite`) via Google AI API. Each PDF page sent as image, returns structured JSON with full text, document type, date, entity extraction, and metadata. | -| `ocr_source = 'tesseract-community'` | 537,622 | Community repositories | Tesseract OCR. Primary source: [rhowardstone/Epstein-research-data](https://github.com/rhowardstone/Epstein-research-data) for DataSet 9 (531K files). Remaining community files fill gaps in DataSets 2-5, 12, FBIVault, and HouseOversightEstate. | +- Gemini documents: entities come from the structured JSON extraction prompt (types: person, organization, location, date, reference_number, email_address, phone_number, monetary_amount). +- Community documents: entities from post-processing NER pipeline. +- Entity `normalized_value` provides cleaned/canonical forms where available. -**Identifying OCR source**: Check the `ocr_source` column. NULL = Gemini, `"tesseract-community"` = community Tesseract. +## chunks -### `entities` (8,542,849 rows) +**2,193,090 rows.** Text chunks for RAG (retrieval-augmented generation). -Extracted entities (people, organizations, locations, dates, reference numbers) linked to their parent document via `file_key`. +- ~800 token target per chunk with overlap at sentence boundaries. +- `char_start` and `char_end` map back to the parent document's `full_text`. -- **Gemini documents**: Entities extracted by Gemini as part of OCR (structured JSON output includes entity arrays). -- **Community documents**: Entities extracted by post-processing NER on Tesseract OCR text. -- **Entity types**: `person`, `organization`, `location`, `date`, `reference_number`. +## embeddings_chunk -### `chunks` (2,039,205 rows) +**2,111,356 rows.** 768-dimensional float32 vectors from `gemini-embedding-001`. -Text chunks derived from `documents.full_text` using ~800-token splitting with overlap. Used for retrieval-augmented generation (RAG). +- ~96% coverage (documents with malformed text excluded). +- Stored as `list` in Parquet. +- `source_text_hash` links to the chunk text that was embedded. -- **Source**: Derived from the full text of each document. -- **Method**: Token-count-based splitting with character offset tracking. -- **Fields**: `file_key`, `chunk_index`, `content`, `token_count`, `char_start`, `char_end`. +## persons -### `embeddings_chunk` (1,956,803 rows) +**1,614 rows.** Curated person registry with canonical names, aliases, and categories. -768-dimensional float32 embedding vectors for text chunks. +- Categories: perpetrator, victim, associate, other. +- `aliases` field: JSON array of known alternate names/spellings. +- `search_terms`: additional search patterns for entity resolution. +- Community-curated from multiple sources. -- **Model**: `gemini-embedding-001` (Gemini Embedding v1). -- **Input**: `chunks.content` (the chunk text). -- **Coverage**: 96% of chunks (1,956,803 / 2,039,205). 1,249 malformed embeddings excluded. -- **Format**: In Parquet, stored as `list` with fixed size 768. In SQLite, stored as raw `float32` BLOB (3,072 bytes per embedding). -- **Note**: Summary embeddings (one per document) were removed from the published dataset. 92% of documents contain a single chunk, making summary and chunk embeddings identical. For multi-chunk documents, use the first chunk embedding or mean-pool across chunks for a document-level vector. +## kg_entities / kg_relationships -### `persons` (1,614 rows) +**467 entities, 2,198 relationships.