b20199a4e3
- Added environment variables for Vertex AI and Azure OpenAI configurations in docker-compose and user configuration models. - Updated the application logic to handle Vertex and Azure as new LLM providers, including validation and API key management. - Enhanced the UI components to support model selection and API key input for Vertex and Azure. - Updated relevant utility functions and constants to accommodate the new providers. - Ensured proper error handling for configuration requirements specific to Vertex and Azure.
118 lines
3.4 KiB
Python
118 lines
3.4 KiB
Python
from fastapi import HTTPException
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from google import genai
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from openai import OpenAI
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from constants.llm import (
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DEFAULT_ANTHROPIC_MODEL,
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DEFAULT_AZURE_MODEL,
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DEFAULT_CODEX_MODEL,
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DEFAULT_GOOGLE_MODEL,
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DEFAULT_OPENAI_MODEL,
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DEFAULT_VERTEX_MODEL,
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)
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from enums.llm_provider import LLMProvider
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from utils.get_env import (
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get_azure_openai_model_env,
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get_anthropic_model_env,
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get_codex_model_env,
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get_custom_model_env,
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get_google_api_key_env,
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get_google_model_env,
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get_llm_provider_env,
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get_ollama_model_env,
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get_openai_api_key_env,
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get_openai_model_env,
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get_vertex_model_env,
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)
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def get_llm_provider():
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try:
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return LLMProvider(get_llm_provider_env())
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except:
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raise HTTPException(
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status_code=500,
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detail=(
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"Invalid LLM provider. Please select one of: "
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"openai, google, vertex, azure, anthropic, ollama, custom, codex"
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),
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)
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def is_openai_selected():
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return get_llm_provider() == LLMProvider.OPENAI
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def is_google_selected():
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return get_llm_provider() == LLMProvider.GOOGLE
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def is_anthropic_selected():
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return get_llm_provider() == LLMProvider.ANTHROPIC
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def is_vertex_selected():
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return get_llm_provider() == LLMProvider.VERTEX
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def is_azure_selected():
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return get_llm_provider() == LLMProvider.AZURE
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def is_ollama_selected():
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return get_llm_provider() == LLMProvider.OLLAMA
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def is_custom_llm_selected():
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return get_llm_provider() == LLMProvider.CUSTOM
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def is_codex_selected():
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return get_llm_provider() == LLMProvider.CODEX
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def get_model():
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selected_llm = get_llm_provider()
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if selected_llm == LLMProvider.OPENAI:
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return get_openai_model_env() or DEFAULT_OPENAI_MODEL
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elif selected_llm == LLMProvider.GOOGLE:
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return get_google_model_env() or DEFAULT_GOOGLE_MODEL
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elif selected_llm == LLMProvider.VERTEX:
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return get_vertex_model_env() or DEFAULT_VERTEX_MODEL
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elif selected_llm == LLMProvider.AZURE:
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return get_azure_openai_model_env() or DEFAULT_AZURE_MODEL
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elif selected_llm == LLMProvider.ANTHROPIC:
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return get_anthropic_model_env() or DEFAULT_ANTHROPIC_MODEL
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elif selected_llm == LLMProvider.OLLAMA:
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return get_ollama_model_env()
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elif selected_llm == LLMProvider.CUSTOM:
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return get_custom_model_env()
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elif selected_llm == LLMProvider.CODEX:
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return get_codex_model_env() or DEFAULT_CODEX_MODEL
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else:
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raise HTTPException(
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status_code=500,
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detail=(
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"Invalid LLM provider. Please select one of: "
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"openai, google, vertex, azure, anthropic, ollama, custom, codex"
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),
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)
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def get_google_llm_client() -> genai.Client:
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"""Google GenAI client for tests and direct API use (uses GOOGLE_API_KEY from env)."""
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if not get_google_api_key_env():
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raise HTTPException(status_code=400, detail="Google API Key is not set")
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return genai.Client()
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def get_llm_client() -> OpenAI:
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"""OpenAI client for tests and direct API use (uses OPENAI_API_KEY from env)."""
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if not get_openai_api_key_env():
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raise HTTPException(status_code=400, detail="OpenAI API Key is not set")
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return OpenAI()
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def get_large_model() -> str:
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"""Resolved model name for the configured LLM provider (same as runtime `get_model`)."""
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return get_model()
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