Iyeoluwa Akinrinola
272 lines
9.2 KiB
Python
272 lines
9.2 KiB
Python
"""
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Service for model management and interaction.
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"""
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import requests
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import json
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from typing import List, Dict, Any, Optional
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from ai_service.config import config
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from ai_service.models.model_parameters import ModelParameters
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class ModelService:
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"""Service for model management and interaction."""
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# Available models
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AVAILABLE_MODELS = {
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'gemma3': {
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'name': 'Gemma 3',
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'description': 'Google Gemma 3 model via Ollama',
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'provider': 'ollama',
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'max_tokens': 8192
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},
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'llama3.3': {
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'name': 'Llama 3 (70B)',
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'description': 'Meta Llama 3 70B model via Ollama',
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'provider': 'ollama',
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'max_tokens': 8192
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},
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'llama3.1': {
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'name': 'Llama 3 (8B)',
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'description': 'Meta Llama 3 8B model via Ollama',
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'provider': 'ollama',
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'max_tokens': 8192
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},
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'mistral': {
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'name': 'Mistral',
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'description': 'Mistral AI model via Ollama',
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'provider': 'ollama',
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'max_tokens': 8192
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},
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'deepseek': {
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'name': 'DeepSeek',
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'description': 'DeepSeek model via Ollama',
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'provider': 'ollama',
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'max_tokens': 8192
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}
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}
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def __init__(self):
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"""Initialize the model service."""
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self.default_model = config.DEFAULT_MODEL
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self.ollama_api_url = config.OLLAMA_API_URL
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self.openwebui_url = config.OPENWEBUI_URL
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self.openwebui_api_key = config.OPENWEBUI_API_KEY
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def get_available_models(self) -> List[Dict[str, Any]]:
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"""
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Get a list of available models.
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Returns:
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List of model information dictionaries.
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"""
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models = []
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for model_id, model_info in self.AVAILABLE_MODELS.items():
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model_data = {
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'id': model_id,
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'is_default': model_id == self.default_model,
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**model_info
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}
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models.append(model_data)
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# Debug log
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print(f"Model service models: {models}")
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# Ensure we're returning a non-empty list
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if not models:
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# Return a default model if none are found
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return [{
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'id': 'llama3.1',
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'name': 'Llama 3 (8B)',
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'description': 'Meta Llama 3 8B model via Ollama',
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'provider': 'ollama',
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'max_tokens': 8192,
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'is_default': True
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}]
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return models
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def get_model_info(self, model_id: str) -> Optional[Dict[str, Any]]:
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"""
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Get information about a specific model.
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Args:
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model_id: ID of the model.
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Returns:
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Model information dictionary if found, None otherwise.
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"""
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if model_id not in self.AVAILABLE_MODELS:
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return None
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return {
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'id': model_id,
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'is_default': model_id == self.default_model,
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**self.AVAILABLE_MODELS[model_id]
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}
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def generate_response(self, model_id: str, prompt: str,
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context: Optional[List[Dict[str, str]]] = None,
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use_rag: bool = False,
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model_params: Optional[ModelParameters] = None) -> str:
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"""
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Generate a response from the model.
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Args:
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model_id: ID of the model to use.
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prompt: User prompt.
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context: Optional conversation context.
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use_rag: Whether to use RAG (Retrieval Augmented Generation).
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model_params: Optional model parameters.
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Returns:
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Generated response.
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"""
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# Debug configuration information
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print(f"ModelService configuration:")
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print(f" - Ollama API URL: {self.ollama_api_url}")
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print(f" - OpenWebUI URL: {self.openwebui_url}")
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print(f" - Default model: {self.default_model}")
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print(f" - Requested model: {model_id}")
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print(f" - Using RAG: {use_rag}")
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if model_id not in self.AVAILABLE_MODELS:
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model_id = self.default_model
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print(f" - Model not found, using default: {model_id}")
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# Ensure we're using a valid model
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# (model_id is already validated above)
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# Prepare the messages for the API call
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messages = []
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# Use custom system prompt if provided, otherwise use default
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system_content = "You are a helpful assistant."
