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160
README.md
160
README.md
@@ -20,7 +20,7 @@ LEANN is an innovative vector database that democratizes personal AI. Transform
|
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|
||||
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)
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|
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**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)), **[live data](#mcp-integration-rag-on-live-data-from-any-platform)** ([Slack](#slack-messages-search-your-team-conversations), [Twitter](#twitter-bookmarks-your-personal-tweet-library)), **[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.
|
||||
|
||||
|
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\* 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)
|
||||
@@ -72,9 +72,8 @@ uv venv
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source .venv/bin/activate
|
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uv pip install leann
|
||||
```
|
||||
|
||||
<!--
|
||||
> Low-resource? See "Low-resource setups" in the [Configuration Guide](docs/configuration-guide.md#low-resource-setups). -->
|
||||
> Low-resource? See “Low-resource setups” in the [Configuration Guide](docs/configuration-guide.md#low-resource-setups). -->
|
||||
|
||||
<details>
|
||||
<summary>
|
||||
@@ -177,7 +176,7 @@ response = chat.ask("How much storage does LEANN save?", top_k=1)
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|
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## RAG on Everything!
|
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|
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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 **live data from any platform through MCP (Model Context Protocol) servers** - including Slack, Twitter, and more.
|
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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.
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|
||||
|
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|
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@@ -775,155 +774,6 @@ Once your iMessage conversations are indexed, you can search with queries like:
|
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|
||||
</details>
|
||||
|
||||
### MCP Integration: RAG on Live Data from Any Platform
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|
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**NEW!** Connect to live data sources through the Model Context Protocol (MCP). LEANN now supports real-time RAG on platforms like Slack, Twitter, and more through standardized MCP servers.
|
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|
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**Key Benefits:**
|
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- **Live Data Access**: Fetch real-time data without manual exports
|
||||
- **Standardized Protocol**: Use any MCP-compatible server
|
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- **Easy Extension**: Add new platforms with minimal code
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- **Secure Access**: MCP servers handle authentication
|
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|
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<details>
|
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<summary><strong>Slack Messages: Search Your Team Conversations</strong></summary>
|
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|
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Transform your Slack workspace into a searchable knowledge base! Find discussions, decisions, and shared knowledge across all your channels.
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|
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```bash
|
||||
# Test MCP server connection
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python -m apps.slack_rag --mcp-server "slack-mcp-server" --test-connection
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|
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# Index and search Slack messages
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python -m apps.slack_rag \
|
||||
--mcp-server "slack-mcp-server" \
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--workspace-name "my-team" \
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||||
--channels general dev-team random \
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||||
--query "What did we decide about the product launch?"
|
||||
```
|
||||
|
||||
**Setup Requirements:**
|
||||
1. Install a Slack MCP server (e.g., `npm install -g slack-mcp-server`)
|
||||
2. Create a Slack App and get API credentials:
|
||||
- Go to [api.slack.com/apps](https://api.slack.com/apps) and create a new app
|
||||
- Under "OAuth & Permissions", add these Bot Token Scopes: `channels:read`, `channels:history`, `groups:read`, `groups:history`, `im:read`, `im:history`, `mpim:read`, `mpim:history`
|
||||
- Install the app to your workspace and copy the "Bot User OAuth Token" (starts with `xoxb-`)
|
||||
- Under "App-Level Tokens", create a token with `connections:write` scope (starts with `xapp-`)
|
||||
```bash
|
||||
export SLACK_BOT_TOKEN="xoxb-your-bot-token"
|
||||
export SLACK_APP_TOKEN="xapp-your-app-token"
|
||||
```
|
||||
3. Test connection with `--test-connection` flag
|
||||
|
||||
**Arguments:**
|
||||
- `--mcp-server`: Command to start the Slack MCP server
|
||||
- `--workspace-name`: Slack workspace name for organization
|
||||
- `--channels`: Specific channels to index (optional)
|
||||
- `--concatenate-conversations`: Group messages by channel (default: true)
|
||||
- `--max-messages-per-channel`: Limit messages per channel (default: 100)
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><strong>Twitter Bookmarks: Your Personal Tweet Library</strong></summary>
|
||||
|
||||
Search through your Twitter bookmarks! Find that perfect article, thread, or insight you saved for later.
|
||||
|
||||
```bash
|
||||
# Test MCP server connection
|
||||
python -m apps.twitter_rag --mcp-server "twitter-mcp-server" --test-connection
|
||||
|
||||
# Index and search Twitter bookmarks
|
||||
python -m apps.twitter_rag \
|
||||
--mcp-server "twitter-mcp-server" \
|
||||
--max-bookmarks 1000 \
|
||||
--query "What AI articles did I bookmark about machine learning?"
|
||||
```
|
||||
|
||||
**Setup Requirements:**
|
||||
1. Install a Twitter MCP server (e.g., `npm install -g twitter-mcp-server`)
|
||||
2. Get Twitter API credentials:
|
||||
- Apply for a Twitter Developer Account at [developer.twitter.com](https://developer.twitter.com)
|
||||
- Create a new app in the Twitter Developer Portal
|
||||
- Generate API keys and access tokens with "Read" permissions
|
||||
- For bookmarks access, you may need Twitter API v2 with appropriate scopes
|
||||
```bash
|
||||
export TWITTER_API_KEY="your-api-key"
|
||||
export TWITTER_API_SECRET="your-api-secret"
|
||||
export TWITTER_ACCESS_TOKEN="your-access-token"
|
||||
export TWITTER_ACCESS_TOKEN_SECRET="your-access-token-secret"
|
||||
```
|
||||
3. Test connection with `--test-connection` flag
|
||||
|
||||
**Arguments:**
|
||||
- `--mcp-server`: Command to start the Twitter MCP server
|
||||
- `--username`: Filter bookmarks by username (optional)
|
||||
- `--max-bookmarks`: Maximum bookmarks to fetch (default: 1000)
|
||||
- `--no-tweet-content`: Exclude tweet content, only metadata
|
||||
- `--no-metadata`: Exclude engagement metadata
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><strong>💡 Click to expand: Example queries you can try</strong></summary>
|
||||
|
||||
**Slack Queries:**
|
||||
- "What did the team discuss about the project deadline?"
|
||||
- "Find messages about the new feature launch"
|
||||
- "Show me conversations about budget planning"
|
||||
- "What decisions were made in the dev-team channel?"
|
||||
|
||||
**Twitter Queries:**
|
||||
- "What AI articles did I bookmark last month?"
|
||||
- "Find tweets about machine learning techniques"
|
||||
- "Show me bookmarked threads about startup advice"
|
||||
- "What Python tutorials did I save?"
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><strong>🔧 Using MCP with CLI Commands</strong></summary>
|
||||
|
||||
**Want to use MCP data with regular LEANN CLI?** You can combine MCP apps with CLI commands:
|
||||
|
||||
```bash
|
||||
# Step 1: Use MCP app to fetch and index data
|
||||
python -m apps.slack_rag --mcp-server "slack-mcp-server" --workspace-name "my-team"
|
||||
|
||||
# Step 2: The data is now indexed and available via CLI
|
||||
leann search slack_messages "project deadline"
|
||||
leann ask slack_messages "What decisions were made about the product launch?"
|
||||
|
||||
# Same for Twitter bookmarks
|
||||
python -m apps.twitter_rag --mcp-server "twitter-mcp-server"
|
||||
leann search twitter_bookmarks "machine learning articles"
|
||||
```
|
||||
|
||||
**MCP vs Manual Export:**
|
||||
- **MCP**: Live data, automatic updates, requires server setup
|
||||
- **Manual Export**: One-time setup, works offline, requires manual data export
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><strong>🔧 Adding New MCP Platforms</strong></summary>
|
||||
|
||||
Want to add support for other platforms? LEANN's MCP integration is designed for easy extension:
|
||||
|
||||
1. **Find or create an MCP server** for your platform
|
||||
2. **Create a reader class** following the pattern in `apps/slack_data/slack_mcp_reader.py`
|
||||
3. **Create a RAG application** following the pattern in `apps/slack_rag.py`
|
||||
4. **Test and contribute** back to the community!
|
||||
|
||||
**Popular MCP servers to explore:**
|
||||
- GitHub repositories and issues
|
||||
- Discord messages
|
||||
- Notion pages
|
||||
- Google Drive documents
|
||||
- And many more in the MCP ecosystem!
|
||||
|
||||
</details>
|
||||
|
||||
### 🚀 Claude Code Integration: Transform Your Development Workflow!
|
||||
|
||||
<details>
|
||||
@@ -955,7 +805,7 @@ Try our fully agentic pipeline with auto query rewriting, semantic search planni
|
||||
|
||||
**🔥 Ready to supercharge your coding?** [Complete Setup Guide →](packages/leann-mcp/README.md)
|
||||
|
||||
## Command Line Interface
|
||||
## 🖥️ Command Line Interface
|
||||
|
||||
LEANN includes a powerful CLI for document processing and search. Perfect for quick document indexing and interactive chat.
|
||||
|
||||
@@ -1197,7 +1047,7 @@ MIT License - see [LICENSE](LICENSE) for details.
|
||||
|
||||
Core Contributors: [Yichuan Wang](https://yichuan-w.github.io/) & [Zhifei Li](https://github.com/andylizf).
|
||||
|
||||
Active Contributors: [Gabriel Dehan](https://github.com/gabriel-dehan), [Aakash Suresh](https://github.com/ASuresh0524)
|
||||
Active Contributors: [Gabriel Dehan](https://github.com/gabriel-dehan)
|
||||
|
||||
|
||||
We welcome more contributors! Feel free to open issues or submit PRs.
|
||||
|
||||
@@ -10,6 +10,7 @@ from typing import Any
|
||||
|
||||
import dotenv
|
||||
from leann.api import LeannBuilder, LeannChat
|
||||
from leann.interactive_utils import create_rag_session
|
||||
from leann.registry import register_project_directory
|
||||
from leann.settings import resolve_ollama_host, resolve_openai_api_key, resolve_openai_base_url
|
||||
|
||||
@@ -307,37 +308,26 @@ class BaseRAGExample(ABC):
|
||||
complexity=args.search_complexity,
|
||||
)
|
||||
|
||||
print(f"\n[Interactive Mode] Chat with your {self.name} data!")
|
||||
print("Type 'quit' or 'exit' to stop.\n")
|
||||
# Create interactive session
|
||||
session = create_rag_session(
|
||||
app_name=self.name.lower().replace(" ", "_"), data_description=self.name
|
||||
)
|
||||
|
||||
while True:
|
||||
try:
|
||||
query = input("You: ").strip()
|
||||
if query.lower() in ["quit", "exit", "q"]:
|
||||
print("Goodbye!")
|
||||
break
|
||||
def handle_query(query: str):
|
||||
# Prepare LLM kwargs with thinking budget if specified
|
||||
llm_kwargs = {}
|
||||
if hasattr(args, "thinking_budget") and args.thinking_budget:
|
||||
llm_kwargs["thinking_budget"] = args.thinking_budget
|
||||
|
||||
if not query:
|
||||
continue
|
||||
response = chat.ask(
|
||||
query,
|
||||
top_k=args.top_k,
|
||||
complexity=args.search_complexity,
|
||||
llm_kwargs=llm_kwargs,
|
||||
)
|
||||
print(f"\nAssistant: {response}\n")
|
||||
|
||||
# Prepare LLM kwargs with thinking budget if specified
|
||||
llm_kwargs = {}
|
||||
if hasattr(args, "thinking_budget") and args.thinking_budget:
|
||||
llm_kwargs["thinking_budget"] = args.thinking_budget
|
||||
|
||||
response = chat.ask(
|
||||
query,
|
||||
top_k=args.top_k,
|
||||
complexity=args.search_complexity,
|
||||
llm_kwargs=llm_kwargs,
|
||||
)
|
||||
print(f"\nAssistant: {response}\n")
|
||||
|
||||
except KeyboardInterrupt:
|
||||
print("\nGoodbye!")
|
||||
break
|
||||
except Exception as e:
|
||||
print(f"Error: {e}")
|
||||
session.run_interactive_loop(handle_query)
|
||||
|
||||
async def run_single_query(self, args, index_path: str, query: str):
|
||||
"""Run a single query against the index."""
|
||||
|
||||
183
apps/semantic_file_search/leann-plus-temporal-search.py
Normal file
183
apps/semantic_file_search/leann-plus-temporal-search.py
Normal file
@@ -0,0 +1,183 @@
|
||||
#!/usr/bin/env python3
|
||||
import re
|
||||
import sys
|
||||
from datetime import datetime, timedelta
|
||||
from pathlib import Path
|
||||
|
||||
from leann import LeannSearcher
|
||||
|
||||
INDEX_PATH = str(Path("./").resolve() / "demo.leann")
|
||||
|
||||
|
||||
class TimeParser:
|
||||
def __init__(self):
|
||||
# Main pattern: captures optional fuzzy modifier, number, unit, and optional "ago"
|
||||
self.pattern = r"(?:(around|about|roughly|approximately)\s+)?(\d+)\s+(hour|day|week|month|year)s?(?:\s+ago)?"
|
||||
|
||||
# Compile for performance
|
||||
self.regex = re.compile(self.pattern, re.IGNORECASE)
|
||||
|
||||
# Stop words to remove before regex parsing
|
||||
self.stop_words = {
|
||||
"in",
|
||||
"at",
|
||||
"of",
|
||||
"by",
|
||||
"as",
|
||||
"me",
|
||||
"the",
|
||||
"a",
|
||||
"an",
|
||||
"and",
|
||||
"any",
|
||||
"find",
|
||||
"search",
|
||||
"list",
|
||||
"ago",
|
||||
"back",
|
||||
"past",
|
||||
"earlier",
|
||||
}
|
||||
|
||||
def clean_text(self, text):
|
||||
"""Remove stop words from text"""
|
||||
words = text.split()
|
||||
cleaned = " ".join(word for word in words if word.lower() not in self.stop_words)
|
||||
return cleaned
|
||||
|
||||
def parse(self, text):
|
||||
"""Extract all time expressions from text"""
|
||||
# Clean text first
|
||||
cleaned_text = self.clean_text(text)
|
||||
|
||||
matches = []
|
||||
for match in self.regex.finditer(cleaned_text):
|
||||
fuzzy = match.group(1) # "around", "about", etc.
|
||||
number = int(match.group(2))
|
||||
unit = match.group(3).lower()
|
||||
|
||||
matches.append(
|
||||
{
|
||||
"full_match": match.group(0),
|
||||
"fuzzy": bool(fuzzy),
|
||||
"number": number,
|
||||
"unit": unit,
|
||||
"range": self.calculate_range(number, unit, bool(fuzzy)),
|
||||
}
|
||||
)
|
||||
|
||||
return matches
|
||||
|
||||
def calculate_range(self, number, unit, is_fuzzy):
|
||||
"""Convert to actual datetime range and return ISO format strings"""
|
||||
units = {
|
||||
"hour": timedelta(hours=number),
|
||||
"day": timedelta(days=number),
|
||||
"week": timedelta(weeks=number),
|
||||
"month": timedelta(days=number * 30),
|
||||
"year": timedelta(days=number * 365),
|
||||
}
|
||||
|
||||
delta = units[unit]
|
||||
now = datetime.now()
|
||||
target = now - delta
|
||||
|
||||
if is_fuzzy:
|
||||
buffer = delta * 0.2 # 20% buffer for fuzzy
|
||||
start = (target - buffer).isoformat()
|
||||
end = (target + buffer).isoformat()
|
||||
else:
|
||||
start = target.isoformat()
|
||||
end = now.isoformat()
|
||||
|
||||
return (start, end)
|
||||
|
||||
|
||||
def search_files(query, top_k=15):
|
||||
"""Search the index and return results"""
|
||||
# Parse time expressions
|
||||
parser = TimeParser()
|
||||
time_matches = parser.parse(query)
|
||||
|
||||
# Remove time expressions from query for semantic search
|
||||
clean_query = query
|
||||
if time_matches:
|
||||
for match in time_matches:
|
||||
clean_query = clean_query.replace(match["full_match"], "").strip()
|
||||
|
||||
# Check if clean_query is less than 4 characters
|
||||
if len(clean_query) < 4:
|
||||
print("Error: add more input for accurate results.")
