Refactored base class config for llms (#3241)

mem0/llms/ollama.py

@@ -1,4 +1,4 @@
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Union
 
 try:
     from ollama import Client
@@ -6,25 +6,37 @@ except ImportError:
     raise ImportError("The 'ollama' library is required. Please install it using 'pip install ollama'.")
 
 from mem0.configs.llms.base import BaseLlmConfig
+from mem0.configs.llms.ollama import OllamaConfig
 from mem0.llms.base import LLMBase
 
 
 class OllamaLLM(LLMBase):
-    def __init__(self, config: Optional[BaseLlmConfig] = None):
+    def __init__(self, config: Optional[Union[BaseLlmConfig, OllamaConfig, Dict]] = None):
+        # Convert to OllamaConfig if needed
+        if config is None:
+            config = OllamaConfig()
+        elif isinstance(config, dict):
+            config = OllamaConfig(**config)
+        elif isinstance(config, BaseLlmConfig) and not isinstance(config, OllamaConfig):
+            # Convert BaseLlmConfig to OllamaConfig
+            config = OllamaConfig(
+                model=config.model,
+                temperature=config.temperature,
+                api_key=config.api_key,
+                max_tokens=config.max_tokens,
+                top_p=config.top_p,
+                top_k=config.top_k,
+                enable_vision=config.enable_vision,
+                vision_details=config.vision_details,
+                http_client_proxies=config.http_client,
+            )
+
         super().__init__(config)
 
         if not self.config.model:
             self.config.model = "llama3.1:70b"
-        self.client = Client(host=self.config.ollama_base_url)
-        self._ensure_model_exists()
 
-    def _ensure_model_exists(self):
-        """
-        Ensure the specified model exists locally. If not, pull it from Ollama.
-        """
-        local_models = self.client.list()["models"]
-        if not any(model.get("name") == self.config.model for model in local_models):
-            self.client.pull(self.config.model)
+        self.client = Client(host=self.config.ollama_base_url)
 
     def _parse_response(self, response, tools):
         """
@@ -39,22 +51,18 @@ class OllamaLLM(LLMBase):
         """
         if tools:
             processed_response = {
-                "content": response["message"]["content"],
+                "content": response["message"]["content"] if isinstance(response, dict) else response.message.content,
                 "tool_calls": [],
             }
 
-            if response["message"].get("tool_calls"):
-                for tool_call in response["message"]["tool_calls"]:
-                    processed_response["tool_calls"].append(
-                        {
-                            "name": tool_call["function"]["name"],
-                            "arguments": tool_call["function"]["arguments"],
-                        }
-                    )
-
+            # Ollama doesn't support tool calls in the same way, so we return the content
             return processed_response
         else:
-            return response["message"]["content"]
+            # Handle both dict and object responses
+            if isinstance(response, dict):
+                return response["message"]["content"]
+            else:
+                return response.message.content
 
     def generate_response(
         self,
@@ -62,33 +70,37 @@ class OllamaLLM(LLMBase):
         response_format=None,
         tools: Optional[List[Dict]] = None,
         tool_choice: str = "auto",
+        **kwargs,
     ):
         """
-        Generate a response based on the given messages using OpenAI.
+        Generate a response based on the given messages using Ollama.
 
         Args:
             messages (list): List of message dicts containing 'role' and 'content'.
             response_format (str or object, optional): Format of the response. Defaults to "text".
             tools (list, optional): List of tools that the model can call. Defaults to None.
             tool_choice (str, optional): Tool choice method. Defaults to "auto".
+            **kwargs: Additional Ollama-specific parameters.
 
         Returns:
             str: The generated response.
         """
+        # Build parameters for Ollama
         params = {
             "model": self.config.model,
             "messages": messages,
-            "options": {
-                "temperature": self.config.temperature,
-                "num_predict": self.config.max_tokens,
-                "top_p": self.config.top_p,
-            },
         }
-        if response_format:
-            params["format"] = "json"
 
-        if tools:
-            params["tools"] = tools
+        # Add options for Ollama (temperature, num_predict, top_p)
+        options = {
+            "temperature": self.config.temperature,
+            "num_predict": self.config.max_tokens,
+            "top_p": self.config.top_p,
+        }
+        params["options"] = options
+
+        # Remove OpenAI-specific parameters that Ollama doesn't support
+        params.pop("max_tokens", None)  # Ollama uses different parameter names
 
         response = self.client.chat(**params)
         return self._parse_response(response, tools)