### Install epstein-files Package

Source: https://context7.com/michelcrypt4d4mus/epstein_text_messages/llms.txt

Instructions for installing the epstein-files package using poetry, pip, or pipx. Poetry is recommended for managing dependencies.

```bash
# Using poetry (recommended)
poetry install

# Using pip
pip install epstein-files

# Using pipx
pipx install epstein-files
```

--------------------------------

### Epstein Files Command Line Tools

Source: https://github.com/michelcrypt4d4mus/epstein_text_messages/blob/master/README.md

Examples of using various command-line tools provided by the epstein-files package, including generating highlighted files, searching text, displaying files, counting words, and diffing files.

```bash
# Generate color highlighted texts/emails/other files
epstein_generate

# Search for a string:
epstein_grep Bannon
# Or a regex:
epstein_grep '\bSteve\s*Bannon|Jeffrey\s*Epstein\b'

# Show a file with color highlighting of keywords:
epstein_show 030999
# Show both the highlighted and raw versions of the file:
epstein_show --raw 030999
# The full filename is also accepted:
epstein_show HOUSE_OVERSIGHT_030999

# Count words used by Epstein and Bannon
epstein_word_count --name 'Jeffrey Epstein' --name 'Steve Bannon'

# Diff two epstein files after all the cleanup (stripping BOMs, matching newline chars, etc):
epstein_diff 030999 020442
```

--------------------------------

### Install Epstein Files Package

Source: https://github.com/michelcrypt4d4mus/epstein_text_messages/blob/master/README.md

Instructions for installing the epstein-files package using poetry, pip, or pipx. This package provides the core functionality for processing Epstein's documents.

```bash
poetry install
pip install epstein-files
pipx install epstein-files
```

--------------------------------

### Email Access and Filtering Methods

Source: https://context7.com/michelcrypt4d4mus/epstein_text_messages/llms.txt

Provides examples of retrieving emails based on sender, recipient, or both. It covers getting emails by file ID, counting authors and recipients, and accessing the earliest and latest emails for a person.

```python
from epstein_files.epstein_files import EpsteinFiles

epstein_files = EpsteinFiles.get_files()

# Get all emails sent by a specific person
emails_from_bannon = epstein_files.emails_by("Steve Bannon")
for email in emails_from_bannon:
    print(f"{email.timestamp}: {email.subject}")

# Get all emails sent to a specific person
emails_to_epstein = epstein_files.emails_to("Jeffrey Epstein")
print(f"Total emails to Epstein: {len(emails_to_epstein)}")

# Get all emails to or from a person (combined, deduplicated, sorted)
all_emails_for_person = epstein_files.emails_for("Ghislaine Maxwell")
print(f"First email: {all_emails_for_person[0].timestamp}")
print(f"Last email: {all_emails_for_person[-1].timestamp}")

# Get email by file ID
email = epstein_files.email_for_id("030999")
print(f"Subject: {email.subject}, From: {email.author}")

# Get email author counts
author_counts = epstein_files.email_author_counts()
for author, count in sorted(author_counts.items(), key=lambda x: -x[1])[:10]:
    print(f"{author}: {count} emails sent")

# Get recipient counts
recipient_counts = epstein_files.email_recipient_counts()
for recipient, count in sorted(recipient_counts.items(), key=lambda x: -x[1])[:10]:
    print(f"{recipient}: {count} emails received")
```

--------------------------------

### Manage Epstein Files Data Cache (Pickle)

Source: https://context7.com/michelcrypt4d4mus/epstein_text_messages/llms.txt

Demonstrates how to work with the cached data (pickle file) used by the EpsteinFiles library for faster loading. It shows how to load from the cache, force a rebuild by deleting the cache file, use a pre-packaged cache, save the current state, and reload specific subsets of data like DOJ files.

```python
from epstein_files.epstein_files import EpsteinFiles
import os

# Load from pickle (default behavior if pickle exists)
epstein_files = EpsteinFiles.get_files()

# Force rebuild of pickle cache
# (set args.overwrite_pickle = True before calling, or delete pickle file)
os.remove("the_epstein_files.pkl.gz")  # Remove existing cache
epstein_files = EpsteinFiles.get_files()  # Will rebuild

# Use pre-packaged pickle from repo
# cp emails_extracted_from_legal_filings/the_epstein_files.pkl.gz .

# Save current state to disk
epstein_files.save_to_disk()

# Reload only DOJ files (keeps HOUSE_OVERSIGHT unchanged)
epstein_files.reload_doj_files()
```

