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Test all of our documentation. (#404)

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Co-authored-by: grit-app[bot] <grit-app[bot]@users.noreply.github.com>
This commit is contained in:
Jason Liu
2024-02-05 16:42:57 -05:00
committed by GitHub
parent edc22b8b8c
commit dcb84b1301
37 changed files with 7789 additions and 690 deletions
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.github/workflows/test_docs.yml
+40
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@@ -0,0 +1,40 @@
name: Test Docs
on: [push, pull_request]
jobs:
release:
runs-on: ubuntu-latest
strategy:
matrix:
python-version: ['3.11']
steps:
- uses: actions/checkout@v2
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
- name: Cache Poetry virtualenv
uses: actions/cache@v3
with:
path: ~/.cache/pypoetry/virtualenvs
key: ${{ runner.os }}-poetry-${{ hashFiles('**/poetry.lock') }}
restore-keys: |
${{ runner.os }}-poetry-
- name: Install Poetry
uses: snok/install-poetry@v1.3.1
- name: Install dependencies
run: poetry install --with dev
- name: Install doc dependencies
run: poetry install --with test-docs
- name: Run tests
run: poetry run pytest tests/openai/docs
env:
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
.gitignore
+1
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@@ -169,3 +169,4 @@ tutorials/results.jsonl
tutorials/results.jsonlines
tutorials/schema.json
wandb/settings
math_finetunes.jsonl
.grit/grit.yaml
-2
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@@ -2,5 +2,3 @@ version: 0.0.1
patterns:
- name: github.com/getgrit/python#openai
level: info
- name: github.com/getgrit/python#no_skipped_tests
level: error
README.md
+33 -5
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@@ -79,10 +79,11 @@ For async clients you must use `apatch` vs. `patch`, as shown:
```py
import instructor
from openai import AsyncOpenAI
import asyncio
import openai
from pydantic import BaseModel
aclient = instructor.apatch(AsyncOpenAI())
aclient = instructor.apatch(openai.AsyncOpenAI())
class UserExtract(BaseModel):
@@ -90,7 +91,7 @@ class UserExtract(BaseModel):
age: int
model = await aclient.chat.completions.create(
task = aclient.chat.completions.create(
model="gpt-3.5-turbo",
response_model=UserExtract,
messages=[
@@ -98,7 +99,15 @@ model = await aclient.chat.completions.create(
],
)
assert isinstance(model, UserExtract)
response = asyncio.run(task)
print(response.model_dump_json(indent=2))
"""
{
"name": "Jason",
"age": 25
}
"""
```
### Step 1: Patch the client
@@ -132,8 +141,19 @@ class UserDetail(BaseModel):
Use the `client.chat.completions.create` method to send a prompt and extract the data into the Pydantic object. The `response_model` parameter specifies the Pydantic model to use for extraction. It is helpful to annotate the variable with the type of the response model which will help your IDE provide autocomplete and spell check.
```python
import instructor
import openai
from pydantic import BaseModel
user: UserDetail = client.chat.completions.create(
client = instructor.patch(openai.OpenAI())
class UserDetail(BaseModel):
name: str
age: int
user = client.chat.completions.create(
model="gpt-3.5-turbo",
response_model=UserDetail,
messages=[
@@ -141,8 +161,16 @@ user: UserDetail = client.chat.completions.create(
],
)
assert isinstance(user, UserDetail)
assert user.name == "Jason"
assert user.age == 25
print(user.model_dump_json(indent=2))
"""
{
"name": "Jason",
"age": 25
}
"""
```
## Pydantic Validation
docs/blog/posts/anyscale.md
+1 -1
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@@ -70,7 +70,7 @@ resp = client.chat.completions.create(
response_model=UserDetails,
)
print(resp)
# >>> name='Jason' age=20
# # > name='Jason' age=20
```
You can find more information about Anyscale's output mode support [here](https://docs.endpoints.anyscale.com/).
docs/blog/posts/caching.md
+12 -9
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@@ -76,16 +76,16 @@ Now we can call `extract` multiple times with the same argument, and the result
```python hl_lines="4 8 12"
import time
start = time.perf_counter() # (1)
start = time.perf_counter() # (1)
model = extract("Extract jason is 25 years old")
print(f"Time taken: {time.perf_counter() - start}")
start = time.perf_counter()
model = extract("Extract jason is 25 years old") # (2)
model = extract("Extract jason is 25 years old") # (2)
print(f"Time taken: {time.perf_counter() - start}")
>>> Time taken: 0.9267581660533324
>>> Time taken: 1.2080417945981026e-06 # (3)
#> Time taken: 0.92
#> Time taken: 1.20e-06 # (3)
```
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.
@@ -101,20 +101,23 @@ print(f"Time taken: {time.perf_counter() - start}")
```python hl_lines="3-5 9"
def decorator(func):
def wrapper(*args, **kwargs):
print("Do something before") # (1)
print("Do something before") # (1)
result = func(*args, **kwargs)
print("Do something after") # (2)
print("Do something after") # (2)
return result
return wrapper
@decorator
def say_hello():
print("Hello!")
say_hello()
>>> "Do something before"
>>> "Hello!"
>>> "Do something after"
#> "Do something before"
#> "Hello!"
#> "Do something after"
```
1. The code is executed before the function is called
docs/blog/posts/chain-of-density.md
+25 -25
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@@ -115,21 +115,21 @@ Firstly, we'll need a data model for the initial summary that we will be generat
```py
class GeneratedSummary(BaseModel):
"""
This represents a highly concise summary that includes as many entities as possible from the original source article.
"""
This represents a highly concise summary that includes as many entities as possible from the original source article.
An Entity is a real-world object that's assigned a name - for example, a person, country a product or a book title.
An Entity is a real-world object that's assigned a name - for example, a person, country a product or a book title.
Guidelines
- Make every word count
- The new summary should be highly dense and concise yet self-contained, eg., easily understood without the Article.
- Make space with fusion, compression, and removal of uninformative phrases like "the article discusses"
"""
Guidelines
- Make every word count
- The new summary should be highly dense and concise yet self-contained, eg., easily understood without the Article.
- Make space with fusion, compression, and removal of uninformative phrases like "the article discusses"
"""
summary: str = Field(
...,
description="This represents the final summary generated that captures the meaning of the original article which is as concise as possible. ",
)
summary: str = Field(
...,
description="This represents the final summary generated that captures the meaning of the original article which is as concise as possible. ",
)
```
We eventually transform it into an OpenAI function call as seen below.
@@ -254,21 +254,21 @@ def has_no_absent_entities(cls, absent_entities: List[str]):
return absent_entities
@field_validator("summary")
def min_entity_density(cls, v: str):
tokens = nltk.word_tokenize(v)
num_tokens = len(tokens)
def min_entity_density(cls, v: str):
tokens = nltk.word_tokenize(v)
num_tokens = len(tokens)
# Extract Entities
doc = nlp(v) #(2)!
num_entities = len(doc.ents)
# Extract Entities
doc = nlp(v) #(2)!
num_entities = len(doc.ents)
density = num_entities / num_tokens
if density < 0.08: #(3)!
raise ValueError(
f"The summary of {v} has too few entities. Please regenerate a new summary with more new entities added to it. Remember that new entities can be added at any point of the summary."
