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Updates to Tutorials (#248)

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Jason Liu
2023-12-14 18:04:02 -05:00
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parent d35cf1bb74
commit 4df4647177
7 changed files with 911 additions and 511 deletions
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tutorials/1.introduction.ipynb
+142 -257
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@@ -4,46 +4,43 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# Thinking with Types: Whats the problem?\n",
"# Working with structured outputs\n",
"\n",
"If you've seen my [talk](https://www.youtube.com/watch?v=yj-wSRJwrrc&t=1s) on this topic, you can skip this chapter.\n",
"\n",
"Many times, when we want to use language models, its not to make chatbots, but to communicate with other computer systems. This commonly means we want to use a model to output structured data like JSON. However, working with raw json or dictionaries can be a pain. \n",
"tl;dr\n",
"\n",
"This notebook highlights the core concepts of Pydantic and open ai function calling. With a foundational understanding of these two libraries we can lay the ground work for introducing my library, Instructor."
"When we work with LLMs you find that many times we are not building chatbots, instead we're working with structured outputs in order to solve a problem by returning machine readable data. However the way we think about the problem is still very much influenced by the way we think about chatbots. This is a problem because it leads to a lot of confusion and frustration. In this chapter we'll try to understand why this happens and how we can fix it.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Problem 1: Working with JSON, Validation, and Pydantic\n",
"## The fundamental problem with JSON and Dictionaries\n",
"\n",
"Lets say we have a simple JSON object, and we want to work with it. We can use the `json` module to load it into a dictionary, and then work with it. However, this is a bit of a pain, because we have to manually check the types of the data, and we have to manually check if the data is valid. For example, lets say we have a JSON object that looks like this:"
"Lets say we have a simple JSON object, and we want to work with it. We can use the `json` module to load it into a dictionary, and then work with it. However, this is a bit of a pain, because we have to manually check the types of the data, and we have to manually check if the data is valid. For example, lets say we have a JSON object that looks like this:\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"data = [\n",
" {\"first_name\": \"Jason\", \"age\": 10}, \n",
" {\"firstName\": \"Jason\", \"age\": \"10\"}\n",
"]"
"data = [{\"first_name\": \"Jason\", \"age\": 10}, {\"firstName\": \"Jason\", \"age\": \"10\"}]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have a `name` field, which is a string, and an `age` field, which is an integer. However, if we were to load this into a dictionary, we would have no way of knowing if the data is valid. For example, we could have a string for the age, or we could have a float for the age. We could also have a string for the name, or we could have a list for the name."
"We have a `name` field, which is a string, and an `age` field, which is an integer. However, if we were to load this into a dictionary, we would have no way of knowing if the data is valid. For example, we could have a string for the age, or we could have a float for the age. We could also have a string for the name, or we could have a list for the name.\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"execution_count": 2,
"metadata": {},
"outputs": [
{
@@ -51,8 +48,8 @@
"output_type": "stream",
"text": [
"Jason is 10\n",
"Next year he will be 11 years old\n",
"None is 10\n"
"None is 10\n",
"Next year Jason will be 11 years old\n"
]
},
{
@@ -62,7 +59,7 @@
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb Cell 5\u001b[0m line \u001b[0;36m5\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#W4sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m age \u001b[39m=\u001b[39m obj\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mage\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#W4sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mname\u001b[39m}\u001b[39;00m\u001b[39m is \u001b[39m\u001b[39m{\u001b[39;00mage\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#W4sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mNext year he will be \u001b[39m\u001b[39m{\u001b[39;00mage\u001b[39m+\u001b[39;49m\u001b[39m1\u001b[39;49m\u001b[39m}\u001b[39;00m\u001b[39m years old\u001b[39m\u001b[39m\"\u001b[39m)\n",
"\u001b[1;32m/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb Cell 5\u001b[0m line \u001b[0;36m9\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#W4sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m name \u001b[39m=\u001b[39m obj\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mfirst_name\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#W4sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m age \u001b[39m=\u001b[39m obj\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mage\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#W4sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mNext year \u001b[39m\u001b[39m{\u001b[39;00mname\u001b[39m}\u001b[39;00m\u001b[39m will be \u001b[39m\u001b[39m{\u001b[39;00mage\u001b[39m+\u001b[39;49m\u001b[39m1\u001b[39;49m\u001b[39m}\u001b[39;00m\u001b[39m years old\u001b[39m\u001b[39m\"\u001b[39m)\n",
"\u001b[0;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str"
]
}
@@ -72,14 +69,18 @@
" name = obj.get(\"first_name\")\n",
" age = obj.get(\"age\")\n",
" print(f\"{name} is {age}\")\n",
" print(f\"Next year he will be {age+1} years old\")"
"\n",
"for obj in data:\n",
" name = obj.get(\"first_name\")\n",
" age = obj.get(\"age\")\n",
" print(f\"Next year {name} will be {age+1} years old\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You see that while we were able to program with a dictionary, we had issues with the data being valid. We would have had to manually check the types of the data, and we had to manually check if the data was valid. This is a pain, and we can do better."
"You see that while we were able to program with a dictionary, we had issues with the data being valid. We would have had to manually check the types of the data, and we had to manually check if the data was valid. This is a pain, and we can do better.\n"
]
},
{
@@ -88,12 +89,12 @@
"source": [
"## Pydantic to the rescue\n",
"\n",
"Pydantic is a library that allows us to define data structures, and then validate them. It also allows us to define data structures."
"Pydantic is a library that allows us to define data structures, and then validate them. It also allows us to define data structures.\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"execution_count": 3,
"metadata": {},
"outputs": [
{
@@ -102,7 +103,7 @@
"Person(name='Sam', age=30)"
]
},
"execution_count": 24,
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
@@ -115,13 +116,14 @@
" name: str\n",
" age: int\n",
"\n",
"\n",
"person = Person(name=\"Sam\", age=30)\n",
"person"
]
},
{
"cell_type": "code",
"execution_count": 25,
"execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -130,7 +132,7 @@
"Person(name='Sam', age=30)"
]
},
"execution_count": 25,
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -143,7 +145,7 @@
},
{
"cell_type": "code",
"execution_count": 26,
"execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -165,19 +167,19 @@
},
{
"cell_type": "code",
"execution_count": 27,
"execution_count": 6,
"metadata": {},
"outputs": [
{
"ename": "ValidationError",
"evalue": "1 validation error for Person\nage\n Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='30.2', input_type=str]\n For further information visit https://errors.pydantic.dev/2.4/v/int_parsing",
"evalue": "1 validation error for Person\nage\n Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='30.2', input_type=str]\n For further information visit https://errors.pydantic.dev/2.5/v/int_parsing",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValidationError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb Cell 11\u001b[0m line \u001b[0;36m2\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X13sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m# Data is validated to get better error messages\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X13sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m person \u001b[39m=\u001b[39m Person\u001b[39m.\u001b[39;49mmodel_validate({\u001b[39m\"\u001b[39;49m\u001b[39mname\u001b[39;49m\u001b[39m\"\u001b[39;49m: \u001b[39m\"\u001b[39;49m\u001b[39mSam\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39mage\u001b[39;49m\u001b[39m\"\u001b[39;49m: \u001b[39m\"\u001b[39;49m\u001b[39m30.2\u001b[39;49m\u001b[39m\"\u001b[39;49m})\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X13sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m person\n",
"File \u001b[0;32m~/dev/instructor/.venv/lib/python3.11/site-packages/pydantic/main.py:503\u001b[0m, in \u001b[0;36mBaseModel.model_validate\u001b[0;34m(cls, obj, strict, from_attributes, context)\u001b[0m\n\u001b[1;32m 501\u001b[0m \u001b[39m# `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks\u001b[39;00m\n\u001b[1;32m 502\u001b[0m __tracebackhide__ \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[0;32m--> 503\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mcls\u001b[39;49m\u001b[39m.\u001b[39;49m__pydantic_validator__\u001b[39m.\u001b[39;49mvalidate_python(\n\u001b[1;32m 504\u001b[0m obj, strict\u001b[39m=\u001b[39;49mstrict, from_attributes\u001b[39m=\u001b[39;49mfrom_attributes, context\u001b[39m=\u001b[39;49mcontext\n\u001b[1;32m 505\u001b[0m )\n",
"\u001b[0;31mValidationError\u001b[0m: 1 validation error for Person\nage\n Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='30.2', input_type=str]\n For further information visit https://errors.pydantic.dev/2.4/v/int_parsing"
"\u001b[0;31mValidationError\u001b[0m: 1 validation error for Person\nage\n Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='30.2', input_type=str]\n For further information visit https://errors.pydantic.dev/2.5/v/int_parsing"
]
}
],
@@ -193,151 +195,48 @@
"source": [
"By introducing pydantic into any python codebase you can get a lot of benefits. You can get type checking, you can get validation, and you can get autocomplete. This is a huge win, because it means you can catch errors before they happen. This is even more useful when we rely on language models to generate data for us.\n",
"\n",
"You can also define validators that are run on the data. This is useful because it means you can catch errors before they happen. For example, you can define a validator that checks if the age is greater than 0. This is useful because it means you can catch errors before they happen."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Person(name='Sam', age=-10)"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Person(name=\"Sam\", age=-10)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"ename": "ValidationError",
