mirror of
https://github.com/Pythagora-io/gpt-pilot.git
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5b474ccc1f
This is a complete rewrite of the GPT Pilot core, from the ground up, making the agentic architecture front and center, and also fixing some long-standing problems with the database architecture that weren't feasible to solve without breaking compatibility. As the database structure and config file syntax have changed, we have automatic imports for projects and current configs, see the README.md file for details. This also relicenses the project to FSL-1.1-MIT license.
80 lines
2.9 KiB
Python
80 lines
2.9 KiB
Python
from datetime import datetime
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from typing import TYPE_CHECKING, Optional
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from uuid import UUID
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from sqlalchemy import ForeignKey, inspect
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from sqlalchemy.orm import Mapped, mapped_column, relationship
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from sqlalchemy.sql import func
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from core.db.models import Base
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from core.llm.request_log import LLMRequestLog
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if TYPE_CHECKING:
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from core.agents.base import BaseAgent
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from core.db.models import Branch, ProjectState
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class LLMRequest(Base):
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__tablename__ = "llm_requests"
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# ID and parent FKs
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id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True)
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branch_id: Mapped[UUID] = mapped_column(ForeignKey("branches.id", ondelete="CASCADE"))
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project_state_id: Mapped[Optional[UUID]] = mapped_column(ForeignKey("project_states.id", ondelete="SET NULL"))
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# Attributes
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started_at: Mapped[datetime] = mapped_column(server_default=func.now())
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agent: Mapped[Optional[str]] = mapped_column()
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provider: Mapped[str] = mapped_column()
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model: Mapped[str] = mapped_column()
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temperature: Mapped[float] = mapped_column()
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messages: Mapped[list[dict]] = mapped_column()
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response: Mapped[Optional[str]] = mapped_column()
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prompt_tokens: Mapped[int] = mapped_column()
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completion_tokens: Mapped[int] = mapped_column()
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duration: Mapped[float] = mapped_column()
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status: Mapped[str] = mapped_column()
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error: Mapped[Optional[str]] = mapped_column()
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# Relationships
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branch: Mapped["Branch"] = relationship(back_populates="llm_requests")
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project_state: Mapped["ProjectState"] = relationship(back_populates="llm_requests")
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@classmethod
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def from_request_log(
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cls,
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project_state: "ProjectState",
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agent: Optional["BaseAgent"],
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request_log: LLMRequestLog,
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) -> "LLMRequest":
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"""
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Store the request log in the database.
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Note this just creates the request log object. It is committed to the
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database only when the DB session itself is comitted.
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:param project_state: Project state to associate the request log with.
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:param agent: Agent that made the request (if the caller was an agent).
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:param request_log: Request log.
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:return: Newly created LLM request log in the database.
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"""
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session = inspect(project_state).async_session
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obj = cls(
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project_state=project_state,
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branch=project_state.branch,
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agent=agent.agent_type,
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provider=request_log.provider,
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model=request_log.model,
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temperature=request_log.temperature,
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messages=request_log.messages,
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response=request_log.response,
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prompt_tokens=request_log.prompt_tokens,
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completion_tokens=request_log.completion_tokens,
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duration=request_log.duration,
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status=request_log.status,
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error=request_log.error,
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)
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session.add(obj)
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return obj
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