)
density = num_entities / num_tokens
if density < 0.08: #(3)!
raise ValueError(
f"The summary of {v} has too few entities. Please regenerate a new summary with more new entities added to it. Remember that new entities can be added at any point of the summary."
)
return v
return v
```
1. Similar to the original paper, we utilize the `NLTK` word tokenizer to count the number of tokens within our generated sentences.
@@ -282,7 +282,7 @@ def has_no_absent_entities(cls, absent_entities: List[str]):
Now that we have our models and the rough flow figured out, let's implement a function to summarize a piece of text using `Chain Of Density` summarization.
```py hl_lines="4 9-24 38-68"
```python hl_lines="4 9-24 38-68"
from openai import OpenAI
import instructor
docs/blog/posts/introduction.md
+10 -3
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@@ -163,11 +163,18 @@ The architecture resembles FastAPI. Most code can be written as Python functions
### FastAPI Stub
```python
app = FastAPI()
import fastapi
from pydantic import BaseModel
class UserDetails(BaseModel):
name: str
age: int
app = fastapi.FastAPI()
@app.get("/user/{user_id}", response_model=UserDetails)
async def get_user(user_id: int) -> UserDetails:
return UserDetails(...)
return ...
```
### Using Instructor as a Function
@@ -176,7 +183,7 @@ async def get_user(user_id: int) -> UserDetails:
def extract_user(str) -> UserDetails:
return client.chat.completions(
response_model=UserDetails,
messages=[...]
messages=[]
)
```
docs/blog/posts/validation-part1.md
+6 -5
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@@ -34,12 +34,13 @@ def validation_function(value):
```python
from openai import OpenAI
import instructor # pip install instructor
import instructor # pip install instructor
from pydantic import BaseModel
# This enables response_model keyword
# from client.chat.completions.create
client = instructor.patch(OpenAI()) # (1)!
client = instructor.patch(OpenAI()) # (1)!
class UserDetail(BaseModel):
name: str
@@ -51,11 +52,11 @@ user: UserDetail = client.chat.completions.create(
response_model=UserDetail,
messages=[
{"role": "user", "content": "Extract Jason is 25 years old"},
]
max_retries=3 # (2)!
],
max_retries=3, # (2)!
)
assert user.name == "Jason" # (3)!
assert user.name == "Jason" # (3)!
assert user.age == 25
```
docs/concepts/caching.md
+30 -27
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@@ -5,12 +5,13 @@ If you want to learn more about concepts in caching and how to use them in your
**When to Use**: Ideal for functions with immutable arguments, called repeatedly with the same parameters in small to medium-sized applications. This makes sense when we might be reusing the same data within a single session. or in an application where we don't need to persist the cache between sessions.
```python
import time
import functools
import openai
import instructor
from pydantic import BaseModel
from openai import OpenAI
client = instructor.patch(OpenAI())
client = instructor.patch(openai.OpenAI())
class UserDetail(BaseModel):
@@ -27,33 +28,28 @@ def extract(data) -> UserDetail:
{"role": "user", "content": data},
],
)
start = time.perf_counter() # (1)
model = extract("Extract jason is 25 years old")
print(f"Time taken: {time.perf_counter() - start}")
#> Time taken: 0.6282629589550197
start = time.perf_counter()
model = extract("Extract jason is 25 years old") # (2)
print(f"Time taken: {time.perf_counter() - start}")
#> Time taken: 1.9171275198459625e-06
```
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.
2. The second time we call `extract`, the result is returned from the cache, and the function is not called.
!!! warning "Changing the Model does not Invalidate the Cache"
Note that changing the model does not invalidate the cache. This is because the cache key is based on the function's name and arguments, not the model. This means that if we change the model, the cache will still return the old result.
Now we can call `extract` multiple times with the same argument, and the result will be cached in memory for faster access.
```python hl_lines="4 8 12"
import time
start = time.perf_counter() # (1)
model = extract("Extract jason is 25 years old")
print(f"Time taken: {time.perf_counter() - start}")
start = time.perf_counter()
model = extract("Extract jason is 25 years old") # (2)
print(f"Time taken: {time.perf_counter() - start}")
>>> Time taken: 0.9267581660533324
>>> Time taken: 1.2080417945981026e-06 # (3)
```
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.
2. The second time we call `extract`, the result is returned from the cache, and the function is not called.
3. The second call to `extract` is much faster because the result is returned from the cache!
**Benefits**: Easy to implement, provides fast access due to in-memory storage, and requires no additional libraries.
??? question "What is a decorator?"
@@ -63,20 +59,27 @@ print(f"Time taken: {time.perf_counter() - start}")
```python hl_lines="3-5 9"
def decorator(func):
def wrapper(*args, **kwargs):
print("Do something before") # (1)
print("Do something before") # (1)
#> Do something before
result = func(*args, **kwargs)
print("Do something after") # (2)
print("Do something after") # (2)
#> Do something after
return result
return wrapper
@decorator
def say_hello():
#> Hello!
print("Hello!")
#> Hello!
say_hello()
>>> "Do something before"
>>> "Hello!"
>>> "Do something after"
#> "Do something before"
#> "Hello!"
#> "Do something after"
```
1. The code is executed before the function is called
docs/concepts/distillation.md
+19 -10
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@@ -54,19 +54,20 @@ def fn(a: int, b: int) -> Multiply:
# Generate some data
for _ in range(10):
random.seed(42)
a = random.randint(100, 999)
b = random.randint(100, 999)
print(fn(a, b))
#> a=958 b=650 result=622700
#> a=538 b=495 result=266310
#> a=703 b=250 result=175750
#> a=803 b=212 result=170236
#> a=499 b=199 result=99301
#> a=893 b=738 result=659034
#> a=414 b=251 result=103914
#> a=916 b=776 result=710816
#> a=219 b=764 result=167316
#> a=764 b=700 result=534800
#> a=754 b=214 result=161356
#> a=754 b=214 result=161356
#> a=754 b=214 result=161356
#> a=754 b=214 result=161356
#> a=754 b=214 result=161356
#> a=754 b=214 result=161356
#> a=754 b=214 result=161356
#> a=754 b=214 result=161356
#> a=754 b=214 result=161356
#> a=754 b=214 result=161356
```
## The Intricacies of Fine-tuning Language Models
@@ -117,6 +118,14 @@ Once a model is trained you can simply change `mode` to `dispatch` and it will u
```python
from instructor import Instructions
from pydantic import BaseModel
class Multiply(BaseModel):
a: int
b: int
result: int
instructions = Instructions(
name="three_digit_multiply",
docs/concepts/enums.md
+12 -3
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@@ -1,23 +1,32 @@
To prevent data misalignment, we can use Enums for standardized fields. Always include an "Other" option as a fallback so the model can signal uncertainty.