** Knowledge graph connecting people, organizations, and locations. -Curated registry of people mentioned across the corpus. +- Relationship types: traveled_with, associated_with, employed_by, legal_representative, financial_connection, etc. +- `weight` indicates strength of connection (co-occurrence frequency). +- `evidence` field links to source document file_keys. -- **Source**: Community curation, combining automated entity extraction with manual verification. -- **Fields**: `canonical_name`, `slug`, `category` (perpetrator/victim/associate/other), `aliases` (JSON array), `search_terms`, `sources`, `notes`. -- **Upstream**: `release/persons_registry.json`. +## recovered_redactions -### `kg_entities` (467 rows) +**37,870 rows.** Text recovered from under redaction bars using ML reconstruction. -Knowledge graph entities with metadata. +- `interest_score` (0-100) ranks significance of recovered content. +- `names_found` lists person names detected in recovered text. +- Experimental quality — treat as leads, not verified text. -- **Source**: Community curation -- manually identified key entities with occupation, legal status, and mention counts. -- **Fields**: `name`, `entity_type`, `description`, `metadata` (JSON with occupation, legal_status, mention counts). -- **Upstream**: `release/knowledge_graph_entities.json`. +## financial_transactions (NEW in v2) -### `kg_relationships` (4,190 rows) +**49,770 rows.** Credit card and bank transaction records extracted with DeepSeek. -Knowledge graph relationships between entities. +- Source: DOJ-released credit card statements and bank records (DataSet 10/11). +- Extraction model: DeepSeek (per-page structured extraction). +- 31% of raw extractions quarantined for quality issues (hallucinated amounts, duplicate entries, garbled OCR). Only clean records included. +- Flight fields (flight_from, flight_to, flight_carrier, flight_passenger) populated for airline purchases. +- Key cardholders: JEFFREY E EPSTEIN, GHISLAINE MAXWELL, KARYNA SHULIAK, HBRK ASSOCIATES, TERRAMAR PROJECT. -- **Source**: Community curation -- relationships extracted from document analysis. -- **Fields**: `source_name`, `target_name`, `relationship_type` (e.g., traveled_with, associated_with, employed_by), `weight`, `evidence`, `metadata`. -- **Relationship types**: traveled_with, associated_with, employed_by, legal_representative, financial_connection, and others. -- **Upstream**: `release/knowledge_graph_relationships.json`. +## communication_records (NEW in v2) -### `recovered_redactions` (39,588 rows) +**128 rows.** Phone call and cell-site/CDR records. -Text recovered from behind redaction bars in DOJ documents. +- Source: DOJ-released phone records. +- 99.8% of raw extractions quarantined (CDR data has very high hallucination rates). Only 128 high-confidence records included. +- Fields: call date/time, duration, direction, location, number called, provider. -- **Source**: Community ML analysis of redacted document pages. -- **Method**: Machine learning model trained to reconstruct text obscured by redaction bars. -- **Fields**: `file_key`, `page_number`, `reconstructed_text`, `interest_score`, `names_found`, `document_type`. -- **Quality**: Experimental. Higher `interest_score` indicates more significant recovered content. -- **Upstream**: `release/redacted_text_recovered.json.gz`. +## investigative_records (NEW in v2) -### `provenance` (pipeline audit trail) +**143 rows.** Law enforcement reports, evidence recovery logs, and vehicle/property records. -Three sub-tables from the OCR processing pipeline: +- Source: FBI, PBSO, and other agency reports in DOJ release. +- 87% of raw extractions quarantined. Only 143 verified records included. +- Record types: law_enforcement_report, evidence_recovery_log, vehicle_property_record. -| Sub-table | Rows | Description | -|-----------|------|-------------| -| `provenance/files` | 1,386,322 | Per-file processing record: SHA-256 hashes, status, tokens, latency, model | -| `provenance/audit_log` | ~3.6M | Append-only event log: every processing step recorded | -| `provenance/runs` | 120 | Pipeline run metadata: start/end times, success/failure counts | +## derived_events (NEW in v2) -- **Source**: `epstein_provenance.db` -- the forensic audit trail from the OCR pipeline. -- **Key fields on `files`**: `file_key`, `pdf_sha256`, `output_sha256`, `status`, `input_tokens`, `output_tokens`, `api_latency_ms`, `model_used`. +**3,038 events** with **5,751 participants** and **21,910 source document links.