Files

aakash c76a1e2c71 Add real RAG example showing intelligent Slack query functionality

- Add detailed example of asking 'What is LEANN about?'
- Show retrieved messages from Slack channels
- Demonstrate intelligent answer generation based on context
- Add command example for running real RAG queries
- Explain the 4-step process: retrieve, index, generate, cite

2025-10-12 15:49:42 -07:00

12 KiB

Raw Blame History

Slack Integration Setup Guide

This guide provides step-by-step instructions for setting up Slack integration with LEANN.

Overview

LEANN's Slack integration uses MCP (Model Context Protocol) servers to fetch and index your Slack messages for RAG (Retrieval-Augmented Generation). This allows you to search through your Slack conversations using natural language queries.

Prerequisites

Slack Workspace Access: You need admin or owner permissions in your Slack workspace to create apps and configure OAuth tokens.
Slack MCP Server: Install a Slack MCP server (e.g., slack-mcp-server via npm)
LEANN: Ensure you have LEANN installed and working

Step 1: Create a Slack App

1.1 Go to Slack API Dashboard

Visit https://api.slack.com/apps
Click "Create New App"
Choose "From scratch"
Enter your app name (e.g., "LEANN Slack Integration")
Select your workspace
Click "Create App"

1.2 Configure App Permissions

Bot Token Scopes

In your app dashboard, go to "OAuth & Permissions" in the left sidebar
Scroll down to "Scopes" section
Under "Bot Token Scopes", click "Add an OAuth Scope"
Add the following scopes:
- channels:read - Read public channel information
- channels:history - Read messages in public channels
- groups:read - Read private channel information
- groups:history - Read messages in private channels
- im:read - Read direct message information
- im:history - Read direct messages
- mpim:read - Read group direct message information
- mpim:history - Read group direct messages
- users:read - Read user information
- team:read - Read workspace information

App-Level Tokens (Optional)

Some MCP servers may require app-level tokens:

Go to "Basic Information" in the left sidebar
Scroll down to "App-Level Tokens"
Click "Generate Token and Scopes"
Enter a name (e.g., "LEANN Integration")
Add the connections:write scope
Click "Generate"
Copy the token (starts with xapp-)

1.3 Install App to Workspace

Go to "OAuth & Permissions" in the left sidebar
Click "Install to Workspace"
Review the permissions and click "Allow"
Copy the "Bot User OAuth Token" (starts with xoxb-)

Step 2: Install Slack MCP Server

Option A: Using npm (Recommended)

# Install globally
npm install -g slack-mcp-server

# Or install locally
npm install slack-mcp-server

Option B: Using npx (No installation required)

# Use directly without installation
npx slack-mcp-server

Step 3: Configure Environment Variables

Create a .env file or set environment variables:

# Required: Bot User OAuth Token
SLACK_BOT_TOKEN=xoxb-your-bot-token-here

# Optional: App-Level Token (if your MCP server requires it)
SLACK_APP_TOKEN=xapp-your-app-token-here

# Optional: Workspace-specific settings
SLACK_WORKSPACE_ID=T1234567890  # Your workspace ID (optional)

Step 4: Test the Setup

4.1 Test MCP Server Connection

python -m apps.slack_rag \
  --mcp-server "slack-mcp-server" \
  --test-connection \
  --workspace-name "Your Workspace Name"

This will test the connection and list available tools without indexing any data.

4.2 Index a Specific Channel

python -m apps.slack_rag \
  --mcp-server "slack-mcp-server" \
  --workspace-name "Your Workspace Name" \
  --channels general \
  --query "What did we discuss about the project?"

4.3 Real RAG Query Example

To ask intelligent questions about your Slack conversations:

# Ask about a specific topic discussed in your channels
python -m apps.slack_rag \
  --mcp-server "slack-mcp-server" \
  --workspace-name "Sky Lab Computing" \
  --channels random general \
  --query "What is LEANN about?"

This will:

Retrieve relevant messages from the specified channels
Index the content for semantic search
Generate an intelligent answer based on the retrieved context
Provide citations showing which messages were used

Success Example: Working Integration

Here's what a successful Slack integration looks like in practice:

Terminal Output

When you run the connection test, you should see output similar to this:

Testing Slack MCP Connection...
Environment: SLACK_MCP_XOXP_TOKEN = xoxb-16753592806-967...