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if model_params and model_params.system_prompt:
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system_content = model_params.system_prompt
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messages.append({
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"role": "system",
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"content": system_content
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})
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# Add conversation context if provided
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if context:
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messages.extend(context)
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# If RAG is enabled, use OpenWebUI's knowledge database
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if use_rag:
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# We'll use OpenWebUI's built-in RAG capabilities
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# This is handled by sending the request to OpenWebUI instead of Ollama directly
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try:
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# Prepare the request for OpenWebUI
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openwebui_request = {
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"model": model_id,
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"messages": messages + [{"role": "user", "content": prompt}],
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"use_knowledge": True, # Enable RAG
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"stream": False
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}
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# Add model parameters if provided
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if model_params:
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params = model_params.to_dict()
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# Map parameters to OpenWebUI format
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if 'temperature' in params:
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openwebui_request['temperature'] = params['temperature']
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if 'max_tokens' in params:
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openwebui_request['max_tokens'] = params['max_tokens']
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if 'top_p' in params:
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openwebui_request['top_p'] = params['top_p']
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# Make the API call to OpenWebUI
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headers = {"Content-Type": "application/json"}
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if self.openwebui_api_key:
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headers["Authorization"] = f"Bearer {self.openwebui_api_key}"
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# Debug logs
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print(f"Sending RAG request to OpenWebUI at: {self.openwebui_url}/api/chat/completions")
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print(f"OpenWebUI request: {json.dumps(openwebui_request, indent=2)}")
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print(f"Headers: {headers}")
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# OpenWebUI API endpoint is /api/chat/completions
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response = requests.post(
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f"{self.openwebui_url}/api/chat/completions",
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headers=headers,
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json=openwebui_request,
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timeout=60 # Longer timeout for RAG
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)
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response.raise_for_status()
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result = response.json()
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# Extract the response content
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if 'message' in result:
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return result['message']['content']
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else:
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return "Error: Unexpected response format from OpenWebUI"
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except Exception as e:
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print(f"Error calling OpenWebUI API: {str(e)}")
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# Fall back to direct Ollama call without RAG
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print("Falling back to direct Ollama call without RAG")
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# Continue to the Ollama API call below
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# Add user prompt
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messages.append({
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"role": "user",
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"content": prompt
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})
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# Prepare API request parameters for Ollama
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request_json = {
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"model": model_id,
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"messages": messages,
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"stream": False
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}
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# Add model parameters if provided
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if model_params:
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params = model_params.to_dict()
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# Map parameters to Ollama format
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if 'temperature' in params:
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request_json['temperature'] = params['temperature']
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if 'top_p' in params:
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request_json['top_p'] = params['top_p']
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if 'top_k' in params:
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request_json['top_k'] = params['top_k']
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if 'max_tokens' in params:
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request_json['max_tokens'] = params['max_tokens']
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# Make the API call to Ollama
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try:
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# Prepare headers
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headers = {"Content-Type": "application/json"}
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# Direct Ollama API call
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print(f"Sending request to Ollama API at: {self.ollama_api_url}/api/chat")
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print(f"Request JSON: {json.dumps(request_json, indent=2)}")
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response = requests.post(
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f"{self.ollama_api_url}/api/chat",
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headers={"Content-Type": "application/json"},
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json=request_json,
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timeout=60
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)
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response.raise_for_status()
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result = response.json()
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# Extract the response content from Ollama
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# The response format for Ollama API
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if 'message' in result and 'content' in result['message']:
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return result['message']['content']
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else:
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return "Error: Unexpected response format from Ollama"
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except Exception as e:
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return f"Error generating response: {str(e)}"
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# Create a singleton instance
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model_service = ModelService()
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