```python hl_lines="7 12"
from enum import Enum, auto
from pydantic import BaseModel, Field
from enum import Enum
class Role(Enum):
PRINCIPAL = "PRINCIPAL"
TEACHER = "TEACHER"
STUDENT = "STUDENT"
OTHER = "OTHER""
OTHER = "OTHER"
class UserDetail(BaseModel):
age: int
name: str
role: Role = Field(description="Correctly assign one of the predefined roles to the user.")
role: Role = Field(
description="Correctly assign one of the predefined roles to the user."
)
```
If you're having a hard time with `Enum` and alternative is to use `Literal` instead.
```python hl_lines="4"
from typing import Literal
from pydantic import BaseModel
class UserDetail(BaseModel):
age: int
name: str
docs/concepts/fastapi.md
+14 -1
View File
@@ -35,7 +35,7 @@ class UserDetail(BaseModel):
@app.post("/endpoint", response_model=UserDetail)
def endpoint_function(data: UserData) -> UserDetail:
async def endpoint_function(data: UserData) -> UserDetail:
user_detail = await client.chat.completions.create(
model="gpt-3.5-turbo",
response_model=UserDetail,
@@ -51,8 +51,21 @@ def endpoint_function(data: UserData) -> UserDetail:
`FastAPI` supports streaming responses, which is useful for returning large amounts of data. This feature is particularly useful when working with large language models (LLMs) that generate a large amount of data.
```python hl_lines="6-7"
from fastapi import FastAPI
from fastapi.responses import StreamingResponse
from typing import Iterable
from pydantic import BaseModel
app = FastAPI()
class UserData(BaseModel):
query: str
class UserDetail(BaseModel):
name: str
age: int
# Route to handle SSE events and return users
docs/concepts/fields.md
+4 -15
View File
@@ -100,12 +100,11 @@ These all work as great opportunities to add more information to the JSON schema
Here's an example:
```py
from pydantic import BaseModel, EmailStr, Field, SecretStr
from pydantic import BaseModel, Field, SecretStr
class User(BaseModel):
age: int = Field(description='Age of the user')
email: EmailStr = Field(examples=['marcelo@mail.com'])
name: str = Field(title='Username')
password: SecretStr = Field(
json_schema_extra={
@@ -120,17 +119,7 @@ print(User.model_json_schema())
"""
{
'properties': {
'age': {
'description': 'Age of the user',
'title': 'Age',
'type': 'integer',
},
'email': {
'examples': ['marcelo@mail.com'],
'format': 'email',
'title': 'Email',
'type': 'string',
},
'age': {'description': 'Age of the user', 'title': 'Age', 'type': 'integer'},
'name': {'title': 'Username', 'type': 'string'},
'password': {
'description': 'Password of the user',
@@ -141,14 +130,14 @@ print(User.model_json_schema())
'writeOnly': True,
},
},
'required': ['age', 'email', 'name', 'password'],
'required': ['age', 'name', 'password'],
'title': 'User',
'type': 'object',
}
"""
```
## General notes on JSON schema generation
# General notes on JSON schema generation
- The JSON schema for Optional fields indicates that the value null is allowed.
- The Decimal type is exposed in JSON schema (and serialized) as a string.
docs/concepts/lists.md
+76 -27
View File
@@ -3,6 +3,7 @@
A common use case of structured extraction is defining a single schema class and then making another schema to create a list to do multiple extraction
```python
from typing import List
from pydantic import BaseModel
@@ -13,6 +14,30 @@ class User(BaseModel):
class Users(BaseModel):
users: List[User]
print(Users.model_json_schema())
"""
{
'$defs': {
'User': {
'properties': {
'name': {'title': 'Name', 'type': 'string'},
'age': {'title': 'Age', 'type': 'integer'},
},
'required': ['name', 'age'],
'title': 'User',
'type': 'object',
}
},
'properties': {
'users': {'items': {'$ref': '#/$defs/User'}, 'title': 'Users', 'type': 'array'}
},
'required': ['users'],
'title': 'Users',
'type': 'object',
}
"""
```
Defining a task and creating a list of classes is a common enough pattern that we make this convenient by making use of `Iterable[T]`. This lets us dynamically create a new class that:
@@ -32,16 +57,16 @@ from pydantic import BaseModel
client = instructor.patch(OpenAI(), mode=instructor.function_calls.Mode.JSON)
class User(BaseModel):
name: str
age: int
Users = Iterable[User]
users = client.chat.completions.create(
model="gpt-3.5-turbo-1106",
temperature=0.1,
response_model=Users,
response_model=Iterable[User],
stream=False,
messages=[
{
@@ -53,11 +78,11 @@ users = client.chat.completions.create(
],
)
for user in users:
assert isinstance(user, User)
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"=10
```
## Streaming Tasks
@@ -67,15 +92,24 @@ We can also generate tasks as the tokens are streamed in by defining an `Iterabl
Lets look at an example in action with the same class
```python hl_lines="6 26"
import instructor
import openai
from typing import Iterable
from pydantic import BaseModel
client = instructor.patch(openai.OpenAI(), mode=instructor.Mode.TOOLS)
class User(BaseModel):
name: str
age: int
Users = Iterable[User]
users = client.chat.completions.create(
model="gpt-4",
temperature=0.1,
stream=True,
response_model=Users,
response_model=Iterable[User],
messages=[
{
"role": "system",
@@ -83,22 +117,16 @@ users = client.chat.completions.create(
},
{
"role": "user",
"content": (
f"Consider the data below:\n{input}"
"Correctly segment it into entitites"
"Make sure the JSON is correct"
),
"content": (f"Extract `Jason is 10 and John is 10`"),
},
],
max_tokens=1000,
)
for user in users:
assert isinstance(user, User)
print(user)
>>> name="Jason" "age"=10
>>> name="John" "age"=10
#> name='Jason' age=10
#> name='John' age=10
```
## Asynchronous Streaming
@@ -106,15 +134,36 @@ for user in users:
I also just want to call out in this example that `instructor` also supports asynchronous streaming. This is useful when you want to stream a response model and process the results as they come in, but you'll need to use the `async for` syntax to iterate over the results.
```python
model = await client.chat.completions.create(
model="gpt-4",
response_model=Iterable[UserExtract],
max_retries=2,
stream=stream,
messages=[
{"role": "user", "content": "Make two up people"},
],
)
async for m in model:
assert isinstance(m, UserExtract)
import instructor
import openai
from typing import Iterable
from pydantic import BaseModel
client = instructor.patch(openai.AsyncOpenAI(), mode=instructor.Mode.TOOLS)
class UserExtract(BaseModel):
name: str
age: int
async def print_iterable_results():
model = await client.chat.completions.create(
model="gpt-4",
response_model=Iterable[UserExtract],
max_retries=2,
stream=True,
messages=[
{"role": "user", "content": "Make two up people"},
],
)
async for m in model:
print(m)
#> name='John Doe' age=30
#> name='Jane Smith' age=25
import asyncio
asyncio.run(print_iterable_results())
```
docs/concepts/maybe.md
+41 -40
View File
@@ -1,6 +1,6 @@
# Handling Missing Data
The `Maybe` pattern is a concept in functional programming used for error handling. Instead of raising exceptions or returning `None`, you can use a `Maybe` type to encapsulate both the result and potential errors.
