mirror of
https://github.com/arc53/DocsGPT.git
synced 2025-11-29 08:33:20 +00:00
fix: google parser, llm handler and other errors
This commit is contained in:
Siddhant Rai
@@ -1,60 +1,77 @@
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from application.llm.base import BaseLLM
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import google.generativeai as genai
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from application.core.settings import settings
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import logging
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from application.llm.base import BaseLLM
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class GoogleLLM(BaseLLM):
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def __init__(self, api_key=None, user_api_key=None, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.api_key = settings.API_KEY
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self.user_api_key = user_api_key
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genai.configure(api_key=self.api_key)
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def _clean_messages_google(self, messages):
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return [
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{
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"role": "model" if message["role"] == "system" else message["role"],
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"parts": [message["content"]],
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}
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for message in messages[1:]
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]
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cleaned_messages = []
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for message in messages[1:]:
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cleaned_messages.append(
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{
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"role": "model" if message["role"] == "system" else message["role"],
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"parts": [message["content"]],
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}
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)
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return cleaned_messages
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def _clean_tools_format(self, tools_data):
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"""
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Cleans the tools data format, converting string type representations
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to the expected dictionary structure for google-generativeai.
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"""
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if isinstance(tools_data, list):
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return [self._clean_tools_format(item) for item in tools_data]
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elif isinstance(tools_data, dict):
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if 'function' in tools_data and 'type' in tools_data and tools_data['type'] == 'function':
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if (
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"function" in tools_data
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and "type" in tools_data
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and tools_data["type"] == "function"
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):
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# Handle the case where tools are nested under 'function'
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cleaned_function = self._clean_tools_format(tools_data['function'])
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return {'function_declarations': [cleaned_function]}
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elif 'function' in tools_data and 'type_' in tools_data and tools_data['type_'] == 'function':
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cleaned_function = self._clean_tools_format(tools_data["function"])
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return {"function_declarations": [cleaned_function]}
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elif (
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"function" in tools_data
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and "type_" in tools_data
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and tools_data["type_"] == "function"
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):
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# Handle the case where tools are nested under 'function' and type is already 'type_'
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cleaned_function = self._clean_tools_format(tools_data['function'])
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return {'function_declarations': [cleaned_function]}
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cleaned_function = self._clean_tools_format(tools_data["function"])
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return {"function_declarations": [cleaned_function]}
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else:
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new_tools_data = {}
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for key, value in tools_data.items():
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if key == 'type':
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if value == 'string':
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new_tools_data['type_'] = 'STRING' # Keep as string for now
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elif value == 'object':
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new_tools_data['type_'] = 'OBJECT' # Keep as string for now
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elif key == 'additionalProperties':
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if key == "type":
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if value == "string":
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new_tools_data["type_"] = "STRING"
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elif value == "object":
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new_tools_data["type_"] = "OBJECT"
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elif key == "additionalProperties":
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continue
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elif key == 'properties':
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elif key == "properties":
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if isinstance(value, dict):
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new_properties = {}
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for prop_name, prop_value in value.items():
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if isinstance(prop_value, dict) and 'type' in prop_value:
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if prop_value['type'] == 'string':
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new_properties[prop_name] = {'type_': 'STRING', 'description': prop_value.get('description')}
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if (
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isinstance(prop_value, dict)
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and "type" in prop_value
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):
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if prop_value["type"] == "string":
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new_properties[prop_name] = {
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"type_": "STRING",
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"description": prop_value.get(
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"description"
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),
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}
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# Add more type mappings as needed
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else:
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new_properties[prop_name] = self._clean_tools_format(prop_value)
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new_properties[prop_name] = (
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self._clean_tools_format(prop_value)
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)
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new_tools_data[key] = new_properties
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else:
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new_tools_data[key] = self._clean_tools_format(value)
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@@ -74,65 +91,64 @@ class GoogleLLM(BaseLLM):
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tools=None,
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formatting="openai",
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**kwargs
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):
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from google import genai
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from google.genai import types
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client = genai.Client(api_key=self.api_key)
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):
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config = {}
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model_name = "gemini-2.0-flash-exp"
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config = {
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}
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model = 'gemini-2.0-flash-exp'
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if formatting=="raw":
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response = client.models.generate_content(
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model=model,
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contents=messages
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)
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else:
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model = genai.GenerativeModel(
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model_name=model,
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generation_config=config,
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system_instruction=messages[0]["content"],
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tools=self._clean_tools_format(tools)
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)
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chat_session = model.start_chat(
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history=self._clean_messages_google(messages)[:-1]
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)
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response = chat_session.send_message(
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self._clean_messages_google(messages)[-1]
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)
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logging.info(response)
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if formatting == "raw":
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client = genai.GenerativeModel(model_name=model_name)
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response = client.generate_content(contents=messages)
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return response.text
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else:
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if tools:
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client = genai.GenerativeModel(
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model_name=model_name,
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generation_config=config,
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system_instruction=messages[0]["content"],
