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docs: update code snippets and text across multiple documentation files (#450)

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This commit is contained in:
Jason Liu
2024-02-19 21:02:00 -05:00
committed by GitHub
parent 2319fff07e
commit 62919b2cf2
16 changed files with 535 additions and 176 deletions
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README.md
+1 -1
View File
@@ -200,7 +200,7 @@ except ValidationError as e:
"""
1 validation error for QuestionAnswer
answer
Assertion failed, The statement promotes objectionable behavior. [type=assertion_error, input_value='The meaning of life is to be evil and steal', input_type=str]
Assertion failed, The statement promotes objectionable behavior by encouraging evil and stealing, which goes against the rule of not saying objectionable things. [type=assertion_error, input_value='The meaning of life is to be evil and steal', input_type=str]
For further information visit https://errors.pydantic.dev/2.6/v/assertion_error
"""
```
docs/concepts/caching.md
+2 -2
View File
@@ -33,12 +33,12 @@ def extract(data) -> UserDetail:
start = time.perf_counter() # (1)
model = extract("Extract jason is 25 years old")
print(f"Time taken: {time.perf_counter() - start}")
#> Time taken: 0.41433916706591845
#> Time taken: 0.8392175831831992
start = time.perf_counter()
model = extract("Extract jason is 25 years old") # (2)
print(f"Time taken: {time.perf_counter() - start}")
#> Time taken: 1.7080456018447876e-06
#> Time taken: 8.33999365568161e-07
```
1. Using `time.perf_counter()` to measure the time taken to run the function is better than using `time.time()` because it's more accurate and less susceptible to system clock changes.
docs/concepts/lists.md
+4 -6
View File
@@ -79,10 +79,8 @@ users = client.chat.completions.create(
)
for user in users:
print(user)
#> ('tasks', [User(name='Jason', age=10), User(name='John', age=30)])
#> name="Jason" "age"=10
#> name="John" "age"=10
#> name='Jason' age=10
#> name='John' age=30
```
## Streaming Tasks
@@ -159,8 +157,8 @@ async def print_iterable_results():
)
async for m in model:
print(m)
#> name='John Doe' age=32
#> name='Jane Smith' age=28
#> name='John Smith' age=30
#> name='Mary Jane' age=28
import asyncio
docs/concepts/maybe.md
+1 -1
View File
@@ -89,7 +89,7 @@ print(user2.model_dump_json(indent=2))
{
"result": null,
"error": false,
"message": "Unknown user"
"message": null
}
"""
```
docs/concepts/models.md
+1 -1
View File
@@ -150,7 +150,7 @@ class SearchQuery(BaseModel):
def execute(self):
print(f"Searching for {self.query} of type {self.query_type}")
#> Searching for cat of type image
#> Searching for cat pictures of type image
return "Results for cat"
docs/concepts/parallel.md
+2 -2
View File
@@ -44,9 +44,9 @@ function_calls = client.chat.completions.create(
for fc in function_calls:
print(fc)
#> location='Toronto' units='metric'
#> location='Toronto' units='imperial'
#> location='Dallas' units='imperial'
#> query='super bowl winner'
#> query='who won the super bowl'
