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Merge pull request #493 from guardrails-ai/input-validation
Input validation
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‎docs/examples/input_validation.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Input Validation\n",
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"\n",
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"Guardrails supports validating inputs (prompts, instructions, msg_history) with string validators."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In XML, specify the validators on the `prompt` or `instructions` tag, as such:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"is_executing": true
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},
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"outputs": [],
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"source": [
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"rail_spec = \"\"\"\n",
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"<rail version=\"0.1\">\n",
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"<prompt\n",
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" validators=\"two-words\"\n",
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" on-fail-two-words=\"exception\"\n",
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">\n",
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"This is not two words\n",
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"</prompt>\n",
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"<output type=\"string\">\n",
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"</output>\n",
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"</rail>\n",
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"\"\"\"\n",
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"\n",
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"from guardrails import Guard\n",
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"guard = Guard.from_rail_string(rail_spec)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"When `fix` is specified as the on-fail handler, the prompt will automatically be amended before calling the LLM.\n",
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"\n",
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"In any other case (for example, `exception`), a `ValidationException` will be returned in the outcome."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"is_executing": true
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},
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"outputs": [],
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"source": [
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"import openai\n",
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"\n",
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"outcome = guard(\n",
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" openai.ChatCompletion.create,\n",
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")\n",
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"outcome.error"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"When using pydantic to initialize a `Guard`, input validators can be specified by composition, as such:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from guardrails.validators import TwoWords\n",
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"from pydantic import BaseModel\n",
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"\n",
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"\n",
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"class Pet(BaseModel):\n",
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" name: str\n",
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" age: int\n",
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"\n",
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"\n",
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"guard = Guard.from_pydantic(Pet)\n",
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"guard.with_prompt_validation([TwoWords(on_fail=\"exception\")])\n",
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"\n",
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"outcome = guard(\n",
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" openai.ChatCompletion.create,\n",
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" prompt=\"This is not two words\",\n",
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")\n",
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"outcome.error"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}

‎guardrails/classes/history/call.py

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@@ -281,6 +281,13 @@ def error(self) -> Optional[str]:
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return None
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return self.iterations.last.error # type: ignore
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@property
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def exception(self) -> Optional[Exception]:
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"""The exception that interrupted the run."""
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if self.iterations.empty():
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return None
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return self.iterations.last.exception # type: ignore
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@property
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def failed_validations(self) -> Stack[ValidatorLogs]:
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"""The validator logs for any validations that failed during the

‎guardrails/classes/history/iteration.py

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@@ -111,6 +111,11 @@ def error(self) -> Optional[str]:
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this iteration."""
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return self.outputs.error
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@property
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def exception(self) -> Optional[Exception]:
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"""The exception that interrupted this iteration."""
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return self.outputs.exception
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@property
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def failed_validations(self) -> List[ValidatorLogs]:
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"""The validator logs for any validations that failed during this

‎guardrails/classes/history/outputs.py

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@@ -43,6 +43,9 @@ class Outputs(ArbitraryModel):
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"that raised and interrupted the process.",
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default=None,
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)
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exception: Optional[Exception] = Field(
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description="The exception that interrupted the process.", default=None
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)
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def _all_empty(self) -> bool:
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return (

‎guardrails/guard.py

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@@ -1,5 +1,6 @@
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import asyncio
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import contextvars
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import warnings
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from typing import (
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Any,
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Awaitable,
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from guardrails.prompt import Instructions, Prompt
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from guardrails.rail import Rail
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from guardrails.run import AsyncRunner, Runner
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from guardrails.schema import Schema
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from guardrails.schema import Schema, StringSchema
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from guardrails.validators import Validator
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add_destinations(logger.debug)
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self.base_model = base_model
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@property
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def input_schema(self) -> Optional[Schema]:
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def prompt_schema(self) -> Optional[StringSchema]:
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"""Return the input schema."""
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return self.rail.input_schema
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return self.rail.prompt_schema
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@property
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def instructions_schema(self) -> Optional[StringSchema]:
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"""Return the input schema."""
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return self.rail.instructions_schema
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@property
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def msg_history_schema(self) -> Optional[StringSchema]:
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"""Return the input schema."""
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return self.rail.msg_history_schema
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@property
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def output_schema(self) -> Schema:
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prompt=prompt_obj,
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msg_history=msg_history_obj,
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api=get_llm_ask(llm_api, *args, **kwargs),
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input_schema=self.input_schema,
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prompt_schema=self.prompt_schema,
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instructions_schema=self.instructions_schema,
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msg_history_schema=self.msg_history_schema,
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output_schema=self.output_schema,
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num_reasks=num_reasks,
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metadata=metadata,
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prompt=prompt_obj,
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msg_history=msg_history_obj,
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api=get_async_llm_ask(llm_api, *args, **kwargs),
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input_schema=self.input_schema,
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prompt_schema=self.prompt_schema,
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instructions_schema=self.instructions_schema,
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msg_history_schema=self.msg_history_schema,
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output_schema=self.output_schema,
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num_reasks=num_reasks,
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metadata=metadata,
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prompt=kwargs.pop("prompt", None),
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msg_history=kwargs.pop("msg_history", None),
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api=get_llm_ask(llm_api, *args, **kwargs) if llm_api else None,
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input_schema=None,
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prompt_schema=self.prompt_schema,
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instructions_schema=self.instructions_schema,
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msg_history_schema=self.msg_history_schema,
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output_schema=self.output_schema,
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num_reasks=num_reasks,
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metadata=metadata,
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prompt=kwargs.pop("prompt", None),
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msg_history=kwargs.pop("msg_history", None),
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api=get_async_llm_ask(llm_api, *args, **kwargs) if llm_api else None,
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input_schema=None,
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prompt_schema=self.prompt_schema,
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instructions_schema=self.instructions_schema,
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msg_history_schema=self.msg_history_schema,
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output_schema=self.output_schema,
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num_reasks=num_reasks,
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metadata=metadata,
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)
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return ValidationOutcome[OT].from_guard_history(call, error_message)
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def with_prompt_validation(
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self,
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validators: Sequence[Validator],
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):
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"""Add prompt validation to the Guard.
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Args:
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validators: The validators to add to the prompt.
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"""
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if self.rail.prompt_schema:
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warnings.warn("Overriding existing prompt validators.")
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schema = StringSchema.from_string(
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validators=validators,
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)
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self.rail.prompt_schema = schema
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return self
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def with_instructions_validation(
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self,
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validators: Sequence[Validator],
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):
707+
"""Add instructions validation to the Guard.
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Args:
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validators: The validators to add to the instructions.
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"""
712+
if self.rail.instructions_schema:
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warnings.warn("Overriding existing instructions validators.")
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schema = StringSchema.from_string(
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validators=validators,
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)
717+
self.rail.instructions_schema = schema
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return self
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def with_msg_history_validation(
721+
self,
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validators: Sequence[Validator],
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):
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"""Add msg_history validation to the Guard.
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Args:
727+
validators: The validators to add to the msg_history.
728+
"""
729+
if self.rail.msg_history_schema:
730+
warnings.warn("Overriding existing msg_history validators.")
731+
schema = StringSchema.from_string(
732+
validators=validators,
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)
734+
self.rail.msg_history_schema = schema
735+
return self

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