|
||||
return
|
||||
|
||||
# Single query to vector DB
|
||||
searcher = LeannSearcher(INDEX_PATH)
|
||||
results = searcher.search(
|
||||
clean_query if clean_query else query, top_k=top_k, recompute_embeddings=False
|
||||
)
|
||||
|
||||
# Filter by time if time expression found
|
||||
if time_matches:
|
||||
time_range = time_matches[0]["range"] # Use first time expression
|
||||
start_time, end_time = time_range
|
||||
|
||||
filtered_results = []
|
||||
for result in results:
|
||||
# Access metadata attribute directly (not .get())
|
||||
metadata = result.metadata if hasattr(result, "metadata") else {}
|
||||
|
||||
if metadata:
|
||||
# Check modification date first, fall back to creation date
|
||||
date_str = metadata.get("modification_date") or metadata.get("creation_date")
|
||||
|
||||
if date_str:
|
||||
# Convert strings to datetime objects for proper comparison
|
||||
try:
|
||||
file_date = datetime.fromisoformat(date_str)
|
||||
start_dt = datetime.fromisoformat(start_time)
|
||||
end_dt = datetime.fromisoformat(end_time)
|
||||
|
||||
# Compare dates properly
|
||||
if start_dt <= file_date <= end_dt:
|
||||
filtered_results.append(result)
|
||||
except (ValueError, TypeError):
|
||||
# Handle invalid date formats
|
||||
print(f"Warning: Invalid date format in metadata: {date_str}")
|
||||
continue
|
||||
|
||||
results = filtered_results
|
||||
|
||||
# Print results
|
||||
print(f"\nSearch results for: '{query}'")
|
||||
if time_matches:
|
||||
print(
|
||||
f"Time filter: {time_matches[0]['number']} {time_matches[0]['unit']}(s) {'(fuzzy)' if time_matches[0]['fuzzy'] else ''}"
|
||||
)
|
||||
print(
|
||||
f"Date range: {time_matches[0]['range'][0][:10]} to {time_matches[0]['range'][1][:10]}"
|
||||
)
|
||||
print("-" * 80)
|
||||
|
||||
for i, result in enumerate(results, 1):
|
||||
print(f"\n[{i}] Score: {result.score:.4f}")
|
||||
print(f"Content: {result.text}")
|
||||
|
||||
# Show metadata if present
|
||||
metadata = result.metadata if hasattr(result, "metadata") else None
|
||||
if metadata:
|
||||
if "creation_date" in metadata:
|
||||
print(f"Created: {metadata['creation_date']}")
|
||||
if "modification_date" in metadata:
|
||||
print(f"Modified: {metadata['modification_date']}")
|
||||
print("-" * 80)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
if len(sys.argv) < 2:
|
||||
print('Usage: python search_index.py "<search query>" [top_k]')
|
||||
sys.exit(1)
|
||||
|
||||
query = sys.argv[1]
|
||||
top_k = int(sys.argv[2]) if len(sys.argv) > 2 else 15
|
||||
|
||||
search_files(query, top_k)
|
||||
82
apps/semantic_file_search/leann_index_builder.py
Normal file
82
apps/semantic_file_search/leann_index_builder.py
Normal file
@@ -0,0 +1,82 @@
|
||||
#!/usr/bin/env python3
|
||||
import json
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
from leann import LeannBuilder
|
||||
|
||||
|
||||
def process_json_items(json_file_path):
|
||||
"""Load and process JSON file with metadata items"""
|
||||
|
||||
with open(json_file_path, encoding="utf-8") as f:
|
||||
items = json.load(f)
|
||||
|
||||
# Guard against empty JSON
|
||||
if not items:
|
||||
print("⚠️ No items found in the JSON file. Exiting gracefully.")
|
||||
return
|
||||
|
||||
INDEX_PATH = str(Path("./").resolve() / "demo.leann")
|
||||
builder = LeannBuilder(backend_name="hnsw", is_recompute=False)
|
||||
|
||||
total_items = len(items)
|
||||
items_added = 0
|
||||
print(f"Processing {total_items} items...")
|
||||
|
||||
for idx, item in enumerate(items):
|
||||
try:
|
||||
# Create embedding text sentence
|
||||
embedding_text = f"{item.get('Name', 'unknown')} located at {item.get('Path', 'unknown')} and size {item.get('Size', 'unknown')} bytes with content type {item.get('ContentType', 'unknown')} and kind {item.get('Kind', 'unknown')}"
|
||||
|
||||
# Prepare metadata with dates
|
||||
metadata = {}
|
||||
if "CreationDate" in item:
|
||||
metadata["creation_date"] = item["CreationDate"]
|
||||
if "ContentChangeDate" in item:
|
||||
metadata["modification_date"] = item["ContentChangeDate"]
|
||||
|
||||
# Add to builder
|
||||
builder.add_text(embedding_text, metadata=metadata)
|
||||
items_added += 1
|
||||
|
||||
except Exception as e:
|
||||
print(f"\n⚠️ Warning: Failed to process item {idx}: {e}")
|
||||
continue
|
||||
|
||||
# Show progress
|
||||
progress = (idx + 1) / total_items * 100
|
||||
sys.stdout.write(f"\rProgress: {idx + 1}/{total_items} ({progress:.1f}%)")
|
||||
sys.stdout.flush()
|
||||
|
||||
print() # New line after progress
|
||||
|
||||
# Guard against no successfully added items
|
||||
if items_added == 0:
|
||||
print("⚠️ No items were successfully added to the index. Exiting gracefully.")
|
||||
return
|
||||
|
||||
print(f"\n✅ Successfully processed {items_added}/{total_items} items")
|
||||
print("Building index...")
|
||||
|
||||
try:
|
||||
builder.build_index(INDEX_PATH)
|
||||
print(f"✓ Index saved to {INDEX_PATH}")
|
||||
except ValueError as e:
|
||||
if "No chunks added" in str(e):
|
||||
print("⚠️ No chunks were added to the builder. Index not created.")
|
||||
else:
|
||||
raise
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
if len(sys.argv) != 2:
|
||||
print("Usage: python build_index.py <json_file>")
|
||||
sys.exit(1)
|
||||
|
||||
json_file = sys.argv[1]
|
||||
if not Path(json_file).exists():
|
||||
print(f"Error: File {json_file} not found")
|
||||
sys.exit(1)
|
||||
|
||||
process_json_items(json_file)
|
||||
265
apps/semantic_file_search/spotlight_index_dump.py
Normal file
265
apps/semantic_file_search/spotlight_index_dump.py
Normal file
@@ -0,0 +1,265 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Spotlight Metadata Dumper for Vector DB
|
||||
Extracts only essential metadata for semantic search embeddings
|
||||
Output is optimized for vector database storage with minimal fields
|
||||
"""
|
||||
|
||||
import json
|
||||
import sys
|
||||
from datetime import datetime
|
||||
|
||||
# Check platform before importing macOS-specific modules
|
||||
if sys.platform != "darwin":
|
||||
print("This script requires macOS (uses Spotlight)")
|
||||
sys.exit(1)
|
||||
|
||||
from Foundation import NSDate, NSMetadataQuery, NSPredicate, NSRunLoop
|
||||
|
||||
# EDIT THIS LIST: Add or remove folders to search
|
||||
# Can be either:
|
||||
# - Folder names relative to home directory (e.g., "Desktop", "Downloads")
|
||||
# - Absolute paths (e.g., "/Applications", "/System/Library")
|
||||
SEARCH_FOLDERS = [
|
||||
"Desktop",
|
||||
"Downloads",
|
||||
"Documents",
|
||||
"Music",
|
||||
"Pictures",
|
||||
"Movies",
|
||||
# "Library", # Uncomment to include
|
||||
# "/Applications", # Absolute path example
|
||||
# "Code/Projects", # Subfolder example
|
||||
# Add any other folders here
|
||||
]
|
||||
|
||||
|
||||
def convert_to_serializable(obj):
|
||||
"""Convert NS objects to Python serializable types"""
|
||||
if obj is None:
|
||||
return None
|
||||
|
||||
# Handle NSDate
|
||||
if hasattr(obj, "timeIntervalSince1970"):
|
||||
return datetime.fromtimestamp(obj.timeIntervalSince1970()).isoformat()
|
||||
|
||||
# Handle NSArray
|
||||
if hasattr(obj, "count") and hasattr(obj, "objectAtIndex_"):
|
||||
return [convert_to_serializable(obj.objectAtIndex_(i)) for i in range(obj.count())]
|
||||
|
||||
# Convert to string
|
||||
try:
|
||||
return str(obj)
|
||||
except Exception:
|
||||
return repr(obj)
|
||||
|
||||
|
||||
def dump_spotlight_data(max_items=10, output_file="spotlight_dump.json"):
|
||||
"""
|
||||
Dump Spotlight data using public.item predicate
|
||||
"""
|
||||
# Build full paths from SEARCH_FOLDERS
|
||||
import os
|
||||
|
||||
home_dir = os.path.expanduser("~")
|
||||
search_paths = []
|
||||
|
||||
print("Search locations:")
|
||||
for folder in SEARCH_FOLDERS:
|
||||
# Check if it's an absolute path or relative
|
||||
if folder.startswith("/"):
|
||||
full_path = folder
|
||||
else:
|
||||
full_path = os.path.join(home_dir, folder)
|
||||
|
||||
if os.path.exists(full_path):
|
||||
search_paths.append(full_path)
|
||||
print(f" ✓ {full_path}")
|
||||
else:
|
||||
print(f" ✗ {full_path} (not found)")
|
||||
|
||||
if not search_paths:
|
||||
print("No valid search paths found!")
|
||||
return []
|
||||
|
||||
print(f"\nDumping {max_items} items from Spotlight (public.item)...")
|
||||
|
||||
# Create query with public.item predicate
|
||||
query = NSMetadataQuery.alloc().init()
|
||||
predicate = NSPredicate.predicateWithFormat_("kMDItemContentTypeTree CONTAINS 'public.item'")
|
||||
query.setPredicate_(predicate)
|
||||
|
||||
# Set search scopes to our specific folders
|
||||
query.setSearchScopes_(search_paths)
|
||||
|
||||
print("Starting query...")
|
||||
query.startQuery()
|
||||
|
||||
# Wait for gathering to complete
|
||||
run_loop = NSRunLoop.currentRunLoop()
|
||||
print("Gathering results...")
|
||||
|
||||
# Let it gather for a few seconds
|
||||
for i in range(50): # 5 seconds max
|
||||
run_loop.runMode_beforeDate_(
|
||||
"NSDefaultRunLoopMode", NSDate.dateWithTimeIntervalSinceNow_(0.1)
|
||||
)
|
||||
# Check gathering status periodically
|
||||
if i % 10 == 0:
|
||||
current_count = query.resultCount()
|
||||
if current_count > 0:
|
||||
print(f" Found {current_count} items so far...")
|
||||
|
||||
# Continue while still gathering (up to 2 more seconds)
|
||||
timeout = NSDate.dateWithTimeIntervalSinceNow_(2.0)
|
||||
while query.isGathering() and timeout.timeIntervalSinceNow() > 0:
|
||||
run_loop.runMode_beforeDate_(
|
||||
"NSDefaultRunLoopMode", NSDate.dateWithTimeIntervalSinceNow_(0.1)
|
||||
)
|
||||
|
||||
query.stopQuery()
|
||||
|
||||
total_results = query.resultCount()
|
||||
print(f"Found {total_results} total items")
|
||||
|
||||
if total_results == 0:
|
||||
print("No results found")
|
||||
return []
|
||||
|
||||
# Process items
|
||||
items_to_process = min(total_results, max_items)
|
||||
results = []
|
||||
|
||||
# ONLY relevant attributes for vector embeddings
|
||||
# These provide essential context for semantic search without bloat
|
||||
attributes = [
|
||||
"kMDItemPath", # Full path for file retrieval
|
||||
"kMDItemFSName", # Filename for display & embedding
|
||||
"kMDItemFSSize", # Size for filtering/ranking
|
||||
"kMDItemContentType", # File type for categorization
|
||||
"kMDItemKind", # Human-readable type for embedding
|
||||
"kMDItemFSCreationDate", # Temporal context
|
||||
"kMDItemFSContentChangeDate", # Recency for ranking
|
||||
]
|
||||
|
||||
print(f"Processing {items_to_process} items...")
|
||||
|
||||
for i in range(items_to_process):
|
||||
try:
|
||||
item = query.resultAtIndex_(i)
|
||||
metadata = {}
|
||||
|
||||
# Extract ONLY the relevant attributes
|
||||
for attr in attributes:
|
||||
try:
|
||||
value = item.valueForAttribute_(attr)
|
||||
if value is not None:
|
||||
# Keep the attribute name clean (remove kMDItem prefix for cleaner JSON)
|
||||
clean_key = attr.replace("kMDItem", "").replace("FS", "")
|
||||
metadata[clean_key] = convert_to_serializable(value)
|
||||
except (AttributeError, ValueError, TypeError):
|
||||
continue
|
||||
|
||||
# Only add if we have at least a path
|
||||
if metadata.get("Path"):
|
||||
results.append(metadata)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error processing item {i}: {e}")
|
||||
continue
|
||||
|
||||
# Save to JSON
|
||||
with open(output_file, "w", encoding="utf-8") as f:
|
||||
json.dump(results, f, indent=2, ensure_ascii=False)
|
||||
|
||||
print(f"\n✓ Saved {len(results)} items to {output_file}")
|
||||
|
||||
# Show summary
|
||||
print("\nSample items:")
|
||||
import os
|
||||
|
||||
home_dir = os.path.expanduser("~")
|
||||
|
||||
for i, item in enumerate(results[:3]):
|
||||
print(f"\n[Item {i + 1}]")
|
||||
print(f" Path: {item.get('Path', 'N/A')}")
|
||||
print(f" Name: {item.get('Name', 'N/A')}")
|
||||
print(f" Type: {item.get('ContentType', 'N/A')}")
|
||||
print(f" Kind: {item.get('Kind', 'N/A')}")
|
||||
|
||||
# Handle size properly
|
||||
size = item.get("Size")
|
||||
if size:
|
||||
try:
|
||||
size_int = int(size)
|
||||
if size_int > 1024 * 1024:
|
||||
print(f" Size: {size_int / (1024 * 1024):.2f} MB")
|
||||
elif size_int > 1024:
|
||||
print(f" Size: {size_int / 1024:.2f} KB")
|
||||
else:
|
||||
print(f" Size: {size_int} bytes")
|
||||
except (ValueError, TypeError):
|
||||
print(f" Size: {size}")
|
||||
|
||||
# Show dates
|
||||
if "CreationDate" in item:
|
||||
print(f" Created: {item['CreationDate']}")
|
||||
if "ContentChangeDate" in item:
|
||||
print(f" Modified: {item['ContentChangeDate']}")
|
||||
|
||||
# Count by type
|
||||
type_counts = {}
|
||||
for item in results:
|
||||
content_type = item.get("ContentType", "unknown")
|
||||
type_counts[content_type] = type_counts.get(content_type, 0) + 1
|
||||
|
||||
print(f"\nTotal items saved: {len(results)}")
|
||||
|
||||
if type_counts:
|
||||
print("\nTop content types:")
|
||||
for ct, count in sorted(type_counts.items(), key=lambda x: x[1], reverse=True)[:5]:
|
||||
print(f" {ct}: {count} items")
|
||||
|
||||
# Count by folder
|
||||
folder_counts = {}
|
||||
for item in results:
|
||||
path = item.get("Path", "")
|
||||
for folder in SEARCH_FOLDERS:
|
||||
# Build the full folder path
|
||||
if folder.startswith("/"):
|
||||
folder_path = folder
|
||||
else:
|
||||
folder_path = os.path.join(home_dir, folder)
|
||||
|
||||
if path.startswith(folder_path):
|
||||
folder_counts[folder] = folder_counts.get(folder, 0) + 1
|
||||
break
|
||||
|
||||
if folder_counts:
|
||||
print("\nItems by location:")
|
||||
for folder, count in sorted(folder_counts.items(), key=lambda x: x[1], reverse=True):
|
||||
print(f" {folder}: {count} items")
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def main():
|
||||
# Parse arguments
|
||||
if len(sys.argv) > 1:
|
||||
try:
|
||||
max_items = int(sys.argv[1])
|
||||
except ValueError:
|
||||
print("Usage: python spot.py [number_of_items]")
|
||||
print("Default: 10 items")
|
||||
sys.exit(1)
|
||||
else:
|
||||
max_items = 10
|
||||
|
||||
output_file = sys.argv[2] if len(sys.argv) > 2 else "spotlight_dump.json"
|
||||
|
||||
# Run dump
|
||||
dump_spotlight_data(max_items=max_items, output_file=output_file)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1 +0,0 @@
|
||||
# Slack MCP data integration for LEANN
|
||||
@@ -1,334 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Slack MCP Reader for LEANN
|
||||
|
||||
This module provides functionality to connect to Slack MCP servers and fetch message data
|
||||
for indexing in LEANN. It supports various Slack MCP server implementations and provides
|
||||
flexible message processing options.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
from typing import Any, Optional
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class SlackMCPReader:
|
||||
"""
|
||||
Reader for Slack data via MCP (Model Context Protocol) servers.