--------------------------------

### EpsteinFiles Class: Load and Access Documents

Source: https://context7.com/michelcrypt4d4mus/epstein_text_messages/llms.txt

Demonstrates how to load all documents using the EpsteinFiles class, access them as a sequence, and filter by type (emails, iMessages, JSON, other). It also shows how to retrieve unique emails and lists of email senders.

```python
from epstein_files.epstein_files import EpsteinFiles

# Load all files (uses cached pickle if available, otherwise parses all documents)
epstein_files = EpsteinFiles.get_files()

# Access all documents as a single sequence
for document in epstein_files.all_documents:
    print(f"{document.file_id}: {document.author} - {document.timestamp}")

# Access emails specifically
for email in epstein_files.emails:
    print(f"From: {email.author}, To: {email.recipients}, Subject: {email.subject}")

# Access iMessage logs
for imessage_log in epstein_files.imessage_logs:
    print(f"Conversation with: {imessage_log.author}, Messages: {len(imessage_log.messages)}")

# Access JSON files extracted from document dump
for json_file in epstein_files.json_files:
    print(f"JSON file: {json_file.file_id}")

# Access other files (non-email, non-iMessage documents)
for other_file in epstein_files.other_files:
    print(f"Other: {other_file.file_id}, Type: {other_file._class_name}")

# Get non-duplicate emails only
unique_emails = epstein_files.non_duplicate_emails

# Get list of all people who sent or received emails
emailers = epstein_files.emailers
for person in emailers:
    print(f"{person.name}: {len(person.emails)} emails")
```

--------------------------------

### Document Search with docs_matching()

Source: https://context7.com/michelcrypt4d4mus/epstein_text_messages/llms.txt

Shows how to search for text patterns (simple strings or regex) across all documents using the `docs_matching` method. It also demonstrates filtering search results by author.

```python
from epstein_files.epstein_files import EpsteinFiles

epstein_files = EpsteinFiles.get_files()

# Search for a simple string across all documents
results = epstein_files.docs_matching("Bitcoin")
for result in results:
    print(f"Found in {result.document.file_id}:")
    for line in result.lines:
        print(f"  Line {line.line_number}: {line.line}")

# Search using regex pattern
results = epstein_files.docs_matching(r"\bSteve\s*Bannon\b")
for result in results:
    print(f"Match in: {result.document.file_id}")

# Filter search to specific author
results = epstein_files.docs_matching("meeting", names=["Jeffrey Epstein"])
print(f"Found {len(results)} documents from Jeffrey Epstein mentioning 'meeting'")
```

--------------------------------

### Set Epstein Documents Directory Environment Variable

Source: https://github.com/michelcrypt4d4mus/epstein_text_messages/blob/master/README.md

Demonstrates how to set the EPSTEIN_DOCS_DIR environment variable, which is required for the command-line tools. This can be done temporarily for a single command or permanently via a .env file.

```bash
EPSTEIN_DOCS_DIR=/path/to/epstein/ocr_txt_files epstein_generate --help
```

--------------------------------

### Display Epstein Documents with epstein_show

Source: https://context7.com/michelcrypt4d4mus/epstein_text_messages/llms.txt

Displays individual Epstein documents, identified by a 6-digit ID or a full prefix, with color highlighting. It supports showing multiple documents, raw text comparison, opening source PDFs or URLs, and filtering documents by sender name.

```bash
# Show document by 6-digit ID
epstein_show 030999

# Show document with full prefix
epstein_show HOUSE_OVERSIGHT_030999

# Show multiple documents
epstein_show 030999 020442

# Show with raw text comparison
epstein_show --raw 030999

# Open source PDF (if available locally)
epstein_show --open-pdf 030999

# Open source URL in browser
epstein_show --open-url 030999

# Show all emails for specific person
epstein_show --name "Steve Bannon"
```

--------------------------------

### Set DOJ Text Directory Environment Variable

Source: https://github.com/michelcrypt4d4mus/epstein_text_messages/blob/master/README.md

