progress

.env

@@ -20,7 +20,7 @@ EMBEDDING_MODEL=all-MiniLM-L6-v2
 #########################
 # Local LLM (Remote Machine)
 #########################
-LOCAL_LLM_ENDPOINT=https://api.chat.pathcore.org/v1/chat/completions
+LOCAL_LLM_ENDPOINT=http://172.168.10.10:11434/v1/chat/completions
 LOCAL_LLM_MODEL=mistral
 LOCAL_LLM_TIMEOUT=60
 

bots/deafult_bot.py

@@ -0,0 +1,9 @@
+BOT_IDENTIFIER = "default_bot"
+
+SYSTEM_PROMPT = """You are a helpful AI assistant in Slack.
+You answer questions clearly and concisely.
+You are friendly and professional."""
+
+ENABLED_TOOL_NAMES = []  # Add tool names here as you create them
+
+ENABLE_RAG_INSERT = False  # Set to True to enable vector storage

channel_cache.json

@@ -0,0 +1,9 @@
+{
+  "C08L3JJDSDV": {
+    "id": "C08L3JJDSDV",
+    "name": "project-ai",
+    "is_private": false,
+    "is_dm": false,
+    "cached_at": "2026-01-14T15:55:02.290619"
+  }
+}

channel_map.json

@@ -5,5 +5,6 @@
   "C0DQ40MH8": "sales",
   "C2RGSA4GL": "billing",
   "C0DUFQ4BB": "wireless",
-  "C09KNPDT481": "abot_scripting_bot"
+  "C09KNPDT481": "abot_scripting_bot",
+  "C08L3JJDSDV": "default_bot"
 }

config.py

@@ -1,4 +1,4 @@
-BOT_USER_ID = ""
+BOT_USER_ID = "U08LF3N25QE"
 CONVERSATION_TIMEOUT_MINUTES = 15
 MAX_HISTORY_LENGTH = 10
 MAX_MESSAGE_LENGTH = 4000

llm/local_llm_client.py

@@ -1,27 +1,84 @@
-import requests
 import logging
-import os
+from typing import Dict, Any
+from slack_sdk import WebClient
+from slack_sdk.errors import SlackApiError
 
-LLM_ENDPOINT = os.getenv("LOCAL_LLM_ENDPOINT")
-MODEL_NAME = os.getenv("LOCAL_LLM_MODEL", "llama3")
+logger = logging.getLogger(__name__)
 
-def chat_completion(messages, temperature=0.3, max_tokens=1024):
-    payload = {
-        "model": MODEL_NAME,
-        "messages": messages,
-        "temperature": temperature,
-        "max_tokens": max_tokens
-    }
 
+def process_mention(
+    event_data: dict,
+    slack_client: WebClient,
+    vector_store: Any,
+    bot_profile: Any,
+    tool_registry: Dict[str, Any]
+) -> None:
+    """
+    Process messages that mention the bot.
+    """
+    event = event_data.get("event", {})
+    channel = event.get("channel")
+    user = event.get("user")
+    text = event.get("text", "")
+    ts = event.get("ts")  # This is the message timestamp
+    
+    logger.info(f"Processing mention from {user} in {channel}")
+    
+    # Remove bot mention from text
+    from config import BOT_USER_ID
+    clean_text = text.replace(f"<@{BOT_USER_ID}>", "").strip()
+    
+    # Get bot configuration
+    bot_name = getattr(bot_profile, "BOT_IDENTIFIER", "Bot")
+    
     try:
-        resp = requests.post(
-            LLM_ENDPOINT,
-            json=payload,
-            timeout=60
+        # Try to get RAG context if enabled
+        rag_enabled = getattr(bot_profile, "ENABLE_RAG_INSERT", False)
+        context = ""
+        
+        if rag_enabled:
+            try:
+                # Search for similar messages
+                similar = vector_store.search_similar(clean_text, limit=3)
+                if similar:
+                    context = "\nRelevant context:\n" + "\n".join(similar)
+            except AttributeError:
+                logger.warning("RAG retrieval failed: search_similar not implemented")
+            except Exception as e:
+                logger.error(f"RAG retrieval error: {e}")
+        
+        # TODO: Integrate with your LLM here
+        # For now, simple echo response
+        
+        response_text = f"You said: {clean_text}"
+        
+        if context:
+            response_text += f"\n{context}"
+        
+        # Send message to channel (NOT as a thread reply)
+        slack_client.chat_postMessage(
+            channel=channel,
+            text=response_text
         )
-        resp.raise_for_status()
-        return resp.json()["choices"][0]["message"]["content"]
-
+        
+        logger.info(f"Sent response to {channel}")
+        
+    except SlackApiError as e:
+        logger.error(f"Slack API error: {e.response['error']}", exc_info=True)
+        try:
+            slack_client.chat_postMessage(
+                channel=channel,
+                text="Sorry, I encountered a Slack API error."
+            )
+        except:
+            pass
+            
     except Exception as e:
-        logging.error(f"Local LLM call failed: {e}", exc_info=True)
-        return "⚠️ Local LLM is currently unavailable."
+        logger.error(f"Error processing mention: {e}", exc_info=True)
+        try:
+            slack_client.chat_postMessage(
+                channel=channel,
+                text="⚠️ Sorry, I encountered an internal error."
+            )
+        except:
+            pass

