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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>
56 lines
1.7 KiB
Python
56 lines
1.7 KiB
Python
import logging
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from application.llm.anthropic import AnthropicLLM
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from application.llm.docsgpt_provider import DocsGPTAPILLM
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from application.llm.google_ai import GoogleLLM
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from application.llm.groq import GroqLLM
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from application.llm.huggingface import HuggingFaceLLM
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from application.llm.llama_cpp import LlamaCpp
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from application.llm.novita import NovitaLLM
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from application.llm.openai import AzureOpenAILLM, OpenAILLM
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from application.llm.premai import PremAILLM
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from application.llm.sagemaker import SagemakerAPILLM
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logger = logging.getLogger(__name__)
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class LLMCreator:
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llms = {
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"openai": OpenAILLM,
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"azure_openai": AzureOpenAILLM,
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"sagemaker": SagemakerAPILLM,
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"huggingface": HuggingFaceLLM,
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"llama.cpp": LlamaCpp,
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"anthropic": AnthropicLLM,
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"docsgpt": DocsGPTAPILLM,
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"premai": PremAILLM,
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"groq": GroqLLM,
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"google": GoogleLLM,
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"novita": NovitaLLM,
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}
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@classmethod
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def create_llm(
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cls, type, api_key, user_api_key, decoded_token, model_id=None, *args, **kwargs
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):
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from application.core.model_utils import get_base_url_for_model
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llm_class = cls.llms.get(type.lower())
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if not llm_class:
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raise ValueError(f"No LLM class found for type {type}")
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# Extract base_url from model configuration if model_id is provided
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base_url = None
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if model_id:
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base_url = get_base_url_for_model(model_id)
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return llm_class(
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api_key,
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user_api_key,
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decoded_token=decoded_token,
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model_id=model_id,
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base_url=base_url,
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*args,
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**kwargs,
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)
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