guardrails-ai · dtam · Sep 27, 2024 · Sep 27, 2024 · Sep 27, 2024
diff --git a/README.md b/README.md
@@ -147,7 +147,7 @@ prompt = """
 
     ${gr.complete_json_suffix_v2}
 """
-guard = Guard.from_pydantic(output_class=Pet, prompt=prompt)
+guard = Guard.for_pydantic(output_class=Pet, prompt=prompt)
 
 raw_output, validated_output, *rest = guard(
     llm_api=openai.completions.create,

diff --git a/docs/api_reference/async_guard.md b/docs/api_reference/async_guard.md
@@ -5,10 +5,10 @@
     options:
         members: 
             - "__init__"
-            - "from_rail"
-            - "from_rail_string"
-            - "from_pydantic"
-            - "from_string"
+            - "for_rail"
+            - "for_rail_string"
+            - "for_pydantic"
+            - "for_string"
             - "configure"
             - "use"
             - "use_many"

diff --git a/docs/api_reference/guard.md b/docs/api_reference/guard.md
@@ -5,10 +5,10 @@
     options:
         members: 
             - "__init__"
-            - "from_rail"
-            - "from_rail_string"
-            - "from_pydantic"
-            - "from_string"
+            - "for_rail"
+            - "for_rail_string"
+            - "for_pydantic"
+            - "for_string"
             - "configure"
             - "use"
             - "use_many"

diff --git a/docs/concepts/logs.md b/docs/concepts/logs.md
@@ -9,7 +9,7 @@ Calls can be further decomposed into a stack of `Iteration` objects. These are s
 ## General Access
 Given:
 ```py
-my_guard = Guard.from_pydantic(...)
+my_guard = Guard.for_pydantic(...)
 
 response_1 = my_guard(...)
 

diff --git a/docs/concepts/streaming_structured_data.ipynb b/docs/concepts/streaming_structured_data.ipynb
diff --git a/docs/concepts/validators.md b/docs/concepts/validators.md
@@ -15,7 +15,7 @@ Occasionally, validators need additional metadata that is only available during
 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).
 
 ```python
-guard = Guard.from_rail("my_railspec.rail")
+guard = Guard.for_rail("my_railspec.rail")
 
 outcome = guard(
     llm_api=openai.chat.completions.create,

diff --git a/docs/examples/constrained_decoding.ipynb b/docs/examples/constrained_decoding.ipynb
@@ -1,93 +1,93 @@
 {
-    "cells": [
-     {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {},
-      "outputs": [
-       {
-        "name": "stdout",
-        "output_type": "stream",
-        "text": [
-         "\n",
-         "\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",
-         "\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",
-         "\n",
-         "\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",
-         "\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"
-        ]
-       }
-      ],
-      "source": [
-       "! pip install ipywidgets -q"
-      ]
-     },
-     {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {},
-      "outputs": [
-       {
-        "data": {
-         "application/vnd.jupyter.widget-view+json": {
-          "model_id": "81b91198c45249a2b0a7075d13ebf838",
-          "version_major": 2,
-          "version_minor": 0
-         },
-         "text/plain": [
-          "model.safetensors:   0%|          | 10.5M/2.20G [00:00<?, ?B/s]"
-         ]
-        },
-        "metadata": {},
-        "output_type": "display_data"
-       }
-      ],
-      "source": [
-       "from guardrails import Guard\n",
-       "from pydantic import BaseModel\n",
-       "\n",
-       "class Dog(BaseModel):\n",
-       "    name: str\n",
-       "    color: str\n",
-       "    weight_kg: float\n",
-       "\n",
-       "class NewFriends(BaseModel):\n",
-       "    dogs: list[Dog]\n",
-       "\n",
-       "guard = Guard.from_pydantic(NewFriends, output_formatter=\"jsonformer\")\n",
-       "\n",
-       "# JSONFormer is only compatible with HF Pipelines and HF Models:\n",
-       "from transformers import pipeline\n",
-       "tiny_llama_pipeline = pipeline(\"text-generation\", \"TinyLlama/TinyLlama-1.1B-Chat-v1.0\")\n",
-       "\n",
-       "# Inference is straightforward:\n",
-       "response = guard(tiny_llama_pipeline, prompt=\"Please enjoy this list of good dogs:\")\n",
-       "\n",
-       "# `out` is a dict.  Format it as JSON for readability:\n",
-       "import json\n",
-       "print(json.dumps(response.validated_output, indent=2))"
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\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",
+      "\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",
+      "\n",
+      "\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",
+      "\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"
+     ]
+    }
+   ],
+   "source": [
+    "! pip install ipywidgets -q"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81b91198c45249a2b0a7075d13ebf838",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors:   0%|          | 10.5M/2.20G [00:00<?, ?B/s]"
       ]
-     }
-    ],
-    "metadata": {
-     "kernelspec": {
-      "display_name": ".venv",
-      "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": "3.12.3"
-     }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 2
-   }
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from guardrails import Guard\n",
+    "from pydantic import BaseModel\n",
+    "\n",
+    "class Dog(BaseModel):\n",
+    "    name: str\n",
+    "    color: str\n",
+    "    weight_kg: float\n",
+    "\n",
+    "class NewFriends(BaseModel):\n",
+    "    dogs: list[Dog]\n",
+    "\n",
+    "guard = Guard.for_pydantic(NewFriends, output_formatter=\"jsonformer\")\n",
+    "\n",
+    "# JSONFormer is only compatible with HF Pipelines and HF Models:\n",
+    "from transformers import pipeline\n",
+    "tiny_llama_pipeline = pipeline(\"text-generation\", \"TinyLlama/TinyLlama-1.1B-Chat-v1.0\")\n",
+    "\n",
+    "# Inference is straightforward:\n",
+    "response = guard(tiny_llama_pipeline, prompt=\"Please enjoy this list of good dogs:\")\n",
+    "\n",
+    "# `out` is a dict.  Format it as JSON for readability:\n",
+    "import json\n",
+    "print(json.dumps(response.validated_output, indent=2))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/examples/extracting_entities.ipynb b/docs/examples/extracting_entities.ipynb
@@ -197,7 +197,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "guard = gd.Guard.from_pydantic(output_class=CreditCardAgreement)"
+    "guard = gd.Guard.for_pydantic(output_class=CreditCardAgreement)"
    ]
   },
   {