```
1. Set the mode to `PARALLEL_TOOLS` to enable parallel function calling.
docs/concepts/partial.md
+33 -109
View File
@@ -99,9 +99,31 @@ for extraction in extraction_stream:
console.clear()
console.print(obj)
print(extraction.model_dump_json())
print(extraction.model_dump_json(indent=2))
"""
{"users":[{"name":"John Doe","email":"johndoe@email.com","twitter":"@TechGuru44"},{"name":"Jane Smith","email":"janesmith@email.com","twitter":"@DigitalDiva88"},{"name":"Alex Johnson","email":"alexj@email.com","twitter":"@CodeMaster2023"}],"date":"2024-03-15","location":"Grand Tech Arena located at 4521 Innovation Drive","budget":50000,"deadline":"2024-02-20"}
{
"users": [
{
"name": "John Doe",
"email": "johndoe@email.com",
"twitter": "@TechGuru44"
},
{
"name": "Jane Smith",
"email": "janesmith@email.com",
"twitter": "@DigitalDiva88"
},
{
"name": "Alex Johnson",
"email": "alexj@email.com",
"twitter": "@CodeMaster2023"
}
],
"date": "2024-03-15",
"location": "Grand Tech Arena located at 4521 Innovation Drive",
"budget": 50000,
"deadline": "2024-02-20"
}
"""
```
@@ -118,10 +140,10 @@ import instructor
from openai import AsyncOpenAI
from pydantic import BaseModel
client = instructor.apatch(AsyncOpenAI())
client = instructor.patch(AsyncOpenAI())
class UserExtract(BaseModel):
class User(BaseModel):
name: str
age: int
@@ -129,117 +151,19 @@ class UserExtract(BaseModel):
async def print_partial_results():
user = await client.chat.completions.create(
model="gpt-4-turbo-preview",
response_model=instructor.Partial[UserExtract],
response_model=instructor.Partial[User],
max_retries=2,
stream=True,
messages=[
{"role": "user", "content": "Jason Liu is 12 years old"},
{"role": "user", "content": "Jason is 12 years old"},
],
)
async for m in user:
print(m.model_dump_json(indent=2))
"""
{
"name": null,
"age": null
}
"""
"""
{
"name": "",
"age": null
}
"""
"""
{
"name": "Jason",
"age": null
}
"""
"""
{
"name": "Jason Liu",
"age": null
}
"""
"""
{
"name": "Jason Liu",
"age": 12
}
"""
"""
{
"name": "",
"age": null
}
"""
"""
{
"name": "Jason",
"age": null
}
"""
"""
{
"name": "Jason Liu",
"age": null
}
"""
"""
{
"name": "Jason Liu",
"age": 12
}
"""
"""
{
"name": "",
"age": null
}
"""
"""
{
"name": "Jason",
"age": null
}
"""
"""
{
"name": "Jason Liu",
"age": null
}
"""
"""
{
"name": "Jason Liu",
"age": 12
}
"""
"""
{
"name": "",
"age": null
}
"""
"""
{
"name": "Jason",
"age": null
}
"""
"""
{
"name": "Jason Liu",
"age": null
}
"""
"""
{
"name": "Jason Liu",
"age": 12
}
"""
print(m)
#> name=None age=None
#> name='' age=None
#> name='Jason' age=None
#> name='Jason' age=12
import asyncio
docs/concepts/raw_response.md
+15 -10
View File
@@ -25,7 +25,7 @@ user: UserExtract = client.chat.completions.create(
print(user._raw_response)
"""
ChatCompletion(
id='chatcmpl-8pOAsSOIHAmngMBBki3BLN3p552L0',
id='chatcmpl-8u9bsrmmf5YjZyfCtQymoZV8LK1qg',
choices=[
Choice(
finish_reason='stop',
@@ -34,19 +34,24 @@ ChatCompletion(
message=ChatCompletionMessage(
content=None,
role='assistant',
function_call=FunctionCall(
arguments='{\n "name": "Jason",\n "age": 25\n}',
name='UserExtract',
),
tool_calls=None,
function_call=None,
tool_calls=[
ChatCompletionMessageToolCall(
id='call_O5rpXf47YgXiYrYWv45yZUeM',
function=Function(
arguments='{"name":"Jason","age":25}', name='UserExtract'
),
type='function',
)
],
),
)
],
created=1707258346,
model='gpt-3.5-turbo-0613',
created=1708394000,
model='gpt-3.5-turbo-0125',
object='chat.completion',
system_fingerprint=None,
usage=CompletionUsage(completion_tokens=16, prompt_tokens=73, total_tokens=89),
system_fingerprint='fp_69829325d0',
usage=CompletionUsage(completion_tokens=9, prompt_tokens=82, total_tokens=91),
)
"""
```
docs/concepts/reask_validation.md
+1 -1
View File
@@ -91,7 +91,7 @@ except ValidationError as e:
"""
1 validation error for QuestionAnswer
answer
Assertion failed, The statement promotes objectionable behavior. [type=assertion_error, input_value='The meaning of life is to be evil and steal', input_type=str]
Assertion failed, The statement promotes objectionable behavior by encouraging evil and theft. [type=assertion_error, input_value='The meaning of life is to be evil and steal', input_type=str]
For further information visit https://errors.pydantic.dev/2.6/v/assertion_error
"""
```
docs/concepts/types.md
+322 -2
View File
@@ -1,3 +1,323 @@
!!! warning "This page is a work in progress"
# Support for Simple Types
This page is a work in progress. Check out [Pydantic's documentation](https://docs.pydantic.dev/latest/concepts/types/)
Aside from the recommended `pydantic.BaseModel`, and [Iterable](lists.md), and [Partial](partial.md),
Instructor supports simple types like `str`, `int`, `float`, `bool`, `Union`, `Literal`, out of the box. You can use these types directly in your response models.
To add more descriptions you can also use `typing.Annotated` to include more information about the type.