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tools=self._clean_tools_format(tools),
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)
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chat_session = gen_model.start_chat(
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history=self._clean_messages_google(messages)[:-1]
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)
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response = chat_session.send_message(
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self._clean_messages_google(messages)[-1]
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)
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return response
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else:
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gen_model = genai.GenerativeModel(
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model_name=model_name,
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generation_config=config,
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system_instruction=messages[0]["content"],
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)
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chat_session = gen_model.start_chat(
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history=self._clean_messages_google(messages)[:-1]
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)
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response = chat_session.send_message(
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self._clean_messages_google(messages)[-1]
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)
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return response.text
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def _raw_gen_stream(
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self,
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baseself,
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model,
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messages,
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stream=True,
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tools=None,
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**kwargs
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):
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import google.generativeai as genai
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genai.configure(api_key=self.api_key)
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config = {
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}
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model = genai.GenerativeModel(
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model_name=model,
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self, baseself, model, messages, stream=True, tools=None, **kwargs
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):
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config = {}
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model_name = "gemini-2.0-flash-exp"
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gen_model = genai.GenerativeModel(
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model_name=model_name,
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generation_config=config,
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system_instruction=messages[0]["content"]
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)
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chat_session = model.start_chat(
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system_instruction=messages[0]["content"],
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tools=self._clean_tools_format(tools),
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)
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chat_session = gen_model.start_chat(
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history=self._clean_messages_google(messages)[:-1],
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)
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response = chat_session.send_message(
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self._clean_messages_google(messages)[-1]
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, stream=stream
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self._clean_messages_google(messages)[-1], stream=stream
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)
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for line in response:
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if line.text is not None:
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yield line.text
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for chunk in response:
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if chunk.text is not None:
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yield chunk.text
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def _supports_tools(self):
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return True
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return True
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@@ -89,7 +89,7 @@ class Agent:
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if isinstance(resp, str):
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yield resp
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return
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if resp.message.content:
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if hasattr(resp, "message") and hasattr(resp.message, "content"):
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yield resp.message.content
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return
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@@ -98,7 +98,7 @@ class Agent:
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# If no tool calls are needed, generate the final response
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if isinstance(resp, str):
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yield resp
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elif resp.message.content:
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elif hasattr(resp, "message") and hasattr(resp.message, "content"):
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yield resp.message.content
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else:
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completion = self.llm.gen_stream(
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@@ -47,23 +47,43 @@ class OpenAILLMHandler(LLMHandler):
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class GoogleLLMHandler(LLMHandler):
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def handle_response(self, agent, resp, tools_dict, messages):
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from google.genai import types
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import google.generativeai as genai
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while resp.content.parts[0].function_call:
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function_call_part = resp.candidates[0].content.parts[0]
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tool_response, call_id = agent._execute_tool_action(
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tools_dict, function_call_part.function_call
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)
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function_response_part = types.Part.from_function_response(
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name=function_call_part.function_call.name, response=tool_response
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)
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messages.append(function_call_part, function_response_part)
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while (
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hasattr(resp.candidates[0].content.parts[0], "function_call")
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and resp.candidates[0].content.parts[0].function_call
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):
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responses = {}
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for part in resp.candidates[0].content.parts:
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if hasattr(part, "function_call") and part.function_call:
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function_call_part = part
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messages.append(
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genai.protos.Part(
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function_call=genai.protos.FunctionCall(
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name=function_call_part.function_call.name,
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args=function_call_part.function_call.args,
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)
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)
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)
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tool_response, call_id = agent._execute_tool_action(
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tools_dict, function_call_part.function_call
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)
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responses[function_call_part.function_call.name] = tool_response
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response_parts = [
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genai.protos.Part(
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function_response=genai.protos.FunctionResponse(
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name=tool_name, response={"result": response}
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)
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)
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for tool_name, response in responses.items()
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]
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if response_parts:
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messages.append(response_parts)
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resp = agent.llm.gen(
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model=agent.gpt_model, messages=messages, tools=agent.tools
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)
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return resp
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return resp.text
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def get_llm_handler(llm_type):
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@@ -1,5 +1,7 @@
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import json
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from google.protobuf.json_format import MessageToDict
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class ToolActionParser:
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def __init__(self, llm_type):
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@@ -20,7 +22,8 @@ class ToolActionParser:
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return tool_id, action_name, call_args
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def _parse_google_llm(self, call):
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call_args = json.loads(call.args)
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tool_id = call.name.split("_")[-1]
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action_name = call.name.rsplit("_", 1)[0]
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call = MessageToDict(call._pb)
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call_args = call["args"]
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tool_id = call["name"].split("_")[-1]
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action_name = call["name"].rsplit("_", 1)[0]
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return tool_id, action_name, call_args
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