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101 lines
3.5 KiB
Python
101 lines
3.5 KiB
Python
"""LLM configuration resolution and token usage accumulation."""
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from api.constants import MPS_API_URL
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from api.db import db_client
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from api.db.models import WorkflowRunModel
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def _provider_base_url(provider: str | None, endpoint: str = "") -> str | None:
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"""Return the base URL for a given LLM provider."""
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if provider == "openrouter":
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return "https://openrouter.ai/api/v1"
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if provider == "groq":
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return "https://api.groq.com/openai/v1"
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if provider == "google":
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return "https://generativelanguage.googleapis.com/v1beta/openai/"
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if provider == "azure":
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return endpoint or None
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if provider == "dograh":
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return f"{MPS_API_URL}/api/v1/llm"
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return None
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async def resolve_llm_config(
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qa_node_data: dict, workflow_run: WorkflowRunModel
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) -> tuple[str, str, str | None]:
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"""Resolve the LLM model, API key, and base URL for QA analysis.
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If the QA node has its own LLM configuration (qa_use_workflow_llm=False),
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use those settings directly. Otherwise, fall back to the user's configured LLM.
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Returns:
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(model, api_key, base_url) tuple
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"""
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if not qa_node_data.get("qa_use_workflow_llm", True):
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return (
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qa_node_data.get("qa_model"),
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qa_node_data.get("qa_api_key"),
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_provider_base_url(
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qa_node_data.get("qa_provider"),
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qa_node_data.get("qa_endpoint", ""),
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),
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)
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# Fall back to user's configured LLM
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model, api_key, base_url = await resolve_user_llm_config(workflow_run)
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qa_model = qa_node_data.get("qa_model", "default")
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if qa_model and qa_model != "default":
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model = qa_model
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return model, api_key, base_url
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async def resolve_user_llm_config(
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workflow_run: WorkflowRunModel,
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) -> tuple[str, str, str | None]:
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"""Resolve the user's configured LLM (from UserConfiguration).
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Returns:
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(model, api_key, base_url) tuple
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"""
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user_id = None
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if workflow_run.workflow and workflow_run.workflow.user:
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user_id = workflow_run.workflow.user.id
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llm_config: dict = {}
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if user_id:
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user_configuration = await db_client.get_user_configurations(user_id)
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llm_config = user_configuration.model_dump(exclude_none=True).get("llm", {})
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provider = llm_config.get("provider", "openai")
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api_key = llm_config.get("api_key", "")
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model = llm_config.get("model", "gpt-4.1")
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base_url = _provider_base_url(provider, llm_config.get("endpoint", ""))
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if provider == "openrouter" and llm_config.get("base_url"):
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base_url = llm_config["base_url"]
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return model, api_key, base_url
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def accumulate_token_usage(total: dict, response) -> None:
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"""Add token counts from an LLM response to the running total dict."""
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if not response.usage:
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return
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total["prompt_tokens"] = total.get("prompt_tokens", 0) + (
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response.usage.prompt_tokens or 0
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)
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total["completion_tokens"] = total.get("completion_tokens", 0) + (
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response.usage.completion_tokens or 0
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)
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total["total_tokens"] = total.get("total_tokens", 0) + (
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response.usage.total_tokens or 0
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)
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total["cache_read_input_tokens"] = total.get("cache_read_input_tokens", 0) + (
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getattr(response.usage, "cache_read_input_tokens", 0) or 0
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)
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cache_creation = getattr(response.usage, "cache_creation_input_tokens", None)
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if cache_creation is not None:
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total["cache_creation_input_tokens"] = (
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total.get("cache_creation_input_tokens") or 0
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) + cache_creation
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