## What happens behind the scenes?
We will actually wrap the response model with a `pydantic.BaseModel` of the following form:
```python
from typing import Annotated
from pydantic import create_model, Field, BaseModel
typehint = Annotated[bool, Field(description="Sample Description")]
model = create_model("Response", content=(typehint, ...), __base__=BaseModel)
print(model.model_json_schema())
"""
{
'properties': {
'content': {
'description': 'Sample Description',
'title': 'Content',
'type': 'boolean',
}
},
'title': 'Response',
'type': 'object',
}
"""
```
## Primitive Types (str, int, float, bool)
```python
import instructor
import openai
client = instructor.patch(openai.OpenAI())
# Response model with simple types like str, int, float, bool
resp = client.chat.completions.create(
model="gpt-3.5-turbo",
response_model=bool,
messages=[
{
"role": "user",
"content": "Is it true that Paris is the capital of France?",
},
],
)
assert resp is True, "Paris is the capital of France"
print(resp)
#> True
```
## Annotated
Annotations can be used to add more information about the type. This can be useful for adding descriptions to the type, along with more complex information like field names, and more.
```python
import instructor
import openai
from typing import Annotated
from pydantic import Field
client = instructor.patch(openai.OpenAI())
UpperCaseStr = Annotated[str, Field(description="string must be upper case")]
# Response model with simple types like str, int, float, bool
resp = client.chat.completions.create(
model="gpt-3.5-turbo",
response_model=UpperCaseStr,
messages=[
{
"role": "user",
"content": "What is the capital of france?",
},
],
)
assert resp == "PARIS", "Paris is the capital of France"
print(resp)
#> PARIS
```
## Literal
When doing simple classification Literals go quite well, they support literal of string, int, bool.
```python
import instructor
import openai
from typing import Literal
client = instructor.patch(openai.OpenAI())
resp = client.chat.completions.create(
model="gpt-3.5-turbo",
response_model=Literal["BILLING", "SHIPPING"],
messages=[
{
"role": "user",
"content": "Classify the following messages: 'I am having trouble with my billing'",
},
],
)
assert resp == "BILLING"
print(resp)
#> BILLING
```
## Enum
Enums are harder to get right without some addition promping but are useful if these are values that are shared across the application.
```python
import instructor
import openai
from enum import Enum
class Label(str, Enum):
BILLING = "BILLING"
SHIPPING = "SHIPPING"
client = instructor.patch(openai.OpenAI())
resp = client.chat.completions.create(
model="gpt-3.5-turbo",
response_model=Label,
messages=[
{
"role": "user",
"content": "Classify the following messages: 'I am having trouble with my billing'",
},
],
)
assert resp == Label.BILLING
print(resp)
#> BILLING
```
## List
```python
import instructor
import openai
from typing import List
client = instructor.patch(openai.OpenAI())
resp = client.chat.completions.create(
model="gpt-3.5-turbo",
response_model=List[int],
messages=[
{
"role": "user",
"content": "Give me the first 5 prime numbers",
},
],
)
assert resp == [2, 3, 5, 7, 11]
print(resp)
#> [2, 3, 5, 7, 11]
```
## Union
Union is a great way to handle multiple types of responses, similar to multiple function calls but not limited to the function calling api, like in JSON_SCHEMA modes.
```python
import instructor
import openai
from pydantic import BaseModel
from typing import Union
client = instructor.patch(openai.OpenAI())
class Add(BaseModel):
a: int
b: int
class Weather(BaseModel):
location: str
resp = client.chat.completions.create(
model="gpt-3.5-turbo",
response_model=Union[Add, Weather],
messages=[
{
"role": "user",
"content": "What is 5 + 5?",
},
],
)
assert resp == Add(a=5, b=5)
print(resp)
#> a=5 b=5
```
## Complex Types
### Pandas DataFrame
This is a more complex example, where we use a custom type to convert markdown to a pandas DataFrame.
```python
from io import StringIO
from typing import Annotated, Any
from pydantic import BeforeValidator, PlainSerializer, InstanceOf, WithJsonSchema
import pandas as pd
import instructor
import openai
def md_to_df(data: Any) -> Any:
# Convert markdown to DataFrame
if isinstance(data, str):
return (
pd.read_csv(
StringIO(data), # Process data
sep="|",
index_col=1,
)
.dropna(axis=1, how="all")
.iloc[1:]
.applymap(lambda x: x.strip())
)
return data
MarkdownDataFrame = Annotated[
# Validates final type
InstanceOf[pd.DataFrame],
# Converts markdown to DataFrame
BeforeValidator(md_to_df),
# Converts DataFrame to markdown on model_dump_json
PlainSerializer(lambda df: df.to_markdown()),
# Adds a description to the type
WithJsonSchema(
{
"type": "string",
"description": """
The markdown representation of the table,
each one should be tidy, do not try to join
tables that should be seperate""",
}
),
]
client = instructor.patch(openai.OpenAI())
resp = client.chat.completions.create(
model="gpt-3.5-turbo",
response_model=MarkdownDataFrame,
messages=[
{
"role": "user",
"content": "Jason is 20, Sarah is 30, and John is 40",
},
],
)
assert isinstance(resp, pd.DataFrame)
print(resp)
"""
Age
Name
Jason 20
Sarah 30
John 40
"""
```
### Lists of Unions
Just like Unions we can use List of Unions to represent multiple types of responses. This will feel similar to the parallel function calls but not limited to the function calling api, like in JSON_SCHEMA modes.
