Fix: Add Google Genai library support (#2941)
This commit is contained in:
Akshat Jain
@@ -2,9 +2,9 @@ import os
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from typing import Dict, List, Optional
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try:
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import google.generativeai as genai
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from google.generativeai import GenerativeModel, protos
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from google.generativeai.types import content_types
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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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@@ -22,66 +22,71 @@ class GeminiLLM(LLMBase):
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self.config.model = "gemini-1.5-flash-latest"
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api_key = self.config.api_key or os.getenv("GEMINI_API_KEY")
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genai.configure(api_key=api_key)
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self.client = GenerativeModel(model_name=self.config.model)
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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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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 API.
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response: The raw response from the 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 if (content := response.candidates[0].content.parts[0].text) else None),
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"content": content,
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"tool_calls": [],
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}
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for part in response.candidates[0].content.parts:
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if fn := part.function_call:
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if isinstance(fn, protos.FunctionCall):
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fn_call = type(fn).to_dict(fn)
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processed_response["tool_calls"].append({"name": fn_call["name"], "arguments": fn_call["args"]})
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continue
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processed_response["tool_calls"].append({"name": fn.name, "arguments": fn.args})
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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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"name": fn.name,
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"arguments": fn.args,
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})
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return processed_response
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else:
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return response.candidates[0].content.parts[0].text
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def _reformat_messages(self, messages: List[Dict[str, str]]):
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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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"""
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Reformat messages for Gemini.
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Reformat messages for Gemini using google.genai.types.
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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: The list of messages in the required format.
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list: A list of types.Content objects with proper role and parts.
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"""
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new_messages = []
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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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else:
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content = message["content"]
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new_messages.append(
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{
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"parts": content,
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"role": "model" if message["role"] == "model" else "user",
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}
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types.Content(
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role="model" if message["role"] == "model" else "user",
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parts=[types.Part(text=content)]
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)
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)
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return new_messages
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def _reformat_tools(self, tools: Optional[List[Dict]]):
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"""
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Reformat tools for Gemini.
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@@ -126,6 +131,7 @@ class GeminiLLM(LLMBase):
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tools: Optional[List[Dict]] = None,
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tool_choice: str = "auto",
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):
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"""
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Generate a response based on the given messages using Gemini.
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@@ -149,23 +155,37 @@ class GeminiLLM(LLMBase):
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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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tool_config = None
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if tool_choice:
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tool_config = content_types.to_tool_config(
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{
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"function_calling_config": {
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"mode": tool_choice,
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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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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=[
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tool["function"]["name"] for tool in tools
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] if tool_choice == "any" else None
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)
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)
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response = self.client.generate_content(
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contents=self._reformat_messages(messages),
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tools=self._reformat_tools(tools),
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generation_config=genai.GenerationConfig(**params),
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tool_config=tool_config,
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)
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print(f"Tool config: {tool_config}")
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print(f"Params: {params}" )
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print(f"Messages: {messages}")
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print(f"Tools: {tools}")
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print(f"Reformatted messages: {self._reformat_messages(messages)}")
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print(f"Reformatted tools: {self._reformat_tools(tools)}")
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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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)
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print(f"Response test: {response}")
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return self._parse_response(response, tools)
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