|
||||
|
||||
This class connects to Slack MCP servers to fetch message data and convert it
|
||||
into a format suitable for LEANN indexing.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
mcp_server_command: str,
|
||||
workspace_name: Optional[str] = None,
|
||||
concatenate_conversations: bool = True,
|
||||
max_messages_per_conversation: int = 100,
|
||||
):
|
||||
"""
|
||||
Initialize the Slack MCP Reader.
|
||||
|
||||
Args:
|
||||
mcp_server_command: Command to start the MCP server (e.g., 'slack-mcp-server')
|
||||
workspace_name: Optional workspace name to filter messages
|
||||
concatenate_conversations: Whether to group messages by channel/thread
|
||||
max_messages_per_conversation: Maximum messages to include per conversation
|
||||
"""
|
||||
self.mcp_server_command = mcp_server_command
|
||||
self.workspace_name = workspace_name
|
||||
self.concatenate_conversations = concatenate_conversations
|
||||
self.max_messages_per_conversation = max_messages_per_conversation
|
||||
self.mcp_process = None
|
||||
|
||||
async def start_mcp_server(self):
|
||||
"""Start the MCP server process."""
|
||||
try:
|
||||
self.mcp_process = await asyncio.create_subprocess_exec(
|
||||
*self.mcp_server_command.split(),
|
||||
stdin=asyncio.subprocess.PIPE,
|
||||
stdout=asyncio.subprocess.PIPE,
|
||||
stderr=asyncio.subprocess.PIPE,
|
||||
)
|
||||
logger.info(f"Started MCP server: {self.mcp_server_command}")
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to start MCP server: {e}")
|
||||
raise
|
||||
|
||||
async def stop_mcp_server(self):
|
||||
"""Stop the MCP server process."""
|
||||
if self.mcp_process:
|
||||
self.mcp_process.terminate()
|
||||
await self.mcp_process.wait()
|
||||
logger.info("Stopped MCP server")
|
||||
|
||||
async def send_mcp_request(self, request: dict[str, Any]) -> dict[str, Any]:
|
||||
"""Send a request to the MCP server and get response."""
|
||||
if not self.mcp_process:
|
||||
raise RuntimeError("MCP server not started")
|
||||
|
||||
request_json = json.dumps(request) + "\n"
|
||||
self.mcp_process.stdin.write(request_json.encode())
|
||||
await self.mcp_process.stdin.drain()
|
||||
|
||||
response_line = await self.mcp_process.stdout.readline()
|
||||
if not response_line:
|
||||
raise RuntimeError("No response from MCP server")
|
||||
|
||||
return json.loads(response_line.decode().strip())
|
||||
|
||||
async def initialize_mcp_connection(self):
|
||||
"""Initialize the MCP connection."""
|
||||
init_request = {
|
||||
"jsonrpc": "2.0",
|
||||
"id": 1,
|
||||
"method": "initialize",
|
||||
"params": {
|
||||
"protocolVersion": "2024-11-05",
|
||||
"capabilities": {},
|
||||
"clientInfo": {"name": "leann-slack-reader", "version": "1.0.0"},
|
||||
},
|
||||
}
|
||||
|
||||
response = await self.send_mcp_request(init_request)
|
||||
if "error" in response:
|
||||
raise RuntimeError(f"MCP initialization failed: {response['error']}")
|
||||
|
||||
logger.info("MCP connection initialized successfully")
|
||||
|
||||
async def list_available_tools(self) -> list[dict[str, Any]]:
|
||||
"""List available tools from the MCP server."""
|
||||
list_request = {"jsonrpc": "2.0", "id": 2, "method": "tools/list", "params": {}}
|
||||
|
||||
response = await self.send_mcp_request(list_request)
|
||||
if "error" in response:
|
||||
raise RuntimeError(f"Failed to list tools: {response['error']}")
|
||||
|
||||
return response.get("result", {}).get("tools", [])
|
||||
|
||||
async def fetch_slack_messages(
|
||||
self, channel: Optional[str] = None, limit: int = 100
|
||||
) -> list[dict[str, Any]]:
|
||||
"""
|
||||
Fetch Slack messages using MCP tools.
|
||||
|
||||
Args:
|
||||
channel: Optional channel name to filter messages
|
||||
limit: Maximum number of messages to fetch
|
||||
|
||||
Returns:
|
||||
List of message dictionaries
|
||||
"""
|
||||
# This is a generic implementation - specific MCP servers may have different tool names
|
||||
# Common tool names might be: 'get_messages', 'list_messages', 'fetch_channel_history'
|
||||
|
||||
tools = await self.list_available_tools()
|
||||
message_tool = None
|
||||
|
||||
# Look for a tool that can fetch messages
|
||||
for tool in tools:
|
||||
tool_name = tool.get("name", "").lower()
|
||||
if any(
|
||||
keyword in tool_name
|
||||
for keyword in ["message", "history", "channel", "conversation"]
|
||||
):
|
||||
message_tool = tool
|
||||
break
|
||||
|
||||
if not message_tool:
|
||||
raise RuntimeError("No message fetching tool found in MCP server")
|
||||
|
||||
# Prepare tool call parameters
|
||||
tool_params = {"limit": limit}
|
||||
if channel:
|
||||
# Try common parameter names for channel specification
|
||||
for param_name in ["channel", "channel_id", "channel_name"]:
|
||||
tool_params[param_name] = channel
|
||||
break
|
||||
|
||||
fetch_request = {
|
||||
"jsonrpc": "2.0",
|
||||
"id": 3,
|
||||
"method": "tools/call",
|
||||
"params": {"name": message_tool["name"], "arguments": tool_params},
|
||||
}
|
||||
|
||||
response = await self.send_mcp_request(fetch_request)
|
||||
if "error" in response:
|
||||
raise RuntimeError(f"Failed to fetch messages: {response['error']}")
|
||||
|
||||
# Extract messages from response - format may vary by MCP server
|
||||
result = response.get("result", {})
|
||||
if "content" in result and isinstance(result["content"], list):
|
||||
# Some MCP servers return content as a list
|
||||
content = result["content"][0] if result["content"] else {}
|
||||
if "text" in content:
|
||||
try:
|
||||
messages = json.loads(content["text"])
|
||||
except json.JSONDecodeError:
|
||||
# If not JSON, treat as plain text
|
||||
messages = [{"text": content["text"], "channel": channel or "unknown"}]
|
||||
else:
|
||||
messages = result["content"]
|
||||
else:
|
||||
# Direct message format
|
||||
messages = result.get("messages", [result])
|
||||
|
||||
return messages if isinstance(messages, list) else [messages]
|
||||
|
||||
def _format_message(self, message: dict[str, Any]) -> str:
|
||||
"""Format a single message for indexing."""
|
||||
text = message.get("text", "")
|
||||
user = message.get("user", message.get("username", "Unknown"))
|
||||
channel = message.get("channel", message.get("channel_name", "Unknown"))
|
||||
timestamp = message.get("ts", message.get("timestamp", ""))
|
||||
|
||||
# Format timestamp if available
|
||||
formatted_time = ""
|
||||
if timestamp:
|
||||
try:
|
||||
import datetime
|
||||
|
||||
if isinstance(timestamp, str) and "." in timestamp:
|
||||
dt = datetime.datetime.fromtimestamp(float(timestamp))
|
||||
formatted_time = dt.strftime("%Y-%m-%d %H:%M:%S")
|
||||
elif isinstance(timestamp, (int, float)):
|
||||
dt = datetime.datetime.fromtimestamp(timestamp)
|
||||
formatted_time = dt.strftime("%Y-%m-%d %H:%M:%S")
|
||||
else:
|
||||
formatted_time = str(timestamp)
|
||||
except (ValueError, TypeError):
|
||||
formatted_time = str(timestamp)
|
||||
|
||||
# Build formatted message
|
||||
parts = []
|
||||
if channel:
|
||||
parts.append(f"Channel: #{channel}")
|
||||
if user:
|
||||
parts.append(f"User: {user}")
|
||||
if formatted_time:
|
||||
parts.append(f"Time: {formatted_time}")
|
||||
if text:
|
||||
parts.append(f"Message: {text}")
|
||||
|
||||
return "\n".join(parts)
|
||||
|
||||
def _create_concatenated_content(self, messages: list[dict[str, Any]], channel: str) -> str:
|
||||
"""Create concatenated content from multiple messages in a channel."""
|
||||
if not messages:
|
||||
return ""
|
||||
|
||||
# Sort messages by timestamp if available
|
||||
try:
|
||||
messages.sort(key=lambda x: float(x.get("ts", x.get("timestamp", 0))))
|
||||
except (ValueError, TypeError):
|
||||
pass # Keep original order if timestamps aren't numeric
|
||||
|
||||
# Limit messages per conversation
|
||||
if len(messages) > self.max_messages_per_conversation:
|
||||
messages = messages[-self.max_messages_per_conversation :]
|
||||
|
||||
# Create header
|
||||
content_parts = [
|
||||
f"Slack Channel: #{channel}",
|
||||
f"Message Count: {len(messages)}",
|
||||
f"Workspace: {self.workspace_name or 'Unknown'}",
|
||||
"=" * 50,
|
||||
"",
|
||||
]
|
||||
|
||||
# Add messages
|
||||
for message in messages:
|
||||
formatted_msg = self._format_message(message)
|
||||
if formatted_msg.strip():
|
||||
content_parts.append(formatted_msg)
|
||||
content_parts.append("-" * 30)
|
||||
content_parts.append("")
|
||||
|
||||
return "\n".join(content_parts)
|
||||
|
||||
async def read_slack_data(self, channels: Optional[list[str]] = None) -> list[str]:
|
||||
"""
|
||||
Read Slack data and return formatted text chunks.
|
||||
|
||||
Args:
|
||||
channels: Optional list of channel names to fetch. If None, fetches from all available channels.
|
||||
|
||||
Returns:
|
||||
List of formatted text chunks ready for LEANN indexing
|
||||
"""
|
||||
try:
|
||||
await self.start_mcp_server()
|
||||
await self.initialize_mcp_connection()
|
||||
|
||||
all_texts = []
|
||||
|
||||
if channels:
|
||||
# Fetch specific channels
|
||||
for channel in channels:
|
||||
try:
|
||||
messages = await self.fetch_slack_messages(channel=channel, limit=1000)
|
||||
if messages:
|
||||
if self.concatenate_conversations:
|
||||
text_content = self._create_concatenated_content(messages, channel)
|
||||
if text_content.strip():
|
||||
all_texts.append(text_content)
|
||||
else:
|
||||
# Process individual messages
|
||||
for message in messages:
|
||||
formatted_msg = self._format_message(message)
|
||||
if formatted_msg.strip():
|
||||
all_texts.append(formatted_msg)
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to fetch messages from channel {channel}: {e}")
|
||||
continue
|
||||
else:
|
||||
# Fetch from all available channels/conversations
|
||||
# This is a simplified approach - real implementation would need to
|
||||
# discover available channels first
|
||||
try:
|
||||
messages = await self.fetch_slack_messages(limit=1000)
|
||||
if messages:
|
||||
# Group messages by channel if concatenating
|
||||
if self.concatenate_conversations:
|
||||
channel_messages = {}
|
||||
for message in messages:
|
||||
channel = message.get(
|
||||
"channel", message.get("channel_name", "general")
|
||||
)
|
||||
if channel not in channel_messages:
|
||||
channel_messages[channel] = []
|
||||
channel_messages[channel].append(message)
|
||||
|
||||
# Create concatenated content for each channel
|
||||
for channel, msgs in channel_messages.items():
|
||||
text_content = self._create_concatenated_content(msgs, channel)
|
||||
if text_content.strip():
|
||||
all_texts.append(text_content)
|
||||
else:
|
||||
# Process individual messages
|
||||
for message in messages:
|
||||
formatted_msg = self._format_message(message)
|
||||
if formatted_msg.strip():
|
||||
all_texts.append(formatted_msg)
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to fetch messages: {e}")
|
||||
|
||||
return all_texts
|
||||
|
||||
finally:
|
||||
await self.stop_mcp_server()
|
||||
|
||||
async def __aenter__(self):
|
||||
"""Async context manager entry."""
|
||||
await self.start_mcp_server()
|
||||
await self.initialize_mcp_connection()
|
||||
return self
|
||||
|
||||
async def __aexit__(self, exc_type, exc_val, exc_tb):
|
||||
"""Async context manager exit."""
|
||||
await self.stop_mcp_server()
|
||||
@@ -1,206 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Slack RAG Application with MCP Support
|
||||
|
||||
This application enables RAG (Retrieval-Augmented Generation) on Slack messages
|
||||
by connecting to Slack MCP servers to fetch live data and index it in LEANN.
|
||||
|
||||
Usage:
|
||||
python -m apps.slack_rag --mcp-server "slack-mcp-server" --query "What did the team discuss about the project?"
