Tools is broken
This commit is contained in:
eweeman
@@ -1,84 +1,155 @@
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import logging
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from typing import Dict, Any
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from slack_sdk import WebClient
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from slack_sdk.errors import SlackApiError
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import requests
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import os
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from typing import List, Dict, Any
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import urllib3
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# Disable SSL warnings (only for development with self-signed certs!)
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urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
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logger = logging.getLogger(__name__)
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# Get LLM configuration from environment
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LLM_API_URL = os.getenv("LLM_API_URL", "http://api.chat.pathcore.org")
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LLM_API_KEY = os.getenv("LLM_API_KEY") # Optional API key
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LLM_MODEL = os.getenv("LLM_MODEL", "llama3") # Default Ollama model
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def process_mention(
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event_data: dict,
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slack_client: WebClient,
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vector_store: Any,
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bot_profile: Any,
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tool_registry: Dict[str, Any]
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) -> None:
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def chat_completion(
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messages: List[Dict[str, Any]],
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model: str = None,
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temperature: float = 0.7,
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max_tokens: int = 1000,
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tools: List[Dict[str, Any]] | None = None,
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) -> Dict[str, Any]:
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"""
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Process messages that mention the bot.
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Call Ollama API for chat completion.
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Args:
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messages: List of message dicts with 'role' and 'content'
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model: Model name to use (defaults to LLM_MODEL from env)
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temperature: Sampling temperature (0.0 to 2.0)
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max_tokens: Maximum tokens to generate
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Returns:
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Dict with 'content' key containing the response text
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Raises:
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Exception: If the API call fails
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"""
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event = event_data.get("event", {})
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channel = event.get("channel")
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user = event.get("user")
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text = event.get("text", "")
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ts = event.get("ts") # This is the message timestamp
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logger.info(f"Processing mention from {user} in {channel}")
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# Use provided model or fall back to env variable
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model = model or LLM_MODEL
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# Remove bot mention from text
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from config import BOT_USER_ID
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clean_text = text.replace(f"<@{BOT_USER_ID}>", "").strip()
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# Ollama chat endpoint
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url = f"{LLM_API_URL}/api/chat"
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# Get bot configuration
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bot_name = getattr(bot_profile, "BOT_IDENTIFIER", "Bot")
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# Convert OpenAI-style messages to Ollama format
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# Ollama expects messages in the same format, but we need to ensure proper structure
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logger.info(f"Calling Ollama API at {url} with model: {model}")
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payload = {
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"model": model,
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"messages": messages,
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"stream": False,
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"options": {
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"temperature": temperature,
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"num_predict": max_tokens,
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}
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}
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if tools:
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payload["tools"] = tools
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headers = {
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"Content-Type": "application/json"
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}
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# Add API key if configured (Ollama doesn't usually need this, but just in case)
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if LLM_API_KEY:
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headers["Authorization"] = f"Bearer {LLM_API_KEY}"
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try:
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# Try to get RAG context if enabled
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rag_enabled = getattr(bot_profile, "ENABLE_RAG_INSERT", False)
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context = ""
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if rag_enabled:
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try:
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# Search for similar messages
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similar = vector_store.search_similar(clean_text, limit=3)
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if similar:
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context = "\nRelevant context:\n" + "\n".join(similar)
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except AttributeError:
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logger.warning("RAG retrieval failed: search_similar not implemented")
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except Exception as e:
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logger.error(f"RAG retrieval error: {e}")
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# TODO: Integrate with your LLM here
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# For now, simple echo response
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response_text = f"You said: {clean_text}"
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if context:
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response_text += f"\n{context}"
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# Send message to channel (NOT as a thread reply)
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slack_client.chat_postMessage(
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channel=channel,
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text=response_text
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resp = requests.post(
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url,
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json=payload,
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headers=headers,
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timeout=120, # Ollama can be slow on first request
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verify=False
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)
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logger.info(f"Sent response to {channel}")
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# Raise exception for HTTP errors
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resp.raise_for_status()
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except SlackApiError as e:
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logger.error(f"Slack API error: {e.response['error']}", exc_info=True)
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try:
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slack_client.chat_postMessage(
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channel=channel,
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text="Sorry, I encountered a Slack API error."
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)
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except:
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pass
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data = resp.json()
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logger.debug(f"Ollama API response: {data}")
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# Extract the assistant's response from Ollama format
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# Ollama returns: {"message": {"role": "assistant", "content": "..."}}
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message = data.get("message", {})
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content = message.get("content", "")
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tool_calls = message.get("tool_calls", [])
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logger.info(
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f"Ollama response received "
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f"(content={len(content)} chars, tool_calls={len(tool_calls)})"
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)
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return {
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"content": content,
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"tool_calls": tool_calls,
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"raw": data
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}
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except requests.exceptions.Timeout:
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logger.error("Ollama API request timed out after 120 seconds")
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raise Exception("LLM request timed out. The model might be loading for the first time.")
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except requests.exceptions.ConnectionError as e:
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logger.error(f"Cannot connect to Ollama API: {e}")
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raise Exception("Cannot connect to Ollama server. Is it running?")
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except requests.exceptions.HTTPError as e:
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logger.error(f"HTTP error from Ollama API: {e}")
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if e.response.status_code == 404:
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raise Exception(f"Model '{model}' not found. Try: ollama pull {model}")
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raise Exception(f"Ollama API error: {e}")
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except ValueError as e:
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logger.error(f"Invalid response from Ollama API: {e}")
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raise
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except Exception as e:
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logger.error(f"Error processing mention: {e}", exc_info=True)
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try:
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slack_client.chat_postMessage(
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channel=channel,
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text="⚠️ Sorry, I encountered an internal error."
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)
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except:
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pass
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logger.error(f"Unexpected error calling Ollama API: {e}", exc_info=True)
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raise
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def list_models():
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"""
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List available Ollama models.
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Returns:
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List of model names
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"""
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url = f"{LLM_API_URL}/api/tags"
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try:
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resp = requests.get(url, timeout=10, verify=False)
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resp.raise_for_status()
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data = resp.json()
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if "models" in data:
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models = [model["name"] for model in data["models"]]
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logger.info(f"Available models: {models}")
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return models
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return []
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except Exception as e:
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logger.error(f"Error listing models: {e}")
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return []
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# Make functions available for import
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__all__ = ['chat_completion', 'list_models']
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