Add Litellm support (#1493)

mem0/llms/litellm.py

@@ -0,0 +1,71 @@
+import json
+from typing import Dict, List, Optional
+
+import litellm
+
+from mem0.llms.base import LLMBase
+
+
+class LiteLLM(LLMBase):
+    def __init__(self, model="gpt-4o"):
+        self.model = model
+    
+    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.
+        """
+        if tools:
+            processed_response = {
+                "content": response.choices[0].message.content,
+                "tool_calls": []
+            }
+            
+            if response.choices[0].message.tool_calls:
+                for tool_call in response.choices[0].message.tool_calls:
+                    processed_response["tool_calls"].append({
+                        "name": tool_call.function.name,
+                        "arguments": json.loads(tool_call.function.arguments)
+                    })
+            
+            return processed_response
+        else:
+            return response.choices[0].message.content
+
+    def generate_response(
+        self,
+        messages: List[Dict[str, str]],
+        response_format=None,
+        tools: Optional[List[Dict]] = None,
+        tool_choice: str = "auto",
+    ):
+        """
+        Generate a response based on the given messages using Litellm.
+
+        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".
+
+        Returns:
+            str: The generated response.
+        """
+        if not litellm.supports_function_calling(self.model):
+            raise ValueError(f"Model '{self.model}' in litellm does not support function calling.")
+
+        params = {"model": self.model, "messages": messages}
+        if response_format:
+            params["response_format"] = response_format
+        if tools:
+            params["tools"] = tools
+            params["tool_choice"] = tool_choice
+
+        response = litellm.completion(**params)
+        return self._parse_response(response, tools)

mem0/utils/factory.py

@@ -13,7 +13,8 @@ class LlmFactory:
         "openai": "mem0.llms.openai.OpenAILLM",
         "groq": "mem0.llms.groq.GroqLLM",
         "together": "mem0.llms.together.TogetherLLM",
-        "aws_bedrock": "mem0.llms.aws_bedrock.AWSBedrockLLM"
+        "aws_bedrock": "mem0.llms.aws_bedrock.AWSBedrockLLM",
+        "litellm": "mem0.llms.litellm.LiteLLM",
     }
 
     @classmethod