Fix: Gemini Embeddings and LLM (#3050)

mem0/embeddings/gemini.py

@@ -1,7 +1,8 @@
 import os
 from typing import Literal, Optional
 
-import google.genai as genai
+from google import genai
+from google.genai import types
 
 from mem0.configs.embeddings.base import BaseEmbedderConfig
 from mem0.embeddings.base import EmbeddingBase
@@ -16,24 +17,23 @@ class GoogleGenAIEmbedding(EmbeddingBase):
 
         api_key = self.config.api_key or os.getenv("GOOGLE_API_KEY")
 
-        if api_key:
+        self.client = genai.Client(api_key=api_key)
-            self.client = genai.Client(api_key="api_key")
-        else:
-            self.client = genai.Client()
 
     def embed(self, text, memory_action: Optional[Literal["add", "search", "update"]] = None):
         """
         Get the embedding for the given text using Google Generative AI.
         Args:
             text (str): The text to embed.
-            memory_action (optional): The type of embedding to use. (Currently not used by Gemini for task_type)
+            memory_action (optional): The type of embedding to use. Must be one of "add", "search", or "update". Defaults to None.
         Returns:
             list: The embedding vector.
         """
         text = text.replace("\n", " ")
 
-        response = self.client.models.embed_content(
+        # Create config for embedding parameters
-            model=self.config.model, content=text, output_dimensionality=self.config.embedding_dims
+        config = types.EmbedContentConfig(output_dimensionality=self.config.embedding_dims)
-        )
 
-        return response["embedding"]
+        # Call the embed_content method with the correct parameters
+        response = self.client.models.embed_content(model=self.config.model, contents=text, config=config)
+
+        return response.embeddings[0].values

mem0/llms/gemini.py

@@ -4,11 +4,8 @@ from typing import Dict, List, Optional
 try:
     from google import genai
     from google.genai import types
-
 except ImportError:
-    raise ImportError(
+    raise ImportError("The 'google-genai' library is required. Please install it using 'pip install google-genai'.")
-        "The 'google-generativeai' library is required. Please install it using 'pip install google-generativeai'."
-    )
 
 from mem0.configs.llms.base import BaseLlmConfig
 from mem0.llms.base import LLMBase
@@ -19,70 +16,79 @@ class GeminiLLM(LLMBase):
         super().__init__(config)
 
         if not self.config.model:
-            self.config.model = "gemini-1.5-flash-latest"
+            self.config.model = "gemini-2.0-flash"
 
-        api_key = self.config.api_key or os.getenv("GEMINI_API_KEY")
+        api_key = self.config.api_key or os.getenv("GOOGLE_API_KEY")
-        self.client_gemini = genai.Client(
+        self.client = genai.Client(api_key=api_key)
-            api_key=api_key,
-        )
 
     def _parse_response(self, response, tools):
         """
         Process the response based on whether tools are used or not.
 
         Args:
-            response: The raw response from the API.
+            response: The raw response from API.
             tools: The list of tools provided in the request.
 
         Returns:
             str or dict: The processed response.
         """
-        candidate = response.candidates[0]
-        content = candidate.content.parts[0].text if candidate.content.parts else None
-
         if tools:
             processed_response = {
-                "content": content,
+                "content": None,
                 "tool_calls": [],
             }
 
-            for part in candidate.content.parts:
+            # Extract content from the first candidate
-                fn = getattr(part, "function_call", None)
+            if response.candidates and response.candidates[0].content.parts:
-                if fn:
+                for part in response.candidates[0].content.parts:
-                    processed_response["tool_calls"].append(
+                    if hasattr(part, "text") and part.text:
-                        {
+                        processed_response["content"] = part.text
-                            "name": fn.name,
+                        break
-                            "arguments": fn.args,
+
-                        }
+            # Extract function calls
-                    )
+            if response.candidates and response.candidates[0].content.parts:
+                for part in response.candidates[0].content.parts:
+                    if hasattr(part, "function_call") and part.function_call:
+                        fn = part.function_call
+                        processed_response["tool_calls"].append(
+                            {
+                                "name": fn.name,
+                                "arguments": dict(fn.args) if fn.args else {},
+                            }
+                        )
 
             return processed_response
+        else:
+            if response.candidates and response.candidates[0].content.parts:
+                for part in response.candidates[0].content.parts:
+                    if hasattr(part, "text") and part.text:
+                        return part.text
+            return ""
 
-        return content
+    def _reformat_messages(self, messages: List[Dict[str, str]]):
-
-    def _reformat_messages(self, messages: List[Dict[str, str]]) -> List[types.Content]:
         """
-        Reformat messages for Gemini using google.genai.types.
+        Reformat messages for Gemini.
 