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import asyncio
|
||||
|
||||
from apps.base_rag_example import BaseRAGExample
|
||||
from apps.slack_data.slack_mcp_reader import SlackMCPReader
|
||||
|
||||
|
||||
class SlackMCPRAG(BaseRAGExample):
|
||||
"""
|
||||
RAG application for Slack messages via MCP servers.
|
||||
|
||||
This class provides a complete RAG pipeline for Slack data, including
|
||||
MCP server connection, data fetching, indexing, and interactive chat.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__(
|
||||
name="Slack MCP RAG",
|
||||
description="RAG application for Slack messages via MCP servers",
|
||||
default_index_name="slack_messages",
|
||||
)
|
||||
|
||||
def _add_specific_arguments(self, parser: argparse.ArgumentParser):
|
||||
"""Add Slack MCP-specific arguments."""
|
||||
parser.add_argument(
|
||||
"--mcp-server",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Command to start the Slack MCP server (e.g., 'slack-mcp-server' or 'npx slack-mcp-server')",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--workspace-name",
|
||||
type=str,
|
||||
help="Slack workspace name for better organization and filtering",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--channels",
|
||||
nargs="+",
|
||||
help="Specific Slack channels to index (e.g., general random). If not specified, fetches from all available channels",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--concatenate-conversations",
|
||||
action="store_true",
|
||||
default=True,
|
||||
help="Group messages by channel/thread for better context (default: True)",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--no-concatenate-conversations",
|
||||
action="store_true",
|
||||
help="Process individual messages instead of grouping by channel",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--max-messages-per-channel",
|
||||
type=int,
|
||||
default=100,
|
||||
help="Maximum number of messages to include per channel (default: 100)",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--test-connection",
|
||||
action="store_true",
|
||||
help="Test MCP server connection and list available tools without indexing",
|
||||
)
|
||||
|
||||
async def test_mcp_connection(self, args) -> bool:
|
||||
"""Test the MCP server connection and display available tools."""
|
||||
print(f"Testing connection to MCP server: {args.mcp_server}")
|
||||
|
||||
try:
|
||||
reader = SlackMCPReader(
|
||||
mcp_server_command=args.mcp_server,
|
||||
workspace_name=args.workspace_name,
|
||||
concatenate_conversations=not args.no_concatenate_conversations,
|
||||
max_messages_per_conversation=args.max_messages_per_channel,
|
||||
)
|
||||
|
||||
async with reader:
|
||||
tools = await reader.list_available_tools()
|
||||
|
||||
print("\n✅ Successfully connected to MCP server!")
|
||||
print(f"Available tools ({len(tools)}):")
|
||||
|
||||
for i, tool in enumerate(tools, 1):
|
||||
name = tool.get("name", "Unknown")
|
||||
description = tool.get("description", "No description available")
|
||||
print(f"\n{i}. {name}")
|
||||
print(
|
||||
f" Description: {description[:100]}{'...' if len(description) > 100 else ''}"
|
||||
)
|
||||
|
||||
# Show input schema if available
|
||||
schema = tool.get("inputSchema", {})
|
||||
if schema.get("properties"):
|
||||
props = list(schema["properties"].keys())[:3] # Show first 3 properties
|
||||
print(
|
||||
f" Parameters: {', '.join(props)}{'...' if len(schema['properties']) > 3 else ''}"
|
||||
)
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"\n❌ Failed to connect to MCP server: {e}")
|
||||
print("\nTroubleshooting tips:")
|
||||
print("1. Make sure the MCP server is installed and accessible")
|
||||
print("2. Check if the server command is correct")
|
||||
print("3. Ensure you have proper authentication/credentials configured")
|
||||
print("4. Try running the MCP server command directly to test it")
|
||||
return False
|
||||
|
||||
async def load_data(self, args) -> list[str]:
|
||||
"""Load Slack messages via MCP server."""
|
||||
print(f"Connecting to Slack MCP server: {args.mcp_server}")
|
||||
|
||||
if args.workspace_name:
|
||||
print(f"Workspace: {args.workspace_name}")
|
||||
|
||||
if args.channels:
|
||||
print(f"Channels: {', '.join(args.channels)}")
|
||||
else:
|
||||
print("Fetching from all available channels")
|
||||
|
||||
concatenate = not args.no_concatenate_conversations
|
||||
print(
|
||||
f"Processing mode: {'Concatenated conversations' if concatenate else 'Individual messages'}"
|
||||
)
|
||||
|
||||
try:
|
||||
reader = SlackMCPReader(
|
||||
mcp_server_command=args.mcp_server,
|
||||
workspace_name=args.workspace_name,
|
||||
concatenate_conversations=concatenate,
|
||||
max_messages_per_conversation=args.max_messages_per_channel,
|
||||
)
|
||||
|
||||
texts = await reader.read_slack_data(channels=args.channels)
|
||||
|
||||
if not texts:
|
||||
print("❌ No messages found! This could mean:")
|
||||
print("- The MCP server couldn't fetch messages")
|
||||
print("- The specified channels don't exist or are empty")
|
||||
print("- Authentication issues with the Slack workspace")
|
||||
return []
|
||||
|
||||
print(f"✅ Successfully loaded {len(texts)} text chunks from Slack")
|
||||
|
||||
# Show sample of what was loaded
|
||||
if texts:
|
||||
sample_text = texts[0][:200] + "..." if len(texts[0]) > 200 else texts[0]
|
||||
print("\nSample content:")
|
||||
print("-" * 40)
|
||||
print(sample_text)
|
||||
print("-" * 40)
|
||||
|
||||
return texts
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error loading Slack data: {e}")
|
||||
print("\nThis might be due to:")
|
||||
print("- MCP server connection issues")
|
||||
print("- Authentication problems")
|
||||
print("- Network connectivity issues")
|
||||
print("- Incorrect channel names")
|
||||
raise
|
||||
|
||||
async def run(self):
|
||||
"""Main entry point with MCP connection testing."""
|
||||
args = self.parser.parse_args()
|
||||
|
||||
# Test connection if requested
|
||||
if args.test_connection:
|
||||
success = await self.test_mcp_connection(args)
|
||||
if not success:
|
||||
return
|
||||
print(
|
||||
"\n🎉 MCP server is working! You can now run without --test-connection to start indexing."
|
||||
)
|
||||
return
|
||||
|
||||
# Run the standard RAG pipeline
|
||||
await super().run()
|
||||
|
||||
|
||||
async def main():
|
||||
"""Main entry point for the Slack MCP RAG application."""
|
||||
app = SlackMCPRAG()
|
||||
await app.run()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -1 +0,0 @@
|
||||
# Twitter MCP data integration for LEANN
|
||||
@@ -1,295 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Twitter MCP Reader for LEANN
|
||||
|
||||
This module provides functionality to connect to Twitter MCP servers and fetch bookmark data
|
||||
for indexing in LEANN. It supports various Twitter MCP server implementations and provides
|
||||
flexible bookmark processing options.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
from typing import Any, Optional
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class TwitterMCPReader:
|
||||
"""
|
||||
Reader for Twitter bookmark data via MCP (Model Context Protocol) servers.
|
||||
|
||||
This class connects to Twitter MCP servers to fetch bookmark data and convert it
|
||||
into a format suitable for LEANN indexing.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
mcp_server_command: str,
|
||||
username: Optional[str] = None,
|
||||
include_tweet_content: bool = True,
|
||||
include_metadata: bool = True,
|
||||
max_bookmarks: int = 1000,
|
||||
):
|
||||
"""
|
||||
Initialize the Twitter MCP Reader.
|
||||
|
||||
Args:
|
||||
mcp_server_command: Command to start the MCP server (e.g., 'twitter-mcp-server')
|
||||
username: Optional Twitter username to filter bookmarks
|
||||
include_tweet_content: Whether to include full tweet content
|
||||
include_metadata: Whether to include tweet metadata (likes, retweets, etc.)
|
||||
max_bookmarks: Maximum number of bookmarks to fetch
|
||||
"""
|
||||
self.mcp_server_command = mcp_server_command
|
||||
self.username = username
|
||||
self.include_tweet_content = include_tweet_content
|
||||
self.include_metadata = include_metadata
|
||||
self.max_bookmarks = max_bookmarks
|
||||
self.mcp_process = None
|
||||
|
||||
async def start_mcp_server(self):
|
||||
"""Start the MCP server process."""
|
||||
try:
|
||||
self.mcp_process = await asyncio.create_subprocess_exec(
|
||||
*self.mcp_server_command.split(),
|
||||
stdin=asyncio.subprocess.PIPE,
|
||||
stdout=asyncio.subprocess.PIPE,
|
||||
stderr=asyncio.subprocess.PIPE,
|
||||
)
|
||||
logger.info(f"Started MCP server: {self.mcp_server_command}")
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to start MCP server: {e}")
|
||||
raise
|
||||
|
||||
async def stop_mcp_server(self):
|
||||
"""Stop the MCP server process."""
|
||||
if self.mcp_process:
|
||||
self.mcp_process.terminate()
|
||||
await self.mcp_process.wait()
|
||||
logger.info("Stopped MCP server")
|
||||
|
||||
async def send_mcp_request(self, request: dict[str, Any]) -> dict[str, Any]:
|
||||
"""Send a request to the MCP server and get response."""
|
||||
if not self.mcp_process:
|
||||
raise RuntimeError("MCP server not started")
|
||||
|
||||
request_json = json.dumps(request) + "\n"
|
||||
self.mcp_process.stdin.write(request_json.encode())
|
||||
await self.mcp_process.stdin.drain()
|
||||
|
||||
response_line = await self.mcp_process.stdout.readline()
|
||||
if not response_line:
|
||||
raise RuntimeError("No response from MCP server")
|
||||
|
||||
return json.loads(response_line.decode().strip())
|
||||
|
||||
async def initialize_mcp_connection(self):
|
||||
"""Initialize the MCP connection."""
|
||||
init_request = {
|
||||
"jsonrpc": "2.0",
|
||||
"id": 1,
|
||||
"method": "initialize",
|
||||
"params": {
|
||||
"protocolVersion": "2024-11-05",
|
||||
"capabilities": {},
|
||||
"clientInfo": {"name": "leann-twitter-reader", "version": "1.0.0"},
|
||||
},
|
||||
}
|
||||
|
||||
response = await self.send_mcp_request(init_request)
|
||||
if "error" in response:
|
||||
raise RuntimeError(f"MCP initialization failed: {response['error']}")
|
||||
|
||||
logger.info("MCP connection initialized successfully")
|
||||
|
||||
async def list_available_tools(self) -> list[dict[str, Any]]:
|
||||
"""List available tools from the MCP server."""
|
||||
list_request = {"jsonrpc": "2.0", "id": 2, "method": "tools/list", "params": {}}
|
||||
|
||||
response = await self.send_mcp_request(list_request)
|
||||
if "error" in response:
|
||||
raise RuntimeError(f"Failed to list tools: {response['error']}")
|
||||
|
||||
return response.get("result", {}).get("tools", [])
|
||||
|
||||
async def fetch_twitter_bookmarks(self, limit: Optional[int] = None) -> list[dict[str, Any]]:
|
||||
"""
|
||||
Fetch Twitter bookmarks using MCP tools.
|
||||
|
||||
Args:
|
||||
limit: Maximum number of bookmarks to fetch
|
||||
|
||||
Returns:
|
||||
List of bookmark dictionaries
|
||||
"""
|
||||
tools = await self.list_available_tools()
|
||||
bookmark_tool = None
|
||||
|
||||
# Look for a tool that can fetch bookmarks
|
||||
for tool in tools:
|
||||
tool_name = tool.get("name", "").lower()
|
||||
if any(keyword in tool_name for keyword in ["bookmark", "saved", "favorite"]):
|
||||
bookmark_tool = tool
|
||||
break
|
||||
|
||||
if not bookmark_tool:
|
||||
raise RuntimeError("No bookmark fetching tool found in MCP server")
|
||||
|
||||
# Prepare tool call parameters
|
||||
tool_params = {}
|
||||
if limit or self.max_bookmarks:
|
||||
tool_params["limit"] = limit or self.max_bookmarks
|
||||
if self.username:
|
||||
tool_params["username"] = self.username
|
||||
|
||||
fetch_request = {
|
||||
"jsonrpc": "2.0",
|
||||
"id": 3,
|
||||
"method": "tools/call",
|
||||
"params": {"name": bookmark_tool["name"], "arguments": tool_params},
|
||||
}
|
||||
|
||||
response = await self.send_mcp_request(fetch_request)
|
||||
if "error" in response:
|
||||
raise RuntimeError(f"Failed to fetch bookmarks: {response['error']}")
|
||||
|
||||
# Extract bookmarks from response
|
||||
result = response.get("result", {})
|
||||
if "content" in result and isinstance(result["content"], list):
|
||||
content = result["content"][0] if result["content"] else {}
|
||||
if "text" in content:
|
||||
try:
|
||||
bookmarks = json.loads(content["text"])
|
||||
except json.JSONDecodeError:
|
||||
# If not JSON, treat as plain text
|
||||
bookmarks = [{"text": content["text"], "source": "twitter"}]
|
||||
else:
|
||||
bookmarks = result["content"]
|
||||
else:
|
||||
bookmarks = result.get("bookmarks", result.get("tweets", [result]))
|
||||
|
||||
return bookmarks if isinstance(bookmarks, list) else [bookmarks]
|
||||
|
||||
def _format_bookmark(self, bookmark: dict[str, Any]) -> str:
|
||||
"""Format a single bookmark for indexing."""
|
||||
# Extract tweet information
|
||||
text = bookmark.get("text", bookmark.get("content", ""))
|
||||
author = bookmark.get(
|
||||
"author", bookmark.get("username", bookmark.get("user", {}).get("username", "Unknown"))
|
||||
)
|
||||
timestamp = bookmark.get("created_at", bookmark.get("timestamp", ""))
|
||||
url = bookmark.get("url", bookmark.get("tweet_url", ""))
|
||||
|
||||
# Extract metadata if available
|
||||
likes = bookmark.get("likes", bookmark.get("favorite_count", 0))
|
||||
retweets = bookmark.get("retweets", bookmark.get("retweet_count", 0))
|
||||
replies = bookmark.get("replies", bookmark.get("reply_count", 0))
|
||||
|
||||
# Build formatted bookmark
|
||||
parts = []
|
||||
|
||||
# Header
|
||||
parts.append("=== Twitter Bookmark ===")
|
||||
|
||||
if author:
|
||||
parts.append(f"Author: @{author}")
|
||||
|
||||
if timestamp:
|
||||
# Format timestamp if it's a standard format
|
||||
try:
|
||||
import datetime
|
||||
|
||||
if "T" in str(timestamp): # ISO format
|
||||
dt = datetime.datetime.fromisoformat(timestamp.replace("Z", "+00:00"))
|
||||
formatted_time = dt.strftime("%Y-%m-%d %H:%M:%S")
|
||||
else:
|
||||
formatted_time = str(timestamp)
|
||||
parts.append(f"Date: {formatted_time}")
|
||||
except (ValueError, TypeError):
|
||||
parts.append(f"Date: {timestamp}")
|
||||
|
||||
if url:
|
||||
parts.append(f"URL: {url}")
|
||||
|
||||
# Tweet content
|
||||
if text and self.include_tweet_content:
|
||||
parts.append("")
|
||||
parts.append("Content:")
|
||||
parts.append(text)
|
||||
|
||||
# Metadata
|
||||
if self.include_metadata and any([likes, retweets, replies]):
|
||||
parts.append("")
|
||||
parts.append("Engagement:")
|
||||
if likes:
|
||||
parts.append(f" Likes: {likes}")
|
||||
if retweets:
|
||||
parts.append(f" Retweets: {retweets}")
|
||||
if replies:
|
||||
parts.append(f" Replies: {replies}")
|
||||
|
||||
# Extract hashtags and mentions if available
|
||||
hashtags = bookmark.get("hashtags", [])
|
||||
mentions = bookmark.get("mentions", [])
|
||||
|
||||
if hashtags or mentions:
|
||||
parts.append("")
|
||||
if hashtags:
|
||||
parts.append(f"Hashtags: {', '.join(hashtags)}")
|
||||
if mentions:
|
||||
parts.append(f"Mentions: {', '.join(mentions)}")
|
||||
|
||||
return "\n".join(parts)
|
||||
|
||||
async def read_twitter_bookmarks(self) -> list[str]:
|
||||
"""
|
||||
Read Twitter bookmark data and return formatted text chunks.
|
||||
|
||||
Returns:
|
||||
List of formatted text chunks ready for LEANN indexing
|
||||
"""
|
||||
try:
|
||||
await self.start_mcp_server()
|
||||
await self.initialize_mcp_connection()
|
||||
|
||||
print(f"Fetching up to {self.max_bookmarks} bookmarks...")
|
||||
if self.username:
|
||||
print(f"Filtering for user: @{self.username}")
|
||||
|
||||
bookmarks = await self.fetch_twitter_bookmarks()
|
||||
|
||||
if not bookmarks:
|
||||
print("No bookmarks found")
|
||||
return []
|
||||
|
||||
print(f"Processing {len(bookmarks)} bookmarks...")
|
||||
|
||||
all_texts = []
|
||||
processed_count = 0
|
||||
|
||||
for bookmark in bookmarks:
|
||||
try:
|
||||
formatted_bookmark = self._format_bookmark(bookmark)
|
||||
if formatted_bookmark.strip():
|
||||
all_texts.append(formatted_bookmark)
|
||||
processed_count += 1
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to format bookmark: {e}")
|
||||
continue
|
||||
|
||||
print(f"Successfully processed {processed_count} bookmarks")
|
||||
return all_texts
|
||||
|
||||
finally:
|
||||
await self.stop_mcp_server()
|
||||
|
||||
async def __aenter__(self):
|
||||
"""Async context manager entry."""
|
||||
await self.start_mcp_server()
|
||||
await self.initialize_mcp_connection()
|
||||
return self
|
||||
|
||||
async def __aexit__(self, exc_type, exc_val, exc_tb):
|
||||
"""Async context manager exit."""
|
||||
await self.stop_mcp_server()
|
||||
@@ -1,195 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Twitter RAG Application with MCP Support
|
||||
|
||||
This application enables RAG (Retrieval-Augmented Generation) on Twitter bookmarks
|
||||
by connecting to Twitter MCP servers to fetch live data and index it in LEANN.
|
||||
|
||||
Usage:
|
||||
python -m apps.twitter_rag --mcp-server "twitter-mcp-server" --query "What articles did I bookmark about AI?"
