Feature/imessage rag support (#131)

This commit is contained in:
Aakash Suresh
2025-10-02 10:40:57 -07:00
committed by GitHub
parent 6b399ad8d2
commit 658bce47ef
10 changed files with 1910 additions and 2 deletions

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README.md
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@@ -20,7 +20,7 @@ LEANN is an innovative vector database that democratizes personal AI. Transform
LEANN achieves this through *graph-based selective recomputation* with *high-degree preserving pruning*, computing embeddings on-demand instead of storing them all. [Illustration Fig →](#-architecture--how-it-works) | [Paper →](https://arxiv.org/abs/2506.08276)
**Ready to RAG Everything?** Transform your laptop into a personal AI assistant that can semantic search your **[file system](#-personal-data-manager-process-any-documents-pdf-txt-md)**, **[emails](#-your-personal-email-secretary-rag-on-apple-mail)**, **[browser history](#-time-machine-for-the-web-rag-your-entire-browser-history)**, **[chat history](#-wechat-detective-unlock-your-golden-memories)**, **[codebase](#-claude-code-integration-transform-your-development-workflow)**\* , or external knowledge bases (i.e., 60M documents) - all on your laptop, with zero cloud costs and complete privacy.
**Ready to RAG Everything?** Transform your laptop into a personal AI assistant that can semantic search your **[file system](#-personal-data-manager-process-any-documents-pdf-txt-md)**, **[emails](#-your-personal-email-secretary-rag-on-apple-mail)**, **[browser history](#-time-machine-for-the-web-rag-your-entire-browser-history)**, **[chat history](#-wechat-detective-unlock-your-golden-memories)** ([WeChat](#-wechat-detective-unlock-your-golden-memories), [iMessage](#-imessage-history-your-personal-conversation-archive)), **[agent memory](#-chatgpt-chat-history-your-personal-ai-conversation-archive)** ([ChatGPT](#-chatgpt-chat-history-your-personal-ai-conversation-archive), [Claude](#-claude-chat-history-your-personal-ai-conversation-archive)), **[codebase](#-claude-code-integration-transform-your-development-workflow)**\* , or external knowledge bases (i.e., 60M documents) - all on your laptop, with zero cloud costs and complete privacy.
\* Claude Code only supports basic `grep`-style keyword search. **LEANN** is a drop-in **semantic search MCP service fully compatible with Claude Code**, unlocking intelligent retrieval without changing your workflow. 🔥 Check out [the easy setup →](packages/leann-mcp/README.md)
@@ -176,7 +176,7 @@ response = chat.ask("How much storage does LEANN save?", top_k=1)
## RAG on Everything!
LEANN supports RAG on various data sources including documents (`.pdf`, `.txt`, `.md`), Apple Mail, Google Search History, WeChat, and more.
LEANN supports RAG on various data sources including documents (`.pdf`, `.txt`, `.md`), Apple Mail, Google Search History, WeChat, ChatGPT conversations, Claude conversations, iMessage conversations, and more.
@@ -542,6 +542,238 @@ Once the index is built, you can ask questions like:
</details>
### 🤖 ChatGPT Chat History: Your Personal AI Conversation Archive!
Transform your ChatGPT conversations into a searchable knowledge base! Search through all your ChatGPT discussions about coding, research, brainstorming, and more.
```bash
python -m apps.chatgpt_rag --export-path chatgpt_export.html --query "How do I create a list in Python?"
```
**Unlock your AI conversation history.** Never lose track of valuable insights from your ChatGPT discussions again.
<details>
<summary><strong>📋 Click to expand: How to Export ChatGPT Data</strong></summary>
**Step-by-step export process:**
1. **Sign in to ChatGPT**
2. **Click your profile icon** in the top right corner
3. **Navigate to Settings** → **Data Controls**
4. **Click "Export"** under Export Data
5. **Confirm the export** request
6. **Download the ZIP file** from the email link (expires in 24 hours)
7. **Extract or use directly** with LEANN
**Supported formats:**
- `.html` files from ChatGPT exports
- `.zip` archives from ChatGPT
- Directories with multiple export files
</details>
<details>
<summary><strong>📋 Click to expand: ChatGPT-Specific Arguments</strong></summary>
#### Parameters
```bash
--export-path PATH # Path to ChatGPT export file (.html/.zip) or directory (default: ./chatgpt_export)
--separate-messages # Process each message separately instead of concatenated conversations
--chunk-size N # Text chunk size (default: 512)
--chunk-overlap N # Overlap between chunks (default: 128)
```
#### Example Commands
```bash
# Basic usage with HTML export
python -m apps.chatgpt_rag --export-path conversations.html
# Process ZIP archive from ChatGPT
python -m apps.chatgpt_rag --export-path chatgpt_export.zip
# Search with specific query
python -m apps.chatgpt_rag --export-path chatgpt_data.html --query "Python programming help"
# Process individual messages for fine-grained search
python -m apps.chatgpt_rag --separate-messages --export-path chatgpt_export.html
# Process directory containing multiple exports
python -m apps.chatgpt_rag --export-path ./chatgpt_exports/ --max-items 1000
```
</details>
<details>
<summary><strong>💡 Click to expand: Example queries you can try</strong></summary>
Once your ChatGPT conversations are indexed, you can search with queries like:
- "What did I ask ChatGPT about Python programming?"