Connected to Slack MCP server!
Authenticated with Slack.

Listing available MCP tools...
Found 5 available tools:
  1. channels_list - Get list of channels
  2. conversations_add_message - Add messages to channels
  3. conversations_history - Get messages from channels
  4. conversations_replies - Get thread messages
  5. conversations_search_messages - Search messages with filters

Testing message fetch from 'random' channel...
Successfully fetched messages from channel random.

Visual Example

The following screenshot shows a successful integration with VS Code displaying the retrieved Slack channel data:

Key Success Indicators

Authentication Success: Connected to your Slack workspace
Tool Availability: 5 MCP tools ready for interaction
Data Access: Retrieved channel directory with member counts and purposes
Comprehensive Coverage: Access to multiple channels including specialized research groups

This demonstrates that your Slack integration is fully functional and ready for RAG queries across your entire workspace.

Real RAG Example: Querying Slack Messages

Here's what happens when you ask a real question about your Slack conversations:

Query: "What is LEANN about?"

System Response: The system first retrieves relevant messages from your Slack channels, then uses an LLM to provide an intelligent answer based on the retrieved context.

Retrieved Messages:

We'd like to share LEANN — a local RAG system with a 97% smaller index that lets you chat with all your emails, file system, and more.

It's fully Claude Code–compatible via a built-in semantic search MCP server.

:loudspeaker: Tweet: https://x.com/YichuanM/status/1953886752240013803 (reposts appreciated :raised_hands:)

:computer: Code: https://github.com/yichuan-w/LEANN (stars/shares welcome)

1/N :rocket: Launching LEANN — the tiniest vector index on Earth!
Fast, accurate, and 100% private RAG on your MacBook.
0% internet. 97% smaller. Semantic search on everything.
Your personal Jarvis, ready to dive into your emails, chats, and more.

Generated Answer: "LEANN is a local RAG (Retrieval-Augmented Generation) system designed to be extremely efficient with storage and privacy. Key features include:

97% smaller index compared to traditional vector databases
100% private - runs entirely on your local device with no internet required
Universal compatibility - works with emails, file systems, and more
Claude Code integration via built-in semantic search MCP server
Fast and accurate semantic search capabilities

The system acts as your personal AI assistant that can search through all your personal data while maintaining complete privacy."

This example shows how LEANN can intelligently search through your Slack conversations and provide contextual answers based on the actual messages shared in your workspace.

Common Issues and Solutions

Issue 1: "users cache is not ready yet" Error

Problem: You see this warning:

WARNING - Failed to fetch messages from channel random: Failed to fetch messages: {'code': -32603, 'message': 'users cache is not ready yet, sync process is still running... please wait'}

Solution: This is a common timing issue. The LEANN integration now includes automatic retry logic:

Wait and Retry: The system will automatically retry with exponential backoff (2s, 4s, 8s, etc.)

Increase Retry Parameters: If needed, you can customize retry behavior:

python -m apps.slack_rag \
  --mcp-server "slack-mcp-server" \
  --max-retries 10 \
  --retry-delay 3.0 \
  --channels general \
  --query "Your query here"

Keep MCP Server Running: Start the MCP server separately and keep it running:

# Terminal 1: Start MCP server
slack-mcp-server

# Terminal 2: Run LEANN (it will connect to the running server)
python -m apps.slack_rag --mcp-server "slack-mcp-server" --channels general --query "test"

Issue 2: "No message fetching tool found"

Problem: The MCP server doesn't have the expected tools.

Solution:

Check if your MCP server is properly installed and configured
Verify your Slack tokens are correct
Try a different MCP server implementation
Check the MCP server documentation for required configuration

Issue 3: Permission Denied Errors

Problem: You get permission errors when trying to access channels.

Solutions:

Check Bot Permissions: Ensure your bot has been added to the channels you want to access
Verify Token Scopes: Make sure you have all required scopes configured
Channel Access: For private channels, the bot needs to be explicitly invited
Workspace Permissions: Ensure your Slack app has the necessary workspace permissions

Issue 4: Empty Results

Problem: No messages are returned even though the channel has messages.