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import asyncio
|
||||
|
||||
from apps.base_rag_example import BaseRAGExample
|
||||
from apps.twitter_data.twitter_mcp_reader import TwitterMCPReader
|
||||
|
||||
|
||||
class TwitterMCPRAG(BaseRAGExample):
|
||||
"""
|
||||
RAG application for Twitter bookmarks via MCP servers.
|
||||
|
||||
This class provides a complete RAG pipeline for Twitter bookmark data, including
|
||||
MCP server connection, data fetching, indexing, and interactive chat.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__(
|
||||
name="Twitter MCP RAG",
|
||||
description="RAG application for Twitter bookmarks via MCP servers",
|
||||
default_index_name="twitter_bookmarks",
|
||||
)
|
||||
|
||||
def _add_specific_arguments(self, parser: argparse.ArgumentParser):
|
||||
"""Add Twitter MCP-specific arguments."""
|
||||
parser.add_argument(
|
||||
"--mcp-server",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Command to start the Twitter MCP server (e.g., 'twitter-mcp-server' or 'npx twitter-mcp-server')",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--username", type=str, help="Twitter username to filter bookmarks (without @)"
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--max-bookmarks",
|
||||
type=int,
|
||||
default=1000,
|
||||
help="Maximum number of bookmarks to fetch (default: 1000)",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--no-tweet-content",
|
||||
action="store_true",
|
||||
help="Exclude tweet content, only include metadata",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--no-metadata",
|
||||
action="store_true",
|
||||
help="Exclude engagement metadata (likes, retweets, etc.)",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--test-connection",
|
||||
action="store_true",
|
||||
help="Test MCP server connection and list available tools without indexing",
|
||||
)
|
||||
|
||||
async def test_mcp_connection(self, args) -> bool:
|
||||
"""Test the MCP server connection and display available tools."""
|
||||
print(f"Testing connection to MCP server: {args.mcp_server}")
|
||||
|
||||
try:
|
||||
reader = TwitterMCPReader(
|
||||
mcp_server_command=args.mcp_server,
|
||||
username=args.username,
|
||||
include_tweet_content=not args.no_tweet_content,
|
||||
include_metadata=not args.no_metadata,
|
||||
max_bookmarks=args.max_bookmarks,
|
||||
)
|
||||
|
||||
async with reader:
|
||||
tools = await reader.list_available_tools()
|
||||
|
||||
print("\n✅ Successfully connected to MCP server!")
|
||||
print(f"Available tools ({len(tools)}):")
|
||||
|
||||
for i, tool in enumerate(tools, 1):
|
||||
name = tool.get("name", "Unknown")
|
||||
description = tool.get("description", "No description available")
|
||||
print(f"\n{i}. {name}")
|
||||
print(
|
||||
f" Description: {description[:100]}{'...' if len(description) > 100 else ''}"
|
||||
)
|
||||
|
||||
# Show input schema if available
|
||||
schema = tool.get("inputSchema", {})
|
||||
if schema.get("properties"):
|
||||
props = list(schema["properties"].keys())[:3] # Show first 3 properties
|
||||
print(
|
||||
f" Parameters: {', '.join(props)}{'...' if len(schema['properties']) > 3 else ''}"
|
||||
)
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"\n❌ Failed to connect to MCP server: {e}")
|
||||
print("\nTroubleshooting tips:")
|
||||
print("1. Make sure the Twitter MCP server is installed and accessible")
|
||||
print("2. Check if the server command is correct")
|
||||
print("3. Ensure you have proper Twitter API credentials configured")
|
||||
print("4. Verify your Twitter account has bookmarks to fetch")
|
||||
print("5. Try running the MCP server command directly to test it")
|
||||
return False
|
||||
|
||||
async def load_data(self, args) -> list[str]:
|
||||
"""Load Twitter bookmarks via MCP server."""
|
||||
print(f"Connecting to Twitter MCP server: {args.mcp_server}")
|
||||
|
||||
if args.username:
|
||||
print(f"Username filter: @{args.username}")
|
||||
|
||||
print(f"Max bookmarks: {args.max_bookmarks}")
|
||||
print(f"Include tweet content: {not args.no_tweet_content}")
|
||||
print(f"Include metadata: {not args.no_metadata}")
|
||||
|
||||
try:
|
||||
reader = TwitterMCPReader(
|
||||
mcp_server_command=args.mcp_server,
|
||||
username=args.username,
|
||||
include_tweet_content=not args.no_tweet_content,
|
||||
include_metadata=not args.no_metadata,
|
||||
max_bookmarks=args.max_bookmarks,
|
||||
)
|
||||
|
||||
texts = await reader.read_twitter_bookmarks()
|
||||
|
||||
if not texts:
|
||||
print("❌ No bookmarks found! This could mean:")
|
||||
print("- You don't have any bookmarks on Twitter")
|
||||
print("- The MCP server couldn't access your bookmarks")
|
||||
print("- Authentication issues with Twitter API")
|
||||
print("- The username filter didn't match any bookmarks")
|
||||
return []
|
||||
|
||||
print(f"✅ Successfully loaded {len(texts)} bookmarks from Twitter")
|
||||
|
||||
# Show sample of what was loaded
|
||||
if texts:
|
||||
sample_text = texts[0][:300] + "..." if len(texts[0]) > 300 else texts[0]
|
||||
print("\nSample bookmark:")
|
||||
print("-" * 50)
|
||||
print(sample_text)
|
||||
print("-" * 50)
|
||||
|
||||
return texts
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error loading Twitter bookmarks: {e}")
|
||||
print("\nThis might be due to:")
|
||||
print("- MCP server connection issues")
|
||||
print("- Twitter API authentication problems")
|
||||
print("- Network connectivity issues")
|
||||
print("- Rate limiting from Twitter API")
|
||||
raise
|
||||
|
||||
async def run(self):
|
||||
"""Main entry point with MCP connection testing."""
|
||||
args = self.parser.parse_args()
|
||||
|
||||
# Test connection if requested
|
||||
if args.test_connection:
|
||||
success = await self.test_mcp_connection(args)
|
||||
if not success:
|
||||
return
|
||||
print(
|
||||
"\n🎉 MCP server is working! You can now run without --test-connection to start indexing."
|
||||
)
|
||||
return
|
||||
|
||||
# Run the standard RAG pipeline
|
||||
await super().run()
|
||||
|
||||
|
||||
async def main():
|
||||
"""Main entry point for the Twitter MCP RAG application."""
|
||||
app = TwitterMCPRAG()
|
||||
await app.run()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -54,29 +54,51 @@ def extract_thinking_answer(response):
|
||||
return response.strip()
|
||||
|
||||
|
||||
def load_hf_model(model_name="Qwen/Qwen3-8B"):
|
||||
"""Load HuggingFace model"""
|
||||
def load_hf_model(model_name="Qwen/Qwen3-8B", trust_remote_code=False):
|
||||
"""Load HuggingFace model
|
||||
|
||||
Args:
|
||||
model_name (str): Name of the model to load
|
||||
trust_remote_code (bool): Whether to allow execution of code from the model repository.
|
||||
Defaults to False for security. Only enable for trusted models.
|
||||
"""
|
||||
if not HF_AVAILABLE:
|
||||
raise ImportError("transformers not available")
|
||||
|
||||
if trust_remote_code:
|
||||
print(
|
||||
"⚠️ WARNING: Loading model with trust_remote_code=True. This can execute arbitrary code."
|
||||
)
|
||||
|
||||
print(f"Loading HF: {model_name}")
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=trust_remote_code)
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
model_name,
|
||||
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
|
||||
device_map="auto",
|
||||
trust_remote_code=True,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
return tokenizer, model
|
||||
|
||||
|
||||
def load_vllm_model(model_name="Qwen/Qwen3-8B"):
|
||||
"""Load vLLM model"""
|
||||
def load_vllm_model(model_name="Qwen/Qwen3-8B", trust_remote_code=False):
|
||||
"""Load vLLM model
|
||||
|
||||
Args:
|
||||
model_name (str): Name of the model to load
|
||||
trust_remote_code (bool): Whether to allow execution of code from the model repository.
|
||||
Defaults to False for security. Only enable for trusted models.
|
||||
"""
|
||||
if not VLLM_AVAILABLE:
|
||||
raise ImportError("vllm not available")
|
||||
|
||||
if trust_remote_code:
|
||||
print(
|
||||
"⚠️ WARNING: Loading model with trust_remote_code=True. This can execute arbitrary code."
|
||||
)
|
||||
|
||||
print(f"Loading vLLM: {model_name}")
|
||||
llm = LLM(model=model_name, trust_remote_code=True)
|
||||
llm = LLM(model=model_name, trust_remote_code=trust_remote_code)
|
||||
|
||||
# Qwen3 specific config
|
||||
if is_qwen3_model(model_name):
|
||||
@@ -178,19 +200,33 @@ def evaluate_rag(searcher, llm_func, queries, domain="default", top_k=3, complex
|
||||
}
|
||||
|
||||
|
||||
def load_qwen_vl_model(model_name="Qwen/Qwen2.5-VL-7B-Instruct"):
|
||||
"""Load Qwen2.5-VL multimodal model"""
|
||||
def load_qwen_vl_model(model_name="Qwen/Qwen2.5-VL-7B-Instruct", trust_remote_code=False):
|
||||
"""Load Qwen2.5-VL multimodal model
|
||||
|
||||
Args:
|
||||
model_name (str): Name of the model to load
|
||||
trust_remote_code (bool): Whether to allow execution of code from the model repository.
|
||||
Defaults to False for security. Only enable for trusted models.
|
||||
"""
|
||||
if not HF_AVAILABLE:
|
||||
raise ImportError("transformers not available")
|
||||
|
||||
if trust_remote_code:
|
||||
print(
|
||||
"⚠️ WARNING: Loading model with trust_remote_code=True. This can execute arbitrary code."
|
||||
)
|
||||
|
||||
print(f"Loading Qwen2.5-VL: {model_name}")
|
||||
|
||||
try:
|
||||
from transformers import AutoModelForVision2Seq, AutoProcessor
|
||||
|
||||
processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
|
||||
processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=trust_remote_code)
|
||||
model = AutoModelForVision2Seq.from_pretrained(
|
||||
model_name, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
|
||||
model_name,
|
||||
torch_dtype=torch.bfloat16,
|
||||
device_map="auto",
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
|
||||
return processor, model
|
||||
@@ -202,9 +238,14 @@ def load_qwen_vl_model(model_name="Qwen/Qwen2.5-VL-7B-Instruct"):
|
||||
try:
|
||||
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
|
||||
|
||||
processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
|
||||
processor = AutoProcessor.from_pretrained(
|
||||
model_name, trust_remote_code=trust_remote_code
|
||||
)
|
||||
model = Qwen2VLForConditionalGeneration.from_pretrained(
|
||||
model_name, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
|
||||
model_name,
|
||||
torch_dtype=torch.bfloat16,
|
||||
device_map="auto",
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
|
||||
return processor, model
|
||||
|
||||
@@ -455,5 +455,5 @@ Conclusion:
|
||||
|
||||
- [Lessons Learned Developing LEANN](https://yichuan-w.github.io/blog/lessons_learned_in_dev_leann/)
|
||||
- [LEANN Technical Paper](https://arxiv.org/abs/2506.08276)
|
||||
- [DiskANN Original Paper](https://papers.nips.cc/paper/2019/file/09853c7fb1d3f8ee67a61b6bf4a7f8e6-Paper.pdf)
|
||||
- [DiskANN Original Paper](https://suhasjs.github.io/files/diskann_neurips19.pdf)
|
||||
- [SSD-based Graph Partitioning](https://github.com/SonglinLife/SSD_BASED_PLAN)
|
||||
|
||||
@@ -1,178 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
MCP Integration Examples for LEANN
|
||||
|
||||
This script demonstrates how to use LEANN with different MCP servers for
|
||||
RAG on various platforms like Slack and Twitter.
|
||||
|
||||
Examples:
|
||||
1. Slack message RAG via MCP
|
||||
2. Twitter bookmark RAG via MCP
|
||||
3. Testing MCP server connections
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
# Add the parent directory to the path so we can import from apps
|
||||
sys.path.append(str(Path(__file__).parent.parent))
|
||||
|
||||
|
||||
async def demo_slack_mcp():
|
||||
"""Demonstrate Slack MCP integration."""
|
||||
print("=" * 60)
|
||||
print("🔥 Slack MCP RAG Demo")
|
||||
print("=" * 60)
|
||||
|
||||
print("\n1. Testing Slack MCP server connection...")
|
||||
|
||||
# This would typically use a real MCP server command
|
||||
# For demo purposes, we show what the command would look like
|
||||
# slack_app = SlackMCPRAG() # Would be used for actual testing
|
||||
|
||||
# Simulate command line arguments for testing
|
||||
class MockArgs:
|
||||
mcp_server = "slack-mcp-server" # This would be the actual MCP server command
|
||||
workspace_name = "my-workspace"
|
||||
channels = ["general", "random", "dev-team"]
|
||||
no_concatenate_conversations = False
|
||||
max_messages_per_channel = 50
|
||||
test_connection = True
|
||||
|
||||
print(f"MCP Server Command: {MockArgs.mcp_server}")
|
||||
print(f"Workspace: {MockArgs.workspace_name}")
|
||||
print(f"Channels: {', '.join(MockArgs.channels)}")
|
||||
|
||||
# In a real scenario, you would run:
|
||||
# success = await slack_app.test_mcp_connection(MockArgs)
|
||||
|
||||
print("\n📝 Example usage:")
|
||||
print("python -m apps.slack_rag \\")
|
||||
print(" --mcp-server 'slack-mcp-server' \\")
|
||||
print(" --workspace-name 'my-team' \\")
|
||||
print(" --channels general dev-team \\")
|
||||
print(" --test-connection")
|
||||
|
||||
print("\n🔍 After indexing, you could query:")
|
||||
print("- 'What did the team discuss about the project deadline?'")
|
||||
print("- 'Find messages about the new feature launch'")
|
||||
print("- 'Show me conversations about budget planning'")
|
||||
|
||||
|
||||
async def demo_twitter_mcp():
|
||||
"""Demonstrate Twitter MCP integration."""
|
||||
print("\n" + "=" * 60)
|
||||
print("🐦 Twitter MCP RAG Demo")
|
||||
print("=" * 60)
|
||||
|
||||
print("\n1. Testing Twitter MCP server connection...")
|
||||
|
||||
# twitter_app = TwitterMCPRAG() # Would be used for actual testing
|
||||
|
||||
class MockArgs:
|
||||
mcp_server = "twitter-mcp-server"
|
||||
username = None # Fetch all bookmarks
|
||||
max_bookmarks = 500
|
||||
no_tweet_content = False
|
||||
no_metadata = False
|
||||
test_connection = True
|
||||
|
||||
print(f"MCP Server Command: {MockArgs.mcp_server}")
|
||||
print(f"Max Bookmarks: {MockArgs.max_bookmarks}")
|
||||
print(f"Include Content: {not MockArgs.no_tweet_content}")
|
||||
print(f"Include Metadata: {not MockArgs.no_metadata}")
|
||||
|
||||
print("\n📝 Example usage:")
|
||||
print("python -m apps.twitter_rag \\")
|
||||
print(" --mcp-server 'twitter-mcp-server' \\")
|
||||
print(" --max-bookmarks 1000 \\")
|
||||
print(" --test-connection")
|
||||
|
||||
print("\n🔍 After indexing, you could query:")
|
||||
print("- 'What AI articles did I bookmark last month?'")
|
||||
print("- 'Find tweets about machine learning techniques'")
|
||||
print("- 'Show me bookmarked threads about startup advice'")
|
||||
|
||||
|
||||
async def show_mcp_server_setup():
|
||||
"""Show how to set up MCP servers."""
