first commit

llms/ollama.py

@@ -0,0 +1,117 @@
+from typing import Dict, List, Optional, Union
+
+try:
+    from ollama import Client
+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[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)
+
+    def _parse_response(self, response, tools):
+        """
+        Process the response based on whether tools are used or not.
+
+        Args:
+            response: The raw response from API.
+            tools: The list of tools provided in the request.
+
+        Returns:
+            str or dict: The processed response.
+        """
+        # Get the content from response
+        if isinstance(response, dict):
+            content = response["message"]["content"]
+        else:
+            content = response.message.content
+
+        if tools:
+            processed_response = {
+                "content": content,
+                "tool_calls": [],
+            }
+
+            # Ollama doesn't support tool calls in the same way, so we return the content
+            return processed_response
+        else:
+            return content
+
+    def generate_response(
+        self,
+        messages: List[Dict[str, str]],
+        response_format=None,
+        tools: Optional[List[Dict]] = None,
+        tool_choice: str = "auto",
+        **kwargs,
+    ):
+        """
+        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,
+        }
+
+        # Handle JSON response format by using Ollama's native format parameter
+        if response_format and response_format.get("type") == "json_object":
+            params["format"] = "json"
+            # Also add JSON format instruction to the last message as a fallback
+            if messages and messages[-1]["role"] == "user":
+                messages[-1]["content"] += "\n\nPlease respond with valid JSON only."
+            else:
+                messages.append({"role": "user", "content": "Please respond with valid JSON only."})
+
+        # 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)