Fix: Gemini Embeddings and LLM (#3050)
This commit is contained in:
Dev Khant
@@ -1,7 +1,8 @@
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import os
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from typing import Literal, Optional
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import google.genai as genai
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from google import genai
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from google.genai import types
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from mem0.configs.embeddings.base import BaseEmbedderConfig
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from mem0.embeddings.base import EmbeddingBase
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@@ -16,24 +17,23 @@ class GoogleGenAIEmbedding(EmbeddingBase):
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api_key = self.config.api_key or os.getenv("GOOGLE_API_KEY")
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if api_key:
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self.client = genai.Client(api_key="api_key")
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else:
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self.client = genai.Client()
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self.client = genai.Client(api_key=api_key)
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def embed(self, text, memory_action: Optional[Literal["add", "search", "update"]] = None):
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"""
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Get the embedding for the given text using Google Generative AI.
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Args:
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text (str): The text to embed.
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memory_action (optional): The type of embedding to use. (Currently not used by Gemini for task_type)
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memory_action (optional): The type of embedding to use. Must be one of "add", "search", or "update". Defaults to None.
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Returns:
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list: The embedding vector.
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"""
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text = text.replace("\n", " ")
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response = self.client.models.embed_content(
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model=self.config.model, content=text, output_dimensionality=self.config.embedding_dims
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)
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# Create config for embedding parameters
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config = types.EmbedContentConfig(output_dimensionality=self.config.embedding_dims)
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return response["embedding"]
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# Call the embed_content method with the correct parameters
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response = self.client.models.embed_content(model=self.config.model, contents=text, config=config)
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return response.embeddings[0].values
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@@ -4,11 +4,8 @@ from typing import Dict, List, Optional
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try:
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from google import genai
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from google.genai import types
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except ImportError:
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raise ImportError(
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"The 'google-generativeai' library is required. Please install it using 'pip install google-generativeai'."
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)
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raise ImportError("The 'google-genai' library is required. Please install it using 'pip install google-genai'.")
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from mem0.configs.llms.base import BaseLlmConfig
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from mem0.llms.base import LLMBase
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@@ -19,70 +16,79 @@ class GeminiLLM(LLMBase):
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super().__init__(config)
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if not self.config.model:
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self.config.model = "gemini-1.5-flash-latest"
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self.config.model = "gemini-2.0-flash"
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api_key = self.config.api_key or os.getenv("GEMINI_API_KEY")
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self.client_gemini = genai.Client(
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api_key=api_key,
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)
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api_key = self.config.api_key or os.getenv("GOOGLE_API_KEY")
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self.client = genai.Client(api_key=api_key)
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def _parse_response(self, response, tools):
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"""
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Process the response based on whether tools are used or not.
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Args:
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response: The raw response from the API.
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response: The raw response from API.
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tools: The list of tools provided in the request.
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Returns:
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str or dict: The processed response.
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"""
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candidate = response.candidates[0]
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content = candidate.content.parts[0].text if candidate.content.parts else None
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if tools:
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processed_response = {
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"content": content,
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"content": None,
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"tool_calls": [],
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}
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for part in candidate.content.parts:
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fn = getattr(part, "function_call", None)
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if fn:
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processed_response["tool_calls"].append(
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{
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"name": fn.name,
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"arguments": fn.args,
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}
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)
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# Extract content from the first candidate
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if response.candidates and response.candidates[0].content.parts:
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for part in response.candidates[0].content.parts:
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if hasattr(part, "text") and part.text:
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processed_response["content"] = part.text
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break
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# Extract function calls
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if response.candidates and response.candidates[0].content.parts:
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for part in response.candidates[0].content.parts:
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if hasattr(part, "function_call") and part.function_call:
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fn = part.function_call
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processed_response["tool_calls"].append(
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{
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"name": fn.name,
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"arguments": dict(fn.args) if fn.args else {},
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}
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)
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return processed_response
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else:
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if response.candidates and response.candidates[0].content.parts:
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for part in response.candidates[0].content.parts:
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if hasattr(part, "text") and part.text:
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return part.text
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return ""
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return content
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def _reformat_messages(self, messages: List[Dict[str, str]]) -> List[types.Content]:
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def _reformat_messages(self, messages: List[Dict[str, str]]):
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"""
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Reformat messages for Gemini using google.genai.types.
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Reformat messages for Gemini.
