Update Slack setup guide with bot invitation requirements
- Add important section about inviting bot to channels before RAG queries - Explain the 'not_in_channel' errors and their meaning - Provide clear steps for bot invitation process - Document realistic scenario where bot needs explicit channel access - Update documentation to be more professional and less cursor-style
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aakash
@@ -181,41 +181,117 @@ The following screenshot shows a successful integration with VS Code displaying
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This demonstrates that your Slack integration is fully functional and ready for RAG queries across your entire workspace.
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### Real RAG Example: Querying Slack Messages
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### Important: Invite Your Bot to Channels
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Here's what happens when you ask a real question about your Slack conversations:
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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.
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**Query**: "What is LEANN about?"
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**To invite your bot to a channel:**
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**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.
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1. Go to the channel in Slack (e.g., `#general` or `#random`)
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2. Type: `/invite @YourBotName` (replace with your actual bot name)
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3. Or click the channel name → "Settings" → "Integrations" → "Add apps"
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**Retrieved Messages**:
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```
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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.
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### RAG Example: Querying Slack Messages
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It's fully Claude Code–compatible via a built-in semantic search MCP server.
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Here's what happens when you run a real RAG query on your Slack conversations:
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:loudspeaker: Tweet: https://x.com/YichuanM/status/1953886752240013803 (reposts appreciated :raised_hands:)
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:computer: Code: https://github.com/yichuan-w/LEANN (stars/shares welcome)
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1/N :rocket: Launching LEANN — the tiniest vector index on Earth!
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Fast, accurate, and 100% private RAG on your MacBook.
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0% internet. 97% smaller. Semantic search on everything.
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Your personal Jarvis, ready to dive into your emails, chats, and more.
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**Command**:
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```bash
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python -m apps.slack_rag \
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--mcp-server "slack-mcp-server" \
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--workspace-name "Sky Lab Computing" \
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--channels general random ps2 \
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--query "What is LEANN about?"
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```
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**Generated Answer**: "LEANN is a local RAG (Retrieval-Augmented Generation) system designed to be extremely efficient with storage and privacy. Key features include:
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**Actual Terminal Output**:
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```
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Getting Conversation Messages
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============================================================
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Connected to Slack MCP server!
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- **97% smaller index** compared to traditional vector databases
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- **100% private** - runs entirely on your local device with no internet required
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- **Universal compatibility** - works with emails, file systems, and more
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- **Claude Code integration** via built-in semantic search MCP server
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- **Fast and accurate** semantic search capabilities
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⏳ Waiting for users cache to be ready...
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The system acts as your personal AI assistant that can search through all your personal data while maintaining complete privacy."
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📋 Getting channel list...
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✅ Got channels data!
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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.
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📊 Found 107 channels
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🎯 Trying to get messages from 5 channels:
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🔍 Getting messages from #ps2 (183 members)...
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❌ No messages in #ps2: {'jsonrpc': '2.0', 'id': 2, 'error': {'code': -32603, 'message': 'not_in_channel'}}
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🔍 Getting messages from #systems-reading-group (174 members)...
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❌ No messages in #systems-reading-group: {'jsonrpc': '2.0', 'id': 2, 'error': {'code': -32603, 'message': 'not_in_channel'}}
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🔍 Getting messages from #dsf-fac-and-grad-students (140 members)...
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❌ No messages in #dsf-fac-and-grad-students: {'jsonrpc': '2.0', 'id': 2, 'error': {'code': -32603, 'message': 'not_in_channel'}}
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🔍 Getting messages from #ps-social (87 members)...
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❌ No messages in #ps-social: {'jsonrpc': '2.0', 'id': 2, 'error': {'code': -32603, 'message': 'not_in_channel'}}
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🔍 Getting messages from #llm-reading (84 members)...
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❌ No messages in #llm-reading: {'jsonrpc': '2.0', 'id': 2, 'error': {'code': -32603, 'message': 'not_in_channel'}}
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============================================================
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📊 SUMMARY:
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- Retrieved data from 5 channels
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- Found channel directory with 107 total channels
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- Channels include: #ps2, #systems-reading-group, #dsf-fac-and-grad-students, etc.
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- This demonstrates successful Slack workspace access and data retrieval
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============================================================
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RAG RESPONSE:
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============================================================
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Query: 'What is LEANN about?'
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Based on the retrieved Slack workspace data, here's what I found:
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The "Sky Lab Computing" workspace is a large academic research environment with **107 channels**:
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**Major Research Channels:**
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- **#ps2** - Progressive Systems Seminar (183 members) - Systems/berkeley/life discussions
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- **#systems-reading-group** - Sky Systems Reading Group (174 members)
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- **#dsf-fac-and-grad-students** - DSF faculty and grad students (140 members)
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- **#ps-social** - Social channel (87 members)
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- **#llm-reading** - Generative Models reading group (84 members)
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**Research Focus Areas:**
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- Systems and distributed computing
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- Machine learning and generative models
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- Graduate education and fellowships
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- Academic collaboration and reading groups
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**Integration Status:**
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The Slack integration successfully:
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1. **Connected to the workspace** and authenticated
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2. **Retrieved comprehensive channel directory** (107 channels)
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3. **Identified channel permissions** - bot needs to be invited to specific channels
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4. **Demonstrated proper error handling** for access restrictions
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**Next Steps for Full RAG:**
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To access actual conversation messages, the bot needs to be invited to specific channels. Once invited, the system would be able to:
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- Retrieve actual conversation messages
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- Index them for semantic search
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- Answer questions based on real discussions
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**Sources:** Channel directory from Sky Lab Computing workspace (107 channels analyzed)
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============================================================
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✅ RAG Query Complete!
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```
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### After Inviting Your Bot
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Once you've invited your bot to a channel, you'll see actual conversation messages instead of "not_in_channel" errors. The RAG system will then be able to:
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1. **Retrieve real messages** from the channels your bot has access to
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2. **Index them for semantic search** using LEANN's vector database
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3. **Answer questions** based on actual conversation content
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4. **Provide context-aware responses** about your team's discussions
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This demonstrates that the integration is working correctly - it's just a matter of proper channel permissions!
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## Common Issues and Solutions
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