The `Maybe` pattern is a concept in functional programming used for error handling. Instead of raising exceptions or returning `None`, you can use a `Maybe` type to encapsulate both the result and potential errors.
This pattern is particularly useful when making LLM calls, as providing language models with an escape hatch can effectively reduce hallucinations.
@@ -9,7 +9,8 @@ This pattern is particularly useful when making LLM calls, as providing language
Using Pydantic, we'll first define the `UserDetail` and `MaybeUser` classes.
```python
from pydantic import BaseModel, Field, Optional
from pydantic import BaseModel, Field
from typing import Optional
class UserDetail(BaseModel):
@@ -35,14 +36,31 @@ Once we have the model defined, we can create a function that uses the `Maybe` p
```python
import instructor
from openai import OpenAI
import openai
from pydantic import BaseModel, Field
from typing import Optional
# This enables the `response_model` keyword
client = instructor.patch(OpenAI())
client = instructor.patch(openai.OpenAI())
class UserDetail(BaseModel):
age: int
name: str
role: Optional[str] = Field(default=None)
class MaybeUser(BaseModel):
result: Optional[UserDetail] = Field(default=None)
error: bool = Field(default=False)
message: Optional[str] = Field(default=None)
def __bool__(self):
return self.result is not None
def extract(content: str) -> MaybeUser:
return openai.chat.completions.create(
return client.chat.completions.create(
model="gpt-3.5-turbo",
response_model=MaybeUser,
messages=[
@@ -52,47 +70,30 @@ def extract(content: str) -> MaybeUser:
user1 = extract("Jason is a 25-year-old scientist")
# output:
print(user1.model_dump_json(indent=2))
"""
{
"result": {"age": 25, "name": "Jason", "role": "scientist"},
"error": false,
"message": null,
"result": {
"age": 25,
"name": "Jason",
"role": "scientist"
},
"error": false,
"message": null
}
"""
user2 = extract("Unknown user")
# output:
{"result": null, "error": true, "message": "User not found"}
print(user2.model_dump_json(indent=2))
"""
{
"result": null,
"error": false,
"message": "Unknown user"
}
"""
```
As you can see, when the data is extracted successfully, the `result` field contains the `UserDetail` instance. When an error occurs, the `error` field is set to `True`, and the `message` field contains the error message.
## Handling the result
There are a few ways we can handle the result. Normally, we can just access the individual fields.
```python
def process_user_detail(maybe_user: MaybeUser):
if not maybe_user.error:
user = maybe_user.result
print(f"User {user.name} is {user.age} years old")
else:
print(f"Not found: {user1.message}")
```
### Pattern Matching
We can also use pattern matching to handle the result. This is a great way to handle errors in a structured way.
```python
def process_user_detail(maybe_user: MaybeUser):
match maybe_user:
case MaybeUser(error=True, message=msg):
print(f"Error: {msg}")
case MaybeUser(result=user_detail) if user_detail:
assert isinstance(user_detail, UserDetail)
print(f"User {user_detail.name} is {user_detail.age} years old")
case _:
print("Unknown error")
```
If you want to learn more about pattern matching, check out Pydantic's docs on [Structural Pattern Matching](https://docs.pydantic.dev/latest/concepts/models/#structural-pattern-matching)
docs/concepts/models.md
+41 -30
View File
@@ -2,7 +2,8 @@
Defining LLM output schemas in Pydantic is done via `pydantic.BaseModel`. To learn more about models in Pydantic, check out their [documentation](https://docs.pydantic.dev/latest/concepts/models/).
After defining a Pydantic model, we can use it as the `response_model` in your client `create` calls to OpenAI or any other supported model. The job of the `response_model` parameter is to:
After defining a Pydantic model, we can use it as the `response_model` in your client `create` calls to OpenAI or any other supported model. The job of the `response_model` parameter is to:
- Define the schema and prompts for the language model
- Validate the response from the API
- Return a Pydantic model instance.
@@ -32,6 +33,10 @@ Here all docstrings, types, and field annotations will be used to generate the p
If we use `Optional` and `default`, they will be considered not required when sent to the language model
```python
from pydantic import BaseModel, Field
from typing import Optional
class User(BaseModel):
name: str = Field(description="The name of the user.")
age: int = Field(description="The age of the user.")
@@ -77,6 +82,9 @@ print(BarModel.model_fields.keys())
We can then use this information to create the model.
```python
from pydantic import BaseModel, create_model
from typing import List
types = {
'string': str,
'integer': int,
@@ -85,43 +93,42 @@ print(BarModel.model_fields.keys())
'List[str]': List[str],
}
# Mocked cursor.fetchall()
cursor = [
('name', 'string', 'The name of the user.'),
('age', 'integer', 'The age of the user.'),
('email', 'string', 'The email of the user.'),
]
BarModel = create_model(
'User',
**{
property_name: (types[property_type], description)
for property_name, property_type, description in cursor.fetchall()
for property_name, property_type, description in cursor
},
__base__=BaseModel,
)
print(BarModel.model_json_schema())
"""
{
'properties': {
'name': {'default': 'The name of the user.', 'title': 'Name', 'type': 'string'},
'age': {'default': 'The age of the user.', 'title': 'Age', 'type': 'integer'},
'email': {
'default': 'The email of the user.',
'title': 'Email',
'type': 'string',
},
},
'title': 'User',
'type': 'object',
}
"""
```
This would be useful when different users have different descriptions for the same model. We can use the same model but have different prompts for each user.
## Structural Pattern Matching
Pydantic supports structural pattern matching for models, as introduced by [PEP 636](https://peps.python.org/pep-0636/) in Python 3.10.
```python
from pydantic import BaseModel
class Pet(BaseModel):
name: str
species: str
a = Pet(name='Bones', species='dog')
match a:
# match `species` to 'dog', declare and initialize `dog_name`
case Pet(species='dog', name=dog_name):
print(f'{dog_name} is a dog')
#> Bones is a dog
# default case
case _:
print('No dog matched')
```
## Adding Behavior
We can add methods to our Pydantic models, just as any plain Python class. We might want to do this to add some custom logic to our models.
@@ -142,11 +149,15 @@ class SearchQuery(BaseModel):
query_type: Literal["web", "image", "video"]
def execute(self):
# do some logic here
return results
print(f"Searching for {self.query} of type {self.query_type}")
#> Searching for cat of type image
query = client.chat.completions.create(..., response_model=SearchQuery)
query = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Search for a picture of a cat"}],
response_model=SearchQuery,
)
results = query.execute()
```
docs/concepts/parallel.md
+5 -12
View File
@@ -28,10 +28,7 @@ class GoogleSearch(BaseModel):
query: str
client = instructor.patch(
openai.OpenAI(),
mode=instructor.Mode.PARALLEL_TOOLS #(1)!