- "Show me conversations about machine learning algorithms"
- "Find discussions about web development frameworks"
- "What coding advice did ChatGPT give me?"
- "Search for conversations about debugging techniques"
- "Find ChatGPT's recommendations for learning resources"
</details>
### 🤖 Claude Chat History: Your Personal AI Conversation Archive!
Transform your Claude conversations into a searchable knowledge base! Search through all your Claude discussions about coding, research, brainstorming, and more.
```bash
python -m apps.claude_rag --export-path claude_export.json --query "What did I ask about Python dictionaries?"
```
**Unlock your AI conversation history.** Never lose track of valuable insights from your Claude discussions again.
<details>
<summary><strong>📋 Click to expand: How to Export Claude Data</strong></summary>
**Step-by-step export process:**
1. **Open Claude** in your browser
2. **Navigate to Settings** (look for gear icon or settings menu)
3. **Find Export/Download** options in your account settings
4. **Download conversation data** (usually in JSON format)
5. **Place the file** in your project directory
*Note: Claude export methods may vary depending on the interface you're using. Check Claude's help documentation for the most current export instructions.*
**Supported formats:**
- `.json` files (recommended)
- `.zip` archives containing JSON data
- Directories with multiple export files
</details>
<details>
<summary><strong>📋 Click to expand: Claude-Specific Arguments</strong></summary>
#### Parameters
```bash
--export-path PATH # Path to Claude export file (.json/.zip) or directory (default: ./claude_export)
--separate-messages # Process each message separately instead of concatenated conversations
--chunk-size N # Text chunk size (default: 512)
--chunk-overlap N # Overlap between chunks (default: 128)
```
#### Example Commands
```bash
# Basic usage with JSON export
python -m apps.claude_rag --export-path my_claude_conversations.json
# Process ZIP archive from Claude
python -m apps.claude_rag --export-path claude_export.zip
# Search with specific query
python -m apps.claude_rag --export-path claude_data.json --query "machine learning advice"
# Process individual messages for fine-grained search
python -m apps.claude_rag --separate-messages --export-path claude_export.json
# Process directory containing multiple exports
python -m apps.claude_rag --export-path ./claude_exports/ --max-items 1000
```
</details>
<details>
<summary><strong>💡 Click to expand: Example queries you can try</strong></summary>
Once your Claude conversations are indexed, you can search with queries like:
- "What did I ask Claude about Python programming?"
- "Show me conversations about machine learning algorithms"
- "Find discussions about software architecture patterns"
- "What debugging advice did Claude give me?"
- "Search for conversations about data structures"
- "Find Claude's recommendations for learning resources"
</details>
### 💬 iMessage History: Your Personal Conversation Archive!
Transform your iMessage conversations into a searchable knowledge base! Search through all your text messages, group chats, and conversations with friends, family, and colleagues.
```bash
python -m apps.imessage_rag --query "What did we discuss about the weekend plans?"
```
**Unlock your message history.** Never lose track of important conversations, shared links, or memorable moments from your iMessage history.
<details>
<summary><strong>📋 Click to expand: How to Access iMessage Data</strong></summary>
**iMessage data location:**
iMessage conversations are stored in a SQLite database on your Mac at:
```
~/Library/Messages/chat.db
```
**Important setup requirements:**
1. **Grant Full Disk Access** to your terminal or IDE:
- Open **System Preferences** → **Security & Privacy** → **Privacy**
- Select **Full Disk Access** from the left sidebar
- Click the **+** button and add your terminal app (Terminal, iTerm2) or IDE (VS Code, etc.)
- Restart your terminal/IDE after granting access
2. **Alternative: Use a backup database**
- If you have Time Machine backups or manual copies of the database
- Use `--db-path` to specify a custom location
**Supported formats:**
- Direct access to `~/Library/Messages/chat.db` (default)
- Custom database path with `--db-path`
- Works with backup copies of the database
</details>
<details>
<summary><strong>📋 Click to expand: iMessage-Specific Arguments</strong></summary>
#### Parameters
```bash
--db-path PATH # Path to chat.db file (default: ~/Library/Messages/chat.db)
--concatenate-conversations # Group messages by conversation (default: True)
--no-concatenate-conversations # Process each message individually
--chunk-size N # Text chunk size (default: 1000)
--chunk-overlap N # Overlap between chunks (default: 200)
```
#### Example Commands
```bash
# Basic usage (requires Full Disk Access)
python -m apps.imessage_rag
# Search with specific query
python -m apps.imessage_rag --query "family dinner plans"
# Use custom database path
python -m apps.imessage_rag --db-path /path/to/backup/chat.db
# Process individual messages instead of conversations
python -m apps.imessage_rag --no-concatenate-conversations
# Limit processing for testing
python -m apps.imessage_rag --max-items 100 --query "weekend"
```
</details>
<details>
<summary><strong>💡 Click to expand: Example queries you can try</strong></summary>
Once your iMessage conversations are indexed, you can search with queries like:
- "What did we discuss about vacation plans?"
- "Find messages about restaurant recommendations"
- "Show me conversations with John about the project"
- "Search for shared links about technology"
- "Find group chat discussions about weekend events"
- "What did mom say about the family gathering?"
</details>
### 🚀 Claude Code Integration: Transform Your Development Workflow!
<details>