|
||||
print("\n" + "=" * 60)
|
||||
print("⚙️ MCP Server Setup Guide")
|
||||
print("=" * 60)
|
||||
|
||||
print("\n🔧 Setting up Slack MCP Server:")
|
||||
print("1. Install a Slack MCP server (example commands):")
|
||||
print(" npm install -g slack-mcp-server")
|
||||
print(" # OR")
|
||||
print(" pip install slack-mcp-server")
|
||||
|
||||
print("\n2. Configure Slack credentials:")
|
||||
print(" export SLACK_BOT_TOKEN='xoxb-your-bot-token'")
|
||||
print(" export SLACK_APP_TOKEN='xapp-your-app-token'")
|
||||
|
||||
print("\n3. Test the server:")
|
||||
print(" slack-mcp-server --help")
|
||||
|
||||
print("\n🔧 Setting up Twitter MCP Server:")
|
||||
print("1. Install a Twitter MCP server:")
|
||||
print(" npm install -g twitter-mcp-server")
|
||||
print(" # OR")
|
||||
print(" pip install twitter-mcp-server")
|
||||
|
||||
print("\n2. Configure Twitter API credentials:")
|
||||
print(" export TWITTER_API_KEY='your-api-key'")
|
||||
print(" export TWITTER_API_SECRET='your-api-secret'")
|
||||
print(" export TWITTER_ACCESS_TOKEN='your-access-token'")
|
||||
print(" export TWITTER_ACCESS_TOKEN_SECRET='your-access-token-secret'")
|
||||
|
||||
print("\n3. Test the server:")
|
||||
print(" twitter-mcp-server --help")
|
||||
|
||||
|
||||
async def show_integration_benefits():
|
||||
"""Show the benefits of MCP integration."""
|
||||
print("\n" + "=" * 60)
|
||||
print("🌟 Benefits of MCP Integration")
|
||||
print("=" * 60)
|
||||
|
||||
benefits = [
|
||||
("🔄 Live Data Access", "Fetch real-time data from platforms without manual exports"),
|
||||
("🔌 Standardized Protocol", "Use any MCP-compatible server with minimal code changes"),
|
||||
("🚀 Easy Extension", "Add new platforms by implementing MCP readers"),
|
||||
("🔒 Secure Access", "MCP servers handle authentication and API management"),
|
||||
("📊 Rich Metadata", "Access full platform metadata (timestamps, engagement, etc.)"),
|
||||
("⚡ Efficient Processing", "Stream data directly into LEANN without intermediate files"),
|
||||
]
|
||||
|
||||
for title, description in benefits:
|
||||
print(f"\n{title}")
|
||||
print(f" {description}")
|
||||
|
||||
|
||||
async def main():
|
||||
"""Main demo function."""
|
||||
print("🎯 LEANN MCP Integration Examples")
|
||||
print("This demo shows how to integrate LEANN with MCP servers for various platforms.")
|
||||
|
||||
await demo_slack_mcp()
|
||||
await demo_twitter_mcp()
|
||||
await show_mcp_server_setup()
|
||||
await show_integration_benefits()
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("✨ Next Steps")
|
||||
print("=" * 60)
|
||||
print("1. Install and configure MCP servers for your platforms")
|
||||
print("2. Test connections using --test-connection flag")
|
||||
print("3. Run indexing to build your RAG knowledge base")
|
||||
print("4. Start querying your personal data!")
|
||||
|
||||
print("\n📚 For more information:")
|
||||
print("- Check the README for detailed setup instructions")
|
||||
print("- Look at the apps/slack_rag.py and apps/twitter_rag.py for implementation details")
|
||||
print("- Explore other MCP servers for additional platforms")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -18,14 +18,16 @@ dependencies = [
|
||||
"pyzmq>=23.0.0",
|
||||
"msgpack>=1.0.0",
|
||||
"torch>=2.0.0",
|
||||
"sentence-transformers>=2.2.0",
|
||||
"sentence-transformers>=3.0.0",
|
||||
"llama-index-core>=0.12.0",
|
||||
"llama-index-readers-file>=0.4.0", # Essential for document reading
|
||||
"llama-index-embeddings-huggingface>=0.5.5", # For embeddings
|
||||
"python-dotenv>=1.0.0",
|
||||
"openai>=1.0.0",
|
||||
"huggingface-hub>=0.20.0",
|
||||
"transformers>=4.30.0",
|
||||
# Keep transformers below 4.46: 4.46.0 adds Python 3.10-only return type syntax and
|
||||
# breaks Python 3.9 environments.
|
||||
"transformers>=4.30.0,<4.46",
|
||||
"requests>=2.25.0",
|
||||
"accelerate>=0.20.0",
|
||||
"PyPDF2>=3.0.0",
|
||||
@@ -40,7 +42,7 @@ dependencies = [
|
||||
[project.optional-dependencies]
|
||||
colab = [
|
||||
"torch>=2.0.0,<3.0.0", # Limit torch version to avoid conflicts
|
||||
"transformers>=4.30.0,<5.0.0", # Limit transformers version
|
||||
"transformers>=4.30.0,<4.46", # 4.46.0 switches to PEP 604 typing (int | None), breaks Py3.9
|
||||
"accelerate>=0.20.0,<1.0.0", # Limit accelerate version
|
||||
]
|
||||
|
||||
|
||||
@@ -18,6 +18,7 @@ from typing import Any, Literal, Optional, Union
|
||||
import numpy as np
|
||||
from leann_backend_hnsw.convert_to_csr import prune_hnsw_embeddings_inplace
|
||||
|
||||
from leann.interactive_utils import create_api_session
|
||||
from leann.interface import LeannBackendSearcherInterface
|
||||
|
||||
from .chat import get_llm
|
||||
@@ -1242,19 +1243,14 @@ class LeannChat:
|
||||
return ans
|
||||
|
||||
def start_interactive(self):
|
||||
print("\nLeann Chat started (type 'quit' to exit)")
|
||||
while True:
|
||||
try:
|
||||
user_input = input("You: ").strip()
|
||||
if user_input.lower() in ["quit", "exit"]:
|
||||
break
|
||||
if not user_input:
|
||||
continue
|
||||
response = self.ask(user_input)
|
||||
print(f"Leann: {response}")
|
||||
except (KeyboardInterrupt, EOFError):
|
||||
print("\nGoodbye!")
|
||||
break
|
||||
"""Start interactive chat session."""
|
||||
session = create_api_session()
|
||||
|
||||
def handle_query(user_input: str):
|
||||
response = self.ask(user_input)
|
||||
print(f"Leann: {response}")
|
||||
|
||||
session.run_interactive_loop(handle_query)
|
||||
|
||||
def cleanup(self):
|
||||
"""Explicitly cleanup embedding server resources.
|
||||
|
||||
@@ -546,11 +546,30 @@ class OllamaChat(LLMInterface):
|
||||
|
||||
|
||||
class HFChat(LLMInterface):
|
||||
"""LLM interface for local Hugging Face Transformers models with proper chat templates."""
|
||||
"""LLM interface for local Hugging Face Transformers models with proper chat templates.
|
||||
|
||||
def __init__(self, model_name: str = "deepseek-ai/deepseek-llm-7b-chat"):
|
||||
Args:
|
||||
model_name (str): Name of the Hugging Face model to load.
|
||||
trust_remote_code (bool): Whether to allow execution of code from the model repository.
|
||||
Defaults to False for security. Only enable for trusted models as this can pose
|
||||
a security risk if the model repository is compromised.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self, model_name: str = "deepseek-ai/deepseek-llm-7b-chat", trust_remote_code: bool = False
|
||||
):
|
||||
logger.info(f"Initializing HFChat with model='{model_name}'")
|
||||
|
||||
# Security warning when trust_remote_code is enabled
|
||||
if trust_remote_code:
|
||||
logger.warning(
|
||||
"SECURITY WARNING: trust_remote_code=True allows execution of arbitrary code from the model repository. "
|
||||
"Only enable this for models from trusted sources. This creates a potential security risk if the model "
|
||||
"repository is compromised."
|
||||
)
|
||||
|
||||
self.trust_remote_code = trust_remote_code
|
||||
|
||||
# Pre-check model availability with helpful suggestions
|
||||
model_error = validate_model_and_suggest(model_name, "hf")
|
||||
if model_error:
|
||||
@@ -588,14 +607,16 @@ class HFChat(LLMInterface):
|
||||
|
||||
try:
|
||||
logger.info(f"Loading tokenizer for {model_name}...")
|
||||
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
|
||||
self.tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_name, trust_remote_code=self.trust_remote_code
|
||||
)
|
||||
|
||||
logger.info(f"Loading model {model_name}...")
|
||||
self.model = AutoModelForCausalLM.from_pretrained(
|
||||
model_name,
|
||||
torch_dtype=torch.float16 if self.device != "cpu" else torch.float32,
|
||||
device_map="auto" if self.device != "cpu" else None,
|
||||
trust_remote_code=True,
|
||||
trust_remote_code=self.trust_remote_code,
|
||||
)
|
||||
logger.info(f"Successfully loaded {model_name}")
|
||||
finally:
|
||||
@@ -859,7 +880,10 @@ def get_llm(llm_config: Optional[dict[str, Any]] = None) -> LLMInterface:
|
||||
host=llm_config.get("host"),
|
||||
)
|
||||
elif llm_type == "hf":
|
||||
return HFChat(model_name=model or "deepseek-ai/deepseek-llm-7b-chat")
|
||||
return HFChat(
|
||||
model_name=model or "deepseek-ai/deepseek-llm-7b-chat",
|
||||
trust_remote_code=llm_config.get("trust_remote_code", False),
|
||||
)
|
||||
elif llm_type == "openai":
|
||||
return OpenAIChat(
|
||||
model=model or "gpt-4o",
|
||||
|
||||
@@ -8,6 +8,7 @@ from llama_index.core.node_parser import SentenceSplitter
|
||||
from tqdm import tqdm
|
||||
|
||||
from .api import LeannBuilder, LeannChat, LeannSearcher
|
||||
from .interactive_utils import create_cli_session
|
||||
from .registry import register_project_directory
|
||||
from .settings import resolve_ollama_host, resolve_openai_api_key, resolve_openai_base_url
|
||||
|
||||
@@ -1556,22 +1557,13 @@ Examples:
|
||||
initial_query = (args.query or "").strip()
|
||||
|
||||
if args.interactive:
|
||||
# Create interactive session
|
||||
session = create_cli_session(index_name)
|
||||
|
||||
if initial_query:
|
||||
_ask_once(initial_query)
|
||||
|
||||
print("LEANN Assistant ready! Type 'quit' to exit")
|
||||
print("=" * 40)
|
||||
|
||||
while True:
|
||||
user_input = input("\nYou: ").strip()
|
||||
if user_input.lower() in ["quit", "exit", "q"]:
|
||||
print("Goodbye!")
|
||||
break
|
||||
|
||||
if not user_input:
|
||||
continue
|
||||
|
||||
_ask_once(user_input)
|
||||
session.run_interactive_loop(_ask_once)
|
||||
else:
|
||||
query = initial_query or input("Enter your question: ").strip()
|
||||
if not query:
|
||||
|
||||
@@ -183,73 +183,32 @@ def compute_embeddings_sentence_transformers(
|
||||
}
|
||||
|
||||
try:
|
||||
# Try loading with advanced parameters first (newer versions)
|
||||
local_model_kwargs = model_kwargs.copy()
|
||||
local_tokenizer_kwargs = tokenizer_kwargs.copy()
|
||||
local_model_kwargs["local_files_only"] = True
|
||||
local_tokenizer_kwargs["local_files_only"] = True
|
||||
# Try local loading first
|
||||
model_kwargs["local_files_only"] = True
|
||||
tokenizer_kwargs["local_files_only"] = True
|
||||
|
||||
model = SentenceTransformer(
|
||||
model_name,
|
||||
device=device,
|
||||
model_kwargs=local_model_kwargs,
|
||||
tokenizer_kwargs=local_tokenizer_kwargs,
|
||||
model_kwargs=model_kwargs,
|
||||
tokenizer_kwargs=tokenizer_kwargs,
|
||||
local_files_only=True,
|
||||
)
|
||||
logger.info("Model loaded successfully! (local + optimized)")
|
||||
except TypeError as e:
|
||||
if "model_kwargs" in str(e) or "tokenizer_kwargs" in str(e):
|
||||
logger.warning(
|
||||
f"Advanced parameters not supported ({e}), using basic initialization..."
|
||||
)
|
||||
# Fallback to basic initialization for older versions
|
||||
try:
|
||||
model = SentenceTransformer(
|
||||
model_name,
|
||||
device=device,
|
||||
local_files_only=True,
|
||||
)
|
||||
logger.info("Model loaded successfully! (local + basic)")
|
||||
except Exception as e2:
|
||||
logger.warning(f"Local loading failed ({e2}), trying network download...")
|
||||
model = SentenceTransformer(
|
||||
model_name,
|
||||
device=device,
|
||||
local_files_only=False,
|
||||
)
|
||||
logger.info("Model loaded successfully! (network + basic)")
|
||||
else:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.warning(f"Local loading failed ({e}), trying network download...")
|
||||
# Fallback to network loading with advanced parameters
|
||||
try:
|
||||
network_model_kwargs = model_kwargs.copy()
|
||||
network_tokenizer_kwargs = tokenizer_kwargs.copy()
|
||||
network_model_kwargs["local_files_only"] = False
|
||||
network_tokenizer_kwargs["local_files_only"] = False
|
||||
# Fallback to network loading
|
||||
model_kwargs["local_files_only"] = False
|
||||
tokenizer_kwargs["local_files_only"] = False
|
||||
|
||||
model = SentenceTransformer(
|
||||
model_name,
|
||||
device=device,
|
||||
model_kwargs=network_model_kwargs,
|
||||
tokenizer_kwargs=network_tokenizer_kwargs,
|
||||
local_files_only=False,
|
||||
)
|
||||
logger.info("Model loaded successfully! (network + optimized)")
|
||||
except TypeError as e2:
|
||||
if "model_kwargs" in str(e2) or "tokenizer_kwargs" in str(e2):
|
||||
logger.warning(
|
||||
f"Advanced parameters not supported ({e2}), using basic network loading..."
|
||||
)
|
||||
model = SentenceTransformer(
|
||||
model_name,
|
||||
device=device,
|
||||
local_files_only=False,
|
||||
)
|
||||
logger.info("Model loaded successfully! (network + basic)")
|
||||
else:
|
||||
raise
|
||||
model = SentenceTransformer(
|
||||
model_name,
|
||||
device=device,
|
||||
model_kwargs=model_kwargs,
|
||||
tokenizer_kwargs=tokenizer_kwargs,
|
||||
local_files_only=False,
|
||||
)
|
||||
logger.info("Model loaded successfully! (network + optimized)")
|
||||
|
||||
# Apply additional optimizations based on mode
|
||||
if use_fp16 and device in ["cuda", "mps"]:
|
||||
|
||||
189
packages/leann-core/src/leann/interactive_utils.py
Normal file
189
packages/leann-core/src/leann/interactive_utils.py
Normal file
@@ -0,0 +1,189 @@
|
||||
"""
|
||||
Interactive session utilities for LEANN applications.
|
||||
|
||||
Provides shared readline functionality and command handling across
|
||||
CLI, API, and RAG example interactive modes.
|
||||
"""
|
||||
|
||||
import atexit
|
||||
import os
|
||||
from pathlib import Path
|
||||
from typing import Callable, Optional
|
||||
|
||||
# Try to import readline with fallback for Windows
|
||||
try:
|
||||
import readline
|
||||
|
||||
HAS_READLINE = True
|
||||
except ImportError:
|
||||
# Windows doesn't have readline by default
|
||||
HAS_READLINE = False
|
||||
readline = None
|
||||
|
||||
|
||||
class InteractiveSession:
|
||||
"""Manages interactive session with optional readline support and common commands."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
history_name: str,
|
||||
prompt: str = "You: ",
|
||||
welcome_message: str = "",
|
||||
):
|
||||
"""
|
||||
Initialize interactive session with optional readline support.
|
||||
|
||||
Args:
|
||||
history_name: Name for history file (e.g., "cli", "api_chat")
|
||||
(ignored if readline not available)
|
||||
prompt: Input prompt to display
|
||||
welcome_message: Message to show when starting session
|
||||
|
||||
Note:
|
||||
On systems without readline (e.g., Windows), falls back to basic input()
|
||||
with limited functionality (no history, no line editing).
|
||||
"""
|
||||
self.history_name = history_name
|
||||
self.prompt = prompt
|
||||
self.welcome_message = welcome_message
|
||||
self._setup_complete = False
|
||||
|
||||
def setup_readline(self):
|
||||
"""Setup readline with history support (if available)."""