```python
import instructor
import openai
from pydantic import BaseModel
from typing import Union, List
client = instructor.patch(openai.OpenAI())
class Weather(BaseModel, frozen=True):
location: str
class Add(BaseModel, frozen=True):
a: int
b: int
resp = client.chat.completions.create(
model="gpt-3.5-turbo",
response_model=List[Union[Add, Weather]],
messages=[
{
"role": "user",
"content": "Add 5 and 5, and also whats the weather in Toronto?",
},
],
)
assert resp == [Add(a=5, b=5), Weather(location="Toronto")]
print(resp)
#> [Add(a=5, b=5), Weather(location='Toronto')]
```
docs/concepts/usage.md
+1 -1
View File
@@ -23,5 +23,5 @@ user: UserExtract = client.chat.completions.create(
)
print(user._raw_response.usage)
#> CompletionUsage(completion_tokens=16, prompt_tokens=73, total_tokens=89)
#> CompletionUsage(completion_tokens=9, prompt_tokens=82, total_tokens=91)
```
docs/index.md
+18 -12
View File
@@ -127,7 +127,7 @@ print(response.model_dump_json(indent=2))
print(user._raw_response.model_dump_json(indent=2))
"""
{
"id": "chatcmpl-8pOAKwq8OXZVvOCMw4dv713oKplLF",
"id": "chatcmpl-8u9e2TV3ehCgLsRxNLLeAbzpEmBuZ",
"choices": [
{
"finish_reason": "stop",
@@ -136,22 +136,28 @@ print(response.model_dump_json(indent=2))
"message": {
"content": null,
"role": "assistant",
"function_call": {
"arguments": "{\n \"name\": \"Jason\",\n \"age\": 25\n}",
"name": "UserDetail"
},
"tool_calls": null
"function_call": null,
"tool_calls": [
{
"id": "call_3ZuQhfteTLEy7CUokjwnLBHr",
"function": {
"arguments": "{\"name\":\"Jason\",\"age\":25}",
"name": "UserDetail"
},
"type": "function"
}
]
}
}
],
"created": 1707258312,
"model": "gpt-3.5-turbo-0613",
"created": 1708394134,
"model": "gpt-3.5-turbo-0125",
"object": "chat.completion",
"system_fingerprint": null,
"system_fingerprint": "fp_69829325d0",
"usage": {
"completion_tokens": 16,
"prompt_tokens": 72,
"total_tokens": 88
"completion_tokens": 9,
"prompt_tokens": 81,
"total_tokens": 90
}
}
"""
mkdocs.yml
+2 -2
View File
@@ -128,6 +128,8 @@ nav:
- Philosophy: 'concepts/philosophy.md'
- Models: 'concepts/models.md'
- Fields: 'concepts/fields.md'
- Types: 'concepts/types.md'
- Validators: "concepts/reask_validation.md"
- Usage Tokens: 'concepts/usage.md'
- Missing: "concepts/maybe.md"
- Patching: 'concepts/patching.md'
@@ -138,9 +140,7 @@ nav:
- Raw Response: 'concepts/raw_response.md'
- FastAPI: 'concepts/fastapi.md'
- Caching: 'concepts/caching.md'
- Validators: "concepts/reask_validation.md"
- Distillation: "concepts/distillation.md"
- Types: 'concepts/types.md'
- Union: 'concepts/union.md'
- Alias: 'concepts/alias.md'
- Enums: 'concepts/enums.md'
poetry.lock
Generated
+129 -2
View File
@@ -1871,6 +1871,51 @@ files = [
{file = "nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe"},
]
[[package]]
name = "numpy"
version = "1.26.4"
description = "Fundamental package for array computing in Python"
optional = false
python-versions = ">=3.9"
files = [
{file = "numpy-1.26.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:9ff0f4f29c51e2803569d7a51c2304de5554655a60c5d776e35b4a41413830d0"},
{file = "numpy-1.26.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2e4ee3380d6de9c9ec04745830fd9e2eccb3e6cf790d39d7b98ffd19b0dd754a"},
{file = "numpy-1.26.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d209d8969599b27ad20994c8e41936ee0964e6da07478d6c35016bc386b66ad4"},
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{file = "numpy-1.26.4-cp310-cp310-win32.whl", hash = "sha256:bfe25acf8b437eb2a8b2d49d443800a5f18508cd811fea3181723922a8a82b07"},
{file = "numpy-1.26.4-cp310-cp310-win_amd64.whl", hash = "sha256:b97fe8060236edf3662adfc2c633f56a08ae30560c56310562cb4f95500022d5"},