main.py

@@ -10,6 +10,7 @@ from typing import Dict, Any
 from dotenv import load_dotenv
 from flask import Flask, jsonify
 from slackeventsapi import SlackEventAdapter
+from slack_sdk import WebClient
 import slack
 import json
 from hot_reload import ReloadableRegistry, start_hot_reload
@@ -128,7 +129,7 @@ if not SIGNING_SECRET or not SLACK_TOKEN:
     sys.exit("Missing Slack credentials")
 
 slack_event_adapter = SlackEventAdapter(SIGNING_SECRET, "/slack/events", app)
-slack_client = slack.WebClient(token=SLACK_TOKEN)
+slack_client = WebClient(token=SLACK_TOKEN)
 
 # --------------------------------------------------
 # Deduplication

message_processor.py

@@ -1,96 +1,111 @@
-import json
 import logging
+from typing import Dict, Any
+from slack_sdk import WebClient
+from slack_sdk.errors import SlackApiError
 
-from llm.local_llm_client import chat_completion
+logger = logging.getLogger(__name__)
 
-
-SYSTEM_PROMPT = """
-You are a Slack assistant.
-
-If a tool is required, respond ONLY in valid JSON:
-{
-  "tool": "tool_name",
-  "arguments": { ... }
-}
-
-If no tool is required, respond normally with text.
-"""
-
-
-def try_parse_tool_call(text):
-    try:
-        data = json.loads(text)
-        if isinstance(data, dict) and "tool" in data and "arguments" in data:
-            return data
-    except json.JSONDecodeError:
-        return None
-    return None
+# Try to import LLM client, but don't fail if it's not available
+try:
+    from llm.local_llm_client import chat_completion
+    LLM_AVAILABLE = True
+except ImportError:
+    logger.warning("LLM client not available, using simple echo mode")
+    LLM_AVAILABLE = False
+    chat_completion = None
 
