Reverting the tools commit (#2404)

mem0/llms/gemini.py

@@ -3,7 +3,8 @@ from typing import Dict, List, Optional
 
 try:
     import google.generativeai as genai
-    from google.generativeai import GenerativeModel
+    from google.generativeai import GenerativeModel, protos
+    from google.generativeai.types import content_types
 except ImportError:
     raise ImportError(
         "The 'google-generativeai' library is required. Please install it using 'pip install google-generativeai'."
@@ -14,17 +15,7 @@ from mem0.llms.base import LLMBase
 
 
 class GeminiLLM(LLMBase):
-    """
-    A wrapper for Google's Gemini language model, integrating it with the LLMBase class.
-    """
-
     def __init__(self, config: Optional[BaseLlmConfig] = None):
-        """
-        Initializes the Gemini LLM with the provided configuration.
-
-        Args:
-            config (Optional[BaseLlmConfig]): Configuration object for the model.
-        """
         super().__init__(config)
 
         if not self.config.model:
@@ -34,25 +25,51 @@ class GeminiLLM(LLMBase):
         genai.configure(api_key=api_key)
         self.client = GenerativeModel(model_name=self.config.model)
 
-    def _reformat_messages(
-        self, messages: List[Dict[str, str]]
-    ) -> List[Dict[str, str]]:
+    def _parse_response(self, response, tools):
         """
-        Reformats messages to match the Gemini API's expected structure.
+        Process the response based on whether tools are used or not.
 
         Args:
-            messages (List[Dict[str, str]]): A list of messages with 'role' and 'content' keys.
+            response: The raw response from API.
+            tools: The list of tools provided in the request.
 
         Returns:
-            List[Dict[str, str]]: Reformatted messages in the required format.
+            str or dict: The processed response.
+        """
+        if tools:
+            processed_response = {
+                "content": (content if (content := response.candidates[0].content.parts[0].text) else None),
+                "tool_calls": [],
+            }
+
+            for part in response.candidates[0].content.parts:
+                if fn := part.function_call:
+                    if isinstance(fn, protos.FunctionCall):
+                        fn_call = type(fn).to_dict(fn)
+                        processed_response["tool_calls"].append({"name": fn_call["name"], "arguments": fn_call["args"]})
+                        continue
+                    processed_response["tool_calls"].append({"name": fn.name, "arguments": fn.args})
+
+            return processed_response
+        else:
+            return response.candidates[0].content.parts[0].text
+
+    def _reformat_messages(self, messages: List[Dict[str, str]]):
+        """
+        Reformat messages for Gemini.
+
+        Args:
+            messages: The list of messages provided in the request.
+
+        Returns:
+            list: The list of messages in the required format.
         """
         new_messages = []
 
         for message in messages:
             if message["role"] == "system":
-                content = (
-                    "THIS IS A SYSTEM PROMPT. YOU MUST OBEY THIS: " + message["content"]
-                )
+                content = "THIS IS A SYSTEM PROMPT. YOU MUST OBEY THIS: " + message["content"]
+
             else:
                 content = message["content"]
 
@@ -65,33 +82,90 @@ class GeminiLLM(LLMBase):
 
         return new_messages
 
-    def generate_response(
-        self, messages: List[Dict[str, str]], response_format: Optional[Dict] = None
-    ) -> str:
+    def _reformat_tools(self, tools: Optional[List[Dict]]):
         """
-        Generates a response from Gemini based on the given conversation history.
+        Reformat tools for Gemini.
 
         Args:
-            messages (List[Dict[str, str]]): List of message dictionaries containing 'role' and 'content'.
-            response_format (Optional[Dict]): Specifies the response format (e.g., JSON schema).
+            tools: The list of tools provided in the request.
 
         Returns:
-            str: The generated response as text.
+            list: The list of tools in the required format.
         """
+
+        def remove_additional_properties(data):
+            """Recursively removes 'additionalProperties' from nested dictionaries."""
+
+            if isinstance(data, dict):
+                filtered_dict = {
+                    key: remove_additional_properties(value)
+                    for key, value in data.items()
+                    if not (key == "additionalProperties")
+                }
+                return filtered_dict
+            else:
+                return data
+
+        new_tools = []
+        if tools:
+            for tool in tools:
+                func = tool["function"].copy()
+                new_tools.append({"function_declarations": [remove_additional_properties(func)]})
+
+            # TODO: temporarily ignore it to pass tests, will come back to update according to standards later.
+            # return content_types.to_function_library(new_tools)
+
+            return new_tools
+        else:
+            return None
+
+    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 Gemini.
+
+        Args:
+            messages (list): List of message dicts containing 'role' and 'content'.
+            response_format (str or object, optional): Format for 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.
+        """
+
         params = {
             "temperature": self.config.temperature,
             "max_output_tokens": self.config.max_tokens,
             "top_p": self.config.top_p,
         }
 
-        if response_format and response_format.get("type") == "json_object":
+        if response_format is not None and response_format["type"] == "json_object":
             params["response_mime_type"] = "application/json"
             if "schema" in response_format:
                 params["response_schema"] = response_format["schema"]
+        if tool_choice:
+            tool_config = content_types.to_tool_config(
+                {
+                    "function_calling_config": {
+                        "mode": tool_choice,
+                        "allowed_function_names": (
+                            [tool["function"]["name"] for tool in tools] if tool_choice == "any" else None
+                        ),
+                    }
+                }
+            )
 
         response = self.client.generate_content(
             contents=self._reformat_messages(messages),
+            tools=self._reformat_tools(tools),
             generation_config=genai.GenerationConfig(**params),
+            tool_config=tool_config,
         )
 
-        return response.candidates[0].content.parts[0].text
+        return self._parse_response(response, tools)