138 lines
4.4 KiB
Python
138 lines
4.4 KiB
Python
from __future__ import annotations
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from enum import Enum
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from typing import Any, Dict, List, Optional, Union
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from pydantic import BaseModel, Field, HttpUrl
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from app.settings import DEFAULT_IMPORT_MODEL
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class LLMRole(str, Enum):
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USER = "user"
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ASSISTANT = "assistant"
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SYSTEM = "system"
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class LLMMessage(BaseModel):
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role: LLMRole = Field(..., description="Message author role.")
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content: str = Field(..., description="Plain text content of the message.")
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class LLMProvider(str, Enum):
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OPENAI = "openai"
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ANTHROPIC = "anthropic"
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OPENROUTER = "openrouter"
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GEMINI = "gemini"
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QWEN = "qwen"
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DEEPSEEK = "deepseek"
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class LLMRequest(BaseModel):
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provider: LLMProvider = Field(..., description="Target LLM provider identifier.")
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model: str = Field(..., description="Model name understood by the provider.")
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messages: List[LLMMessage] = Field(..., description="Ordered chat messages.")
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temperature: Optional[float] = Field(
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0.7, description="Sampling temperature when supported."
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)
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top_p: Optional[float] = Field(
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None, description="Top-p nucleus sampling when supported."
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)
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max_tokens: Optional[int] = Field(
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None, description="Maximum tokens to generate when supported."
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)
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stream: Optional[bool] = Field(
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False, description="Enable provider streaming if both sides support it."
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)
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extra_params: Optional[dict[str, Any]] = Field(
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None, description="Provider-specific parameters to merge into the payload."
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)
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class LLMChoice(BaseModel):
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index: int
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message: LLMMessage
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class LLMResponse(BaseModel):
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provider: LLMProvider
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model: str
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choices: List[LLMChoice]
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raw: Optional[dict[str, Any]] = Field(
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None, description="Raw provider response for debugging."
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)
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class DataImportAnalysisRequest(BaseModel):
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import_record_id: str = Field(..., description="Unique identifier for this import run.")
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example_data: str = Field(
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...,
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max_length=30_000,
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description="Sample rows from the import payload. Limited to 30k characters.",
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)
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table_headers: List[str] = Field(
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...,
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min_length=1,
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description="Ordered list of table headers associated with the data.",
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)
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llm_model: str = Field(
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...,
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description="Model identifier. Accepts 'provider:model' format or plain model name.",
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)
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temperature: Optional[float] = Field(
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None,
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description="Optional override for LLM temperature when generating recognition output.",
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)
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max_tokens: Optional[int] = Field(
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None,
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description="Optional override for maximum tokens generated during recognition.",
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)
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class DataImportAnalysisResponse(BaseModel):
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import_record_id: str
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llm_response: LLMResponse
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class DataImportAnalysisJobRequest(BaseModel):
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import_record_id: str = Field(
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..., description="Unique identifier for this import request run."
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)
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rows: List[Union[Dict[str, Any], List[Any]]] = Field(
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...,
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description="Sample rows from the import payload. Accepts list of dicts or list of lists.",
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)
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headers: Optional[List[str]] = Field(
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None,
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description="Ordered list of table headers associated with the data sample.",
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)
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raw_csv: Optional[str] = Field(
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None,
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description="Optional raw CSV representation of the sample rows, if already prepared.",
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)
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table_schema: Optional[Any] = Field(
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None,
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description="Optional schema description for the table. Can be a string or JSON-serialisable structure.",
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)
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callback_url: HttpUrl = Field(
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...,
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description="URL to notify when the analysis completes. Receives JSON payload with status/results.",
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)
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llm_model: str = Field(
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DEFAULT_IMPORT_MODEL,
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description="Target LLM model identifier. Defaults to DEFAULT_IMPORT_MODEL.",
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)
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temperature: Optional[float] = Field(
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None,
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description="Optional override for model temperature when generating analysis output.",
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)
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max_output_tokens: Optional[int] = Field(
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None,
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description="Optional maximum number of tokens to generate in the analysis response.",
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)
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class DataImportAnalysisJobAck(BaseModel):
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import_record_id: str = Field(..., description="Echo of the import record identifier")
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status: str = Field("accepted", description="Processing status acknowledgement.")
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