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python-dateutil = ">=2.8.2"
pytz = ">=2020.1"
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consortium-standard = ["dataframe-api-compat (>=0.1.7)"]
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feather = ["pyarrow (>=10.0.1)"]
fss = ["fsspec (>=2022.11.0)"]
gcp = ["gcsfs (>=2022.11.0)", "pandas-gbq (>=0.19.0)"]
hdf5 = ["tables (>=3.8.0)"]
html = ["beautifulsoup4 (>=4.11.2)", "html5lib (>=1.1)", "lxml (>=4.9.2)"]
mysql = ["SQLAlchemy (>=2.0.0)", "pymysql (>=1.0.2)"]
output-formatting = ["jinja2 (>=3.1.2)", "tabulate (>=0.9.0)"]
parquet = ["pyarrow (>=10.0.1)"]
performance = ["bottleneck (>=1.3.6)", "numba (>=0.56.4)", "numexpr (>=2.8.4)"]
plot = ["matplotlib (>=3.6.3)"]
postgresql = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "psycopg2 (>=2.9.6)"]
spss = ["pyreadstat (>=1.2.0)"]
sql-other = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-driver-sqlite (>=0.8.0)"]
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xml = ["lxml (>=4.9.2)"]
[[package]]
name = "pandocfilters"
version = "1.5.1"
@@ -2372,6 +2489,17 @@ files = [
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six = ">=1.5"
[[package]]
name = "pytz"
version = "2024.1"
description = "World timezone definitions, modern and historical"
optional = false
python-versions = "*"
files = [
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[[package]]
name = "pywin32"
version = "306"
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@@ -3300,4 +3427,4 @@ multidict = ">=4.0"
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lock-version = "2.0"
python-versions = "^3.10"
content-hash = "feb8e2f78640f5be62972a35f24179c09e206ff2d765c764eba7a390561705fd"
content-hash = "8a8af75e70138f0eb799bbb22def7e4d69e29a3b85b66f7d5b50c1096114cf79"
pyproject.toml
+1
View File
@@ -41,6 +41,7 @@ mkdocs-minify-plugin = "^0.8.0"
fastapi = "^0.109.2"
redis = "^5.0.1"
diskcache = "^5.6.3"
pandas = "^2.2.0"
[build-system]
requires = ["poetry-core"]
tests/openai/docs/test_docs.py
+2 -24
View File
@@ -12,27 +12,6 @@ def test_readme(example: CodeExample, eval_example: EvalExample):
eval_example.run(example)
@pytest.mark.parametrize("example", find_examples("docs/index.md"), ids=str)
def test_index(example: CodeExample, eval_example: EvalExample):
if eval_example.update_examples:
eval_example.format(example)
eval_example.run_print_update(example)
else:
eval_example.lint(example)
eval_example.run(example)
@pytest.mark.skip("Blogs have too many small examples")
@pytest.mark.parametrize("example", find_examples("docs/blog"), ids=str)
def test_format_blog(example: CodeExample, eval_example: EvalExample):
if eval_example.update_examples:
eval_example.format(example)
eval_example.run_print_update(example)
else:
eval_example.lint(example)
eval_example.run(example)
@pytest.mark.parametrize("example", find_examples("docs/concepts"), ids=str)
def test_format_concepts(example: CodeExample, eval_example: EvalExample):
if eval_example.update_examples:
@@ -43,9 +22,8 @@ def test_format_concepts(example: CodeExample, eval_example: EvalExample):
eval_example.run(example)
@pytest.mark.skip("Examples are too long")
@pytest.mark.parametrize("example", find_examples("docs/examples"), ids=str)
def test_format_examples(example: CodeExample, eval_example: EvalExample):
@pytest.mark.parametrize("example", find_examples("docs/index.md"), ids=str)
def test_index(example: CodeExample, eval_example: EvalExample):
if eval_example.update_examples:
eval_example.format(example)
eval_example.run_print_update(example)