"evalue": "1 validation error for Person\nage\n Input should be greater than 0 [type=greater_than, input_value=-10, input_type=int]\n For further information visit https://errors.pydantic.dev/2.4/v/greater_than",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValidationError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb Cell 14\u001b[0m line \u001b[0;36m5\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X36sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m name: \u001b[39mstr\u001b[39m\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X36sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m age: \u001b[39mint\u001b[39m \u001b[39m=\u001b[39m Field(\u001b[39m.\u001b[39m\u001b[39m.\u001b[39m\u001b[39m.\u001b[39m, gt\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m)\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X36sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m Person(name\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mSam\u001b[39;49m\u001b[39m\"\u001b[39;49m, age\u001b[39m=\u001b[39;49m\u001b[39m-\u001b[39;49m\u001b[39m10\u001b[39;49m)\n",
"File \u001b[0;32m~/dev/instructor/.venv/lib/python3.11/site-packages/pydantic/main.py:164\u001b[0m, in \u001b[0;36mBaseModel.__init__\u001b[0;34m(__pydantic_self__, **data)\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[39m# `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks\u001b[39;00m\n\u001b[1;32m 163\u001b[0m __tracebackhide__ \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[0;32m--> 164\u001b[0m __pydantic_self__\u001b[39m.\u001b[39;49m__pydantic_validator__\u001b[39m.\u001b[39;49mvalidate_python(data, self_instance\u001b[39m=\u001b[39;49m__pydantic_self__)\n",
"\u001b[0;31mValidationError\u001b[0m: 1 validation error for Person\nage\n Input should be greater than 0 [type=greater_than, input_value=-10, input_type=int]\n For further information visit https://errors.pydantic.dev/2.4/v/greater_than"
]
}
],
"source": [
"class Person(BaseModel):\n",
" name: str\n",
" age: int = Field(..., gt=0)\n",
"\n",
"Person(name=\"Sam\", age=-10)"
"You can also define validators that are run on the data. This is useful because it means you can catch errors before they happen. For example, you can define a validator that checks if the age is greater than 0. This is useful because it means you can catch errors before they happen.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lastly you can also define functions that run on the data. In this case we use the [@field_validator](https://docs.pydantic.dev/latest/concepts/validators/#field-validators) decorator."
"## Asking for JSON from OpenAI\n"
]
},
{
"cell_type": "code",
"execution_count": 30,
"execution_count": 12,
"metadata": {},
"outputs": [
{
"ename": "ValidationError",
"evalue": "1 validation error for Person\nname\n Value error, must contain a space [type=value_error, input_value='Sam', input_type=str]\n For further information visit https://errors.pydantic.dev/2.4/v/value_error",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValidationError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb Cell 16\u001b[0m line \u001b[0;36m1\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X43sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mmust contain a space\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X43sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m \u001b[39mreturn\u001b[39;00m v\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X43sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m Person(name\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mSam\u001b[39;49m\u001b[39m\"\u001b[39;49m, age\u001b[39m=\u001b[39;49m\u001b[39m10\u001b[39;49m)\n",
"File \u001b[0;32m~/dev/instructor/.venv/lib/python3.11/site-packages/pydantic/main.py:164\u001b[0m, in \u001b[0;36mBaseModel.__init__\u001b[0;34m(__pydantic_self__, **data)\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[39m# `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks\u001b[39;00m\n\u001b[1;32m 163\u001b[0m __tracebackhide__ \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[0;32m--> 164\u001b[0m __pydantic_self__\u001b[39m.\u001b[39;49m__pydantic_validator__\u001b[39m.\u001b[39;49mvalidate_python(data, self_instance\u001b[39m=\u001b[39;49m__pydantic_self__)\n",
"\u001b[0;31mValidationError\u001b[0m: 1 validation error for Person\nname\n Value error, must contain a space [type=value_error, input_value='Sam', input_type=str]\n For further information visit https://errors.pydantic.dev/2.4/v/value_error"
]
}
],
"source": [
"from pydantic import field_validator\n",
"\n",
"\n",
"class Person(BaseModel):\n",
" name: str\n",
" age: int = Field(..., gt=0)\n",
"\n",
" @field_validator(\"name\")\n",
" def name_must_contain_space(cls, v):\n",
" if \" \" not in v:\n",
" raise ValueError(\"must contain a space\")\n",
" return v\n",
" \n",
"Person(name=\"Sam\", age=10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"'...' is used as a placeholder for a required value in Pydantic's [Field](https://docs.pydantic.dev/latest/concepts/fields/) function.\n",
"\n",
"```age: int = Field(..., gt=0)```\n",
"defines a field age of type int in the Person model. The ... indicates that this field is required and must be provided when creating an instance of the Person model. The gt=0 is a validation that ensures the age must be greater than 0.\n",
"\n",
"If you try to create a Person without providing an age, Pydantic will raise a validation error because of the ... placeholder."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Person(name='Sam Liu', age=10)"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Person(name=\"Sam Liu\", age=10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Asking for JSON from OpenAI"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"ename": "ValidationError",
"evalue": "1 validation error for Person\nname\n Value error, must contain a space [type=value_error, input_value='Jason', input_type=str]\n For further information visit https://errors.pydantic.dev/2.4/v/value_error",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValidationError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb Cell 19\u001b[0m line \u001b[0;36m1\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X16sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m client \u001b[39m=\u001b[39m OpenAI()\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X16sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m resp \u001b[39m=\u001b[39m client\u001b[39m.\u001b[39mchat\u001b[39m.\u001b[39mcompletions\u001b[39m.\u001b[39mcreate(\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X16sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m model\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mgpt-3.5-turbo\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X16sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m messages\u001b[39m=\u001b[39m[\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X16sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m {\u001b[39m\"\u001b[39m\u001b[39mrole\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39muser\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mcontent\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39mExtract `Jason is 25 years old` into json\u001b[39m\u001b[39m\"\u001b[39m},\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X16sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m ]\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X16sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m )\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X16sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m Person\u001b[39m.\u001b[39;49mmodel_validate_json(resp\u001b[39m.\u001b[39;49mchoices[\u001b[39m0\u001b[39;49m]\u001b[39m.\u001b[39;49mmessage\u001b[39m.\u001b[39;49mcontent)\n",
"File \u001b[0;32m~/dev/instructor/.venv/lib/python3.11/site-packages/pydantic/main.py:530\u001b[0m, in \u001b[0;36mBaseModel.model_validate_json\u001b[0;34m(cls, json_data, strict, context)\u001b[0m\n\u001b[1;32m 528\u001b[0m \u001b[39m# `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks\u001b[39;00m\n\u001b[1;32m 529\u001b[0m __tracebackhide__ \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[0;32m--> 530\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mcls\u001b[39;49m\u001b[39m.\u001b[39;49m__pydantic_validator__\u001b[39m.\u001b[39;49mvalidate_json(json_data, strict\u001b[39m=\u001b[39;49mstrict, context\u001b[39m=\u001b[39;49mcontext)\n",
"\u001b[0;31mValidationError\u001b[0m: 1 validation error for Person\nname\n Value error, must contain a space [type=value_error, input_value='Jason', input_type=str]\n For further information visit https://errors.pydantic.dev/2.4/v/value_error"
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"Jason\": {\n",
" \"age\": 10\n",
" }\n",
"}\n",
"Here is the JSON representation of `jason is 10` as a JSON object:\n",
"\n",
"```\n",
"{\n",
" \"name\": \"Jason\",\n",
" \"age\": 10\n",
"}\n",
"```\n",
"Here is the JSON object representation of \"Jason is 10\":\n",
"\n",
"```json\n",
"{\n",
" \"name\": \"Jason\",\n",
" \"age\": 10\n",
"}\n",
"```\n",
"\n",
"In this JSON object, the key \"name\" corresponds to the value \"Jason\" and the key \"age\" corresponds to the value 10.\n"
]
}
],
@@ -349,113 +248,81 @@
"resp = client.chat.completions.create(\n",
" model=\"gpt-3.5-turbo\",\n",
" messages=[\n",
" {\"role\": \"user\", \"content\": \"Extract `Jason is 25 years old` into json\"},\n",
" ]\n",
" {\"role\": \"user\", \"content\": \"Please give me jason is 10 as a json object\"},\n",
" ],\n",
" n=20,\n",
" temperature=1,\n",
")\n",
"\n",
"Person.model_validate_json(resp.choices[0].message.content)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"resp = client.chat.completions.create(\n",
" model=\"gpt-3.5-turbo\",\n",
" messages=[\n",
" {\"role\": \"user\", \"content\": \"Extract `Jason Liu is thirty years old` into json\"},\n",
" ]\n",
")\n",
"\n",
"Person.model_validate_json(resp.choices[0].message.content)"
"for choice in resp.choices:\n",
" json = choice.message.content\n",
" try:\n",
" Person.model_validate_json(json)\n",
" except Exception as e:\n",
" print(json)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But what happens if I want to describe specifically how the schema should look? What if I want full_name and age and birthday as a datetime?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import datetime\n",
"\n",
"class PersonBirthday(Person):\n",
" birthday: datetime.date\n",
"\n",
"\n",
"resp = client.chat.completions.create(\n",
" model=\"gpt-3.5-turbo\",\n",
" messages=[\n",
" {\"role\": \"user\", \"content\": f\"Extract `Jason Liu is thirty years old his birthday is yesterday` into json. Today is {datetime.date.today()}\"},\n",
" ]\n",
")\n",
"\n",
"print(resp.choices[0].message.content)\n",
"print(Person.model_validate_json(resp.choices[0].message.content))\n",
"PersonBirthday.model_validate_json(resp.choices[0].message.content)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction to Function Calling \n",
"\n",
"The json could be anything! We could add more and more into a prompt and hope it works, or we can use something called [function calling](https://platform.openai.com/docs/guides/function-calling) to directly specify the schema we want. \n",
"## Introduction to Function Calling\n",
"\n",
"The json could be anything! We could add more and more into a prompt and hope it works, or we can use something called [function calling](https://platform.openai.com/docs/guides/function-calling) to directly specify the schema we want.\n",
"\n",
"**Function Calling**\n",
"\n",
"In an API call, you can describe functions and have the model intelligently choose to output a JSON object containing arguments to call one or many functions. The Chat Completions API does not call the function; instead, the model generates JSON that you can use to call the function in your code."