|
||||
if self._setup_complete:
|
||||
return
|
||||
|
||||
if not HAS_READLINE:
|
||||
# Readline not available (likely Windows), skip setup
|
||||
self._setup_complete = True
|
||||
return
|
||||
|
||||
# History file setup
|
||||
history_dir = Path.home() / ".leann" / "history"
|
||||
history_dir.mkdir(parents=True, exist_ok=True)
|
||||
history_file = history_dir / f"{self.history_name}.history"
|
||||
|
||||
# Load history if exists
|
||||
try:
|
||||
readline.read_history_file(str(history_file))
|
||||
readline.set_history_length(1000)
|
||||
except FileNotFoundError:
|
||||
pass
|
||||
|
||||
# Save history on exit
|
||||
atexit.register(readline.write_history_file, str(history_file))
|
||||
|
||||
# Optional: Enable vi editing mode (commented out by default)
|
||||
# readline.parse_and_bind("set editing-mode vi")
|
||||
|
||||
self._setup_complete = True
|
||||
|
||||
def _show_help(self):
|
||||
"""Show available commands."""
|
||||
print("Commands:")
|
||||
print(" quit/exit/q - Exit the chat")
|
||||
print(" help - Show this help message")
|
||||
print(" clear - Clear screen")
|
||||
print(" history - Show command history")
|
||||
|
||||
def _show_history(self):
|
||||
"""Show command history."""
|
||||
if not HAS_READLINE:
|
||||
print(" History not available (readline not supported on this system)")
|
||||
return
|
||||
|
||||
history_length = readline.get_current_history_length()
|
||||
if history_length == 0:
|
||||
print(" No history available")
|
||||
return
|
||||
|
||||
for i in range(history_length):
|
||||
item = readline.get_history_item(i + 1)
|
||||
if item:
|
||||
print(f" {i + 1}: {item}")
|
||||
|
||||
def get_user_input(self) -> Optional[str]:
|
||||
"""
|
||||
Get user input with readline support.
|
||||
|
||||
Returns:
|
||||
User input string, or None if EOF (Ctrl+D)
|
||||
"""
|
||||
try:
|
||||
return input(self.prompt).strip()
|
||||
except KeyboardInterrupt:
|
||||
print("\n(Use 'quit' to exit)")
|
||||
return "" # Return empty string to continue
|
||||
except EOFError:
|
||||
print("\nGoodbye!")
|
||||
return None
|
||||
|
||||
def run_interactive_loop(self, handler_func: Callable[[str], None]):
|
||||
"""
|
||||
Run the interactive loop with a custom handler function.
|
||||
|
||||
Args:
|
||||
handler_func: Function to handle user input that's not a built-in command
|
||||
Should accept a string and handle the user's query
|
||||
"""
|
||||
self.setup_readline()
|
||||
|
||||
if self.welcome_message:
|
||||
print(self.welcome_message)
|
||||
|
||||
while True:
|
||||
user_input = self.get_user_input()
|
||||
|
||||
if user_input is None: # EOF (Ctrl+D)
|
||||
break
|
||||
|
||||
if not user_input: # Empty input or KeyboardInterrupt
|
||||
continue
|
||||
|
||||
# Handle built-in commands
|
||||
command = user_input.lower()
|
||||
if command in ["quit", "exit", "q"]:
|
||||
print("Goodbye!")
|
||||
break
|
||||
elif command == "help":
|
||||
self._show_help()
|
||||
elif command == "clear":
|
||||
os.system("clear" if os.name != "nt" else "cls")
|
||||
elif command == "history":
|
||||
self._show_history()
|
||||
else:
|
||||
# Regular user input - pass to handler
|
||||
try:
|
||||
handler_func(user_input)
|
||||
except Exception as e:
|
||||
print(f"Error: {e}")
|
||||
|
||||
|
||||
def create_cli_session(index_name: str) -> InteractiveSession:
|
||||
"""Create an interactive session for CLI usage."""
|
||||
return InteractiveSession(
|
||||
history_name=index_name,
|
||||
prompt="\nYou: ",
|
||||
welcome_message="LEANN Assistant ready! Type 'quit' to exit, 'help' for commands\n"
|
||||
+ "=" * 40,
|
||||
)
|
||||
|
||||
|
||||
def create_api_session() -> InteractiveSession:
|
||||
"""Create an interactive session for API chat."""
|
||||
return InteractiveSession(
|
||||
history_name="api_chat",
|
||||
prompt="You: ",
|
||||
welcome_message="Leann Chat started (type 'quit' to exit, 'help' for commands)\n"
|
||||
+ "=" * 40,
|
||||
)
|
||||
|
||||
|
||||
def create_rag_session(app_name: str, data_description: str) -> InteractiveSession:
|
||||
"""Create an interactive session for RAG examples."""
|
||||
return InteractiveSession(
|
||||
history_name=f"{app_name}_rag",
|
||||
prompt="You: ",
|
||||
welcome_message=f"[Interactive Mode] Chat with your {data_description} data!\nType 'quit' or 'exit' to stop, 'help' for commands.\n"
|
||||
+ "=" * 40,
|
||||
)
|
||||
@@ -22,7 +22,10 @@ dependencies = [
|
||||
"sglang",
|
||||
"ollama",
|
||||
"requests>=2.25.0",
|
||||
"sentence-transformers>=2.2.0",
|
||||
"sentence-transformers>=3.0.0",
|
||||
# Pin transformers below 4.46: 4.46.0 introduced Python 3.10-only typing (PEP 604) and
|
||||
# breaks our Python 3.9 test matrix when pulled in by sentence-transformers.
|
||||
"transformers<4.46",
|
||||
"openai>=1.0.0",
|
||||
# PDF parsing dependencies - essential for document processing
|
||||
"PyPDF2>=3.0.0",
|
||||
|
||||
@@ -1,208 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Test script for MCP integration implementations.
|
||||
|
||||
This script tests the basic functionality of the MCP readers and RAG applications
|
||||
without requiring actual MCP servers to be running.
|
||||
"""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
# Add the parent directory to the path so we can import from apps
|
||||
sys.path.append(str(Path(__file__).parent.parent))
|
||||
|
||||
from apps.slack_data.slack_mcp_reader import SlackMCPReader
|
||||
from apps.slack_rag import SlackMCPRAG
|
||||
from apps.twitter_data.twitter_mcp_reader import TwitterMCPReader
|
||||
from apps.twitter_rag import TwitterMCPRAG
|
||||
|
||||
|
||||
def test_slack_reader_initialization():
|
||||
"""Test that SlackMCPReader can be initialized with various parameters."""
|
||||
print("Testing SlackMCPReader initialization...")
|
||||
|
||||
# Test basic initialization
|
||||
reader = SlackMCPReader("slack-mcp-server")
|
||||
assert reader.mcp_server_command == "slack-mcp-server"
|
||||
assert reader.concatenate_conversations
|
||||
assert reader.max_messages_per_conversation == 100
|
||||
|
||||
# Test with custom parameters
|
||||
reader = SlackMCPReader(
|
||||
"custom-slack-server",
|
||||
workspace_name="test-workspace",
|
||||
concatenate_conversations=False,
|
||||
max_messages_per_conversation=50,
|
||||
)
|
||||
assert reader.workspace_name == "test-workspace"
|
||||
assert not reader.concatenate_conversations
|
||||
assert reader.max_messages_per_conversation == 50
|
||||
|
||||
print("✅ SlackMCPReader initialization tests passed")
|
||||
|
||||
|
||||
def test_twitter_reader_initialization():
|
||||
"""Test that TwitterMCPReader can be initialized with various parameters."""
|
||||
print("Testing TwitterMCPReader initialization...")
|
||||
|
||||
# Test basic initialization
|
||||
reader = TwitterMCPReader("twitter-mcp-server")
|
||||
assert reader.mcp_server_command == "twitter-mcp-server"
|
||||
assert reader.include_tweet_content
|
||||
assert reader.include_metadata
|
||||
assert reader.max_bookmarks == 1000
|
||||
|
||||
# Test with custom parameters
|
||||
reader = TwitterMCPReader(
|
||||
"custom-twitter-server",
|
||||
username="testuser",
|
||||
include_tweet_content=False,
|
||||
include_metadata=False,
|
||||
max_bookmarks=500,
|
||||
)
|
||||
assert reader.username == "testuser"
|
||||
assert not reader.include_tweet_content
|
||||
assert not reader.include_metadata
|
||||
assert reader.max_bookmarks == 500
|
||||
|
||||
print("✅ TwitterMCPReader initialization tests passed")
|
||||
|
||||
|
||||
def test_slack_message_formatting():
|
||||
"""Test Slack message formatting functionality."""
|
||||
print("Testing Slack message formatting...")
|
||||
|
||||
reader = SlackMCPReader("slack-mcp-server")
|
||||
|
||||
# Test basic message formatting
|
||||
message = {
|
||||
"text": "Hello, world!",
|
||||
"user": "john_doe",
|
||||
"channel": "general",
|
||||
"ts": "1234567890.123456",
|
||||
}
|
||||
|
||||
formatted = reader._format_message(message)
|
||||
assert "Channel: #general" in formatted
|
||||
assert "User: john_doe" in formatted
|
||||
assert "Message: Hello, world!" in formatted
|
||||
assert "Time:" in formatted
|
||||
|
||||
# Test with missing fields
|
||||
message = {"text": "Simple message"}
|
||||
formatted = reader._format_message(message)
|
||||
assert "Message: Simple message" in formatted
|
||||
|
||||
print("✅ Slack message formatting tests passed")
|
||||
|
||||
|
||||
def test_twitter_bookmark_formatting():
|
||||
"""Test Twitter bookmark formatting functionality."""
|
||||
print("Testing Twitter bookmark formatting...")
|
||||
|
||||
reader = TwitterMCPReader("twitter-mcp-server")
|
||||
|
||||
# Test basic bookmark formatting
|
||||
bookmark = {
|
||||
"text": "This is a great article about AI!",
|
||||
"author": "ai_researcher",
|
||||
"created_at": "2024-01-01T12:00:00Z",
|
||||
"url": "https://twitter.com/ai_researcher/status/123456789",
|
||||
"likes": 42,
|
||||
"retweets": 15,
|
||||
}
|
||||
|
||||
formatted = reader._format_bookmark(bookmark)
|
||||
assert "=== Twitter Bookmark ===" in formatted
|
||||
assert "Author: @ai_researcher" in formatted
|
||||
assert "Content:" in formatted
|
||||
assert "This is a great article about AI!" in formatted
|
||||
assert "URL: https://twitter.com" in formatted
|
||||
assert "Likes: 42" in formatted
|
||||
assert "Retweets: 15" in formatted
|
||||
|
||||
# Test with minimal data
|
||||
bookmark = {"text": "Simple tweet"}
|
||||
formatted = reader._format_bookmark(bookmark)
|
||||
assert "=== Twitter Bookmark ===" in formatted
|
||||
assert "Simple tweet" in formatted
|
||||
|
||||
print("✅ Twitter bookmark formatting tests passed")
|
||||
|
||||
|
||||
def test_slack_rag_initialization():
|
||||
"""Test that SlackMCPRAG can be initialized."""
|
||||
print("Testing SlackMCPRAG initialization...")
|
||||
|
||||
app = SlackMCPRAG()
|
||||
assert app.default_index_name == "slack_messages"
|
||||
assert hasattr(app, "parser")
|
||||
|
||||
print("✅ SlackMCPRAG initialization tests passed")
|
||||
|
||||
|
||||
def test_twitter_rag_initialization():
|
||||
"""Test that TwitterMCPRAG can be initialized."""
|
||||
print("Testing TwitterMCPRAG initialization...")
|
||||
|
||||
app = TwitterMCPRAG()
|
||||
assert app.default_index_name == "twitter_bookmarks"
|
||||
assert hasattr(app, "parser")
|
||||
|
||||
print("✅ TwitterMCPRAG initialization tests passed")
|
||||
|
||||
|
||||
def test_concatenated_content_creation():
|
||||
"""Test creation of concatenated content from multiple messages."""
|
||||
print("Testing concatenated content creation...")
|
||||
|
||||
reader = SlackMCPReader("slack-mcp-server", workspace_name="test-workspace")
|
||||
|
||||
messages = [
|
||||
{"text": "First message", "user": "alice", "ts": "1000"},
|
||||
{"text": "Second message", "user": "bob", "ts": "2000"},
|
||||
{"text": "Third message", "user": "charlie", "ts": "3000"},
|
||||
]
|
||||
|
||||
content = reader._create_concatenated_content(messages, "general")
|
||||
|
||||
assert "Slack Channel: #general" in content
|
||||
assert "Message Count: 3" in content
|
||||
assert "Workspace: test-workspace" in content
|
||||
assert "First message" in content
|
||||
assert "Second message" in content
|
||||
assert "Third message" in content
|
||||
|
||||
print("✅ Concatenated content creation tests passed")
|
||||
|
||||
|
||||
def main():
|
||||
"""Run all tests."""
|
||||
print("🧪 Running MCP Integration Tests")
|
||||
print("=" * 50)
|
||||
|
||||
try:
|
||||
test_slack_reader_initialization()
|
||||
test_twitter_reader_initialization()
|
||||
test_slack_message_formatting()
|
||||
test_twitter_bookmark_formatting()
|
||||
test_slack_rag_initialization()
|
||||
test_twitter_rag_initialization()
|
||||
test_concatenated_content_creation()
|
||||
|
||||
print("\n" + "=" * 50)
|
||||
print("🎉 All tests passed! MCP integration is working correctly.")
|
||||
print("\nNext steps:")
|
||||
print("1. Install actual MCP servers for Slack and Twitter")
|
||||
print("2. Configure API credentials")
|
||||
print("3. Test with --test-connection flag")
|
||||
print("4. Start indexing your live data!")
|
||||
|
||||
except Exception as e:
|
||||
print(f"\n❌ Test failed: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,221 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Standalone test script for MCP integration implementations.
|
||||
|
||||
This script tests the basic functionality of the MCP readers
|
||||
without requiring LEANN core dependencies.
|
||||
"""
|
||||
|
||||
import json
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
# Add the parent directory to the path so we can import from apps
|
||||
sys.path.append(str(Path(__file__).parent.parent))
|
||||
|
||||
|
||||
def test_slack_reader_basic():
|
||||
"""Test basic SlackMCPReader functionality without async operations."""
|
||||
print("Testing SlackMCPReader basic functionality...")
|
||||
|
||||
# Import and test initialization
|
||||
from apps.slack_data.slack_mcp_reader import SlackMCPReader
|
||||
|
||||
reader = SlackMCPReader("slack-mcp-server")
|
||||
assert reader.mcp_server_command == "slack-mcp-server"
|
||||
assert reader.concatenate_conversations
|
||||
|
||||
# Test message formatting
|
||||
message = {
|
||||
"text": "Hello team! How's the project going?",
|
||||
"user": "john_doe",
|
||||
"channel": "general",
|
||||
"ts": "1234567890.123456",
|
||||
}
|
||||
|
||||
formatted = reader._format_message(message)
|
||||
assert "Channel: #general" in formatted
|
||||
assert "User: john_doe" in formatted
|
||||
assert "Message: Hello team!" in formatted
|
||||
|
||||
# Test concatenated content creation
|
||||
messages = [
|
||||
{"text": "First message", "user": "alice", "ts": "1000"},
|
||||
{"text": "Second message", "user": "bob", "ts": "2000"},
|
||||
]
|
||||
|
||||
content = reader._create_concatenated_content(messages, "dev-team")
|
||||
assert "Slack Channel: #dev-team" in content
|
||||
assert "Message Count: 2" in content
|
||||
assert "First message" in content
|
||||
assert "Second message" in content
|
||||
|
||||
print("✅ SlackMCPReader basic tests passed")
|
||||
|
||||
|
||||
def test_twitter_reader_basic():
|
||||
"""Test basic TwitterMCPReader functionality."""
|
||||
print("Testing TwitterMCPReader basic functionality...")