 
 def process_mention(
-    event_data,
-    slack_client,
-    vector_store,      # qdrant_functions
-    bot_profile,
-    tool_registry
-):
-    event = event_data["event"]
-    channel = event["channel"]
-    user = event["user"]
-    text = event["text"]
-    ts = event["ts"]
-    thread_ts = event.get("thread_ts", ts)
-
-    # ---- RAG retrieval (if enabled) ----
-    context_blocks = []
-    if getattr(bot_profile, "ENABLE_RAG_RETRIEVAL", True):
-        try:
-            results = vector_store.search_similar(text, limit=5)
-            for r in results:
-                context_blocks.append(f"- {r['text']}")
-        except Exception as e:
-            logging.warning(f"RAG retrieval failed: {e}")
-
-    context_text = "\n".join(context_blocks)
-
-    messages = [
-        {"role": "system", "content": SYSTEM_PROMPT},
-        {
-            "role": "user",
-            "content": f"""
-Context:
-{context_text}
-
-User message:
-{text}
-"""
-        }
-    ]
-
-    llm_output = chat_completion(messages)
-
-    # ---- Tool handling ----
-    tool_call = try_parse_tool_call(llm_output)
-
-    if tool_call:
-        tool_name = tool_call["tool"]
-        args = tool_call["arguments"]
-
-        tool_entry = tool_registry.get(tool_name)
-        if not tool_entry:
-            response_text = f"⚠️ Unknown tool: `{tool_name}`"
-        else:
+    event_data: dict,
+    slack_client: WebClient,
+    vector_store: Any,
+    bot_profile: Any,
+    tool_registry: Dict[str, Any]
+) -> None:
+    """
+    Process messages that mention the bot.
+    """
+    event = event_data.get("event", {})
+    channel = event.get("channel")
+    user = event.get("user")
+    text = event.get("text", "")
+    ts = event.get("ts")  # This is the message timestamp
+    
+    logger.info(f"Processing mention from {user} in {channel}")
+    
+    # Remove bot mention from text
+    from config import BOT_USER_ID
+    clean_text = text.replace(f"<@{BOT_USER_ID}>", "").strip()
+    
+    # Get bot configuration
+    bot_name = getattr(bot_profile, "BOT_IDENTIFIER", "Bot")
+    system_prompt = getattr(bot_profile, "SYSTEM_PROMPT", "You are a helpful assistant.")
+    
+    try:
+        # Try to get RAG context if enabled
+        rag_enabled = getattr(bot_profile, "ENABLE_RAG_INSERT", False)
+        context = ""
+        
+        if rag_enabled:
             try:
-                result = tool_entry["function"](**args)
-                response_text = json.dumps(result, indent=2)
+                # Search for similar messages
+                similar = vector_store.search_similar(clean_text, limit=3)
+                if similar:
+                    context = "\nRelevant context:\n" + "\n".join(similar)
+            except AttributeError:
+                logger.warning("RAG retrieval failed: search_similar not implemented")
             except Exception as e:
-                logging.error(f"Tool execution failed: {e}", exc_info=True)
-                response_text = "⚠️ Tool execution failed."
-    else:
-        response_text = llm_output
-
-    slack_client.chat_postMessage(
-        channel=channel,
-        text=response_text,
-        thread_ts=thread_ts
-    )
+                logger.error(f"RAG retrieval error: {e}")
+        
+        # Generate response
+        if LLM_AVAILABLE and chat_completion:
+            try:
+                # Use LLM to generate response
+                messages = [
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": clean_text}
+                ]
+                
+                if context:
+                    messages.insert(1, {"role": "system", "content": f"Context: {context}"})
+                
+                llm_response = chat_completion(messages)
+                response_text = llm_response.get("content", "Sorry, I couldn't generate a response.")
+                
+            except Exception as e:
+                logger.error(f"LLM error: {e}", exc_info=True)
+                response_text = f"You said: {clean_text}"
+        else:
+            # Simple echo response when LLM not available
+            response_text = f"You said: {clean_text}"
+            
+            if context:
+                response_text += f"\n{context}"
+        
+        # Send message to channel (NOT as a thread reply)
+        slack_client.chat_postMessage(
+            channel=channel,
+            text=response_text
+        )
+        
+        logger.info(f"Sent response to {channel}")
+        
+    except SlackApiError as e:
+        logger.error(f"Slack API error: {e.response['error']}", exc_info=True)
+        try:
+            slack_client.chat_postMessage(
+                channel=channel,
+                text="Sorry, I encountered a Slack API error."
+            )
+        except:
+            pass
+            
+    except Exception as e:
+        logger.error(f"Error processing mention: {e}", exc_info=True)
+        try:
+            slack_client.chat_postMessage(
+                channel=channel,
+                text="⚠️ Sorry, I encountered an internal error."
+            )
+        except:
+            pass

rag/qdrant_functions.py

@@ -1,29 +1,24 @@
-# qdrant_functions.py
+# rag/qdrant_functions.py
 import logging
-from qdrant_client import QdrantClient
-from sentence_transformers import SentenceTransformer
-import uuid
-import os
+from typing import Any, List
 
-QDRANT_HOST = os.getenv("QDRANT_HOST", "localhost")
-QDRANT_PORT = int(os.getenv("QDRANT_PORT", 6333))
-QDRANT_COLLECTION = os.getenv("QDRANT_COLLECTION", "abot-slack")
+logger = logging.getLogger(__name__)
 
-client = QdrantClient(host=QDRANT_HOST, port=QDRANT_PORT)
 
-embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
-VECTOR_SIZE = 384
+def embed_and_store_slack_message(
+    slack_client: Any,
+    channel: str,
+    user: str,
+    text: str,
+    ts: str,
+    bot_user_id: str
+) -> None:
+    """Embed and store a Slack message in the vector database."""
+    logger.info(f"RAG insert for message in {channel}: {text[:50]}...")
+    pass
 
-def ensure_collection():
-    collections = [c.name for c in client.get_collections().collections]
-    if QDRANT_COLLECTION not in collections:
-        client.create_collection(
-            collection_name=QDRANT_COLLECTION,
-            vectors_config={
-                "size": VECTOR_SIZE,
-                "distance": "Cosine"
-            }
-        )
-        logging.info(f"Created Qdrant collection {QDRANT_COLLECTION}")
 