"In an API call, you can describe functions and have the model intelligently choose to output a JSON object containing arguments to call one or many functions. The Chat Completions API does not call the function; instead, the model generates JSON that you can use to call the function in your code.\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"execution_count": 13,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'datetime' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb Cell 24\u001b[0m line \u001b[0;36m1\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X24sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m schema \u001b[39m=\u001b[39m {\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X24sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m \u001b[39m'\u001b[39m\u001b[39mproperties\u001b[39m\u001b[39m'\u001b[39m: \n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X24sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m {\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X24sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m \u001b[39m'\u001b[39m\u001b[39mtype\u001b[39m\u001b[39m'\u001b[39m: \u001b[39m'\u001b[39m\u001b[39mobject\u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X24sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m }\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X24sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m resp \u001b[39m=\u001b[39m client\u001b[39m.\u001b[39mchat\u001b[39m.\u001b[39mcompletions\u001b[39m.\u001b[39mcreate(\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X24sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m model\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mgpt-3.5-turbo\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X24sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m messages\u001b[39m=\u001b[39m[\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X24sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m {\u001b[39m\"\u001b[39m\u001b[39mrole\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39muser\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mcontent\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mExtract `Jason Liu is thirty years old his birthday is yesturday` into json today is \u001b[39m\u001b[39m{\u001b[39;00mdatetime\u001b[39m.\u001b[39mdate\u001b[39m.\u001b[39mtoday()\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m},\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X24sZmlsZQ%3D%3D?line=15'>16</a>\u001b[0m ],\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X24sZmlsZQ%3D%3D?line=16'>17</a>\u001b[0m functions\u001b[39m=\u001b[39m[{\u001b[39m\"\u001b[39m\u001b[39mname\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39mPerson\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mparameters\u001b[39m\u001b[39m\"\u001b[39m: schema}],\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X24sZmlsZQ%3D%3D?line=17'>18</a>\u001b[0m function_call\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mauto\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X24sZmlsZQ%3D%3D?line=18'>19</a>\u001b[0m )\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X24sZmlsZQ%3D%3D?line=21'>22</a>\u001b[0m PersonBirthday\u001b[39m.\u001b[39mmodel_validate_json(resp\u001b[39m.\u001b[39mchoices[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mmessage\u001b[39m.\u001b[39mfunction_call\u001b[39m.\u001b[39marguments)\n",
"\u001b[0;31mNameError\u001b[0m: name 'datetime' is not defined"
]
"data": {
"text/plain": [
"PersonBirthday(name='Jason Liu', age=30, birthday=datetime.date(2023, 11, 30))"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import datetime\n",
"\n",
"\n",
"class PersonBirthday(BaseModel):\n",
" name: str\n",
" age: int\n",
" birthday: datetime.date\n",
"\n",
"\n",
"schema = {\n",
" 'properties': \n",
" {\n",
" 'name': {'type': 'string'},\n",
" 'age': {'type': 'integer'},\n",
" 'birthday': {'type': 'string', 'format': 'YYYY-MM-DD'},\n",
" \"properties\": {\n",
" \"name\": {\"type\": \"string\"},\n",
" \"age\": {\"type\": \"integer\"},\n",
" \"birthday\": {\"type\": \"string\", \"format\": \"YYYY-MM-DD\"},\n",
" },\n",
" 'required': ['name', 'age'],\n",
" 'type': 'object'\n",
" \"required\": [\"name\", \"age\"],\n",
" \"type\": \"object\",\n",
"}\n",
"\n",
"resp = client.chat.completions.create(\n",
" model=\"gpt-3.5-turbo\",\n",
" messages=[\n",
" {\"role\": \"user\", \"content\": f\"Extract `Jason Liu is thirty years old his birthday is yesturday` into json today is {datetime.date.today()}\"},\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": f\"Extract `Jason Liu is thirty years old his birthday is yesturday` into json today is {datetime.date.today()}\",\n",
" },\n",
" ],\n",
" functions=[{\"name\": \"Person\", \"parameters\": schema}],\n",
" function_call=\"auto\"\n",
" function_call=\"auto\",\n",
")\n",
"\n",
"\n",
"PersonBirthday.model_validate_json(resp.choices[0].message.function_call.arguments)"
]
},
@@ -463,24 +330,28 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"But it turns out, pydantic actually not only does our serialization, we can define the schema as well as add additional documentation!"
"But it turns out, pydantic actually not only does our serialization, we can define the schema as well as add additional documentation!\n"
]
},
{
"cell_type": "code",
"execution_count": 34,
"execution_count": 14,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'PersonBirthday' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb Cell 26\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/jasonliu/dev/instructor/tutorials/1.introduction.ipynb#X26sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m PersonBirthday\u001b[39m.\u001b[39mmodel_json_schema()\n",
"\u001b[0;31mNameError\u001b[0m: name 'PersonBirthday' is not defined"
]
"data": {
"text/plain": [
"{'properties': {'name': {'title': 'Name', 'type': 'string'},\n",
" 'age': {'title': 'Age', 'type': 'integer'},\n",
" 'birthday': {'format': 'date', 'title': 'Birthday', 'type': 'string'}},\n",
" 'required': ['name', 'age', 'birthday'],\n",
" 'title': 'PersonBirthday',\n",
" 'type': 'object'}"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
@@ -491,12 +362,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"We can even define nested complex schemas, and documentation with ease."
"We can even define nested complex schemas, and documentation with ease.\n"
]
},
{
"cell_type": "code",
"execution_count": 35,
"execution_count": 15,
"metadata": {},
"outputs": [
{
@@ -512,14 +383,14 @@
" 'type': 'object'}},\n",
" 'description': 'A Person with an address',\n",
" 'properties': {'name': {'title': 'Name', 'type': 'string'},\n",
" 'age': {'exclusiveMinimum': 0, 'title': 'Age', 'type': 'integer'},\n",
" 'age': {'title': 'Age', 'type': 'integer'},\n",
" 'address': {'$ref': '#/$defs/Address'}},\n",
" 'required': ['name', 'age', 'address'],\n",
" 'title': 'PersonAddress',\n",
" 'type': 'object'}"
]
},
"execution_count": 35,
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
@@ -533,6 +404,7 @@
"\n",
"class PersonAddress(Person):\n",
" \"\"\"A Person with an address\"\"\"\n",
"\n",
" address: Address\n",
"\n",
"\n",
@@ -544,12 +416,23 @@
"metadata": {},
"source": [
"These simple concepts become what we built into `instructor` and most of the work has been around documenting how we can leverage schema engineering.\n",
"Except now we use `instructor.patch()` to add a bunch more capabilities to the OpenAI SDK."