Shows how to set both EPSTEIN_DOCS_DIR and EPSTEIN_DOJ_TXTS_20260130_DIR environment variables to include data from the January 2026 DOJ disclosures.

```bash
EPSTEIN_DOCS_DIR=/path/to/epstein/ocr_txt_files EPSTEIN_DOJ_TXTS_20260130_DIR=/path/to/doj/files epstein_generate --help
```

--------------------------------

### Compare Epstein Documents with epstein_diff

Source: https://context7.com/michelcrypt4d4mus/epstein_text_messages/llms.txt

Compares two Epstein documents after applying cleanup processing. The tool outputs the differences in cleaned text and can suggest configurations for duplicate suppression.

```bash
# Diff two documents by ID
epstein_diff 030999 020442

# Shows cleaned text differences and suggests duplicate suppression config
```

--------------------------------

### Use Epstein Files as a Python Library

Source: https://github.com/michelcrypt4d4mus/epstein_text_messages/blob/master/README.md

Python code snippet demonstrating how to import and use the EpsteinFiles class from the epstein_files library to access and iterate through all processed documents.

```python
from epstein_files.epstein_files import EpsteinFiles
epstein_files = EpsteinFiles.get_files()

# All files
for document in epstein_files.all_documents():
    do_stuff(document)
```

--------------------------------

### Load Document by ID and Access Properties (Python)

Source: https://context7.com/michelcrypt4d4mus/epstein_text_messages/llms.txt

Loads a document directly using its file ID and prints various properties such as filename, author, timestamp, length, and URL. It also allows access to raw and processed text, checks for duplicates, retrieves metadata, and searches for matching lines within the document.

```python
from epstein_files.documents.document import Document

# Load a document directly by file ID
doc = Document.from_file_id("030999")
print(f"File: {doc.filename}")
print(f"Author: {doc.author}")
print(f"Timestamp: {doc.timestamp}")
print(f"Length: {doc.length} characters, {doc.num_lines} lines")
print(f"External URL: {doc.external_url}")

# Access raw and processed text
print(f"Processed text preview: {doc.text[:500]}")
print(f"Raw text: {doc.raw_text()[:500]}")

# Check if document is a duplicate
if doc.is_duplicate:
    print(f"This is a duplicate of: {doc.duplicate_of_id}")

# Get document metadata as dict
metadata = doc.metadata
print(f"Metadata: {metadata}")

# Search for matching lines within a document
matching_lines = doc.matching_lines(r"money|payment|transfer")
for match in matching_lines:
    print(f"Line {match.line_number}: {match.line}")
```

--------------------------------

### Document Class: Base Document Properties

Source: https://context7.com/michelcrypt4d4mus/epstein_text_messages/llms.txt

Illustrates the base Document class, which serves as the foundation for all document types within the library. It highlights common properties available for any document, such as file ID, author, timestamp, and text content.

```python
from epstein_files.documents.document import Document
```

--------------------------------

### Generate HTML Reports with epstein_generate CLI Tool (Bash)

Source: https://context7.com/michelcrypt4d4mus/epstein_text_messages/llms.txt

This command-line tool generates color-highlighted HTML output of the documents. It allows for generating the full HTML output, or specific sections for emails or text messages. It can also generate a chronological email timeline.

```bash
# Set environment variable for document directory
export EPSTEIN_DOCS_DIR=/path/to/epstein/ocr_txt_files

# Generate full HTML output
epstein_generate

# Generate only emails section
epstein_generate --output-emails

# Generate only text messages section
epstein_generate --output-texts

# Generate chronological email timeline
epstein_generate --email-timeline
```

--------------------------------

### CLI Tool: epstein_generate

Source: https://context7.com/michelcrypt4d4mus/epstein_text_messages/llms.txt

A command-line interface tool to generate color-highlighted HTML output of the documents. Supports filtering output for emails, text messages, or generating a chronological email timeline.

```APIDOC
## CLI Tool: epstein_generate

### Description
A command-line interface tool to generate color-highlighted HTML output of the documents. Supports filtering output for emails, text messages, or generating a chronological email timeline.

### Method
Command-line execution

### Endpoint
N/A

### Parameters
#### Path Parameters
N/A

#### Query Parameters
N/A

#### Request Body
N/A

### Request Example
```bash
# Set environment variable for document directory
export EPSTEIN_DOCS_DIR=/path/to/epstein/ocr_txt_files

