LEANN/docs/slack-setup-guide.md

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

1. Slack Workspace Access: You need admin or owner permissions in your Slack workspace to create apps and configure OAuth tokens.

2. Slack MCP Server: Install a Slack MCP server (e.g., slack-mcp-server via npm)

3. LEANN: Ensure you have LEANN installed and working

Step 1: Create a Slack App

1.1 Go to Slack API Dashboard

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

1.2 Configure App Permissions

Bot Token Scopes

1. In your app dashboard, go to "OAuth & Permissions" in the left sidebar
2. Scroll down to "Scopes" section
3. Under "Bot Token Scopes", click "Add an OAuth Scope"
4. 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:

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

1.3 Install App to Workspace

1. Go to "OAuth & Permissions" in the left sidebar
2. Click "Install to Workspace"
3. Review the permissions and click "Allow"
4. 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:

1. Retrieve relevant messages from the specified channels
2. Index the content for semantic search
3. Generate an intelligent answer based on the retrieved context
4. 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.

Important: Invite Your Bot to Channels

Before running RAG queries, you need to invite your Slack bot to the channels you want to access. This is a security feature in Slack.

To invite your bot to a channel:

1. Go to the channel in Slack (e.g., #general or #random)
2. Type: /invite @YourBotName (replace with your actual bot name)
3. Or click the channel name → "Settings" → "Integrations" → "Add apps"

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:

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

3. 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:

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

Issue 3: Permission Denied Errors

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

Solutions:

1. Check Bot Permissions: Ensure your bot has been added to the channels you want to access
2. Verify Token Scopes: Make sure you have all required scopes configured
3. Channel Access: For private channels, the bot needs to be explicitly invited
4. 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.

Solutions:

1. Check Channel Names: Ensure channel names are correct (without the # symbol)
2. Verify Bot Access: Make sure the bot can access the channels
3. Check Date Ranges: Some MCP servers have limitations on message history
4. Increase Message Limits: Try increasing the message limit:

   python -m apps.slack_rag \
     --mcp-server "slack-mcp-server" \
     --channels general \
     --max-messages-per-channel 1000 \
     --query "test"
   

Advanced Configuration

Custom MCP Server Commands

If you need to pass additional parameters to your MCP server:

python -m apps.slack_rag \
  --mcp-server "slack-mcp-server --token-file /path/to/tokens.json" \
  --workspace-name "Your Workspace" \
  --channels general \
  --query "Your query"

Multiple Workspaces

To work with multiple Slack workspaces, you can:

1. Create separate apps for each workspace
2. Use different environment variables
3. Run separate instances with different configurations

Performance Optimization

For better performance with large workspaces:

python -m apps.slack_rag \
  --mcp-server "slack-mcp-server" \
  --workspace-name "Your Workspace" \
  --max-messages-per-channel 500 \
  --no-concatenate-conversations \
  --query "Your query"

Screenshot: Real RAG Query Results

Here's what you'll see when running a RAG query on your Slack workspace:

What this screenshot shows:

• ✅ Successful MCP connection to Slack workspace
• ✅ Channel directory retrieval (107 channels discovered)
• ✅ Proper error handling for channel access permissions
• ⚠️ "not_in_channel" errors indicating bot needs invitation to specific channels

This is the expected behavior - the integration is working perfectly, it just needs proper channel permissions to access conversation messages.

Real RAG Query Example (Sky Lab Computing “random”)

This example shows a real query against the Sky Lab Computing workspace’s “random” channel using the Slack MCP server, with an embedded screenshot of the terminal output.

Screenshot

Prerequisites

• Bot is installed in the Sky Lab Computing workspace and invited to the target channel (run /invite @YourBotName in the channel if needed)
• Bot token available and exported in the same terminal session

Commands

1. Set the workspace token for this shell

export SLACK_MCP_XOXP_TOKEN="xoxb-***-redacted-***"

2. Run a real query against the “random” channel by channel ID (C0GN5BX0F)

python test_channel_by_id_or_name.py \
  --channel-id C0GN5BX0F \
  --workspace-name "Sky Lab Computing" \
  --query "PUBPOL 290"

Expected: The output contains a matching message (e.g., “do we have a channel for class PUBPOL 290 this semester?”) followed by a compact RAG-style answer section.

3. Optional: Ask a broader question

python test_channel_by_id_or_name.py \
  --channel-id C0GN5BX0F \
  --workspace-name "Sky Lab Computing" \
  --query "What is LEANN about?"

Notes:

• If you see not_in_channel, invite the bot to the channel and re-run.
• If you see channel_not_found, confirm the channel ID and workspace.
• Deep search via server-side “search” tools may require additional Slack scopes; the example above performs client-side filtering over retrieved history.

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Troubleshooting Checklist

• Slack app created with proper permissions
• Bot token (xoxb-) copied correctly
• App-level token (xapp-) created if needed
• MCP server installed and accessible
• Environment variables set correctly
• Bot invited to relevant channels
• Channel names specified without # symbol
• Sufficient retry attempts configured
• Network connectivity to Slack APIs

Getting Help

If you continue to have issues:

1. Check Logs: Look for detailed error messages in the console output
2. Test MCP Server: Use --test-connection to verify the MCP server is working
3. Verify Tokens: Double-check that your Slack tokens are valid and have the right scopes
4. Community Support: Reach out to the LEANN community for help

Example Commands

Basic Usage

# Test connection
python -m apps.slack_rag --mcp-server "slack-mcp-server" --test-connection

# Index specific channels
python -m apps.slack_rag \
  --mcp-server "slack-mcp-server" \
  --workspace-name "My Company" \
  --channels general random \
  --query "What did we decide about the project timeline?"

Advanced Usage

# With custom retry settings
python -m apps.slack_rag \
  --mcp-server "slack-mcp-server" \
  --workspace-name "My Company" \
  --channels general \
  --max-retries 10 \
  --retry-delay 5.0 \
  --max-messages-per-channel 2000 \
  --query "Show me all decisions made in the last month"