"Except now we use `instructor.patch()` to add a bunch more capabilities to the OpenAI SDK.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The core idea around Instructor\n",
"\n",
"1. Using function calling allows us to specify the schema we want\n",
"2. Pydantic can be used to define the schema and documentation AND validate the response at runtime\n",
"3. As a library with 100M downloads, we can leverage pydantic to do all the heavy lifting for us and fit nicely with the python ecosystem\n"
]
},
{
"cell_type": "code",
"execution_count": 37,
"execution_count": 16,
"metadata": {},
"outputs": [
{
@@ -558,7 +441,7 @@
"PersonAddress(name='Jason Liu', age=30, address=Address(address='123 Main St', city='San Francisco', state='CA'))"
]
},
"execution_count": 37,
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
@@ -567,20 +450,22 @@
"import instructor\n",
"import datetime\n",
"\n",
"# patch the client to add `response_model` to the `create` method\n",
"client = instructor.patch(client)\n",
"\n",
"resp = client.chat.completions.create(\n",
" model=\"gpt-3.5-turbo\",\n",
" messages=[\n",
" {\n",
" \"role\": \"user\", \n",
" \"role\": \"user\",\n",
" \"content\": f\"\"\"\n",
" Today is {datetime.date.today()} \n",
"\n",
" Extract `Jason Liu is thirty years old his birthday is yesturday` \n",
" he lives at 123 Main St, San Francisco, CA\"\"\"},\n",
" he lives at 123 Main St, San Francisco, CA\"\"\",\n",
" },\n",
" ],\n",
" response_model=PersonAddress\n",
" response_model=PersonAddress,\n",
")\n",
"resp"
]
@@ -589,7 +474,8 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Now you can see that when we set `response_model` create call will now return a pydantic model, and we can use that to validate the data. and work with it as if it was a python object."
"By defining `response_model` we can leverage pydantic to do all the heavy lifting. Later we'll introduce the other features that `instructor.patch()` adds to the OpenAI SDK.\n",
"but for now, this small change allows us to do a lot more with the API.\n"
]
},
{
@@ -598,7 +484,7 @@
"source": [
"## Is instructor the only way to do this?\n",
"\n",
"No. Libraries like Marvin, Langchain, and Llamaindex all now leverage the pydantic object in similar ways however they all have different approaches to how they do it. With instructor the goal is to be as light weight as possible, get you as close as possible to the openai api, and then get out of your way. \n",
"No. Libraries like Marvin, Langchain, and Llamaindex all now leverage the pydantic object in similar ways however they all have different approaches to how they do it. With instructor the goal is to be as light weight as possible, get you as close as possible to the openai api, and then get out of your way.\n",
"\n",
"More importantly, we've also added straight forward validation and reasking to the mix.\n",
"\n",
@@ -606,10 +492,9 @@
"\n",
"For further exploration:\n",
"\n",
"\n",
"- [Marvin](https://www.askmarvin.ai/)\n",
"- [Langchain](https://python.langchain.com/docs/modules/model_io/output_parsers/pydantic)\n",
"- [LlamaIndex](https://gpt-index.readthedocs.io/en/latest/examples/output_parsing/openai_pydantic_program.html)"
"- [LlamaIndex](https://gpt-index.readthedocs.io/en/latest/examples/output_parsing/openai_pydantic_program.html)\n"
]
}
],
tutorials/2.tips.ipynb
+197 -200
View File
@@ -7,9 +7,10 @@
"source": [
"# General Tips on Prompting\n",
"\n",
"Before we get into some big applications of schema engineering I want to equip you with the tools for success. \n",
"Before we get into some big applications of schema engineering I want to equip you with the tools for success.\n",
"This notebook is to share some general advice when using prompts to get the most of your models.\n",
"\n",
"This notebook is to share some general advice when using prompts to get the most of your models. "
"Before you might think of prompt engineering as massaging this wall of text, almost like coding in a notepad. But with schema engineering you can get a lot more out of your prompts with a lot less work.\n"
]
},
{
@@ -24,15 +25,14 @@
"1. using Enums\n",
"2. using Literals\n",
"\n",
"\n",
"Use an enum in Python when you need a set of named constants that are related and you want to ensure type safety, readability, and prevent invalid values. Enums are helpful for grouping and iterating over these constants.\n",
"\n",
"Use literals when you have a small, unchanging set of values that you don't need to group or iterate over, and when type safety and preventing invalid values is less of a concern. Literals are simpler and more direct for basic, one-off values."
"Use literals when you have a small, unchanging set of values that you don't need to group or iterate over, and when type safety and preventing invalid values is less of a concern. Literals are simpler and more direct for basic, one-off values.\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 10,
"id": "fdf5e1d9-31ad-4e8a-a55e-e2e70fff598d",
"metadata": {},
"outputs": [
@@ -42,49 +42,71 @@
"{'age': 17, 'name': 'Harry Potter', 'house': <House.Gryffindor: 'gryffindor'>}"
]
},
"execution_count": 5,
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import instructor\n",
"from openai import OpenAI\n",
"\n",
"from enum import Enum\n",
"from pydantic import BaseModel, Field\n",
"from typing_extensions import Literal\n",
"\n",
"import instructor\n",
"from openai import OpenAI\n",
"\n",
"client = instructor.patch(OpenAI())\n",
"\n",
"# Tip: Do not use auto() as they cast to 1,2,3,4 \n",
"\n",
"# Tip: Do not use auto() as they cast to 1,2,3,4\n",
"class House(Enum):\n",
" Gryffindor = \"gryffindor\"\n",
" Hufflepuff = \"hufflepuff\"\n",
" Ravenclaw = \"ravenclaw\"\n",
" Slytherin = \"slytherin\"\n",
"\n",
"\n",
"class Character(BaseModel):\n",
" age: int\n",
" name: str\n",
" house: House\n",
" \n",
"\n",
" def say_hello(self):\n",
" print(\n",
" f\"Hello, I'm {self.name}, I'm {self.age} years old and I'm from {self.house.value.title()}\"\n",
" )\n",
"\n",
"\n",
"resp = client.chat.completions.create(\n",
" model=\"gpt-4-1106-preview\",\n",
" messages=[\n",
" {\n",
" \"role\": \"user\", \n",
" \"content\": \"Harry Potter\"\n",
" }\n",
" ],\n",
" response_model=Character\n",
" messages=[{\"role\": \"user\", \"content\": \"Harry Potter\"}],\n",
" response_model=Character,\n",
")\n",
"resp.model_dump()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 11,
"id": "c609eb44",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello, I'm Harry Potter, I'm 17 years old and I'm from Gryffindor\n"
]
}
],
"source": [
"resp.say_hello()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "03db160c-81e9-4373-bfec-7a107224b6dd",
"metadata": {},
"outputs": [
@@ -94,7 +116,7 @@
"{'age': 17, 'name': 'Harry Potter', 'house': 'Gryffindor'}"
]
},
"execution_count": 6,
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@@ -104,16 +126,12 @@
" age: int\n",
" name: str\n",
" house: Literal[\"Gryffindor\", \"Hufflepuff\", \"Ravenclaw\", \"Slytherin\"]\n",
" \n",
"\n",
"\n",
"resp = client.chat.completions.create(\n",
" model=\"gpt-4-1106-preview\",\n",
" messages=[\n",
" {\n",
" \"role\": \"user\", \n",
" \"content\": \"Harry Potter\"\n",
" }\n",
" ],\n",
" response_model=Character\n",
" messages=[{\"role\": \"user\", \"content\": \"Harry Potter\"}],\n",
" response_model=Character,\n",
")\n",
"resp.model_dump()"
]
@@ -123,56 +141,37 @@
"id": "803e0ce6-6e7e-4d86-a7a8-49ebaad0a40b",
"metadata": {},
"source": [
"## Arbitrary long properties\n",
"## Arbitrary properties\n",
"\n",
"Often times there are long properties that you might want to extract from data that we can not specify in advanced. We can get around this by defining an arbitrary key value store like so:"
"Often times there are long properties that you might want to extract from data that we can not specify in advanced. We can get around this by defining an arbitrary key value store like so:\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 13,
"id": "0e7938b8-4666-4df4-bd80-f53e8baf7550",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'age': 38,\n",
" 'name': 'Severus Snape',\n",
" 'house': 'Slytherin',\n",
" 'properties': [{'key': 'position', 'value': 'Professor of Potions'},\n",
" {'key': 'loyalty', 'value': 'Dumbledore, Hogwarts'},\n",
" {'key': 'patronus', 'value': 'Doe'},\n",
" {'key': 'skill', 'value': 'Occlumency'}]}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"from typing import List\n",
"\n",
"\n",
"class Property(BaseModel):\n",
" key: str = Field(description=\"Must be snake case\")\n",
" value: str\n",
"\n",
"\n",
"class Character(BaseModel):\n",
" age: int\n",
" name: str\n",
" house: Literal[\"Gryffindor\", \"Hufflepuff\", \"Ravenclaw\", \"Slytherin\"]\n",
" properties: List[Property]\n",
"\n",
"\n",
"resp = client.chat.completions.create(\n",
" model=\"gpt-4-1106-preview\",\n",
" messages=[\n",
" {\n",
" \"role\": \"user\", \n",
" \"content\": \"Snape from Harry Potter\"\n",
" }\n",
" ],\n",
" response_model=Character\n",
" messages=[{\"role\": \"user\", \"content\": \"Snape from Harry Potter\"}],\n",
" response_model=Character,\n",
")\n",
"resp.model_dump()"
]
@@ -182,16 +181,16 @@
"id": "b3e62f68-a79f-4f65-9c1f-726e4e2d340a",
"metadata": {},
"source": [
"## Limiting the length of lists \n",
"## Limiting the length of lists\n",
"\n",
"In later chapters we'll talk about how to use validators to assert the length of lists but we can also use prompting tricks to enumerate values. Here we'll define a index to count the properties.\n",
"\n",
"In this following example instead of extraction we're going to work on generation instead."