# Generate full HTML output
epstein_generate

# Generate only emails section
epstein_generate --output-emails

# Generate only text messages section
epstein_generate --output-texts

# Generate chronological email timeline
epstein_generate --email-timeline
```

### Response
#### Success Response (200)
HTML output to standard output or file.

#### Response Example
N/A
```

--------------------------------

### Generate Epstein Document Information Tables

Source: https://context7.com/michelcrypt4d4mus/epstein_text_messages/llms.txt

Generates various informational tables from the Epstein documents. This includes emailer information and JSON metadata for all files. The generation of DOJ files requires the EPSTEIN_DOJ_TXTS_20260130_DIR environment variable to be set.

```bash
epstein_generate --emailers-info
epstein_generate --json-metadata
export EPSTEIN_DOJ_TXTS_20260130_DIR=/path/to/doj/txt/files
epstein_generate --output-doj-files
epstein_generate --help
```

--------------------------------

### Document Class - Document Loading and Access

Source: https://context7.com/michelcrypt4d4mus/epstein_text_messages/llms.txt

Load documents directly by their file ID and access various properties such as filename, author, timestamp, and content. It also supports searching for lines within a document.

```APIDOC
## Document Class - Document Loading and Access

### Description
Load documents directly by their file ID and access various properties such as filename, author, timestamp, and content. It also supports searching for lines within a document.

### Method
N/A (Class methods and instantiation)

### Endpoint
N/A

### Parameters
#### Path Parameters
N/A

#### Query Parameters
N/A

#### Request Body
N/A

### Request Example
```python
from epstein_files.documents.document import Document

# Load a document directly by file ID
doc = Document.from_file_id("030999")
print(f"File: {doc.filename}")
print(f"Author: {doc.author}")
print(f"Timestamp: {doc.timestamp}")
print(f"Length: {doc.length} characters, {doc.num_lines} lines")
print(f"External URL: {doc.external_url}")

# Access raw and processed text
print(f"Processed text preview: {doc.text[:500]}")
print(f"Raw text: {doc.raw_text()[:500]}")

# Check if document is a duplicate
if doc.is_duplicate:
    print(f"This is a duplicate of: {doc.duplicate_of_id}")

# Get document metadata as dict
metadata = doc.metadata
print(f"Metadata: {metadata}")

# Search for matching lines within a document
matching_lines = doc.matching_lines(r"money|payment|transfer")
for match in matching_lines:
    print(f"Line {match.line_number}: {match.line}")
```

### Response
#### Success Response (200)
N/A (This is client-side code)

#### Response Example
N/A
```

--------------------------------

### Aggregate Person Data and Communication Statistics (Python)

Source: https://context7.com/michelcrypt4d4mus/epstein_text_messages/llms.txt

Aggregates all communications for specific individuals, including emails and iMessage logs. It provides details on the person's category, information string, email statistics (total, sent, received, unique), and date ranges for their email activity. It also shows the count of iMessage conversations and allows access to all emailers sorted by activity.

```python
from epstein_files.epstein_files import EpsteinFiles

epstein_files = EpsteinFiles.get_files()

# Get Person objects for specific names
people = epstein_files.person_objs(["Steve Bannon", "Ghislaine Maxwell"])

for person in people:
    print(f"\n=== {person.name} ===")
    print(f"Category: {person.category}")
    print(f"Info: {person.info_str}")

    # Email statistics
    print(f"Total emails: {len(person.emails)}")
    print(f"Emails sent: {len(person.emails_by)}")
    print(f"Emails received: {len(person.emails_to)}")
    print(f"Unique emails: {len(person.unique_emails)}")

    # Date range
    print(f"First email: {person.earliest_email_date}")
    print(f"Last email: {person.last_email_date}")
    print(f"Conversation span: {person.email_conversation_length_in_days} days")

    # iMessage logs
    print(f"iMessage conversations: {len(person.imessage_logs)}")

# Access all emailers sorted by activity
all_emailers = epstein_files.emailers
sorted_emailers = sorted(all_emailers, key=lambda p: len(p.unique_emails), reverse=True)
for person in sorted_emailers[:10]:
    print(f"{person.name}: {len(person.unique_emails)} emails")
```