         Args:
             messages: The list of messages provided in the request.
 
         Returns:
-            list: A list of types.Content objects with proper role and parts.
+            tuple: (system_instruction, contents_list)
         """
-        new_messages = []
+        system_instruction = None
+        contents = []
 
         for message in messages:
             if message["role"] == "system":
-                content = "THIS IS A SYSTEM PROMPT. YOU MUST OBEY THIS: " + message["content"]
+                system_instruction = message["content"]
             else:
-                content = message["content"]
+                content = types.Content(
+                    parts=[types.Part(text=message["content"])],
+                    role=message["role"],
+                )
+                contents.append(content)
 
-            new_messages.append(
+        return system_instruction, contents
-                types.Content(role="model" if message["role"] == "model" else "user", parts=[types.Part(text=content)])
-            )
-
-        return new_messages
 
     def _reformat_tools(self, tools: Optional[List[Dict]]):
         """
@@ -97,7 +103,6 @@ class GeminiLLM(LLMBase):
 
         def remove_additional_properties(data):
             """Recursively removes 'additionalProperties' from nested dictionaries."""
-
             if isinstance(data, dict):
                 filtered_dict = {
                     key: remove_additional_properties(value)
@@ -108,16 +113,21 @@ class GeminiLLM(LLMBase):
             else:
                 return data
 
-        new_tools = []
         if tools:
+            function_declarations = []
             for tool in tools:
                 func = tool["function"].copy()
-                new_tools.append({"function_declarations": [remove_additional_properties(func)]})
+                cleaned_func = remove_additional_properties(func)
 
-            # TODO: temporarily ignore it to pass tests, will come back to update according to standards later.
+                function_declaration = types.FunctionDeclaration(
-            # return content_types.to_function_library(new_tools)
+                    name=cleaned_func["name"],
+                    description=cleaned_func.get("description", ""),
+                    parameters=cleaned_func.get("parameters", {}),
+                )
+                function_declarations.append(function_declaration)
 
-            return new_tools
+            tool_obj = types.Tool(function_declarations=function_declarations)
+            return [tool_obj]
         else:
             return None
 
@@ -141,38 +151,53 @@ class GeminiLLM(LLMBase):
             str: The generated response.
         """
 
-        params = {
+        # Extract system instruction and reformat messages
+        system_instruction, contents = self._reformat_messages(messages)
+
+        # Prepare generation config
+        config_params = {
             "temperature": self.config.temperature,
             "max_output_tokens": self.config.max_tokens,
             "top_p": self.config.top_p,
         }
 
+        # Add system instruction to config if present
+        if system_instruction:
+            config_params["system_instruction"] = system_instruction
+
+
         if response_format is not None and response_format["type"] == "json_object":
-            params["response_mime_type"] = "application/json"
+            config_params["response_mime_type"] = "application/json"
             if "schema" in response_format:
-                params["response_schema"] = response_format["schema"]
+                config_params["response_schema"] = response_format["schema"]
 
-        tool_config = None
+        if tools:
-        if tool_choice:
+            formatted_tools = self._reformat_tools(tools)
-            tool_config = types.ToolConfig(
+            config_params["tools"] = formatted_tools
-                function_calling_config=types.FunctionCallingConfig(
+
-                    mode=tool_choice.upper(),  # Assuming 'any' should become 'ANY', etc.
+
-                    allowed_function_names=[tool["function"]["name"] for tool in tools]
+            if tool_choice:
-                    if tool_choice == "any"
+                if tool_choice == "auto":
-                    else None,
+                    mode = types.FunctionCallingConfigMode.AUTO
+                elif tool_choice == "any":
+                    mode = types.FunctionCallingConfigMode.ANY
+                else:
+                    mode = types.FunctionCallingConfigMode.NONE
+
+                tool_config = types.ToolConfig(
+                    function_calling_config=types.FunctionCallingConfig(
+                        mode=mode,
+                        allowed_function_names=(
+                            [tool["function"]["name"] for tool in tools] if tool_choice == "any" else None
+                        ),
+                    )
                 )
-            )
+                config_params["tool_config"] = tool_config
 
-        response = self.client_gemini.models.generate_content(
+        generation_config = types.GenerateContentConfig(**config_params)
-            model=self.config.model,
+
-            contents=self._reformat_messages(messages),
+        response = self.client.models.generate_content(
-            config=types.GenerateContentConfig(
+            model=self.config.model, contents=contents, config=generation_config
-                temperature=self.config.temperature,
-                max_output_tokens=self.config.max_tokens,
-                top_p=self.config.top_p,
-                tools=self._reformat_tools(tools),
-                tool_config=tool_config,
-            ),
         )
 
         return self._parse_response(response, tools)