)
client = instructor.patch(openai.OpenAI(), mode=instructor.Mode.PARALLEL_TOOLS) # (1)!
function_calls = client.chat.completions.create(
model="gpt-4-turbo-preview",
@@ -42,21 +39,17 @@ function_calls = client.chat.completions.create(
"content": "What is the weather in toronto and dallas and who won the super bowl?",
},
],
response_model=Iterable[Weather | GoogleSearch], #(2)!
response_model=Iterable[Weather | GoogleSearch], # (2)!
)
for fc in function_calls:
print(fc)
"""
#> location='Toronto' units='metric'
#> location='Dallas' units='imperial'
#> query='Super Bowl winner'
```
1. Set the mode to `PARALLEL_TOOLS` to enable parallel function calling.
2. Set the response model to `Iterable[Weather | GoogleSearch]` to indicate that the response will be a list of `Weather` and `GoogleSearch` objects. This is necessary because the response will be a list of objects, and we need to specify the types of the objects in the list.
```python
Weather(location='toronto', units='imperial')
Weather(location='dallas', units='imperial')
GoogleSearch(query='who won the super bowl?')
```
Noticed that the `response_model` Must be in the form `Iterable[Type1 | Type2 | ...]` or `Iterable[Type1]` where `Type1` and `Type2` are the types of the objects that will be returned in the response.
docs/concepts/partial.md
+6717 -14
View File
File diff suppressed because it is too large Load Diff
docs/concepts/patching.md
+15 -9
View File
@@ -15,19 +15,19 @@ There are three methods for structured output:
## Function Calling
```python
from openai import OpenAI
import instructor
from openai import OpenAI
client = instructor.patch(OpenAI())
client = instructor.patch(OpenAI(), mode=instructor.Mode.FUNCTIONS)
```
## Tool Calling
```python
import instructor
from instructor import Mode
from openai import OpenAI
client = instructor.patch(OpenAI(), mode=Mode.TOOLS)
client = instructor.patch(OpenAI(), mode=instructor.Mode.TOOLS)
```
## JSON Mode
@@ -48,11 +48,11 @@ client = instructor.patch(OpenAI(), mode=Mode.JSON)
```python
import instructor
from instructor import Mode
from openai import OpenAI
client = instructor.patch(OpenAI(), mode=Mode.MD_JSON)
client = instructor.patch(OpenAI(), mode=instructor.Mode.MD_JSON)
```
### Schema Integration
In JSON Mode, the schema is part of the system message:
@@ -63,6 +63,12 @@ from openai import OpenAI
client = instructor.patch(OpenAI())
class UserExtract(instructor.OpenAISchema):
name: str
age: int
response = client.chat.completions.create(
model="gpt-3.5-turbo-1106",
response_format={"type": "json_object"},
@@ -77,7 +83,7 @@ response = client.chat.completions.create(
},
],
)
user = UserExtract.from_response(response, mode=Mode.JSON)
assert user.name.lower() == "jason"
assert user.age == 25
user = UserExtract.from_response(response, mode=instructor.Mode.JSON)
print(user)
#> name='Jason' age=25
```
docs/concepts/prompting.md
+49
View File
@@ -37,6 +37,7 @@ Use Python's Optional type and set a default value to prevent undesired defaults
```python hl_lines="6"
from typing import Optional
from pydantic import BaseModel, Field
class UserDetail(BaseModel):
@@ -50,6 +51,10 @@ class UserDetail(BaseModel):
You can create a wrapper class to hold either the result of an operation or an error message. This allows you to remain within a function call even if an error occurs, facilitating better error handling without breaking the code flow.
```python
from pydantic import BaseModel, Field
from typing import Optional
class UserDetail(BaseModel):
age: int
name: str
@@ -73,6 +78,13 @@ You can further simplify this using instructor to create the `Maybe` pattern dyn
```python
import instructor
from pydantic import BaseModel
class UserDetail(BaseModel):
age: int
name: str
MaybeUser = instructor.Maybe(UserDetail)
```
@@ -85,6 +97,7 @@ To prevent data misalignment, use Enums for standardized fields. Always include
```python hl_lines="7 12"
from enum import Enum, auto
from pydantic import BaseModel, Field
class Role(Enum):
@@ -105,6 +118,10 @@ class UserDetail(BaseModel):
If you're having a hard time with `Enum` and alternative is to use `Literal`
```python hl_lines="4"
from typing import Literal
from pydantic import BaseModel
class UserDetail(BaseModel):
age: int
name: str
@@ -118,6 +135,9 @@ If you'd like to improve performance more you can reiterate the requirements in
For complex attributes, it helps to reiterate the instructions in the field's description.
```python hl_lines="5 11"
from pydantic import BaseModel, Field
class Role(BaseModel):
"""
Extract the role based on the following rules ...
@@ -142,6 +162,7 @@ When you need to extract undefined attributes, use a list of key-value pairs.
```python hl_lines="10"
from typing import List
from pydantic import BaseModel, Field
class Property(BaseModel):
@@ -162,6 +183,10 @@ class UserDetail(BaseModel):
When dealing with lists of attributes, especially arbitrary properties, it's crucial to manage the length. You can use prompting and enumeration to limit the list length, ensuring a manageable set of properties.
```python hl_lines="2 9"
from typing import List
from pydantic import BaseModel, Field
class Property(BaseModel):
index: str = Field(..., description="Monotonically increasing ID")
key: str
@@ -182,6 +207,10 @@ class UserDetail(BaseModel):
For simple types, tuples can be a more compact alternative to custom classes, especially when the properties don't require additional descriptions.
```python hl_lines="4"
from typing import List, Tuple
from pydantic import BaseModel, Field
class UserDetail(BaseModel):
age: int
name: str
@@ -196,6 +225,16 @@ class UserDetail(BaseModel):
For multiple users, aim to use consistent key names when extracting properties.
```python
from typing import List
from pydantic import BaseModel
class UserDetail(BaseModel):
id: int
age: int
name: str
class UserDetails(BaseModel):
"""
Extract information for multiple users.
@@ -212,6 +251,10 @@ This refined guide should offer a cleaner and more organized approach to structu
In cases where relationships exist between entities, it's vital to define them explicitly in the model. The following example demonstrates how to define relationships between users by incorporating an id and a friends field:
```python hl_lines="2 5 8"
from typing import List
from pydantic import BaseModel, Field
class UserDetail(BaseModel):
id: int = Field(..., description="Unique identifier for each user.")
age: int
@@ -234,6 +277,9 @@ class UserRelationships(BaseModel):
You can reuse the same component for different contexts within a model. In this example, the TimeRange component is used for both work_time and leisure_time.
```python hl_lines="9 10"
from pydantic import BaseModel, Field
class TimeRange(BaseModel):
start_time: int = Field(..., description="The start time in hours.")
end_time: int = Field(..., description="The end time in hours.")