--------------------------------

### Copy Extracted Emails to Document Directory

Source: https://github.com/michelcrypt4d4mus/epstein_text_messages/blob/master/README.md

Command to copy extracted email text files from the repository into the directory specified by the EPSTEIN_DOCS_DIR environment variable for local analysis.

```bash
cp ./emails_extracted_from_legal_filings/*.txt "$EPSTEIN_DOCS_DIR"
```

--------------------------------

### Process Emails in Epstein Text Messages

Source: https://github.com/michelcrypt4d4mus/epstein_text_messages/blob/master/README.md

Iterates through a collection of email objects within the epstein_files.emails list and applies a 'do_stuff' function to each email. This snippet demonstrates basic iteration over email data.

```python
for email in epstein_files.emails:
    do_stuff(email)
```

--------------------------------

### Search Epstein Documents with epstein_grep

Source: https://context7.com/michelcrypt4d4mus/epstein_text_messages/llms.txt

Searches through the Epstein documents for specified strings or regular expression patterns. It offers options to filter searches to email bodies, specific document types (emails or texts), display the whole file content for each match, and filter by minimum line length. Output is color-highlighted.

```bash
# Search for a name
epstein_grep "Bannon"

# Search with regex
epstein_grep '\bSteve\s*Bannon|Jeffrey\s*Epstein\b'

# Search only in email bodies (exclude headers)
epstein_grep --email-body "Bitcoin"

# Filter to specific document types
epstein_grep --output-emails "meeting"
epstein_grep --output-texts "tomorrow"

# Show whole file for each match
epstein_grep --whole-file "confidential"

# Filter by minimum line length
epstein_grep --min-line-length 50 "money"
```

--------------------------------

### Email Class - Email-Specific Properties

Source: https://context7.com/michelcrypt4d4mus/epstein_text_messages/llms.txt

Access email-specific data including headers, recipients, subjects, and extracted actual text (excluding quoted replies).

```APIDOC
## Email Class - Email-Specific Properties

### Description
Access email-specific data including headers, recipients, subjects, and extracted actual text (excluding quoted replies).

### Method
N/A (Class methods and instantiation)

### Endpoint
N/A

### Parameters
#### Path Parameters
N/A

#### Query Parameters
N/A

#### Request Body
N/A

### Request Example
```python
from epstein_files.documents.email import Email
from epstein_files.epstein_files import EpsteinFiles

epstein_files = EpsteinFiles.get_files()

# Get an email and access its properties
email = epstein_files.email_for_id("030999")

# Basic properties
print(f"From: {email.author}")
print(f"To: {email.recipients}")
print(f"Subject: {email.subject}")
print(f"Sent at: {email.timestamp}")
print(f"Sent from device: {email.sent_from_device}")  # e.g., "iPhone", "BlackBerry"

# Header information
print(f"Header fields: {email.header.field_names}")
print(f"CC: {email.header.cc}")
print(f"BCC: {email.header.bcc}")
print(f"Attachments: {email.attachments}")

# Text content
print(f"Full text length: {len(email.text)}")
print(f"Actual text (excluding quoted replies): {email.actual_text[:300]}")

# Classification flags
print(f"Is junk mail: {email.is_junk_mail}")
print(f"Is mailing list: {email.is_mailing_list}")
print(f"Is forwarded article: {email.is_fwded_article}")
print(f"Is note to self: {email.is_note_to_self}")

# Check if email involves specific person
print(f"Involves Bannon: {email.is_from_or_to('Steve Bannon')}")

# Access attached documents
for attached_doc in email.attached_docs:
    print(f"Attachment: {attached_doc.file_id}")
```

### Response
#### Success Response (200)
N/A (This is client-side code)

#### Response Example
N/A
```

--------------------------------

### Access and Analyze iMessage Logs (Python)

Source: https://context7.com/michelcrypt4d4mus/epstein_text_messages/llms.txt

Provides access to parsed iMessage conversation logs, allowing iteration through conversations and individual messages. It displays conversation details like author, phone number, message count, and timestamps. It also supports retrieving logs for a specific person and counting messages by author across all logs.

```python
from epstein_files.epstein_files import EpsteinFiles

epstein_files = EpsteinFiles.get_files()

# Access all iMessage logs
for log in epstein_files.imessage_logs:
    print(f"Conversation with: {log.author}")
    print(f"Phone number: {log.phone_number}")
    print(f"Number of messages: {len(log.messages)}")
    print(f"First message at: {log.timestamp}")

    # Access individual messages
    for msg in log.messages[:5]:  # First 5 messages
        print(f"  [{msg.timestamp_str}] {msg.author}: {msg.text[:50]}...")

# Get logs for specific person
logs_for_person = epstein_files.imessage_logs_for("Some Name")

# Count messages by author across all logs
author_counts = epstein_files.MessengerLog.count_authors(epstein_files.imessage_logs)
for author, count in sorted(author_counts.items(), key=lambda x: -x[1]):
    print(f"{author}: {count} messages")
```