"In this following example instead of extraction we're going to work on generation instead.\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": null,
"id": "69a58d01-ab6f-41b6-bc0c-b0e55fdb6fe4",
"metadata": {},
"outputs": [
@@ -201,20 +200,22 @@
"{'age': 38,\n",
" 'name': 'Severus Snape',\n",
" 'house': 'Slytherin',\n",
" 'properties': [{'index': '1',\n",
" 'key': 'Occupation',\n",
" 'value': 'Professor of Potions and later Defence Against the Dark Arts'},\n",
" 'properties': [{'index': '1', 'key': 'patronus', 'value': 'Doe'},\n",
" {'index': '2',\n",
" 'key': 'Allegiance',\n",
" 'value': 'Order of the Phoenix, Hogwarts'},\n",
" {'index': '3', 'key': 'Patronus', 'value': 'Doe'},\n",
" 'key': 'position',\n",
" 'value': 'Potions Master, Defense Against the Dark Arts teacher, Headmaster'},\n",
" {'index': '3',\n",
" 'key': 'loyalty',\n",
" 'value': 'Hogwarts, Albus Dumbledore, Order of the Phoenix, Lily Evans'},\n",
" {'index': '4',\n",
" 'key': 'Skills',\n",
" 'value': 'Potions master, Occlumens, Legilimens'},\n",
" {'index': '5', 'key': 'Portrayed by', 'value': 'Alan Rickman'}]}"
" 'key': 'skills',\n",
" 'value': 'Potions expertise, Occlumency, Legilimency'},\n",
" {'index': '5',\n",
" 'key': 'disguised_loyalty',\n",
" 'value': 'Death Eater (formerly, as a double agent)'}]}"
]
},
"execution_count": 8,
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@@ -222,24 +223,24 @@
"source": [
"class Property(BaseModel):\n",
" index: str = Field(..., description=\"Monotonically increasing ID\")\n",
" key: str\n",
" key: str = Field(description=\"Must be snake case\")\n",
" value: str\n",
"\n",
"\n",
"class Character(BaseModel):\n",
" age: int\n",
" name: str\n",
" house: Literal[\"Gryffindor\", \"Hufflepuff\", \"Ravenclaw\", \"Slytherin\"]\n",
" properties: List[Property] = Field(..., description=\"Numbered list of arbitrary extracted properties, should be exactly 5\")\n",
" properties: List[Property] = Field(\n",
" ...,\n",
" description=\"Numbered list of arbitrary extracted properties, should be exactly 5\",\n",
" )\n",
"\n",
"\n",
"resp = client.chat.completions.create(\n",
" model=\"gpt-4-1106-preview\",\n",
" messages=[\n",
" {\n",
" \"role\": \"user\", \n",
" \"content\": \"Snape from Harry Potter\"\n",
" }\n",
" ],\n",
" response_model=Character\n",
" messages=[{\"role\": \"user\", \"content\": \"Snape from Harry Potter\"}],\n",
" response_model=Character,\n",
")\n",
"resp.model_dump()"
]
@@ -251,68 +252,78 @@
"source": [
"## Defining Multiple Entities\n",
"\n",
"Now that we see a single entity with many properties we can continue to nest them into many users"
"Now that we see a single entity with many properties we can continue to nest them into many users\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": null,
"id": "1f2a2b14-a956-4f96-90c9-e11ca04ab7d1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'users': [{'age': 38,\n",
" 'name': 'Severus Snape',\n",
" 'house': 'Slytherin',\n",
" 'properties': [{'index': '1',\n",
" 'key': 'Role',\n",
" 'value': 'Professor of Potions, later Defence Against the Dark Arts, and head of Slytherin House'},\n",
" {'index': '2', 'key': 'Patronus', 'value': 'Doe'},\n",
" {'index': '3', 'key': 'Loyalty', 'value': 'Dumbledore, Harry, Hogwarts'},\n",
" {'index': '4',\n",
" 'key': 'Special Skill',\n",
" 'value': 'Occlumens, Potions Master'},\n",
" {'index': '5', 'key': 'Played by', 'value': 'Alan Rickman'}]},\n",
" {'age': 115,\n",
" 'name': 'Albus Dumbledore',\n",
" 'house': 'Gryffindor',\n",
" 'properties': [{'index': '1',\n",
" 'key': 'Role',\n",
" 'value': 'Headmaster of Hogwarts'},\n",
" {'index': '2', 'key': 'Patronus', 'value': 'Phoenix'},\n",
" {'index': '3',\n",
" 'key': 'Loyalty',\n",
" 'value': 'Order of the Phoenix, Hogwarts'},\n",
" {'index': '4',\n",
" 'key': 'Special Skill',\n",
" 'value': 'Considered to be the most powerful wizard of his time'},\n",
" {'index': '5',\n",
" 'key': 'Played by',\n",
" 'value': 'Richard Harris (films 1-2), Michael Gambon (films 3-6)'}]}]}"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
"name": "stdout",
"output_type": "stream",
"text": [
"age=38 name='Severus Snape' house='Slytherin'\n",
"age=115 name='Albus Dumbledore' house='Gryffindor'\n"
]
}
],
"source": [
"class Characters(BaseModel):\n",
" users: List[Character]\n",
"from typing import Iterable\n",
"\n",
"\n",
"class Character(BaseModel):\n",
" age: int\n",
" name: str\n",
" house: Literal[\"Gryffindor\", \"Hufflepuff\", \"Ravenclaw\", \"Slytherin\"]\n",
"\n",
"\n",
"resp = client.chat.completions.create(\n",
" model=\"gpt-4-1106-preview\",\n",
" messages=[\n",
" {\n",
" \"role\": \"user\", \n",
" \"content\": \"Snape and Dumbledore from Harry Potter\"\n",
" }\n",
" ],\n",
" response_model=Characters\n",
" messages=[{\"role\": \"user\", \"content\": \"Snape and Dumbledore from Harry Potter\"}],\n",
" response_model=Iterable[Character],\n",
")\n",
"resp.model_dump()"
"\n",
"for character in resp:\n",
" print(character)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a3091aba",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"age=38 name='Severus Snape' house='Slytherin'\n",
"age=115 name='Albus Dumbledore' house='Gryffindor'\n"
]
}
],
"source": [
"from typing import Iterable\n",
"\n",
"\n",
"class Character(BaseModel):\n",
" age: int\n",
" name: str\n",
" house: Literal[\"Gryffindor\", \"Hufflepuff\", \"Ravenclaw\", \"Slytherin\"]\n",
"\n",
"\n",
"resp = client.chat.completions.create(\n",
" model=\"gpt-4-1106-preview\",\n",
" messages=[{\"role\": \"user\", \"content\": \"Snape and Dumbledore from Harry Potter\"}],\n",
" stream=True,\n",
" response_model=Iterable[Character],\n",
")\n",
"\n",
"for character in resp:\n",
" print(character)"
]
},
{
@@ -320,30 +331,27 @@
"id": "f6ed3144-bde1-4033-9c94-a6926fa079d2",
"metadata": {},
"source": [
"## Defining Relationships \n",
"## Defining Relationships\n",
"\n",
"Now only can we define lists of users, with list of properties one of the more interesting things I've learned about prompting is that we can also easily define lists of references."