diff --git a/docs/examples/generate_structured_data.ipynb b/docs/examples/generate_structured_data.ipynb
@@ -133,7 +133,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "guard = gd.Guard.from_pydantic(output_class=Orders)"
+    "guard = gd.Guard.for_pydantic(output_class=Orders)"
    ]
   },
   {

diff --git a/docs/examples/generate_structured_data_cohere.ipynb b/docs/examples/generate_structured_data_cohere.ipynb
@@ -154,7 +154,7 @@
    "source": [
     "from rich import print\n",
     "import guardrails as gd\n",
-    "guard = gd.Guard.from_pydantic(output_class=Orders)\n",
+    "guard = gd.Guard.for_pydantic(output_class=Orders)\n",
     "\n",
     "raw_llm_response, validated_response, *rest = guard(\n",
     " \tmessages=[{\"role\":\"user\", \"content\":prompt}],\n",

diff --git a/docs/examples/guard_use.ipynb b/docs/examples/guard_use.ipynb
@@ -60,7 +60,7 @@
     "from guardrails.errors import ValidationError\n",
     "\n",
     "\n",
-    "guard = Guard.from_pydantic(Person)\n",
+    "guard = Guard.for_pydantic(Person)\n",
     "\n",
     "try:\n",
     "    guard.validate(json.dumps({\n",

diff --git a/docs/examples/guardrails_with_chat_models.ipynb b/docs/examples/guardrails_with_chat_models.ipynb
@@ -371,7 +371,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "guard = gd.Guard.from_rail_string(rail_str)"
+    "guard = gd.Guard.for_rail_string(rail_str)"
    ]
   },
   {
@@ -387,7 +387,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "guard = gd.Guard.from_pydantic(output_class=CreditCardAgreement)"
+    "guard = gd.Guard.for_pydantic(output_class=CreditCardAgreement)"
    ]
   },
   {

diff --git a/docs/examples/input_validation.ipynb b/docs/examples/input_validation.ipynb
@@ -59,7 +59,7 @@
     "</rail>\n",
     "\"\"\"\n",
     "\n",
-    "guard = Guard.from_rail_string(rail_spec)"
+    "guard = Guard.for_rail_string(rail_spec)"
    ]
   },
   {
@@ -123,7 +123,7 @@
     "    age: int\n",
     "\n",
     "\n",
-    "guard = Guard.from_pydantic(Pet)\n",
+    "guard = Guard.for_pydantic(Pet)\n",
     "guard.use(TwoWords(on_fail=\"exception\"), on=\"prompt\")\n",
     "\n",
     "try:\n",

diff --git a/docs/examples/json_function_calling_tools.ipynb b/docs/examples/json_function_calling_tools.ipynb
@@ -215,7 +215,7 @@
     "class Schedule(BaseModel):\n",
     "    deliveries: List[Delivery]\n",
     "\n",
-    "pydantic_guard = Guard.from_pydantic(Schedule)\n",
+    "pydantic_guard = Guard.for_pydantic(Schedule)\n",
     "\n",
     "# Generate the function calling tool and add it to the list\n",
     "pydantic_guard_tools = pydantic_guard.json_function_calling_tool([])\n",
@@ -246,7 +246,7 @@
     "</rail>\n",
     "\"\"\"\n",
     "\n",
-    "rail_guard = Guard.from_rail_string(rail)\n",
+    "rail_guard = Guard.for_rail_string(rail)\n",
     "\n",
     "\n",
     "# Generate the function calling tool and add it to the list\n",

diff --git a/docs/examples/llamaindex-output-parsing.ipynb b/docs/examples/llamaindex-output-parsing.ipynb
@@ -190,8 +190,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# You can either define a RailSpec and initialise a Guard object from_rail_string()\n",
-    "# OR define Pydantic classes and initialise a Guard object from_pydantic()\n",
+    "# You can either define a RailSpec and initialise a Guard object for_rail_string()\n",
+    "# OR define Pydantic classes and initialise a Guard object for_pydantic()\n",
     "# For more info: https://docs.guardrailsai.com/defining_guards/pydantic/\n",
     "# Guardrails recommends Pydantic\n",
     "\n",
@@ -237,7 +237,7 @@
    "outputs": [],
    "source": [
     "# Create a guard object\n",
-    "guard = gd.Guard.from_pydantic(output_class=BulletPoints, prompt=prompt)\n",
+    "guard = gd.Guard.for_pydantic(output_class=BulletPoints, prompt=prompt)\n",
     "\n",
     "# Create output parse object\n",
     "output_parser = GuardrailsOutputParser(guard, llm=llm_predictor.llm.complete)"

diff --git a/docs/examples/no_secrets_in_generated_text.ipynb b/docs/examples/no_secrets_in_generated_text.ipynb
@@ -175,7 +175,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "guard = gd.Guard.from_rail_string(rail_str)"
+    "guard = gd.Guard.for_rail_string(rail_str)"
    ]
   },
   {
@@ -191,7 +191,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "guard = gd.Guard.from_pydantic(output_class=ScrubbedCode)"
+    "guard = gd.Guard.for_pydantic(output_class=ScrubbedCode)"
    ]
   },
   {