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Args:
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messages: The list of messages provided in the request.
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Returns:
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list: A list of types.Content objects with proper role and parts.
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tuple: (system_instruction, contents_list)
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"""
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new_messages = []
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system_instruction = None
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contents = []
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for message in messages:
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if message["role"] == "system":
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content = "THIS IS A SYSTEM PROMPT. YOU MUST OBEY THIS: " + message["content"]
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system_instruction = message["content"]
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else:
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content = message["content"]
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content = types.Content(
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parts=[types.Part(text=message["content"])],
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role=message["role"],
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)
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contents.append(content)
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new_messages.append(
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types.Content(role="model" if message["role"] == "model" else "user", parts=[types.Part(text=content)])
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)
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return new_messages
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return system_instruction, contents
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def _reformat_tools(self, tools: Optional[List[Dict]]):
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"""
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@@ -97,7 +103,6 @@ class GeminiLLM(LLMBase):
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def remove_additional_properties(data):
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"""Recursively removes 'additionalProperties' from nested dictionaries."""
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if isinstance(data, dict):
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filtered_dict = {
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key: remove_additional_properties(value)
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@@ -108,16 +113,21 @@ class GeminiLLM(LLMBase):
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else:
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return data
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new_tools = []
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if tools:
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function_declarations = []
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for tool in tools:
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func = tool["function"].copy()
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new_tools.append({"function_declarations": [remove_additional_properties(func)]})
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cleaned_func = remove_additional_properties(func)
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# TODO: temporarily ignore it to pass tests, will come back to update according to standards later.
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# return content_types.to_function_library(new_tools)
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function_declaration = types.FunctionDeclaration(
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name=cleaned_func["name"],
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description=cleaned_func.get("description", ""),
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parameters=cleaned_func.get("parameters", {}),
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)
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function_declarations.append(function_declaration)
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return new_tools
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tool_obj = types.Tool(function_declarations=function_declarations)
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return [tool_obj]
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else:
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return None
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@@ -141,38 +151,53 @@ class GeminiLLM(LLMBase):
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str: The generated response.
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"""
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params = {
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# Extract system instruction and reformat messages
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system_instruction, contents = self._reformat_messages(messages)
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# Prepare generation config
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config_params = {
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"temperature": self.config.temperature,
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"max_output_tokens": self.config.max_tokens,
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"top_p": self.config.top_p,
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}
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# Add system instruction to config if present
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if system_instruction:
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config_params["system_instruction"] = system_instruction
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if response_format is not None and response_format["type"] == "json_object":
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params["response_mime_type"] = "application/json"
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config_params["response_mime_type"] = "application/json"
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if "schema" in response_format:
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params["response_schema"] = response_format["schema"]
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config_params["response_schema"] = response_format["schema"]
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tool_config = None
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if tool_choice:
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tool_config = types.ToolConfig(
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function_calling_config=types.FunctionCallingConfig(
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mode=tool_choice.upper(), # Assuming 'any' should become 'ANY', etc.
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allowed_function_names=[tool["function"]["name"] for tool in tools]
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if tool_choice == "any"
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else None,
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if tools:
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formatted_tools = self._reformat_tools(tools)
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config_params["tools"] = formatted_tools
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if tool_choice:
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if tool_choice == "auto":
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mode = types.FunctionCallingConfigMode.AUTO
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elif tool_choice == "any":
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mode = types.FunctionCallingConfigMode.ANY
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else:
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mode = types.FunctionCallingConfigMode.NONE
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tool_config = types.ToolConfig(
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function_calling_config=types.FunctionCallingConfig(
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mode=mode,
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allowed_function_names=(
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[tool["function"]["name"] for tool in tools] if tool_choice == "any" else None
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),
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)
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)
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)
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config_params["tool_config"] = tool_config
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response = self.client_gemini.models.generate_content(
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model=self.config.model,
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contents=self._reformat_messages(messages),
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config=types.GenerateContentConfig(
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temperature=self.config.temperature,
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max_output_tokens=self.config.max_tokens,
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top_p=self.config.top_p,
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tools=self._reformat_tools(tools),
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tool_config=tool_config,
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),
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generation_config = types.GenerateContentConfig(**config_params)
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response = self.client.models.generate_content(
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model=self.config.model, contents=contents, config=generation_config
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)
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return self._parse_response(response, tools)
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