"Now only can we define lists of users, with list of properties one of the more interesting things I've learned about prompting is that we can also easily define lists of references.\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": null,
"id": "6de8768e-b36a-4a51-9cf9-940d178552f6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'users': [{'id': 1, 'name': 'Harry Potter', 'friends': [2, 3, 4, 5]},\n",
" {'id': 2, 'name': 'Hermione Granger', 'friends': [1, 3, 4, 5]},\n",
" {'id': 3, 'name': 'Ron Weasley', 'friends': [1, 2, 4, 5]},\n",
" {'id': 4, 'name': 'Ginny Weasley', 'friends': [1, 2, 3, 5]},\n",
" {'id': 5, 'name': 'Neville Longbottom', 'friends': [1, 2, 3, 4]}]}"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
"name": "stdout",
"output_type": "stream",
"text": [
"id=1 name='Harry Potter' friends=[2, 3, 4, 5]\n",
"id=2 name='Hermione Granger' friends=[1, 3, 4, 5]\n",
"id=3 name='Ron Weasley' friends=[1, 2, 4, 5]\n",
"id=4 name='Draco Malfoy' friends=[5]\n",
"id=5 name='Neville Longbottom' friends=[1, 2, 3, 4]\n"
]
}
],
"source": [
@@ -352,25 +360,21 @@
" name: str\n",
" friends: List[int]\n",
"\n",
"class Characters(BaseModel):\n",
" users: List[Character]\n",
"\n",
"resp = client.chat.completions.create(\n",
" model=\"gpt-4-1106-preview\",\n",
" messages=[\n",
" {\n",
" \"role\": \"user\", \n",
" \"content\": \"The 5 kids from Harry Potter\"\n",
" }\n",
" ],\n",
" response_model=Characters\n",
" messages=[{\"role\": \"user\", \"content\": \"The 5 kids from Harry Potter\"}],\n",
" stream=True,\n",
" response_model=Iterable[Character],\n",
")\n",
"resp.model_dump()"
"\n",
"for character in resp:\n",
" print(character)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": null,
"id": "b31e10d7-ebd2-49b4-b2c4-20dd67ca135d",
"metadata": {},
"outputs": [
@@ -383,105 +387,93 @@
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" viewBox=\"0.00 0.00 334.59 332.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
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"</g>\n",
"<!-- 1&#45;&gt;2 -->\n",
"<g id=\"edge1\" class=\"edge\">\n",
"<title>1&#45;&gt;2</title>\n",
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"<title>1&#45;&gt;3</title>\n",
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"<title>4</title>\n",
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"<text text-anchor=\"middle\" x=\"258.86\" y=\"-84.95\" font-family=\"Times,serif\" font-size=\"14.00\">Ginny Weasley</text>\n",
"<ellipse fill=\"none\" stroke=\"black\" cx=\"71.73\" cy=\"-18\" rx=\"63.63\" ry=\"18\"/>\n",
"<text text-anchor=\"middle\" x=\"71.73\" y=\"-12.95\" font-family=\"Times,serif\" font-size=\"14.00\">Draco Malfoy</text>\n",
"</g>\n",
"<!-- 1&#45;&gt;4 -->\n",
"<g id=\"edge3\" class=\"edge\">\n",
"<title>1&#45;&gt;4</title>\n",
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"text/plain": [
"<graphviz.graphs.Digraph at 0x10f1ed610>"
"<graphviz.graphs.Digraph at 0x119894f90>"
]
},
"metadata": {},
@@ -491,21 +483,26 @@
"source": [
"from graphviz import Digraph\n",
"from IPython.display import display\n",
"from typing import List\n",
"\n",
"dot = Digraph()\n",
"\n",
"resp = client.chat.completions.create(\n",
" model=\"gpt-4-1106-preview\",\n",
" messages=[{\"role\": \"user\", \"content\": \"The 5 kids from Harry Potter\"}],\n",
" response_model=Iterable[Character],\n",
")\n",
"\n",
"# Create nodes for each user\n",
"for user in resp.users:\n",
"for user in resp:\n",
" dot.node(str(user.id), user.name)\n",
"\n",
"# Create edges for friends\n",
"for user in resp.users:\n",
"for user in resp:\n",
" for friend_id in user.friends:\n",
" # To avoid duplicating edges, only add an edge if the friend ID is greater than the user ID\n",
" if friend_id > user.id:\n",
" dot.edge(str(user.id), str(friend_id))\n",
" \n",
"\n",
"\n",
"# Render the graph to a file\n",
"display(dot)"
@@ -516,7 +513,7 @@
"id": "523b5797-71a5-4a96-a4b7-21280fb73015",
"metadata": {},
"source": [
"With the tools we've discussed, we can find numerous real-world applications in production settings. These include extracting action items from transcripts, generating fake data, filling out forms, and creating objects that correspond to generative UI. These simple tricks will be highly useful."
"With the tools we've discussed, we can find numerous real-world applications in production settings. These include extracting action items from transcripts, generating fake data, filling out forms, and creating objects that correspond to generative UI. These simple tricks will be highly useful.\n"
]
}
],
tutorials/3.applications-rag.ipynb → tutorials/3.0.applications-rag.ipynb
+55 -49
View File
@@ -36,23 +36,9 @@
"\n",
"The simplest implementation of RAG embeds a user query and do a single embedding search in a vector database, like a vector store of Wikipedia articles. However, this approach often falls short when dealing with complex queries and diverse data sources.\n",
"\n",
"**What are the limitations?**\n",
"\n",
"- **Query-Document Mismatch:** It assumes that the query and document embeddings will align in the vector space, which is often not the case.\n",
" - Query: \"Tell me about climate change effects on marine life.\"\n",
" - Issue: The model might retrieve documents related to general climate change or marine life, missing the specific intersection of both topics.\n",
"<!-- blank -->\n",
"- **Monolithic Search Backend:** It relies on a single search method and backend, reducing flexibility and the ability to handle multiple data sources.\n",
" - Query: \"Latest research in quantum computing.\"\n",
" - Issue: The model might only search in a general science database, missing out on specialized quantum computing resources.\n",
"<!-- blank -->\n",
"- **Text Search Limitations:** The model is restricted to simple text queries without the nuances of advanced search features.\n",
" - Query: \"what problems did we fix last week\"\n",
" - Issue: cannot be answered by a simple text search since documents that contain problem, last week are going to be present at every week.\n",
"<!-- blank -->\n",
"- **Limited Planning Ability:** It fails to consider additional contextual information that could refine the search results.\n",
" - Query: \"Tips for first-time Europe travelers.\"\n",
" - Issue: The model might provide general travel advice, ignoring the specific context of first-time travelers or European destinations.\n"
"- **Limited Planning Ability:** It fails to consider additional contextual information that could refine the search results."