--------------------------------

### Export JSON Metadata for Epstein Files

Source: https://context7.com/michelcrypt4d4mus/epstein_text_messages/llms.txt

Exports structured metadata for all documents as a JSON string using the EpsteinFiles Python library. This metadata can be parsed to access information about individual files, such as email subjects, and details about people mentioned in the documents.

```python
from epstein_files.epstein_files import EpsteinFiles
import json

epstein_files = EpsteinFiles.get_files()

# Get JSON metadata string for all files
metadata_json = epstein_files.json_metadata()

# Parse and access
metadata = json.loads(metadata_json)

# Access file metadata by type
for email_meta in metadata['files']['Email'][:5]:
    print(f"Email {email_meta['file_id']}: {email_meta.get('subject', 'No subject')}")

# Access people metadata
for name, info in list(metadata['people'].items())[:5]:
    print(f"{name}: {info}")
```

--------------------------------

### Person Class - Aggregate Person Data

Source: https://context7.com/michelcrypt4d4mus/epstein_text_messages/llms.txt

Aggregate all communications for a specific individual including emails and iMessage logs. Provides statistics on communication volume, dates, and types.

```APIDOC
## Person Class - Aggregate Person Data

### Description
Aggregate all communications for a specific individual including emails and iMessage logs. Provides statistics on communication volume, dates, and types.

### Method
N/A (Class methods and instantiation)

### Endpoint
N/A

### Parameters
#### Path Parameters
N/A

#### Query Parameters
N/A

#### Request Body
N/A

### Request Example
```python
from epstein_files.epstein_files import EpsteinFiles

epstein_files = EpsteinFiles.get_files()

# Get Person objects for specific names
people = epstein_files.person_objs(["Steve Bannon", "Ghislaine Maxwell"])

for person in people:
    print(f"\n=== {person.name} ===")
    print(f"Category: {person.category}")
    print(f"Info: {person.info_str}")

    # Email statistics
    print(f"Total emails: {len(person.emails)}")
    print(f"Emails sent: {len(person.emails_by)}")
    print(f"Emails received: {len(person.emails_to)}")
    print(f"Unique emails: {len(person.unique_emails)}")

    # Date range
    print(f"First email: {person.earliest_email_date}")
    print(f"Last email: {person.last_email_date}")
    print(f"Conversation span: {person.email_conversation_length_in_days} days")

    # iMessage logs
    print(f"iMessage conversations: {len(person.imessage_logs)}")

# Access all emailers sorted by activity
all_emailers = epstein_files.emailers
sorted_emailers = sorted(all_emailers, key=lambda p: len(p.unique_emails), reverse=True)
for person in sorted_emailers[:10]:
    print(f"{person.name}: {len(person.unique_emails)} emails")
```

### Response
#### Success Response (200)
N/A (This is client-side code)

#### Response Example
N/A
```

--------------------------------

### MessengerLog Class - iMessage Logs

Source: https://context7.com/michelcrypt4d4mus/epstein_text_messages/llms.txt

Access parsed iMessage conversation logs with individual message extraction. Allows iteration through conversations and messages, filtering by author, and counting messages.

```APIDOC
## MessengerLog Class - iMessage Logs

### Description
Access parsed iMessage conversation logs with individual message extraction. Allows iteration through conversations and messages, filtering by author, and counting messages.

### Method
N/A (Class methods and instantiation)

### Endpoint
N/A

### Parameters
#### Path Parameters
N/A

#### Query Parameters
N/A

#### Request Body
N/A

### Request Example
```python
from epstein_files.epstein_files import EpsteinFiles

epstein_files = EpsteinFiles.get_files()

# Access all iMessage logs
for log in epstein_files.imessage_logs:
    print(f"Conversation with: {log.author}")
    print(f"Phone number: {log.phone_number}")
    print(f"Number of messages: {len(log.messages)}")
    print(f"First message at: {log.timestamp}")

    # Access individual messages
    for msg in log.messages[:5]:  # First 5 messages
        print(f"  [{msg.timestamp_str}] {msg.author}: {msg.text[:50]}...")

