참고소스 수정본
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283
참고/guardrails-main/docs/examples/value_within_distribution.ipynb
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283
참고/guardrails-main/docs/examples/value_within_distribution.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Installing hub:\u001b[35m/\u001b[0m\u001b[35m/guardrails/\u001b[0m\u001b[95msimilar_to_previous_values...\u001b[0m\n",
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"/Users/calebcourier/Projects/gr-mono/guardrails/docs/examples/.venv/lib/python3.12/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
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" warnings.warn(\n",
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"✅Successfully installed guardrails/similar_to_previous_values!\n",
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"\n",
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"\n"
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]
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}
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],
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"source": [
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"!guardrails hub install hub://guardrails/similar_to_previous_values --quiet"
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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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"# Check whether a value is similar to a set of other values\n",
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"\n",
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"**Using the SimilarToPreviousValues validator**\n",
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"\n",
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"This validator validates whether a new value is similar to a set of previously known values. It is useful for checking whether a new value is within a distribution of known values. It supports both integer and string values.\n",
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"\n",
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"For integer values, this validator checks whether the value lies within the specified standard deviations of the mean of the previous values. (Assumes that the previous values are normally distributed.)\n",
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"\n",
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"For string values, this validator checks whether the average semantic similarity between the generated value and the previous values is less than a threshold.\n"
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create the Guard with the SimilarToList validator\n",
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"from pydantic import BaseModel, Field\n",
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"from guardrails import Guard\n",
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"from guardrails.hub import SimilarToPreviousValues\n",
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"\n",
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"\n",
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"class MyModel(BaseModel):\n",
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" value: int = Field(\n",
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" validators=[\n",
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" SimilarToPreviousValues(standard_deviations=2, threshold=0.8, on_fail=\"fix\")\n",
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" ]\n",
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" )\n",
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"\n",
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"\n",
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"guard = Guard.for_pydantic(MyModel)"
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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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"## Test with integer values\n"
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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": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'value': 3}\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/dtam/dev/guardrails/guardrails/validator_service/__init__.py:85: UserWarning: Could not obtain an event loop. Falling back to synchronous validation.\n",
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" warnings.warn(\n"
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]
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}
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],
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"source": [
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"# Test with a value that is within the distribution\n",
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"output = guard.parse(\n",
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" llm_output='{ \"value\": 3 }',\n",
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" metadata={\"prev_values\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]},\n",
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")\n",
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"\n",
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"print(output.validated_output)"
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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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"As 3 is within the distribution of the given prev_values, the validator returns 3 as it is.\n"
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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": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"None\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/dtam/dev/guardrails/guardrails/validator_service/__init__.py:85: UserWarning: Could not obtain an event loop. Falling back to synchronous validation.\n",
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" warnings.warn(\n"
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]
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}
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],
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"source": [
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"# Test with a value that is outside the distribution\n",
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"output = guard.parse(\n",
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" llm_output='{ \"value\": 300 }',\n",
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" metadata={\"prev_values\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]},\n",
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")\n",
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"\n",
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"print(output.validated_output)"
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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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"As 300 is not within the distribution of the given prev_values, the validator returns None.\n"
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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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"## Test with string values\n"
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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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Define embed function\n",
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"# Create an embedding function that uses openAI Ada to embed the text.\n",
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"import numpy as np\n",
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"import openai\n",
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"\n",
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"\n",
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"# Define the embed function\n",
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"def embed_function(text: str) -> np.ndarray:\n",
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" \"\"\"Embed the text using openAI Ada.\"\"\"\n",
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" response = openai.embeddings.create(\n",
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" model=\"text-embedding-ada-002\",\n",
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" input=text,\n",
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" )\n",
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" embeddings = response.data[0].embedding\n",
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" # response[\"data\"][0][\"embedding\"]\n",
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" return np.array(embeddings)\n",
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"\n",
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"\n",
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"guard = Guard().use(\n",
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" SimilarToPreviousValues(standard_deviations=2, threshold=0.8, on_fail=\"fix\")\n",
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")"
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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": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/dtam/dev/guardrails/guardrails/validator_service/__init__.py:85: UserWarning: Could not obtain an event loop. Falling back to synchronous validation.\n",
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" warnings.warn(\n",
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"HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
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"HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
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"HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
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"HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"cisco\n"
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]
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}
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],
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"source": [
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"# Test with a value that is similar to prev values\n",
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"# As it is, it will return that value\n",
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"output = guard.parse(\n",
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" llm_output=\"cisco\",\n",
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" metadata={\n",
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" \"prev_values\": [\"broadcom\", \"paypal\"],\n",
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" \"embed_function\": embed_function,\n",
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" },\n",
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")\n",
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"\n",
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"print(output.validated_output)"
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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": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/dtam/dev/guardrails/guardrails/validator_service/__init__.py:85: UserWarning: Could not obtain an event loop. Falling back to synchronous validation.\n",
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" warnings.warn(\n",
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"HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
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"HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
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"HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
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"HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"None\n"
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]
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}
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],
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"source": [
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"# Test with a value that is not similar to prev values\n",
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"# As it is not, it will return None\n",
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"output = guard.parse(\n",
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" llm_output=\"taj mahal\",\n",
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" metadata={\n",
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" \"prev_values\": [\"broadcom\", \"paypal\"],\n",
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" \"embed_function\": embed_function,\n",
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" },\n",
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")\n",
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"\n",
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"print(output.validated_output)"
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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": "311lite",
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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": "3.11.3"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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