** -## Dataset-level breakdown +Three analysis tracks reconstruct Epstein's activities: -### DOJ FOIA Releases +- **Calendar track**: Meetings, dinners, appointments from Lesley Groff's daily schedule emails (2011-2016). +- **Travel track**: Flights, hotel stays, ground transport from schedule transitions and AmEx bookings. +- **Financial track**: Gift exchanges, institutional donations, major purchases from bank statements and emails. -| Dataset | Total Files | Gemini | Community | Description | -|---------|-------------|--------|-----------|-------------| -| DataSet 1 | 3,158 | 3,158 | 0 | Initial release | -| DataSet 2 | 574 | 49 | 525 | | -| DataSet 3 | 67 | 49 | 18 | | -| DataSet 4 | 152 | 49 | 103 | | -| DataSet 5 | 120 | 49 | 71 | | -| DataSet 6 | 13 | 13 | 0 | | -| DataSet 7 | 17 | 17 | 0 | | -| DataSet 8 | 10,595 | 10,595 | 0 | | -| DataSet 9 | 531,279 | 0 | 531,279 | Entirely community-processed | -| DataSet 10 | 503,154 | 502,548 | 606 | | -| DataSet 11 | 331,655 | 331,651 | 4 | | -| DataSet 12 | 152 | 50 | 102 | | +Each event links to source EFTA documents via `event_sources` and participants via `event_participants`. -### Non-DOJ Sources +## curated_docs (NEW in v2) -| Dataset | Files | Source | OCR | -|---------|-------|--------|-----| -| FBIVault | 22 | FBI Vault FOIA | Community Tesseract | -| HouseOversightEstate | 4,892 | House Oversight Committee | Community Tesseract | +**5,766 gold documents** across 5 investigation subjects: Hoffman (1,526), Gates (2,069), Summers (739), Clinton (765), Black (667). -## Processing failures +- Each document annotated with: tier (NUCLEAR/CRITICAL/HIGH/MEDIUM/SUPPORTING), category, date, sender, recipient, headline, key quote, investigative detail. +- `also_appears_as`: JSON array of duplicate EFTA file_keys for the same document. +- Only gold-status documents exported (24,706 rejected documents excluded). -472 source PDFs could not be processed. These are documented in `release/epstein_problems.json` with: -- `file_key` -- EFTA identifier -- `doj_url` -- Original DOJ download URL -- `error_message` -- Why processing failed -- `category` -- Failure type (empty_source_pdf, corrupt_source_pdf, api_disconnect, doj_file_unavailable) +## provenance/files -## Reproducibility +**1,387,775 rows.** One row per processed file with full pipeline metadata. -The full processing pipeline is available at [github.com/kevinnbass/epstein](https://github.com/kevinnbass/epstein): -- `process_pdfs_gemini.py` -- Gemini OCR pipeline -- `import_community.py` -- Community data importer -- `validate_dataset.py` -- 14-check validation suite -- `epstein_audit.db` -- Complete audit trail with per-file SHA-256 checksums +- `pdf_sha256`: SHA-256 hash of input PDF. +- `output_sha256`: SHA-256 hash of output JSON. +- `model_used`: OCR model (gemini-2.5-flash-lite, tesseract-community, etc.). +- `input_tokens` / `output_tokens`: API token consumption. +- `validation_score`: automated quality score (0-100). -Every output JSON has a SHA-256 checksum recorded in the provenance database, enabling verification that published data matches pipeline output. +## provenance/audit_log + +**3,711,609 rows.** Append-only forensic audit trail. + +- Every pipeline action (file processed, error, retry, fix applied) is logged. +- `checksum` field provides tamper detection on critical operations. +- Timestamps in ISO-8601 format. + +## provenance/runs + +**123 rows.