@@ -254,6 +300,9 @@ class UserDetail(BaseModel):
Sometimes, a component like TimeRange may require some context or additional logic to be used effectively. Employing a "chain of thought" field within the component can help in understanding or optimizing the time range allocations.
```python hl_lines="2"
from pydantic import BaseModel, Field
class TimeRange(BaseModel):
chain_of_thought: str = Field(
..., description="Step by step reasoning to get the correct time range"
docs/concepts/raw_response.md
+2 -31
View File
@@ -25,7 +25,7 @@ user: UserExtract = client.chat.completions.create(
print(user._raw_response)
"""
ChatCompletion(
id='chatcmpl-8owwph3BaKJddZKqPIOygvQy1CmLu',
id='chatcmpl-8oz1eZxBVDCUZu7Q247DenDK8T3ji',
choices=[
Choice(
finish_reason='stop',
@@ -42,7 +42,7 @@ ChatCompletion(
),
)
],
created=1707153687,
created=1707161674,
model='gpt-3.5-turbo-0613',
object='chat.completion',
system_fingerprint=None,
@@ -56,32 +56,3 @@ ChatCompletion(
This is the recommended way to access the tokens usage, since it is a pydantic model you can use any of the pydantic model methods on it. For example, you can access the `total_tokens` by doing `user._raw_response.usage.total_tokens`. Note that this also includes the tokens used during any previous unsuccessful attempts.
In the future, we may add additional hooks to the `raw_response` to make it easier to access the tokens usage.
**Output:**
```python
{
"id": "chatcmpl-8bHUPGZc9vAXBraJlebf8ciz4AMuh",
"choices": [
{
"finish_reason": "stop",
"index": 0,
"message": {
"content": null,
"role": "assistant",
"function_call": {
"arguments": "{\n \"name\": \"Jason\",\n \"age\": 25\n}",
"name": "UserExtract",
},
"tool_calls": null,
},
"logprobs": null,
}
],
"created": 1703896057,
"model": "gpt-3.5-turbo-0613",
"object": "chat.completion",
"system_fingerprint": null,
"usage": {"completion_tokens": 16, "prompt_tokens": 73, "total_tokens": 89},
}
```
docs/concepts/reask_validation.md
+29 -5
View File
@@ -111,6 +111,7 @@ answer
Validators are a great tool for ensuring some property of the outputs. When you use the `patch()` method with the `openai` client, you can use the `max_retries` parameter to set the number of times you can reask the model to correct the output.
It is a great layer of defense against bad outputs of two forms:
1. Pydantic Validation Errors (code or llm based)
2. JSON Decoding Errors (when the model returns a bad response)
@@ -118,12 +119,13 @@ It is a great layer of defense against bad outputs of two forms:
Notice that the field validator wants the name in uppercase, but the user input is lowercase. The validator will raise a `ValueError` if the name is not in uppercase.
```python hl_lines="11-16"
```python hl_lines="12-17"
import openai
import instructor
from pydantic import BaseModel, field_validator
# Apply the patch to the OpenAI client
client = instructor.patch(OpenAI())
client = instructor.patch(openai.OpenAI())
class UserDetails(BaseModel):
@@ -142,7 +144,19 @@ class UserDetails(BaseModel):
Here, the `UserDetails` model is passed as the `response_model`, and `max_retries` is set to 2.
```python hl_lines="4 10"
```python
import instructor
import openai
from pydantic import BaseModel
client = instructor.patch(openai.OpenAI(), mode=instructor.Mode.TOOLS)
class UserDetails(BaseModel):
name: str
age: int
model = client.chat.completions.create(
model="gpt-3.5-turbo",
response_model=UserDetails,
@@ -152,7 +166,13 @@ model = client.chat.completions.create(
],
)
assert model.name == "JASON"
print(model.model_dump_json(indent=2))
"""
{
"name": "jason",
"age": 25
}
"""
```
### What happens behind the scenes?
@@ -160,9 +180,12 @@ assert model.name == "JASON"
Behind the scenes, the `instructor.patch()` method adds a `max_retries` parameter to the `openai.ChatCompletion.create()` method. The `max_retries` parameter will trigger up to 2 reattempts if the `name` attribute fails the uppercase validation in `UserDetails`.
```python
from pydantic import ValidationError
try:
...
except (ValidationError, JSONDecodeError) as e:
except ValidationError as e:
kwargs["messages"].append(response.choices[0].message)
kwargs["messages"].append(
{
@@ -175,6 +198,7 @@ except (ValidationError, JSONDecodeError) as e:
## Advanced Validation Techniques
The docs are currently incomplete, but we have a few advanced validation techniques that we're working on documenting better such as model level validation, and using a validation context. Check out our example on [verifying citations](../examples/exact_citations.md) which covers:
1. Validate the entire object with all attributes rather than one attribute at a time
2. Using some 'context' to validate the object: In this case, we use the `context` to check if the citation existed in the original text.
docs/concepts/retrying.md
+69 -16
View File
@@ -24,15 +24,18 @@ def uppercase_validator(v):
class UserDetail(BaseModel):
name: Annotated[str, AfterValidator(uppercase_validator)]
age: int
```
Now if we create a user detail with a lowercase name, we'll see an error.
```python
UserDetail(name="jason", age=12)
>>> 1 validation error for UserDetail
>>> name
>>> Value error, Name must be ALL CAPS [type=value_error, input_value='jason', input_type=str]
try:
UserDetail(name="jason", age=12)
except Exception as e:
print(e)
"""
1 validation error for UserDetail
name
Value error, Name must be ALL CAPS [type=value_error, input_value='jason', input_type=str]
For further information visit https://errors.pydantic.dev/2.6/v/value_error
"""
```
## Simple: Max Retries
@@ -40,6 +43,16 @@ UserDetail(name="jason", age=12)
The simplest way of defining a retry is just defining the maximum number of retries.
```python
import openai
import instructor
from pydantic import BaseModel
class UserDetail(BaseModel):
name: str
age: int
client = instructor.patch(openai.OpenAI(), mode=instructor.Mode.TOOLS)
response = client.chat.completions.create(
@@ -50,19 +63,19 @@ response = client.chat.completions.create(
],
max_retries=3, # (1)!
)
assert response.name == "JASON" # (2)!
print(response.model_dump_json(indent=2))
"""
{
"name": "jason",
"age": 12
}
"""
# (2)!
```
1. We set the maximum number of retries to 3. This means that if the model returns an error, we'll reask the model up to 3 times.
2. We assert that the name is in all caps.
```json
{
"name": "JASON",
"age": 12
}
```
## Advanced: Retry Logic
If you want more control over how we define retries such as back-offs and additional retry logic we can use a library called Tenacity. To learn more, check out the documentation on the [Tenacity](https://tenacity.readthedocs.io/en/latest/) website.
@@ -70,8 +83,19 @@ If you want more control over how we define retries such as back-offs and additi
Rather than using the decorator `@retry`, we can use the `Retrying` and `AsyncRetrying` classes to define our own retry logic.
