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57c13e3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 | """
LLM Interface for DeepDrone supporting LiteLLM and Ollama.
"""
import os
from typing import List, Dict, Any, Optional
import json
import logging
from .config import ModelConfig
# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class LLMInterface:
"""Interface for interacting with various LLM providers."""
def __init__(self, model_config: ModelConfig):
self.model_config = model_config
self._setup_client()
def _setup_client(self):
"""Set up the appropriate client based on model provider."""
if self.model_config.provider == "ollama":
self._setup_ollama()
else:
self._setup_litellm()
def _setup_ollama(self):
"""Set up Ollama client."""
try:
import ollama
self.client = ollama
self.client_type = "ollama"
# Test connection
try:
models = self.client.list()
available_models = models.models if hasattr(models, 'models') else []
logger.info(f"Connected to Ollama. Available models: {len(available_models)}")
# Check if the requested model is available
model_names = [model.model for model in available_models]
if self.model_config.model_id not in model_names:
logger.warning(f"Model '{self.model_config.model_id}' not found locally. Available models: {model_names}")
except Exception as e:
logger.warning(f"Could not connect to Ollama: {e}")
logger.info("Make sure Ollama is running: ollama serve")
except ImportError:
raise ImportError("Ollama package not installed. Install with: pip install ollama")
def _setup_litellm(self):
"""Set up LiteLLM client."""
try:
import litellm
# Set API key in environment if provided (skip for local/placeholder keys)
if self.model_config.api_key and self.model_config.api_key != "local":
if self.model_config.provider == "openai":
os.environ["OPENAI_API_KEY"] = self.model_config.api_key
elif self.model_config.provider == "anthropic":
os.environ["ANTHROPIC_API_KEY"] = self.model_config.api_key
elif self.model_config.provider == "huggingface":
os.environ["HUGGINGFACE_API_KEY"] = self.model_config.api_key
elif self.model_config.provider == "mistral":
os.environ["MISTRAL_API_KEY"] = self.model_config.api_key
elif self.model_config.provider == "vertex_ai":
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = self.model_config.api_key
# Set base URL if provided
if self.model_config.base_url:
litellm.api_base = self.model_config.base_url
self.client = litellm
self.client_type = "litellm"
logger.info(f"Set up LiteLLM for {self.model_config.provider}")
except ImportError:
raise ImportError("LiteLLM package not installed. Install with: pip install litellm")
def chat(self, messages: List[Dict[str, str]]) -> str:
"""Send chat messages and get response."""
try:
if self.client_type == "ollama":
return self._chat_ollama(messages)
else:
return self._chat_litellm(messages)
except Exception as e:
logger.error(f"Chat error: {e}")
return f"Error communicating with {self.model_config.provider}: {str(e)}"
def _chat_ollama(self, messages: List[Dict[str, str]]) -> str:
"""Chat using Ollama."""
try:
# Convert messages to Ollama format
prompt = self._messages_to_prompt(messages)
response = self.client.generate(
model=self.model_config.model_id,
prompt=prompt,
options={
'temperature': self.model_config.temperature,
'num_predict': self.model_config.max_tokens,
}
)
return response['response']
except Exception as e:
error_str = str(e).lower()
if "model not found" in error_str or "model does not exist" in error_str:
available_models = []
try:
models = self.client.list()
available_models = [m.model for m in models.models] if hasattr(models, 'models') else []
except:
pass
error_msg = f"❌ Model '{self.model_config.model_id}' not found in Ollama.\n\n"
if available_models:
error_msg += f"📋 Available local models:\n"
for model in available_models:
error_msg += f" • {model}\n"
error_msg += f"\n💡 To install {self.model_config.model_id}, run:\n"
error_msg += f" ollama pull {self.model_config.model_id}\n"
else:
error_msg += "📭 No models found locally.\n\n"
error_msg += f"💡 To install {self.model_config.model_id}, run:\n"
error_msg += f" ollama pull {self.model_config.model_id}\n\n"
error_msg += "🎯 Popular models to try:\n"
error_msg += " • ollama pull llama3.1\n"
error_msg += " • ollama pull codestral\n"
error_msg += " • ollama pull qwen2.5-coder\n"
return error_msg
elif "connection" in error_str or "refused" in error_str:
return "❌ Cannot connect to Ollama.\n\n💡 Make sure Ollama is running:\n ollama serve\n\n📥 Download Ollama from: https://ollama.com/download"
return f"❌ Ollama error: {str(e)}"
def _chat_litellm(self, messages: List[Dict[str, str]]) -> str:
"""Chat using LiteLLM."""
try:
response = self.client.completion(
model=self.model_config.model_id,
messages=messages,
max_tokens=self.model_config.max_tokens,
temperature=self.model_config.temperature,
)
return response.choices[0].message.content
except Exception as e:
if "api key" in str(e).lower():
return f"API key error for {self.model_config.provider}. Please set your API key with: deepdrone models set-key {self.model_config.name}"
elif "quota" in str(e).lower() or "billing" in str(e).lower():
return f"Billing/quota error for {self.model_config.provider}. Please check your account."
elif "model" in str(e).lower() and "not found" in str(e).lower():
return f"Model '{self.model_config.model_id}' not found for {self.model_config.provider}."
raise e
def _messages_to_prompt(self, messages: List[Dict[str, str]]) -> str:
"""Convert messages to a single prompt for models that don't support chat format."""
prompt_parts = []
for message in messages:
role = message["role"]
content = message["content"]
if role == "system":
prompt_parts.append(f"System: {content}")
elif role == "user":
prompt_parts.append(f"Human: {content}")
elif role == "assistant":
prompt_parts.append(f"Assistant: {content}")
prompt_parts.append("Assistant: ")
return "\n\n".join(prompt_parts)
def test_connection(self) -> Dict[str, Any]:
"""Test connection to the LLM service."""
try:
test_messages = [
{"role": "user", "content": "Hello, please respond with 'Connection test successful'"}
]
response = self.chat(test_messages)
return {
"success": True,
"response": response,
"provider": self.model_config.provider,
"model": self.model_config.model_id
}
except Exception as e:
return {
"success": False,
"error": str(e),
"provider": self.model_config.provider,
"model": self.model_config.model_id
}
def get_model_info(self) -> Dict[str, Any]:
"""Get information about the current model."""
info = {
"name": self.model_config.name,
"provider": self.model_config.provider,
"model_id": self.model_config.model_id,
"max_tokens": self.model_config.max_tokens,
"temperature": self.model_config.temperature,
"client_type": self.client_type,
}
if self.model_config.base_url:
info["base_url"] = self.model_config.base_url
return info |