Input validation

docs/examples/input_validation.ipynb

@@ -0,0 +1,123 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Input Validation\n",
+    "\n",
+    "Guardrails supports validating inputs (prompts, instructions, msg_history) with string validators."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In XML, specify the validators on the `prompt` or `instructions` tag, as such:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "is_executing": true
+   },
+   "outputs": [],
+   "source": [
+    "rail_spec = \"\"\"\n",
+    "<rail version=\"0.1\">\n",
+    "<prompt\n",
+    "    validators=\"two-words\"\n",
+    "    on-fail-two-words=\"exception\"\n",
+    ">\n",
+    "This is not two words\n",
+    "</prompt>\n",
+    "<output type=\"string\">\n",
+    "</output>\n",
+    "</rail>\n",
+    "\"\"\"\n",
+    "\n",
+    "from guardrails import Guard\n",
+    "guard = Guard.from_rail_string(rail_spec)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When `fix` is specified as the on-fail handler, the prompt will automatically be amended before calling the LLM.\n",
+    "\n",
+    "In any other case (for example, `exception`), a `ValidationException` will be returned in the outcome."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "is_executing": true
+   },
+   "outputs": [],
+   "source": [
+    "import openai\n",
+    "\n",
+    "outcome = guard(\n",
+    "    openai.ChatCompletion.create,\n",
+    ")\n",
+    "outcome.error"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When using pydantic to initialize a `Guard`, input validators can be specified by composition, as such:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from guardrails.validators import TwoWords\n",
+    "from pydantic import BaseModel\n",
+    "\n",
+    "\n",
+    "class Pet(BaseModel):\n",
+    "    name: str\n",
+    "    age: int\n",
+    "\n",
+    "\n",
+    "guard = Guard.from_pydantic(Pet)\n",
+    "guard.with_prompt_validation([TwoWords(on_fail=\"exception\")])\n",
+    "\n",
+    "outcome = guard(\n",
+    "    openai.ChatCompletion.create,\n",
+    "    prompt=\"This is not two words\",\n",
+    ")\n",
+    "outcome.error"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

guardrails/classes/history/call.py

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

guardrails/classes/history/iteration.py

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

guardrails/classes/history/outputs.py

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

guardrails/guard.py

@@ -1,5 +1,6 @@
 import asyncio
 import contextvars
+import warnings
 from typing import (
     Any,
     Awaitable,
@@ -27,7 +28,7 @@
 from guardrails.prompt import Instructions, Prompt
 from guardrails.rail import Rail
 from guardrails.run import AsyncRunner, Runner
-from guardrails.schema import Schema
+from guardrails.schema import Schema, StringSchema
 from guardrails.validators import Validator
 
 add_destinations(logger.debug)
@@ -64,9 +65,19 @@ def __init__(
         self.base_model = base_model
 
     @property
-    def input_schema(self) -> Optional[Schema]:
+    def prompt_schema(self) -> Optional[StringSchema]:
         """Return the input schema."""
-        return self.rail.input_schema
+        return self.rail.prompt_schema
+
+    @property
+    def instructions_schema(self) -> Optional[StringSchema]:
+        """Return the input schema."""
+        return self.rail.instructions_schema
+
+    @property
+    def msg_history_schema(self) -> Optional[StringSchema]:
+        """Return the input schema."""
+        return self.rail.msg_history_schema
 
     @property
     def output_schema(self) -> Schema:
@@ -377,7 +388,9 @@ def _call_sync(
                 prompt=prompt_obj,
                 msg_history=msg_history_obj,
                 api=get_llm_ask(llm_api, *args, **kwargs),
-                input_schema=self.input_schema,
+                prompt_schema=self.prompt_schema,
+                instructions_schema=self.instructions_schema,
+                msg_history_schema=self.msg_history_schema,
                 output_schema=self.output_schema,
                 num_reasks=num_reasks,
                 metadata=metadata,
@@ -434,7 +447,9 @@ async def _call_async(
                 prompt=prompt_obj,
                 msg_history=msg_history_obj,
                 api=get_async_llm_ask(llm_api, *args, **kwargs),
-                input_schema=self.input_schema,
+                prompt_schema=self.prompt_schema,
+                instructions_schema=self.instructions_schema,
+                msg_history_schema=self.msg_history_schema,
                 output_schema=self.output_schema,
                 num_reasks=num_reasks,
                 metadata=metadata,
@@ -610,7 +625,9 @@ def _sync_parse(
                 prompt=kwargs.pop("prompt", None),
                 msg_history=kwargs.pop("msg_history", None),
                 api=get_llm_ask(llm_api, *args, **kwargs) if llm_api else None,
-                input_schema=None,
+                prompt_schema=self.prompt_schema,
+                instructions_schema=self.instructions_schema,
+                msg_history_schema=self.msg_history_schema,
                 output_schema=self.output_schema,
                 num_reasks=num_reasks,
                 metadata=metadata,
@@ -650,7 +667,9 @@ async def _async_parse(
                 prompt=kwargs.pop("prompt", None),
                 msg_history=kwargs.pop("msg_history", None),
                 api=get_async_llm_ask(llm_api, *args, **kwargs) if llm_api else None,
-                input_schema=None,
+                prompt_schema=self.prompt_schema,
+                instructions_schema=self.instructions_schema,
+                msg_history_schema=self.msg_history_schema,
                 output_schema=self.output_schema,
                 num_reasks=num_reasks,
                 metadata=metadata,
@@ -663,3 +682,54 @@ async def _async_parse(
             )
 
             return ValidationOutcome[OT].from_guard_history(call, error_message)
+
+    def with_prompt_validation(
+        self,
+        validators: Sequence[Validator],
+    ):
+        """Add prompt validation to the Guard.
+
+        Args:
+            validators: The validators to add to the prompt.
+        """
+        if self.rail.prompt_schema:
+            warnings.warn("Overriding existing prompt validators.")
+        schema = StringSchema.from_string(
+            validators=validators,
+        )
+        self.rail.prompt_schema = schema
+        return self
+
+    def with_instructions_validation(
+        self,
+        validators: Sequence[Validator],
+    ):
+        """Add instructions validation to the Guard.
+
+        Args:
+            validators: The validators to add to the instructions.
+        """
+        if self.rail.instructions_schema:
+            warnings.warn("Overriding existing instructions validators.")
+        schema = StringSchema.from_string(
+            validators=validators,
+        )
+        self.rail.instructions_schema = schema
+        return self
+
+    def with_msg_history_validation(
+        self,
+        validators: Sequence[Validator],
+    ):
+        """Add msg_history validation to the Guard.
+
+        Args:
+            validators: The validators to add to the msg_history.
+        """
+        if self.rail.msg_history_schema:
+            warnings.warn("Overriding existing msg_history validators.")
+        schema = StringSchema.from_string(
+            validators=validators,
+        )
+        self.rail.msg_history_schema = schema
+        return self