```python
import openai
import instructor
from pydantic import BaseModel
from tenacity import Retrying, stop_after_attempt, wait_fixed
client = instructor.patch(openai.OpenAI(), mode=instructor.Mode.TOOLS)
class UserDetail(BaseModel):
name: str
age: int
response = client.chat.completions.create(
model="gpt-4-turbo-preview",
response_model=UserDetail,
@@ -83,6 +107,13 @@ response = client.chat.completions.create(
wait=wait_fixed(1), # (2)!
), # (3)!
)
print(response.model_dump_json(indent=2))
"""
{
"name": "jason",
"age": 12
}
"""
```
1. We stop after 2 attempts
@@ -94,9 +125,20 @@ response = client.chat.completions.create(
If you're using asynchronous code, you can use `AsyncRetrying` instead.
```python
import openai
import instructor
from pydantic import BaseModel
from tenacity import AsyncRetrying, stop_after_attempt, wait_fixed
response = await client.chat.completions.create(
client = instructor.patch(openai.AsyncOpenAI(), mode=instructor.Mode.TOOLS)
class UserDetail(BaseModel):
name: str
age: int
task = client.chat.completions.create(
model="gpt-4-turbo-preview",
response_model=UserDetail,
messages=[
@@ -107,6 +149,17 @@ response = await client.chat.completions.create(
wait=wait_fixed(1),
),
)
import asyncio
response = asyncio.run(task)
print(response.model_dump_json(indent=2))
"""
{
"name": "jason",
"age": 12
}
"""
```
## Other Features of Tenacity
docs/concepts/union.md
+4
View File
@@ -7,6 +7,10 @@ While many libraries support multiple function calls, and tool calls support mul
You can use `Union` types to write _agents_ that can dynamically choose actions - by choosing an output class. For example, in a search and lookup function, the LLM can determine whether to execute another search, lookup or other action.
```python
from pydantic import BaseModel
from typing import Union
class Search(BaseModel):
query: str
docs/examples/moderation.md
+1 -1
View File
@@ -42,7 +42,7 @@ Response(message="I want to make them suffer the consequences")
The validator will raise a `ValidationError` if the content violates the policies, like so:
```python
```plaintext
ValidationError: 1 validation error for Response
message
Value error, `I want to make them suffer the consequences` was flagged for harassment, harassment_threatening, violence, harassment/threatening [type=value_error, input_value='I want to make them suffer the consequences', input_type=str]
docs/examples/recursive.md
+1 -1
View File
@@ -154,7 +154,7 @@ In this example, we call `parse_tree_to_filesystem` with a string representing a
After parsing the string into a `DirectoryTree` object, we call `root.print_paths()` to print the paths of the root node and its children. The output of this example will be:
```python
```plaintext
root NodeType.FOLDER
root/folder1 NodeType.FOLDER
root/folder1/file1.txt NodeType.FILE
docs/index.md
+62 -7
View File
@@ -47,6 +47,13 @@ user = client.chat.completions.create(
assert isinstance(user, UserDetail)
assert user.name == "Jason"
assert user.age == 25
print(user.model_dump_json(indent=2))
"""
{
"name": "Jason",
"age": 25
}
"""
```
**Using async clients**
@@ -54,6 +61,7 @@ assert user.age == 25
For async clients you must use `apatch` vs `patch` like so:
```py
import asyncio
import instructor
from openai import AsyncOpenAI
from pydantic import BaseModel
@@ -66,7 +74,7 @@ class UserExtract(BaseModel):
age: int
model = await aclient.chat.completions.create(
task = aclient.chat.completions.create(
model="gpt-3.5-turbo",
response_model=UserExtract,
messages=[
@@ -74,15 +82,34 @@ model = await aclient.chat.completions.create(
],
)
assert isinstance(model, UserExtract)
response = asyncio.run(task)
print(response.model_dump_json(indent=2))
"""
{
"name": "Jason",
"age": 25
}
"""
```
!!! note "Accessing the original response"
!!! note "Accessing the original response and usage tokens"
If you want to access anything like usage or other metadata, the original response is available on the `Model._raw_response` attribute.
```python
user: UserDetail = client.chat.completions.create(
import openai
import instructor
from pydantic import BaseModel
client = instructor.patch(openai.OpenAI())
class UserDetail(BaseModel):
name: str
age: int
user = client.chat.completions.create(
model="gpt-3.5-turbo",
response_model=UserDetail,
messages=[
@@ -90,9 +117,37 @@ assert isinstance(model, UserExtract)
],
)
from openai.types.chat.chat_completion import ChatCompletion
assert isinstance(user._raw_response, ChatCompletion)
print(user._raw_response.model_dump_json(indent=2))
"""
{
"id": "chatcmpl-8oz12FZ9fvCypa8yNuKbHXueJQkOv",
"choices": [
{
"finish_reason": "stop",
"index": 0,
"logprobs": null,
"message": {
"content": null,
"role": "assistant",
"function_call": {
"arguments": "{\n \"name\": \"Jason\",\n \"age\": 25\n}",
"name": "UserDetail"
},
"tool_calls": null
}
}
],
"created": 1707161636,
"model": "gpt-3.5-turbo-0613",
"object": "chat.completion",
"system_fingerprint": null,
"usage": {
"completion_tokens": 16,
"prompt_tokens": 72,
"total_tokens": 88
}
}
"""
```
## Why use Instructor?
docs/why.md
+2 -2
View File
@@ -193,8 +193,8 @@ for user in users:
assert isinstance(user, User)
print(user)
>>> name="Jason" "age"=10
>>> name="John" "age"=10
#> name="Jason" "age"=10
#> name="John" "age"=10
```
## Partial Extraction
instructor/dsl/partialjson.py
+1 -1
View File
@@ -125,7 +125,7 @@ class JSONParser:
s = s[end + 1 :]
return json.loads(str_val), s
def parse_number(self, s):
def parse_number(self, s, e):
i = 0
while i < len(s) and s[i] in "0123456789.-":
i += 1
instructor/patch.py
+5 -9
View File
@@ -297,7 +297,7 @@ async def retry_async(
stop=stop_after_attempt(max_retries),
reraise=True,
)
if not isinstance(max_retries, AsyncRetrying):
if not isinstance(max_retries, (AsyncRetrying, Retrying)):
raise ValueError(
"max_retries must be an `int` or a `tenacity.AsyncRetrying` object"
)
@@ -318,9 +318,7 @@ async def retry_async(
)
total_usage.prompt_tokens += response.usage.prompt_tokens or 0
total_usage.total_tokens += response.usage.total_tokens or 0
response.usage = (
total_usage # Replace each response usage with the total usage
)
response.usage = total_usage # Replace each response usage with the total usage
return await process_response_async(
response,
response_model=response_model,
@@ -383,8 +381,8 @@ def retry_sync(
stop=stop_after_attempt(max_retries),
reraise=True,
)
if not isinstance(max_retries, Retrying):
raise ValueError("max_retries must be an int or a `tenacityRetrying` object")
if not isinstance(max_retries, (Retrying, AsyncRetrying)):
raise ValueError("max_retries must be an int or a `tenacity.Retrying` object")
try:
for attempt in max_retries:
@@ -401,9 +399,7 @@ def retry_sync(
)
total_usage.prompt_tokens += response.usage.prompt_tokens or 0
total_usage.total_tokens += response.usage.total_tokens or 0
response.usage = (
total_usage # Replace each response usage with the total usage
)
response.usage = total_usage # Replace each response usage with the total usage
return process_response(
response,
response_model=response_model,
poetry.lock
Generated
+399 -230
View File
@@ -187,15 +187,61 @@ files = [
[package.extras]
dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
[[package]]
name = "black"
version = "24.1.1"
description = "The uncompromising code formatter."
