107 lines
3.9 KiB
Python
107 lines
3.9 KiB
Python
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from __future__ import annotations
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from typing_extensions import deprecated
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from typing import Any, Dict, List, Optional, Union
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from pydantic import Field, field_serializer, field_validator
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from guardrails.classes.generic.arbitrary_model import ArbitraryModel
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from guardrails.classes.llm.prompt_callable import PromptCallableBase
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from guardrails.prompt.prompt import Prompt
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from guardrails.prompt.messages import Messages
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from guardrails.prompt.instructions import Instructions
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class Inputs(ArbitraryModel):
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"""Inputs represent the input data that is passed into the validation
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loop."""
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llm_api: Optional[PromptCallableBase] = Field(
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description="The constructed class for calling the LLM.", default=None
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)
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llm_output: Optional[str] = Field(
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description="The string output from an external LLM call"
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"provided by the user via Guard.parse.",
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default=None,
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)
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messages: Optional[
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Union[List[Dict[str, Union[str, Prompt, Instructions]]], Messages]
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] = Field(
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description="The message history provided by the user for chat model calls.",
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default=None,
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)
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prompt_params: Optional[Dict] = Field(
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description="The parameters provided by the user"
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"that will be formatted into the final LLM prompt.",
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default=None,
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)
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num_reasks: Optional[int] = Field(
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description="The total number of reasks allowed; user provided or defaulted.",
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default=None,
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)
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metadata: Optional[Dict[str, Any]] = Field(
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description="The metadata provided by the user to be used during validation.",
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default=None,
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)
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full_schema_reask: Optional[bool] = Field(
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description="Whether to perform reasks across the entire schema"
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"or at the field level.",
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default=None,
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)
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stream: Optional[bool] = Field(
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description="Whether to use streaming.",
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default=False,
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)
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@field_serializer("llm_api")
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def serialize_llm_api(self, llm_api: PromptCallableBase | None) -> str | None:
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if llm_api:
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return str(llm_api)
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return None
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@field_validator("llm_api", mode="before")
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@classmethod
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def deserialize_llm_api(cls, llm_api: Any) -> PromptCallableBase | None:
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if isinstance(llm_api, PromptCallableBase):
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return llm_api
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# Note: We can potentially identify the correct
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# PrompCallable Class and reconstruct it,
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# but the previous implementation always just returned None.
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return None
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@field_serializer("messages")
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def serialize_messages(
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self, messages: list[dict[str, str | Prompt | Instructions]] | Messages | None
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) -> list[dict[str, Any]] | None:
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# Legacy serialization logic from previous to_interface implementation
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# TODO: Just make Prompt, Instructions, and Messages pydantic models
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if messages:
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serialized_messages = []
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for msg in messages:
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ser_msg = {**msg}
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content = ser_msg.get("content")
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if content:
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ser_msg["content"] = (
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content.source if isinstance(content, Prompt) else content
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)
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serialized_messages.append(ser_msg)
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return serialized_messages
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return None
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@deprecated("Use Inputs.model_dump() instead.")
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def to_interface(self) -> dict[str, Any]:
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return self.model_dump(exclude_none=True, by_alias=True)
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@deprecated("Use Inputs.model_dump() instead.")
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def to_dict(self) -> dict[str, Any]:
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return self.model_dump(exclude_none=True, by_alias=True)
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@classmethod
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@deprecated("Use Inputs.model_validate() instead.")
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def from_interface(cls, i_inputs: Any) -> "Inputs":
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return cls.model_validate(i_inputs)
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@classmethod
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@deprecated("Use Inputs.model_validate() instead.")
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def from_dict(cls, obj: Any) -> "Inputs":
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return cls.model_validate(obj)
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