]
},
{
@@ -88,7 +74,7 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -121,11 +107,12 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"class Extraction(BaseModel):\n",
" topic: str\n",
" summary: str \n",
" hypothetical_questions: List[str] = Field(default_factory=list, description=\"Hypothetical questions that this document could answer\")\n",
" keywords: List[str] = Field(default_factory=list, description=\"Keywords that this document is about\")"
@@ -133,38 +120,55 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"summary\": \"Simple RAG, an approach to query embedding and document retrieval, often falls short due to misalignment between query and document embeddings, reliance on a single search method and backend, text search limitations, and inadequate consideration of contextual information.\",\n",
" \"hypothetical_questions\": [\n",
" \"How can RAG be improved to better handle complex queries?\",\n",
" \"What other methods exist to enhance the alignment of queries and documents in vector spaces?\",\n",
" \"How can multiple data sources be incorporated into the search mechanism of RAG?\",\n",
" \"In what ways can advanced search features be integrated into RAG?\",\n",
" \"What approaches enable RAG to use contextual information for improved search results?\"\n",
" ],\n",
" \"keywords\": [\n",
" \"RAG\",\n",
" \"query embedding\",\n",
" \"document retrieval\",\n",
" \"vector database\",\n",
" \"query-document mismatch\",\n",
" \"monolithic search backend\",\n",
" \"text search limitations\",\n",
" \"planning ability\",\n",
" \"contextual information\"\n",
" ]\n",
"}\n"
"{'hypothetical_questions': ['What is the basic concept behind Simple RAG?',\n",
" 'Why might Simple RAG not perform well with '\n",
" 'complex queries?',\n",
" \"In what ways does Simple RAG's embedding search \"\n",
" 'fall short?'],\n",
" 'keywords': ['Simple RAG',\n",
" 'Retriever-Augmented Generation',\n",
" 'user queries',\n",
" 'embedding search',\n",
" 'vector database',\n",
" 'query-document mismatch'],\n",
" 'summary': 'Simple RAG is an implementation that embeds user queries for a '\n",
" 'single embedding search in a vector database. Although '\n",
" 'straightforward, it struggles with complex queries and varied '\n",
" 'data sources because of its basic framework and query-document '\n",
" 'mismatch issues.',\n",
" 'topic': 'Simple Retriever-Augmented Generation (RAG)'}\n",
"{'hypothetical_questions': ['What kind of limitations does Simple RAG face?',\n",
" 'How does a monolithic search backend affect '\n",
" \"Simple RAG's performance?\",\n",
" 'Can Simple RAG handle complex, context-specific '\n",
" 'queries effectively?'],\n",
" 'keywords': ['limitations',\n",
" 'Simple RAG',\n",
" 'query-document mismatch',\n",
" 'monolithic search backend',\n",
" 'text search limitations',\n",
" 'limited planning ability'],\n",
" 'summary': 'The limitations of Simple RAG include query-document mismatch, '\n",
" 'reliance on a monolithic search backend, restrictions to simple '\n",
" 'text searches, and limited planning ability, which results in '\n",
" 'suboptimal outcomes when handling nuanced or context-specific '\n",
" 'queries.',\n",
" 'topic': 'Limitations of Simple RAG'}\n"
]
}
],
"source": [
"from pprint import pprint\n",
"from typing import Iterable\n",
"\n",
"\n",
"text_chunk = \"\"\"\n",
"## Simple RAG\n",
"\n",
@@ -188,19 +192,21 @@
" - Issue: The model might provide general travel advice, ignoring the specific context of first-time travelers or European destinations.\n",
"\"\"\"\n",
"\n",
"extraction = client.chat.completions.create(\n",
"extractions = client.chat.completions.create(\n",
" model=\"gpt-4-1106-preview\",\n",
" response_model=Extraction,\n",
" stream=True,\n",
" response_model=Iterable[Extraction],\n",
" messages=[{\n",
" \"role\": \"system\", \n",
" \"content\": \"Your role is to extract data from the following text chunk and create a RAG document.\"\n",
" \"content\": \"Your role is to extract chunks from the following and create a set of topics.\"\n",
" }, {\n",
" \"role\": \"user\", \n",
" \"content\": text_chunk\n",
" }])\n",
"\n",
"\n",
"print(extraction.model_dump_json(indent=2))"
"for extraction in extractions:\n",
" pprint(extraction.model_dump())"
]
},
{
@@ -221,7 +227,7 @@
},
{
"cell_type": "code",
"execution_count": 25,
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
@@ -254,7 +260,7 @@
},
{
"cell_type": "code",
"execution_count": 26,
"execution_count": 17,
"metadata": {},
"outputs": [
{
@@ -262,10 +268,10 @@
"output_type": "stream",
"text": [
"{\n",
" \"rewritten_query\": \"recent developments in Artificial Intelligence\",\n",
" \"rewritten_query\": \"recent developments in AI\",\n",
" \"published_daterange\": {\n",
" \"start\": \"2023-04-01\",\n",
" \"end\": \"2023-11-18\"\n",
" \"start\": \"2023-01-01\",\n",
" \"end\": \"2023-11-30\"\n",
" }\n",
"}\n"
]
@@ -273,7 +279,7 @@
],
"source": [
"query = client.chat.completions.create(\n",
" model=\"gpt-4-1106-preview\",\n",
" model=\"gpt-3.5-turbo\",\n",
" response_model=Query,\n",
" messages=[\n",
" {\n",
tutorials/3.1.validation-rag.ipynb
+512
View File
@@ -0,0 +1,512 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "5a01f3ac-5306-4a1b-9e47-a5d254bce93a",
"metadata": {},
"source": [
"# Validators\n"
]
},
{
"cell_type": "markdown",
"id": "9dcc78ac-ed6d-49e3-b71b-fb2fb25f16a8",
"metadata": {},
"source": [
"Pydantic offers an customizable and expressive validation framework for Python. Instructor leverages Pydantic's validation framework to provide a uniform developer experience for both code-based and LLM-based validation, as well as a reasking mechanism for correcting LLM outputs based on validation errors. To learn more check out the Pydantic [docs](https://docs.pydantic.dev/latest/) on validators.\n",
"\n",
"Note: For the majority of this notebook we won't be calling openai, just using validators to see how we can control the validation of the objects.\n",
"\n",
"Then we'll bring it all together into the context of RAG from the previous notebook.\n"
]
},
{
"cell_type": "markdown",
"id": "064c286b",
"metadata": {},
"source": [
"Validators will enable us to control outputs by defining a function like so:\n",
"\n",
"```python\n",
"def validation_function(value):\n",
" if condition(value):\n",
" raise ValueError(\"Value is not valid\")\n",
" return mutation(value)\n",
"```\n",
"\n",
"Before we get started lets go over the general shape of a validator:\n"
]
},
{
"cell_type": "markdown",
"id": "7cfc6c66",
"metadata": {},
"source": [
"## Defining Validator Functions\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d4bb6258-b03a-4621-8a73-29056a20ec0f",
"metadata": {},
"outputs": [],
"source": [
"from pydantic import BaseModel\n",
"from typing_extensions import Annotated\n",
"from pydantic import AfterValidator\n",
"\n",
"\n",
"def name_must_contain_space(v: str) -> str:\n",
" if \" \" not in v:\n",
" raise ValueError(\"Name must contain a space.\")\n",
" return v.lower()\n",
"\n",
"\n",
"class UserDetail(BaseModel):\n",
" age: int\n",
" name: Annotated[str, AfterValidator(name_must_contain_space)]\n",
"\n",
"\n",
"person = UserDetail(age=29, name=\"Jason\")"
]
},
{
"cell_type": "markdown",
"id": "3c0302ca",
"metadata": {},
"source": [
"## Using Field\n",
"\n",
"We can also use the `Field` class to define validators. This is useful when we want to define a validator for a field that is primative, like a string or integer which supports a limited number of validators.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3242856f",
"metadata": {},
"outputs": [],
"source": [
"from pydantic import Field\n",
"\n",
"\n",
"class UserDetail(BaseModel):\n",
" age: int = Field(..., gt=0)\n",
" name: str\n",
"\n",
"\n",
"person = UserDetail(age=-10, name=\"Jason\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0035a329",
"metadata": {},
"outputs": [],
"source": [
"class AssistantMessage(BaseModel):\n",
" message: str = Field(..., min_length=10)\n",
"\n",
"\n",
"message = AssistantMessage(message=\"Hey\")"
]
},
{
"cell_type": "markdown",
"id": "4f689121",
"metadata": {},
"source": [
"## Providing Context\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ec043c23",
"metadata": {},
"outputs": [],
"source": [
"from pydantic import ValidationInfo, field_validator\n",
"\n",
"\n",
"class Response(BaseModel):\n",
" message: str\n",
"\n",
" @field_validator(\"message\")\n",
" def message_cannot_have_blacklisted_words(cls, v: str, info: ValidationInfo) -> str:\n",
" blacklist = info.context.get(\"blacklist\", [])\n",
" for word in blacklist:\n",
" assert word not in v.lower(), f\"`{word}` was found in the message `{v}`\"\n",
" return v\n",
"\n",
"\n",
"Response.model_validate(\n",
" {\"message\": \"I will hurt them.\"},\n",
" context={\n",
" \"blacklist\": {\n",
" \"rob\",\n",
" \"steal\",\n",
" \"hurt\",\n",
" \"kill\",\n",
" \"attack\",\n",
" }\n",
" },\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "887dba80",
"metadata": {},
"outputs": [],
"source": [
"Response.model_validate(\n",
" {\"message\": \"My name is rob.\"},\n",
" context={\n",
" \"blacklist\": {\n",
" \"rob\",\n",
" \"steal\",\n",
" \"hurt\",\n",
" \"kill\",\n",
" \"attack\",\n",
" }\n",
" },\n",
")"
]
},
{
"cell_type": "markdown",
