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‎.github/workflows/install_from_hub.yml‎

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jobs:
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install_from_hub:
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runs-on: ubuntu-latest
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env:
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GUARDRAILS_API_KEY: ${{ secrets.GUARDRAILS_API_KEY }}
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steps:
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- name: Set up Python
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uses: actions/setup-python@v5
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- name: pip install from main
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run: pip install git+https://github.com/guardrails-ai/guardrails.git@main
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- name: Install PII validator
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run: guardrails hub install hub://guardrails/detect_pii
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run: |
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guardrails configure --token $GUARDRAILS_API_KEY --disable-metrics --enable-remote-inferencing;
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guardrails hub install hub://guardrails/detect_pii;
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- name: Verify PII validator is addressable
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run: echo 'from guardrails.hub import DetectPII' | python

‎README.md‎

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@@ -147,7 +147,7 @@ prompt = """
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${gr.complete_json_suffix_v2}
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"""
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guard = Guard.from_pydantic(output_class=Pet, prompt=prompt)
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guard = Guard.for_pydantic(output_class=Pet, prompt=prompt)
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raw_output, validated_output, *rest = guard(
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llm_api=openai.completions.create,

‎docs/api_reference/async_guard.md‎

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options:
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members:
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- "__init__"
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- "from_rail"
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- "from_rail_string"
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- "from_pydantic"
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- "from_string"
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- "for_rail"
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- "for_rail_string"
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- "for_pydantic"
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- "for_string"
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- "configure"
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- "use"
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- "use_many"

‎docs/api_reference/guard.md‎

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options:
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members:
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- "__init__"
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- "from_rail"
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- "from_rail_string"
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- "from_pydantic"
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- "from_string"
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- "for_rail"
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- "for_rail_string"
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- "for_pydantic"
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- "for_string"
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- "configure"
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- "use"
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- "use_many"

‎docs/concepts/error_remediation.md‎

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| `OnFailAction.FIX_REASK` | First, fix the generated output deterministically, and then rerun validation with the deterministically fixed output. If validation fails, then perform reasking. | No |
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Custom OnFailActions can also be implemented, see the `custom` section in the [how to use on fail actions guide](../how_to_guides/use_on_fail_actions).
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## Guidance on dealing with Validator errors
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When a validator fails, Guardrails does two things

‎docs/concepts/logs.md‎

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## General Access
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Given:
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```py
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my_guard = Guard.from_pydantic(...)
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my_guard = Guard.for_pydantic(...)
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response_1 = my_guard(...)
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‎docs/concepts/streaming_structured_data.ipynb‎

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‎docs/concepts/validators.md‎

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As an example, the `ExtractedSummarySentencesMatch` validator accepts a `filepaths` property in the metadata dictionary to specify what source files to compare the summary against to ensure similarity. Unlike arguments which are specified at validator initialization, metadata is specified when calling `guard.validate` or `guard.__call__` (this is the `guard()` function).
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```python
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guard = Guard.from_rail("my_railspec.rail")
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guard = Guard.for_rail("my_railspec.rail")
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outcome = guard(
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llm_api=openai.chat.completions.create,

‎docs/examples/constrained_decoding.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": 4,
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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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"\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
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"\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
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]
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}
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],
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"source": [
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"! pip install ipywidgets -q"
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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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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "81b91198c45249a2b0a7075d13ebf838",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"model.safetensors: 0%| | 10.5M/2.20G [00:00<?, ?B/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"from guardrails import Guard\n",
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"from pydantic import BaseModel\n",
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"\n",
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"class Dog(BaseModel):\n",
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" name: str\n",
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" color: str\n",
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" weight_kg: float\n",
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"\n",
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"class NewFriends(BaseModel):\n",
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" dogs: list[Dog]\n",
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"\n",
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"guard = Guard.from_pydantic(NewFriends, output_formatter=\"jsonformer\")\n",
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"\n",
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"# JSONFormer is only compatible with HF Pipelines and HF Models:\n",
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"from transformers import pipeline\n",
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"tiny_llama_pipeline = pipeline(\"text-generation\", \"TinyLlama/TinyLlama-1.1B-Chat-v1.0\")\n",
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"\n",
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"# Inference is straightforward:\n",
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"response = guard(tiny_llama_pipeline, prompt=\"Please enjoy this list of good dogs:\")\n",
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"\n",
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"# `out` is a dict. Format it as JSON for readability:\n",
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"import json\n",
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"print(json.dumps(response.validated_output, indent=2))"
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"cells": [
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
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"\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
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]
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}
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],
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"source": [
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"! pip install ipywidgets -q"
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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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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "81b91198c45249a2b0a7075d13ebf838",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"model.safetensors: 0%| | 10.5M/2.20G [00:00<?, ?B/s]"
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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": ".venv",
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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": "3.12.3"
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}
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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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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"from guardrails import Guard\n",
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"from pydantic import BaseModel\n",
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"\n",
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"class Dog(BaseModel):\n",
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" name: str\n",
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" color: str\n",
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" weight_kg: float\n",
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"\n",
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"class NewFriends(BaseModel):\n",
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" dogs: list[Dog]\n",
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"\n",
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"guard = Guard.for_pydantic(NewFriends, output_formatter=\"jsonformer\")\n",
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"\n",
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"# JSONFormer is only compatible with HF Pipelines and HF Models:\n",
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"from transformers import pipeline\n",
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"tiny_llama_pipeline = pipeline(\"text-generation\", \"TinyLlama/TinyLlama-1.1B-Chat-v1.0\")\n",
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"\n",
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"# Inference is straightforward:\n",
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"response = guard(tiny_llama_pipeline, prompt=\"Please enjoy this list of good dogs:\")\n",
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"\n",
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"# `out` is a dict. Format it as JSON for readability:\n",
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"import json\n",
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"print(json.dumps(response.validated_output, indent=2))"
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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": ".venv",
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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": "3.11.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎docs/examples/extracting_entities.ipynb‎

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"metadata": {},
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"outputs": [],
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"source": [
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"guard = gd.Guard.from_pydantic(output_class=CreditCardAgreement)"
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"guard = gd.Guard.for_pydantic(output_class=CreditCardAgreement)"
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]
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},
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{

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