# Epstein Vectorized Database

This repository contains a **vectorized derivative** of publicly released U.S. House Oversight Committee Epstein estate documents. The database provides efficient semantic search and retrieval-augmented generation (RAG) capabilities via FAISS embeddings and metadata.

## Database Contents

- `epstein_index.faiss`: FAISS vector index (384-dim, normalized embeddings via all-MiniLM-L6-v2, IndexFlatIP for cosine similarity)
- `epstein_metadata.parquet`: Metadata for each vector (id, filename, text_snippet, chars, words)
- `epstein_metadata.jsonl`: Same metadata in JSONL format

**Total documents indexed: 25,800**

## Source Attribution

**Original source:** U.S. House Committee on Oversight and Government Reform public release "Oversight Committee Releases Additional Epstein Estate Documents" (November 12, 2025):  
[https://oversight.house.gov/release/oversight-committee-releases-additional-epstein-estate-documents/](https://oversight.house.gov/release/oversight-committee-releases-additional-epstein-estate-documents/)

**Original dataset on HuggingFace:**  
[tensonaut/EPSTEIN_FILES_20K](https://huggingface.co/datasets/tensonaut/EPSTEIN_FILES_20K)

## Usage

### Load the index and metadata:
```python
import faiss
import pandas as pd

index = faiss.read_index('epstein_index.faiss')
meta = pd.read_parquet('epstein_metadata.parquet')
```

### Semantic search example:
```python
from sentence_transformers import SentenceTransformer
import numpy as np

model = SentenceTransformer('all-MiniLM-L6-v2')
query = "your search query"
q_emb = model.encode([query], convert_to_numpy=True)
q_emb = q_emb / np.linalg.norm(q_emb, axis=1, keepdims=True)

D, I = index.search(q_emb.astype('float32'), k=10)  # top-10 results
results = meta.iloc[I[0]]
```

## Usage Responsibilities

Users are responsible for:

- Using the dataset only for **lawful purposes** and in accordance with institutional and ethical review requirements
- Treating individuals mentioned in the documents with respect and avoiding sensationalism or misuse of sensitive material
- **Clearly distinguishing model-generated content** from verified facts, and citing primary sources appropriately
- Complying with applicable copyright law, privacy law, and institutional policies

### NOT intended for:

- Fine-tuning language models without explicit legal review
- Harassment, doxing, or targeted attacks
- Attempts to deanonymize or circumvent existing redactions
- Making or amplifying unverified allegations as factual claims

## Content Warning

The underlying corpus contains sensitive material related to:

- Sexual abuse and exploitation
- Trafficking and violence
- Unverified allegations, opinions, and speculation

Readers should approach with care and appropriate context.

## Legal Disclaimer (Non-Authoritative)

- This is a **derivative vectorized index only**; the original documents are copyrighted by their respective authors and the U.S. House Committee on Oversight and Government Reform
- This dataset does **not grant any license** to reproduce or distribute the underlying documents beyond what may be permitted by law (fair use, etc.)
- Users are **solely responsible** for ensuring compliance with copyright, privacy, and institutional policies
- **Seek independent legal counsel** if using this corpus in a public-facing product or for model training at scale

## Files

- `epstein_index.faiss`: FAISS IndexFlatIP (384-dim, normalized vectors)
- `epstein_metadata.parquet`: Parquet metadata table
- `epstein_metadata.jsonl`: JSONL metadata (one record per line)

## License

This vectorized database is provided under the same legal constraints as the original House release. See "Usage Responsibilities" and "Legal Disclaimer" above. No original content is reproduced; only embeddings and metadata snippets are stored.