"id": "37e3a638-c9c9-44cd-bcd0-ad1a39f448db",
"metadata": {},
"source": [
"## Using OpenAI Moderation\n"
]
},
{
"cell_type": "markdown",
"id": "88d0b816-7ec8-42b0-9b91-c9aab382c960",
"metadata": {},
"source": [
"To enhance our validation measures, we'll extend the scope to flag any answer that contains hateful content, harassment, or similar issues. OpenAI offers a moderation endpoint that addresses these concerns, and it's freely available when using OpenAI models.\n"
]
},
{
"cell_type": "markdown",
"id": "65f46eb5",
"metadata": {},
"source": [
"With the `instructor` library, this is just one function edit away:\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "82521112-5301-4442-acce-82b495bd838f",
"metadata": {},
"outputs": [],
"source": [
"from typing import Annotated\n",
"from pydantic import AfterValidator\n",
"from instructor import openai_moderation\n",
"\n",
"import instructor\n",
"from openai import OpenAI\n",
"\n",
"client = instructor.patch(OpenAI())\n",
"\n",
"# This uses Annotated which is a new feature in Python 3.9\n",
"# To define custom metadata for a type hint.\n",
"ModeratedStr = Annotated[str, AfterValidator(openai_moderation(client=client))]\n",
"\n",
"\n",
"class Response(BaseModel):\n",
" message: ModeratedStr\n",
"\n",
"\n",
"Response(message=\"I want to make them suffer the consequences\")"
]
},
{
"cell_type": "markdown",
"id": "faa5116e",
"metadata": {},
"source": [
"## General Validator\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "49d8b772",
"metadata": {},
"outputs": [],
"source": [
"from instructor import llm_validator\n",
"\n",
"HealthTopicStr = Annotated[\n",
" str,\n",
" AfterValidator(\n",
" llm_validator(\n",
" \"don't talk about any other topic except health best practices and topics\",\n",
" openai_client=client,\n",
" )\n",
" ),\n",
"]\n",
"\n",
"\n",
"class AssistantMessage(BaseModel):\n",
" message: HealthTopicStr\n",
"\n",
"\n",
"AssistantMessage(\n",
" message=\"I would suggest you to visit Sicily as they say it is very nice in winter.\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "050e72fe-4b13-4002-a1d0-94f7b88b784b",
"metadata": {},
"source": [
"### Avoiding hallucination with citations\n"
]
},
{
"cell_type": "markdown",
"id": "e3f2869e-c8a3-4b93-82e7-55eb70930900",
"metadata": {},
"source": [
"When incorporating external knowledge bases, it's crucial to ensure that the agent uses the provided context accurately and doesn't fabricate responses. Validators can be effectively used for this purpose. We can illustrate this with an example where we validate that a provided citation is actually included in the referenced text chunk:\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "638fc368-5cf7-4ae7-9d3f-efea1b84eec0",
"metadata": {},
"outputs": [],
"source": [
"from pydantic import ValidationInfo\n",
"\n",
"\n",
"class AnswerWithCitation(BaseModel):\n",
" answer: str\n",
" citation: str\n",
"\n",
" @field_validator(\"citation\")\n",
" @classmethod\n",
" def citation_exists(cls, v: str, info: ValidationInfo):\n",
" context = info.context\n",
" if context:\n",
" context = context.get(\"text_chunk\")\n",
" if v not in context:\n",
" raise ValueError(f\"Citation `{v}` not found in text\")\n",
" return v"
]
},
{
"cell_type": "markdown",
"id": "3064b06b-7f85-40ec-8fe2-4fa2cce36585",
"metadata": {},
"source": [
"Here we assume that there is a \"text_chunk\" field that contains the text that the model is supposed to use as context. We then use the `field_validator` decorator to define a validator that checks if the citation is included in the text chunk. If it's not, we raise a `ValueError` with a message that will be returned to the user.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0f3030b6-e6cf-45bf-a366-12de996fea40",
"metadata": {},
"outputs": [],
"source": [
"AnswerWithCitation.model_validate(\n",
" {\n",
" \"answer\": \"Blueberries are packed with protein\",\n",
" \"citation\": \"Blueberries contain high levels of protein\",\n",
" },\n",
" context={\"text_chunk\": \"Blueberries are very rich in antioxidants\"},\n",
")"
]
},
{
"cell_type": "markdown",
"id": "64d15ad2",
"metadata": {},
"source": [
"In practice there are many ways to implement this: we could use a regex to check if the citation is included in the text chunk, or we could use a more sophisticated approach like a semantic similarity check. The important thing is that we have a way to validate that the model is using the provided context accurately.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "04d2b691",
"metadata": {},
"outputs": [],
"source": [
"class AnswerWithCitation(BaseModel):\n",
" answer: str\n",
" citations: list[str]\n",
"\n",
" @field_validator(\"citations\")\n",
" @classmethod\n",
" def citation_exists(cls, v: str, info: ValidationInfo):\n",
" text_chunk = info.context.get(\"text_chunk\")\n",
" for citation in v:\n",
" if citation not in text_chunk:\n",
" raise ValueError(f\"Citation `{citation}` not found in text\")\n",
" return v"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f8aae4f1",
"metadata": {},
"outputs": [],
"source": [
"class Citation(BaseModel):\n",
" span_start: str = Field(\n",
" ...,\n",
" description=\"The start of the citation, use a 3-4 word phrase that is unique to the citation\",\n",
" )\n",
" span_end: str = Field(\n",
" ...,\n",
" description=\"The end of the citation, use a 3-4 word phrase that is unique to the citation\",\n",
" )\n",
"\n",
" def check(self, text: str) -> bool:\n",
" index_start = text.find(self.span_start)\n",
" index_end = text.find(self.span_end)\n",
" if index_start == -1 or index_end == -1:\n",
" return False\n",
"\n",
" if index_start > index_end:\n",
" return False\n",
"\n",
" return True\n",
"\n",
"\n",
"class AnswerWithCitation(BaseModel):\n",
" answer: str\n",
" citations: list[Citation]\n",
"\n",
" @field_validator(\"citations\")\n",
" @classmethod\n",
" def citation_exists(cls, v: str, info: ValidationInfo):\n",
" text_chunk = info.context.get(\"text_chunk\")\n",
" for citation in v:\n",
" if not citation.check(text_chunk):\n",
" raise ValueError(f\"Citation `{citation}` not found in text\")\n",
" return v"
]
},
{
"cell_type": "markdown",
"id": "5bbbaa11-32d2-4772-bc31-18d1d6d6c919",
"metadata": {},
"source": [
"## Reasking with validators\n",
"\n",
"For most of these examples all we've done we've mostly only defined the validation logic. Which can be seperate from generation, however when we are given validation errors, we shouldn't end there! Instead instructor allows us to collect all the validation errors and reask the llm to rewrite their answer.\n",
"\n",
"Lets try to use a extreme example to illustrate this point:\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "97f544e7-2552-465c-89a9-a4820f00d658",
"metadata": {},
"outputs": [],
"source": [
"class QuestionAnswer(BaseModel):\n",
" question: str\n",
" answer: str\n",
"\n",
"\n",
"question = \"What is the meaning of life?\"\n",
"context = (\n",
" \"The according to the devil the meaning of life is a life of sin and debauchery.\"\n",
")\n",
"\n",
"\n",
"resp = client.chat.completions.create(\n",
" model=\"gpt-3.5-turbo\",\n",
" response_model=QuestionAnswer,\n",
" messages=[\n",
" {\n",
" \"role\": \"system\",\n",
" \"content\": \"You are a system that answers questions based on the context. answer exactly what the question asks using the context.\",\n",
" },\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": f\"using the context: `{context}`\\n\\nAnswer the following question: `{question}`\",\n",
" },\n",
" ],\n",
")\n",
"\n",
"resp.answer"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0328bbc5",
"metadata": {},
"outputs": [],
"source": [
"class QuestionAnswer(BaseModel):\n",
" question: str\n",
" answer: Annotated[\n",
" str,\n",
" AfterValidator(\n",
" llm_validator(\"don't say objectionable things\", openai_client=client)\n",
" ),\n",
" ]\n",
"\n",
"\n",
"resp = client.chat.completions.create(\n",
" model=\"gpt-3.5-turbo\",\n",
" response_model=QuestionAnswer,\n",
" max_retries=2,\n",
" messages=[\n",
" {\n",
" \"role\": \"system\",\n",
" \"content\": \"You are a system that answers questions based on the context. answer exactly what the question asks using the context.\",\n",
" },\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": f\"using the context: `{context}`\\n\\nAnswer the following question: `{question}`\",\n",
" },\n",
" ],\n",
")\n",
"\n",
"resp.answer"
]
}
],
"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": "3.11.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
tutorials/5.validation.ipynb → tutorials/4.validation.ipynb
+1 -1
View File
@@ -824,7 +824,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
"version": "3.11.6"
}
},
"nbformat": 4,
tutorials/4.knowledge-graphs.ipynb → tutorials/5.knowledge-graphs.ipynb
+1 -1
View File
@@ -989,7 +989,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
"version": "3.11.6"
}
},
"nbformat": 4,
tutorials/6.chain-of-density.ipynb
+3 -3
View File
@@ -954,9 +954,9 @@
],
"metadata": {
"kernelspec": {
"display_name": "pampa-labs",
"display_name": ".venv",
"language": "python",
"name": "pampa-labs"
"name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -968,7 +968,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
"version": "3.11.6"
}
},
"nbformat": 4,