** Pipeline execution records with timing, worker counts, and costs. diff --git a/README.md b/README.md index 1cfbd5919d6b52d165328122220efb82b4287497..4793d3429943dbd8fa05293ad0cd387c41ab233e 100644 --- a/README.md +++ b/README.md @@ -1,178 +1,196 @@ -# Epstein Document Archive - -**1.39 million OCR'd documents** from the DOJ Jeffrey Epstein document release, with extracted entities, text embeddings, a knowledge graph, and full pipeline provenance. - -This is the data behind [epstein.academy](https://epstein.academy). - -## What's in the dataset - -| Layer | Rows | Download Size | Description | -|-------|------|---------------|-------------| -| `documents` | 1,413,765 | ~800 MB | Full text of every document with metadata | -| `entities` | 8,542,849 | ~200 MB | Extracted people, organizations, locations, dates | -| `chunks` | 2,039,205 | ~1.5 GB | ~800-token text chunks for RAG | -| `embeddings_chunk` | 1,956,803 | ~5 GB | 768-dim Gemini embeddings per chunk | -| `provenance` | 4.9M rows | ~400 MB | Full pipeline audit trail | -| `persons` | 1,614 | <1 MB | Curated person registry with aliases | -| `kg_entities` | 467 | <1 MB | Knowledge graph entities | -| `kg_relationships` | 4,190 | <1 MB | Knowledge graph relationships | -| `recovered_redactions` | 39,588 | ~3 MB | Recovered text from redacted pages | - -**Total: ~8 GB compressed** (Parquet with zstd). Each layer is independent -- download only what you need. - -## Quick start - -### With HuggingFace `datasets` +--- +license: cc-by-4.0 +task_categories: + - text-classification + - question-answering + - feature-extraction +language: + - en +tags: + - legal + - ocr + - documents + - foia + - knowledge-graph + - financial +size_categories: + - 1M 1000 +ORDER BY amount DESC +LIMIT 20; + +-- Curated gold documents +SELECT file_key, subject, tier, headline, key_quote +FROM 'hf://datasets/kabasshouse/epstein-data/data/curated_docs/*.parquet' +WHERE tier = 'NUCLEAR' +ORDER BY subject, doc_date; ``` -### With pandas +### Pandas ```python import pandas as pd -# Read a specific shard -df = pd.read_parquet("hf://datasets/kabasshouse/epstein-data/data/documents/documents-00000.parquet") -print(df.shape) -print(df.columns.tolist()) +docs = pd.read_parquet("hf://datasets/kabasshouse/epstein-data/data/documents/") +print(f"{len(docs):,} documents") +print(docs.groupby("dataset").size().sort_values(ascending=False)) ``` -### Assemble a local SQLite database +## Data Layers -```bash -pip install pyarrow numpy tqdm huggingface_hub +### Core Content -# Core tables only (documents + entities, ~1 GB download) -python assemble_db.py --layers core --output epstein.db +| Layer | Rows | Description | +|-------|------|-------------| +| `documents` | 1,424,673 | Full OCR text, document type, date, photo flag | +| `entities` | 10,629,198 | Named entities (person, org, location, date, etc.) | +| `chunks` | 2,193,090 | ~800-token text chunks for RAG | +| `embeddings_chunk` | 2,111,356 | 768-dim Gemini embeddings per chunk | -# With text chunks (~2.5 GB download) -python assemble_db.py --layers text --output epstein.db +### Knowledge & Analysis -# Full database with embeddings (~7.5 GB download) -python assemble_db.py --layers full --output epstein.db +| Layer | Rows | Description | +|-------|------|-------------| +| `persons` | 1,614 | Curated person registry (name, aliases, category) | +| `kg_entities` | 467 | Knowledge graph nodes | +| `kg_relationships` | 2,198 | Knowledge graph edges (traveled_with, associated_with, etc.) | +| `recovered_redactions` | 37,870 | ML-recovered text from redacted pages | +| `curated_docs` | 5,766 | Expert-annotated gold documents (5 subjects, tiered) | -# Everything including provenance (~8 GB download) -python assemble_db.py --layers all --output epstein.db +### Structured Records (NEW in v2) -# From a local Parquet export -python assemble_db.py --local ./hf_export/ --layers core --output epstein.db -``` +| Layer | Rows | Description | +|-------|------|-------------| +| `financial_transactions` | 49,770 | Credit card & bank transactions | +| `derived_events` | 3,038 | Reconstructed calendar/travel/financial events | +| `event_participants` | 5,751 | People linked to derived events | +| `event_sources` | 21,910 | Source documents for derived events | +| `investigative_records` | 143 | Law enforcement reports & evidence logs | +| `communication_records` | 128 | Phone call & CDR records | -## Source documents +### Provenance -The documents come from 12 DOJ FOIA dataset releases plus two community-sourced collections: +| Layer | Rows | Description | +|-------|------|-------------| +| `provenance/files` | 1,387,775 | Per-file processing metadata + SHA-256 checksums | +| `provenance/audit_log` | 3,711,609 | Append-only forensic audit trail | +| `provenance/runs` | 123 | Pipeline execution records | + +## Datasets | Dataset | Files | Source | |---------|-------|--------| -| DataSet 1 | 3,158 | DOJ FOIA | -| DataSet 2 | 574 | DOJ FOIA | -| DataSet 3 | 67 | DOJ FOIA | -| DataSet 4 | 152 | DOJ FOIA | -| DataSet 5 | 120 | DOJ FOIA | -| DataSet 6 | 13 | DOJ FOIA | -| DataSet 7 | 17 | DOJ FOIA | -| DataSet 8 | 10,595 | DOJ FOIA | -| DataSet 9 | 531,279 | DOJ FOIA | -| DataSet 10 | 503,154 | DOJ FOIA | -| DataSet 11 | 331,655 | DOJ FOIA | -| DataSet 12 | 152 | DOJ FOIA | -| FBIVault | 22 | FBI Vault FOIA | +| DataSet 1 | 3,158 | DOJ EFTA release | +| DataSet 2 | 574 | DOJ EFTA release | +| DataSet 3 | 67 | DOJ EFTA release | +| DataSet 4 | 152 | DOJ EFTA release | +| DataSet 5 | 120 | DOJ EFTA release | +| DataSet 6 | 13 | DOJ EFTA release | +| DataSet 7 | 17 | DOJ EFTA release | +| DataSet 8 | 10,595 | DOJ EFTA release | +| DataSet 9 | 531,279 | DOJ EFTA release (community Tesseract OCR) | +| DataSet 10 | 503,154 | DOJ EFTA release | +| DataSet 11 | 331,655 | DOJ EFTA release | +| DataSet 12 | 152 | DOJ EFTA release | +| FBIVault | 22 | FBI Vault FOIA release | | HouseOversightEstate | 4,892 | House Oversight Committee | -**Total: 1,385,850 successful** + 472 unrecoverable failures (documented in `release/epstein_problems.json`). - -## OCR provenance - -Two OCR sources were used: +**468 unrecoverable failures** (corrupt/empty source PDFs). Full failure catalog in `release/epstein_problems.json`. -- **Gemini 2.5 Flash Lite** (848,228 files): Primary OCR engine. These documents have `ocr_source` = NULL. -- **Tesseract (community)** (537,622 files): Gap-fill from community repositories. These have `ocr_source` = `"tesseract-community"`. +## OCR Sources -See [PROVENANCE.md](PROVENANCE.md) for per-table source documentation. +- **Gemini 2.5 Flash Lite**: 856,028 files — structured JSON output with entities, document classification, and metadata +- **Tesseract (community)**: 531,279 files — raw text only (DataSet 9, community gap-fill imports) +- **Upgraded**: 1,377 files originally processed with Tesseract, now re-processed with Gemini -## Schema +Distinguish OCR source via the `ocr_source` column: `NULL` = Gemini, `'tesseract-community'` = community Tesseract. -Every document has a unique `file_key` (e.g., `EFTA00000001`) that serves as the primary identifier across all tables. The Parquet files use `file_key` everywhere -- no opaque integer IDs. +## Curated Documents -Key fields on `documents`: -- `file_key` -- unique identifier (EFTA number) -- `dataset` -- source dataset (e.g., "DataSet10") -- `full_text` -- complete OCR text -- `document_type` -- classified type (Email, Form, Letter, Photo, etc.) -- `date` -- extracted date if available -- `is_photo` -- whether the document is a photograph -- `ocr_source` -- NULL for Gemini, "tesseract-community" for community OCR +The `curated_docs` layer contains 5,766 expert-annotated gold documents across 5 investigation subjects: -See `schema.sql` for the full SQLite schema used by `assemble_db.py`. +| Subject | Gold Docs | Tiers | +|---------|-----------|-------| +| Hoffman | 1,526 | NUCLEAR / CRITICAL / HIGH / MEDIUM / SUPPORTING | +| Gates | 2,069 | NUCLEAR / CRITICAL / HIGH / MEDIUM / SUPPORTING | +| Summers | 739 | NUCLEAR / CRITICAL / HIGH / MEDIUM / SUPPORTING | +| Clinton | 765 | NUCLEAR / CRITICAL / HIGH / MEDIUM / SUPPORTING | +| Black | 667 | NUCLEAR / CRITICAL / HIGH / MEDIUM / SUPPORTING | -## Known issues +Each entry includes: tier, category, date, sender/recipient, headline, key quote, and investigative detail. -- 472 source PDFs could not be processed (corrupt, empty, or unavailable). These are cataloged in `release/epstein_problems.json` with DOJ download URLs. -- DataSet 9 (531K files) was entirely community-processed with Tesseract OCR, which has lower quality than Gemini. -- Some documents are heavily redacted. `recovered_redactions` contains ML-recovered text from 39,588 redacted pages. -- Embedding coverage is ~96% for chunks (1,249 malformed embeddings excluded). Summary embeddings were removed as redundant -- 92% of documents have a single chunk, making summary and chunk embeddings identical. +## Financial Transactions -## Release artifacts +49,770 clean records extracted from credit card statements and bank records using DeepSeek. Includes: -Small reference files are included directly in this repo under `release/`: +- Transaction date, amount, currency, merchant +- Cardholder name (Epstein, Maxwell, Shuliak, etc.) +- Flight data (origin, destination, carrier, passenger) for airline purchases +- Merchant category classification -| File | Size | Description | -|------|------|-------------| -| `epstein_problems.json` | 280 KB | 472 processing failures with DOJ URLs | -| `efta_dataset_mapping.json` | 4 KB | EFTA file key to DOJ URL mapping | -| `persons_registry.json` | 436 KB | 1,614 curated person records | -| `knowledge_graph_entities.json` | 172 KB | 467 KG entities | -| `knowledge_graph_relationships.json` | 932 KB | 4,190 KG relationships | -| `extracted_entities_filtered.json` | 1.9 MB | Filtered entity export | -| `redacted_text_recovered.json.gz` | 2.5 MB | 39,588 recovered redacted pages | -| `document_summary.csv.gz` | 1.8 MB | Document metadata summary | -| `image_catalog.csv.gz` | 15 MB | Photo/image catalog | +31% of raw extractions were quarantined for quality issues and excluded from this release. ## License -This dataset is released under [CC-BY-4.0](LICENSE). The underlying documents are U.S. government records released under FOIA. +CC-BY-4.0. Source documents are U.S. government public records. ## Citation ```bibtex -@dataset{epstein_data_2026, - title={Epstein Document Archive}, - author={Kevin Bass}, +@dataset{epstein_archive_2026, + title={Epstein DOJ Document Archive}, + author={kabasshouse}, year={2026}, url={https://huggingface.co/datasets/kabasshouse/epstein-data}, - note={1.39M OCR'd DOJ documents with entities, embeddings, and knowledge graph} + version={2.0} } ``` diff --git a/data/chunks/chunks-00000-of-00011.parquet b/data/chunks/chunks-00000-of-00011.parquet new file mode 100644 index 0000000000000000000000000000000000000000..b8d97954ae349992a27b0d0607271920c9e9aeb5 --- /dev/null +++ b/data/chunks/chunks-00000-of-00011.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a981d273fec089a29a4974e3a2af050202679a1866d7d697838ff4b0b3a8a652 +size 42764173 diff --git a/data/chunks/chunks-00001-of-00011.parquet b/data/chunks/chunks-00001-of-00011.parquet new file mode 100644 index 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