# Get logs for specific person
logs_for_person = epstein_files.imessage_logs_for("Some Name")

# Count messages by author across all logs
author_counts = epstein_files.MessengerLog.count_authors(epstein_files.imessage_logs)
for author, count in sorted(author_counts.items(), key=lambda x: -x[1]):
    print(f"{author}: {count} messages")
```

### Response
#### Success Response (200)
N/A (This is client-side code)

#### Response Example
N/A
```

--------------------------------

### Access Email Properties and Content (Python)

Source: https://context7.com/michelcrypt4d4mus/epstein_text_messages/llms.txt

Retrieves email-specific data, including headers, recipients, subjects, and the actual text content excluding quoted replies. It provides access to basic properties, header fields, attachment information, and classification flags like junk mail or mailing list status. It can also check if an email involves a specific person and access attached documents.

```python
from epstein_files.documents.email import Email
from epstein_files.epstein_files import EpsteinFiles

epstein_files = EpsteinFiles.get_files()

# Get an email and access its properties
email = epstein_files.email_for_id("030999")

# Basic properties
print(f"From: {email.author}")
print(f"To: {email.recipients}")
print(f"Subject: {email.subject}")
print(f"Sent at: {email.timestamp}")
print(f"Sent from device: {email.sent_from_device}")  # e.g., "iPhone", "BlackBerry"

# Header information
print(f"Header fields: {email.header.field_names}")
print(f"CC: {email.header.cc}")
print(f"BCC: {email.header.bcc}")
print(f"Attachments: {email.attachments}")

# Text content
print(f"Full text length: {len(email.text)}")
print(f"Actual text (excluding quoted replies): {email.actual_text[:300]}")

# Classification flags
print(f"Is junk mail: {email.is_junk_mail}")
print(f"Is mailing list: {email.is_mailing_list}")
print(f"Is forwarded article: {email.is_fwded_article}")
print(f"Is note to self: {email.is_note_to_self}")

# Check if email involves specific person
print(f"Involves Bannon: {email.is_from_or_to('Steve Bannon')}")

# Access attached documents
for attached_doc in email.attached_docs:
    print(f"Attachment: {attached_doc.file_id}")
```

--------------------------------

### Process JSON Files in Epstein Text Messages

Source: https://github.com/michelcrypt4d4mus/epstein_text_messages/blob/master/README.md

Iterates through a collection of JSON file objects within the epstein_files.json_files list and applies a 'do_stuff' function to each file. This demonstrates the processing of structured JSON data.

```python
for json_file in epstein_files.json_files:
    do_stuff(json_file)
```

--------------------------------

### Process Other Files in Epstein Text Messages

Source: https://github.com/michelcrypt4d4mus/epstein_text_messages/blob/master/README.md

Iterates through a collection of miscellaneous file objects within the epstein_files.other_files list and applies a 'do_stuff' function to each file. This serves as a fallback for unprocessed file types.

```python
for file in epstein_files.other_files:
    do_stuff(file)
```

--------------------------------

### Process iMessage Logs in Epstein Text Messages

Source: https://github.com/michelcrypt4d4mus/epstein_text_messages/blob/master/README.md

Iterates through a collection of iMessage log objects within the epstein_files.imessage_logs list and applies a 'do_stuff' function to each log. This snippet shows how iMessage data is handled.

```python
for imessage_log in epstein_files.imessage_logs:
    do_stuff(imessage_log)
```

--------------------------------

### Generate Word Count Statistics with epstein_word_count

Source: https://context7.com/michelcrypt4d4mus/epstein_text_messages/llms.txt

Generates word count statistics for communications within the Epstein documents. It allows filtering by specific individuals' names and produces an HTML output that includes word frequency analysis.

```bash
# Count words by specific people
epstein_word_count --name "Jeffrey Epstein" --name "Steve Bannon"

# Generates HTML output with word frequency analysis
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.