-ensure_collection()
+def search_similar(query: str, limit: int = 5) -> List[str]:
+    """Search for similar messages in the vector database."""
+    logger.info(f"RAG search for: {query[:50]}...")
+    return []  # Return empty list for now

requirements.txt

@@ -1,5 +1,5 @@
 flask
-slack-sdk
+slack_sdk
 slackeventsapi
 python-dotenv
 

robots.txt

@@ -0,0 +1,3 @@
+# robots.txt
+User-agent: *
+Disallow: /

scripts/list_channels.py

@@ -0,0 +1,38 @@
+import os
+import json
+from dotenv import load_dotenv
+from slack_sdk import WebClient
+
+load_dotenv()
+
+client = WebClient(token=os.getenv("SLACK_TOKEN"))
+
+# Get all channels
+response = client.conversations_list(types="public_channel,private_channel")
+
+if response["ok"]:
+    channels = response["channels"]
+    
+    print("Available channels:\n")
+    print(f"{'Channel Name':<30} {'Channel ID':<15} {'Type':<10}")
+    print("-" * 60)
+    
+    channel_map = {}
+    
+    for channel in channels:
+        channel_type = "Private" if channel.get('is_private') else "Public"
+        print(f"{channel['name']:<30} {channel['id']:<15} {channel_type:<10}")
+        
+        # Add to suggested channel_map.json
+        channel_map[channel['id']] = "default_bot"
+    
+    print("\n" + "="*60)
+    print("\nSuggested channel_map.json:\n")
+    print(json.dumps(channel_map, indent=2))
+    
+    # Optionally save it
+    save = input("\nSave this to channel_map.json? (y/n): ")
+    if save.lower() == 'y':
+        with open('channel_map.json', 'w') as f:
+            json.dump(channel_map, f, indent=2)
+        print("✓ Saved to channel_map.json")

scripts/test_slack_connection.py

@@ -0,0 +1,55 @@
+import os
+from dotenv import load_dotenv
+from slack_sdk import WebClient
+from pathlib import Path
+
+load_dotenv()
+
+def test_slack_connection():
+    """Test basic Slack connection and bot setup"""
+    
+    token = os.getenv("SLACK_TOKEN")
+    
+    if not token:
+        print("❌ SLACK_TOKEN not found in .env file")
+        return False
+    
+    print(f"✓ SLACK_TOKEN found: {token[:10]}...")
+    
+    try:
+        client = WebClient(token=token)
+        
+        # Test authentication
+        auth_response = client.auth_test()
+        
+        if auth_response["ok"]:
+            print(f"✓ Bot authenticated successfully!")
+            print(f"  Bot User ID: {auth_response['user_id']}")
+            print(f"  Bot Username: {auth_response['user']}")
+            print(f"  Team: {auth_response['team']}")
+            
+            # Test listing channels
+            channels_response = client.conversations_list(types="public_channel,private_channel")
+            
+            if channels_response["ok"]:
+                channels = channels_response["channels"]
+                print(f"\n✓ Found {len(channels)} channels:")
+                
+                for channel in channels[:5]:  # Show first 5
+                    print(f"  - {channel['name']} (ID: {channel['id']})")
+                
+                if len(channels) > 5:
+                    print(f"  ... and {len(channels) - 5} more")
+            
+            return True
+        else:
+            print(f"❌ Authentication failed: {auth_response}")
+            return False
+            
+    except Exception as e:
+        print(f"❌ Error connecting to Slack: {e}")
+        return False
+
+if __name__ == "__main__":
+    print("Testing Slack Connection...\n")
+    test_slack_connection()

user_cache.json

@@ -0,0 +1,24 @@
+{
+  "U03TRF48XLY": {
+    "id": "U03TRF48XLY",
+    "name": "edwin.weeman",
+    "real_name": "edwin.weeman",
+    "display_name": "",
+    "email": "",
+    "title": "",
+    "first_name": "edwin.weeman",
+    "last_name": "",
+    "cached_at": "2026-01-14T15:55:02.449664"
+  },
+  "U04JA29H6P3": {
+    "id": "U04JA29H6P3",
+    "name": "ryangvenecia",
+    "real_name": "Ryan Venecia",
+    "display_name": "",
+    "email": "",
+    "title": "",
+    "first_name": "Ryan",
+    "last_name": "Venecia",
+    "cached_at": "2026-01-14T16:06:03.750498"
+  }
+}