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https://github.com/arc53/DocsGPT.git
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3f7de867cc
* feat: Implement model registry and capabilities for multi-provider support - Added ModelRegistry to manage available models and their capabilities. - Introduced ModelProvider enum for different LLM providers. - Created ModelCapabilities dataclass to define model features. - Implemented methods to load models based on API keys and settings. - Added utility functions for model management in model_utils.py. - Updated settings.py to include provider-specific API keys. - Refactored LLM classes (Anthropic, OpenAI, Google, etc.) to utilize new model registry. - Enhanced utility functions to handle token limits and model validation. - Improved code structure and logging for better maintainability. * feat: Add model selection feature with API integration and UI component * feat: Add model selection and default model functionality in agent management * test: Update assertions and formatting in stream processing tests * refactor(llm): Standardize model identifier to model_id * fix tests --------- Co-authored-by: Alex <a@tushynski.me>
139 lines
4.9 KiB
Python
139 lines
4.9 KiB
Python
import json
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from openai import OpenAI
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from application.core.settings import settings
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from application.llm.base import BaseLLM
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class DocsGPTAPILLM(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 = "sk-docsgpt-public"
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self.client = OpenAI(api_key=self.api_key, base_url="https://oai.arc53.com")
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self.user_api_key = user_api_key
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def _clean_messages_openai(self, messages):
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cleaned_messages = []
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for message in messages:
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role = message.get("role")
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content = message.get("content")
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if role == "model":
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role = "assistant"
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if role and content is not None:
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if isinstance(content, str):
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cleaned_messages.append({"role": role, "content": content})
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elif isinstance(content, list):
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for item in content:
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if "text" in item:
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cleaned_messages.append(
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{"role": role, "content": item["text"]}
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)
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elif "function_call" in item:
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cleaned_args = self._remove_null_values(
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item["function_call"]["args"]
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)
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tool_call = {
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"id": item["function_call"]["call_id"],
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"type": "function",
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"function": {
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"name": item["function_call"]["name"],
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"arguments": json.dumps(cleaned_args),
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},
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}
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cleaned_messages.append(
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{
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"role": "assistant",
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"content": None,
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"tool_calls": [tool_call],
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}
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)
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elif "function_response" in item:
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cleaned_messages.append(
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{
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"role": "tool",
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"tool_call_id": item["function_response"][
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"call_id"
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],
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"content": json.dumps(
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item["function_response"]["response"]["result"]
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),
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}
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)
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else:
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raise ValueError(
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f"Unexpected content dictionary format: {item}"
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)
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else:
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raise ValueError(f"Unexpected content type: {type(content)}")
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return cleaned_messages
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def _raw_gen(
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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=False,
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tools=None,
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engine=settings.AZURE_DEPLOYMENT_NAME,
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**kwargs,
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):
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messages = self._clean_messages_openai(messages)
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if tools:
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response = self.client.chat.completions.create(
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model="docsgpt",
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messages=messages,
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stream=stream,
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tools=tools,
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**kwargs,
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)
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return response.choices[0]
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else:
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response = self.client.chat.completions.create(
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model="docsgpt", messages=messages, stream=stream, **kwargs
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)
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return response.choices[0].message.content
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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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engine=settings.AZURE_DEPLOYMENT_NAME,
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**kwargs,
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):
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messages = self._clean_messages_openai(messages)
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if tools:
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response = self.client.chat.completions.create(
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model="docsgpt",
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messages=messages,
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stream=stream,
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tools=tools,
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**kwargs,
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)
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else:
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response = self.client.chat.completions.create(
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model="docsgpt", messages=messages, stream=stream, **kwargs
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)
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try:
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for line in response:
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if (
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len(line.choices) > 0
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and line.choices[0].delta.content is not None
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and len(line.choices[0].delta.content) > 0
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):
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yield line.choices[0].delta.content
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elif len(line.choices) > 0:
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yield line.choices[0]
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finally:
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if hasattr(response, "close"):
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response.close()
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def _supports_tools(self):
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return True
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