progress

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