|
||||
|
||||
from apps.twitter_data.twitter_mcp_reader import TwitterMCPReader
|
||||
|
||||
reader = TwitterMCPReader("twitter-mcp-server")
|
||||
assert reader.mcp_server_command == "twitter-mcp-server"
|
||||
assert reader.include_tweet_content
|
||||
assert reader.max_bookmarks == 1000
|
||||
|
||||
# Test bookmark formatting
|
||||
bookmark = {
|
||||
"text": "Amazing article about the future of AI! Must read for everyone interested in tech.",
|
||||
"author": "tech_guru",
|
||||
"created_at": "2024-01-15T14:30:00Z",
|
||||
"url": "https://twitter.com/tech_guru/status/123456789",
|
||||
"likes": 156,
|
||||
"retweets": 42,
|
||||
"replies": 23,
|
||||
"hashtags": ["AI", "tech", "future"],
|
||||
"mentions": ["@openai", "@anthropic"],
|
||||
}
|
||||
|
||||
formatted = reader._format_bookmark(bookmark)
|
||||
assert "=== Twitter Bookmark ===" in formatted
|
||||
assert "Author: @tech_guru" in formatted
|
||||
assert "Amazing article about the future of AI!" in formatted
|
||||
assert "Likes: 156" in formatted
|
||||
assert "Retweets: 42" in formatted
|
||||
assert "Hashtags: AI, tech, future" in formatted
|
||||
assert "Mentions: @openai, @anthropic" in formatted
|
||||
|
||||
# Test with minimal data
|
||||
simple_bookmark = {"text": "Short tweet", "author": "user123"}
|
||||
formatted_simple = reader._format_bookmark(simple_bookmark)
|
||||
assert "=== Twitter Bookmark ===" in formatted_simple
|
||||
assert "Short tweet" in formatted_simple
|
||||
assert "Author: @user123" in formatted_simple
|
||||
|
||||
print("✅ TwitterMCPReader basic tests passed")
|
||||
|
||||
|
||||
def test_mcp_request_format():
|
||||
"""Test MCP request formatting."""
|
||||
print("Testing MCP request formatting...")
|
||||
|
||||
# Test initialization request format
|
||||
init_request = {
|
||||
"jsonrpc": "2.0",
|
||||
"id": 1,
|
||||
"method": "initialize",
|
||||
"params": {
|
||||
"protocolVersion": "2024-11-05",
|
||||
"capabilities": {},
|
||||
"clientInfo": {"name": "leann-slack-reader", "version": "1.0.0"},
|
||||
},
|
||||
}
|
||||
|
||||
# Verify it's valid JSON
|
||||
json_str = json.dumps(init_request)
|
||||
parsed = json.loads(json_str)
|
||||
assert parsed["jsonrpc"] == "2.0"
|
||||
assert parsed["method"] == "initialize"
|
||||
assert parsed["params"]["protocolVersion"] == "2024-11-05"
|
||||
|
||||
# Test tools/list request
|
||||
list_request = {"jsonrpc": "2.0", "id": 2, "method": "tools/list", "params": {}}
|
||||
|
||||
json_str = json.dumps(list_request)
|
||||
parsed = json.loads(json_str)
|
||||
assert parsed["method"] == "tools/list"
|
||||
|
||||
print("✅ MCP request formatting tests passed")
|
||||
|
||||
|
||||
def test_data_processing():
|
||||
"""Test data processing capabilities."""
|
||||
print("Testing data processing capabilities...")
|
||||
|
||||
from apps.slack_data.slack_mcp_reader import SlackMCPReader
|
||||
from apps.twitter_data.twitter_mcp_reader import TwitterMCPReader
|
||||
|
||||
# Test Slack message processing with various formats
|
||||
slack_reader = SlackMCPReader("test-server")
|
||||
|
||||
messages_with_timestamps = [
|
||||
{"text": "Meeting in 5 minutes", "user": "alice", "ts": "1000.123"},
|
||||
{"text": "On my way!", "user": "bob", "ts": "1001.456"},
|
||||
{"text": "Starting now", "user": "charlie", "ts": "1002.789"},
|
||||
]
|
||||
|
||||
content = slack_reader._create_concatenated_content(messages_with_timestamps, "meetings")
|
||||
assert "Meeting in 5 minutes" in content
|
||||
assert "On my way!" in content
|
||||
assert "Starting now" in content
|
||||
|
||||
# Test Twitter bookmark processing with engagement data
|
||||
twitter_reader = TwitterMCPReader("test-server", include_metadata=True)
|
||||
|
||||
high_engagement_bookmark = {
|
||||
"text": "Thread about startup lessons learned 🧵",
|
||||
"author": "startup_founder",
|
||||
"likes": 1250,
|
||||
"retweets": 340,
|
||||
"replies": 89,
|
||||
}
|
||||
|
||||
formatted = twitter_reader._format_bookmark(high_engagement_bookmark)
|
||||
assert "Thread about startup lessons learned" in formatted
|
||||
assert "Likes: 1250" in formatted
|
||||
assert "Retweets: 340" in formatted
|
||||
assert "Replies: 89" in formatted
|
||||
|
||||
# Test with metadata disabled
|
||||
twitter_reader_no_meta = TwitterMCPReader("test-server", include_metadata=False)
|
||||
formatted_no_meta = twitter_reader_no_meta._format_bookmark(high_engagement_bookmark)
|
||||
assert "Thread about startup lessons learned" in formatted_no_meta
|
||||
assert "Likes:" not in formatted_no_meta
|
||||
assert "Retweets:" not in formatted_no_meta
|
||||
|
||||
print("✅ Data processing tests passed")
|
||||
|
||||
|
||||
def main():
|
||||
"""Run all standalone tests."""
|
||||
print("🧪 Running MCP Integration Standalone Tests")
|
||||
print("=" * 60)
|
||||
print("Testing core functionality without LEANN dependencies...")
|
||||
print()
|
||||
|
||||
try:
|
||||
test_slack_reader_basic()
|
||||
test_twitter_reader_basic()
|
||||
test_mcp_request_format()
|
||||
test_data_processing()
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("🎉 All standalone tests passed!")
|
||||
print("\n✨ MCP Integration Summary:")
|
||||
print("- SlackMCPReader: Ready for Slack message processing")
|
||||
print("- TwitterMCPReader: Ready for Twitter bookmark processing")
|
||||
print("- MCP Protocol: Properly formatted JSON-RPC requests")
|
||||
print("- Data Processing: Handles various message/bookmark formats")
|
||||
|
||||
print("\n🚀 Next Steps:")
|
||||
print("1. Install MCP servers: npm install -g slack-mcp-server twitter-mcp-server")
|
||||
print("2. Configure API credentials for Slack and Twitter")
|
||||
print("3. Test connections: python -m apps.slack_rag --test-connection")
|
||||
print("4. Start indexing live data from your platforms!")
|
||||
|
||||
print("\n📖 Documentation:")
|
||||
print("- Check README.md for detailed setup instructions")
|
||||
print("- Run examples/mcp_integration_demo.py for usage examples")
|
||||
print("- Explore apps/slack_rag.py and apps/twitter_rag.py for implementation details")
|
||||
|
||||
except Exception as e:
|
||||
print(f"\n❌ Test failed: {e}")
|
||||
import traceback
|
||||
|
||||
traceback.print_exc()
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
413
uv.lock
generated
413
uv.lock
generated
@@ -14,9 +14,7 @@ version = "1.10.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "huggingface-hub" },
|
||||
{ name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
|
||||
{ name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
|
||||
{ name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
|
||||
{ name = "numpy" },
|
||||
{ name = "packaging" },
|
||||
{ name = "psutil" },
|
||||
{ name = "pyyaml" },
|
||||
@@ -203,9 +201,7 @@ name = "astchunk"
|
||||
version = "0.1.0"
|
||||
source = { editable = "packages/astchunk-leann" }
|
||||
dependencies = [
|
||||
{ name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
|
||||
{ name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
|
||||
{ name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
|
||||
{ name = "numpy" },
|
||||
{ name = "pyrsistent" },
|
||||
{ name = "tree-sitter", version = "0.23.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
|
||||
{ name = "tree-sitter", version = "0.25.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
|
||||
@@ -589,7 +585,7 @@ resolution-markers = [
|
||||
"python_full_version < '3.10'",
|
||||
]
|
||||
dependencies = [
|
||||
{ name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
|
||||
{ name = "numpy", marker = "python_full_version < '3.10'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/f5/f6/31a8f28b4a2a4fa0e01085e542f3081ab0588eff8e589d39d775172c9792/contourpy-1.3.0.tar.gz", hash = "sha256:7ffa0db17717a8ffb127efd0c95a4362d996b892c2904db72428d5b52e1938a4", size = 13464370 }
|
||||
wheels = [
|
||||
@@ -667,7 +663,7 @@ resolution-markers = [
|
||||
"python_full_version == '3.10.*'",
|
||||
]
|
||||
dependencies = [
|
||||
{ name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
|
||||
{ name = "numpy", marker = "python_full_version == '3.10.*'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/66/54/eb9bfc647b19f2009dd5c7f5ec51c4e6ca831725f1aea7a993034f483147/contourpy-1.3.2.tar.gz", hash = "sha256:b6945942715a034c671b7fc54f9588126b0b8bf23db2696e3ca8328f3ff0ab54", size = 13466130 }
|
||||
wheels = [
|
||||
@@ -738,7 +734,7 @@ resolution-markers = [
|
||||
"python_full_version == '3.11.*'",
|
||||
]
|
||||
dependencies = [
|
||||
{ name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
|
||||
{ name = "numpy", marker = "python_full_version >= '3.11'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/58/01/1253e6698a07380cd31a736d248a3f2a50a7c88779a1813da27503cadc2a/contourpy-1.3.3.tar.gz", hash = "sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880", size = 13466174 }
|
||||
wheels = [
|
||||
@@ -1028,9 +1024,7 @@ dependencies = [
|
||||
{ name = "fsspec", extra = ["http"] },
|
||||
{ name = "huggingface-hub" },
|
||||
{ name = "multiprocess" },
|
||||
{ name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
|
||||
{ name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
|
||||
{ name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
|
||||
{ name = "numpy" },
|
||||
{ name = "packaging" },
|
||||
{ name = "pandas" },
|
||||
{ name = "pyarrow" },
|
||||
@@ -1171,9 +1165,7 @@ dependencies = [
|
||||
{ name = "fsspec", extra = ["http"] },
|
||||
{ name = "huggingface-hub" },
|
||||
{ name = "multiprocess" },
|
||||
{ name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
|
||||
{ name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
|
||||
{ name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
|
||||
{ name = "numpy" },
|
||||
{ name = "packaging" },
|
||||
{ name = "pandas" },
|
||||
{ name = "requests" },
|
||||
@@ -1610,7 +1602,7 @@ name = "importlib-metadata"
|
||||
version = "8.7.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "zipp" },
|
||||
{ name = "zipp", marker = "python_full_version < '3.10'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/76/66/650a33bd90f786193e4de4b3ad86ea60b53c89b669a5c7be931fac31cdb0/importlib_metadata-8.7.0.tar.gz", hash = "sha256:d13b81ad223b890aa16c5471f2ac3056cf76c5f10f82d6f9292f0b415f389000", size = 56641 }
|
||||
wheels = [
|
||||
@@ -2167,9 +2159,7 @@ version = "0.3.4"
|
||||
source = { editable = "packages/leann-backend-diskann" }
|
||||
dependencies = [
|
||||
{ name = "leann-core" },
|
||||
{ name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
|
||||
{ name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
|
||||
{ name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
|
||||
{ name = "numpy" },
|
||||
{ name = "protobuf" },
|
||||
]
|
||||
|
||||
@@ -2187,9 +2177,7 @@ source = { editable = "packages/leann-backend-hnsw" }
|
||||
dependencies = [
|
||||
{ name = "leann-core" },
|
||||
{ name = "msgpack" },
|
||||
{ name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
|
||||
{ name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
|
||||
{ name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
|
||||
{ name = "numpy" },
|
||||
{ name = "pyzmq" },
|
||||
]
|
||||
|
||||
@@ -2216,9 +2204,7 @@ dependencies = [
|
||||
{ name = "mlx-lm", marker = "platform_machine == 'arm64' and sys_platform == 'darwin'" },
|
||||
{ name = "msgpack" },
|
||||
{ name = "nbconvert" },
|
||||
{ name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
|
||||
{ name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
|
||||
{ name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
|
||||
{ name = "numpy" },
|
||||
{ name = "openai" },
|
||||
{ name = "pdfplumber" },
|
||||
{ name = "psutil" },
|
||||
@@ -2255,12 +2241,12 @@ requires-dist = [
|
||||
{ name = "python-dotenv", specifier = ">=1.0.0" },
|
||||
{ name = "pyzmq", specifier = ">=23.0.0" },
|
||||
{ name = "requests", specifier = ">=2.25.0" },
|
||||
{ name = "sentence-transformers", specifier = ">=2.2.0" },
|
||||
{ name = "sentence-transformers", specifier = ">=3.0.0" },
|
||||
{ name = "torch", specifier = ">=2.0.0" },
|
||||
{ name = "torch", marker = "extra == 'colab'", specifier = ">=2.0.0,<3.0.0" },
|
||||
{ name = "tqdm", specifier = ">=4.60.0" },
|
||||
{ name = "transformers", specifier = ">=4.30.0" },
|
||||
{ name = "transformers", marker = "extra == 'colab'", specifier = ">=4.30.0,<5.0.0" },
|
||||
{ name = "transformers", specifier = ">=4.30.0,<4.46" },
|
||||
{ name = "transformers", marker = "extra == 'colab'", specifier = ">=4.30.0,<4.46" },
|
||||
]
|
||||
provides-extras = ["colab"]
|
||||
|
||||
@@ -2286,9 +2272,7 @@ dependencies = [
|
||||
{ name = "mlx-lm", marker = "platform_machine == 'arm64' and sys_platform == 'darwin'" },
|
||||
{ name = "msgpack" },
|
||||
{ name = "nbconvert" },
|
||||
{ name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
|
||||
{ name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
|
||||
{ name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
|
||||
{ name = "numpy" },
|
||||
{ name = "ollama" },
|
||||
{ name = "openai" },
|
||||
{ name = "pathspec" },
|
||||
@@ -2306,6 +2290,7 @@ dependencies = [
|
||||
{ name = "torch" },
|
||||
{ name = "torchvision" },
|
||||
{ name = "tqdm" },
|
||||
{ name = "transformers" },
|
||||
{ name = "tree-sitter", version = "0.23.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
|
||||
{ name = "tree-sitter", version = "0.25.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
|
||||
{ name = "tree-sitter-c-sharp" },
|
||||
@@ -2381,11 +2366,12 @@ requires-dist = [
|
||||
{ name = "pypdfium2", specifier = ">=4.30.0" },
|
||||
{ name = "python-docx", marker = "extra == 'documents'", specifier = ">=0.8.11" },
|
||||
{ name = "requests", specifier = ">=2.25.0" },
|
||||
{ name = "sentence-transformers", specifier = ">=2.2.0" },
|
||||
{ name = "sentence-transformers", specifier = ">=3.0.0" },
|
||||
{ name = "sglang" },
|
||||
{ name = "torch" },
|
||||
{ name = "torchvision", specifier = ">=0.23.0" },
|
||||
{ name = "tqdm" },
|
||||
{ name = "transformers", specifier = "<4.46" },
|
||||
{ name = "tree-sitter", specifier = ">=0.20.0" },
|
||||
{ name = "tree-sitter-c-sharp", specifier = ">=0.20.0" },
|
||||
{ name = "tree-sitter-java", specifier = ">=0.20.0" },
|
||||
@@ -2500,9 +2486,7 @@ dependencies = [
|
||||
{ name = "networkx", version = "3.4.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
|
||||
{ name = "networkx", version = "3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
|
||||
{ name = "nltk" },
|
||||
{ name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
|
||||
{ name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
|
||||
{ name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
|
||||
{ name = "numpy" },
|
||||
{ name = "pillow" },
|
||||
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|
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{ url = "https://files.pythonhosted.org/packages/6a/dc/23c26b7b0bce5aaccf2b767db3e9c4f5ae4331bd47688c1f2ef091b23696/transformers-4.42.4-py3-none-any.whl", hash = "sha256:6d59061392d0f1da312af29c962df9017ff3c0108c681a56d1bc981004d16d24", size = 9337817 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
||||
Reference in New Issue
Block a user