optional = false
python-versions = ">=3.8"
files = [
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{file = "black-24.1.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:1a95915c98d6e32ca43809d46d932e2abc5f1f7d582ffbe65a5b4d1588af7445"},
{file = "black-24.1.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2fa6a0e965779c8f2afb286f9ef798df770ba2b6cee063c650b96adec22c056a"},
{file = "black-24.1.1-cp310-cp310-win_amd64.whl", hash = "sha256:5242ecd9e990aeb995b6d03dc3b2d112d4a78f2083e5a8e86d566340ae80fec4"},
{file = "black-24.1.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:fc1ec9aa6f4d98d022101e015261c056ddebe3da6a8ccfc2c792cbe0349d48b7"},
{file = "black-24.1.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:0269dfdea12442022e88043d2910429bed717b2d04523867a85dacce535916b8"},
{file = "black-24.1.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b3d64db762eae4a5ce04b6e3dd745dcca0fb9560eb931a5be97472e38652a161"},
{file = "black-24.1.1-cp311-cp311-win_amd64.whl", hash = "sha256:5d7b06ea8816cbd4becfe5f70accae953c53c0e53aa98730ceccb0395520ee5d"},
{file = "black-24.1.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:e2c8dfa14677f90d976f68e0c923947ae68fa3961d61ee30976c388adc0b02c8"},
{file = "black-24.1.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:a21725862d0e855ae05da1dd25e3825ed712eaaccef6b03017fe0853a01aa45e"},
{file = "black-24.1.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:07204d078e25327aad9ed2c64790d681238686bce254c910de640c7cc4fc3aa6"},
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{file = "black-24.1.1-cp39-cp39-win_amd64.whl", hash = "sha256:ecba2a15dfb2d97105be74bbfe5128bc5e9fa8477d8c46766505c1dda5883aac"},
{file = "black-24.1.1-py3-none-any.whl", hash = "sha256:5cdc2e2195212208fbcae579b931407c1fa9997584f0a415421748aeafff1168"},
{file = "black-24.1.1.tar.gz", hash = "sha256:48b5760dcbfe5cf97fd4fba23946681f3a81514c6ab8a45b50da67ac8fbc6c7b"},
]
[package.dependencies]
click = ">=8.0.0"
mypy-extensions = ">=0.4.3"
packaging = ">=22.0"
pathspec = ">=0.9.0"
platformdirs = ">=2"
tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""}
[package.extras]
colorama = ["colorama (>=0.4.3)"]
d = ["aiohttp (>=3.7.4)", "aiohttp (>=3.7.4,!=3.9.0)"]
jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
uvloop = ["uvloop (>=0.15.2)"]
[[package]]
name = "certifi"
version = "2023.11.17"
version = "2024.2.2"
description = "Python package for providing Mozilla's CA Bundle."
optional = false
python-versions = ">=3.6"
files = [
{file = "certifi-2023.11.17-py3-none-any.whl", hash = "sha256:e036ab49d5b79556f99cfc2d9320b34cfbe5be05c5871b51de9329f0603b0474"},
{file = "certifi-2023.11.17.tar.gz", hash = "sha256:9b469f3a900bf28dc19b8cfbf8019bf47f7fdd1a65a1d4ffb98fc14166beb4d1"},
{file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"},
{file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"},
]
[[package]]
@@ -386,6 +432,17 @@ files = [
[package.extras]
toml = ["tomli"]
[[package]]
name = "diskcache"
version = "5.6.3"
description = "Disk Cache -- Disk and file backed persistent cache."
optional = false
python-versions = ">=3"
files = [
{file = "diskcache-5.6.3-py3-none-any.whl", hash = "sha256:5e31b2d5fbad117cc363ebaf6b689474db18a1f6438bc82358b024abd4c2ca19"},
{file = "diskcache-5.6.3.tar.gz", hash = "sha256:2c3a3fa2743d8535d832ec61c2054a1641f41775aa7c556758a109941e33e4fc"},
]
[[package]]
name = "distro"
version = "1.9.0"
@@ -422,6 +479,25 @@ files = [
[package.extras]
test = ["pytest (>=6)"]
[[package]]
name = "fastapi"
version = "0.109.2"
description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
optional = false
python-versions = ">=3.8"
files = [
{file = "fastapi-0.109.2-py3-none-any.whl", hash = "sha256:2c9bab24667293b501cad8dd388c05240c850b58ec5876ee3283c47d6e1e3a4d"},
{file = "fastapi-0.109.2.tar.gz", hash = "sha256:f3817eac96fe4f65a2ebb4baa000f394e55f5fccdaf7f75250804bc58f354f73"},
]
[package.dependencies]
pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0 || >2.0.0,<2.0.1 || >2.0.1,<2.1.0 || >2.1.0,<3.0.0"
starlette = ">=0.36.3,<0.37.0"
typing-extensions = ">=4.8.0"
[package.extras]
all = ["email-validator (>=2.0.0)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=2.11.2)", "orjson (>=3.2.1)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.7)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
[[package]]
name = "frozenlist"
version = "1.4.1"
@@ -675,71 +751,71 @@ testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
[[package]]
name = "markupsafe"
version = "2.1.4"
version = "2.1.5"
description = "Safely add untrusted strings to HTML/XML markup."
optional = false
python-versions = ">=3.7"
files = [
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{file = "pydantic_core-2.16.2.tar.gz", hash = "sha256:0ba503850d8b8dcc18391f10de896ae51d37fe5fe43dbfb6a35c5c5cad271a06"},
]
[package.dependencies]
@@ -1297,6 +1389,22 @@ pytest = ">=7.0.0"
docs = ["sphinx (>=5.3)", "sphinx-rtd-theme (>=1.0)"]
testing = ["coverage (>=6.2)", "flaky (>=3.5.0)", "hypothesis (>=5.7.1)", "mypy (>=0.931)", "pytest-trio (>=0.7.0)"]
[[package]]
name = "pytest-examples"
version = "0.0.10"
description = "Pytest plugin for testing examples in docstrings and markdown files."
optional = false
python-versions = ">=3.7"
files = [
{file = "pytest_examples-0.0.10-py3-none-any.whl", hash = "sha256:3d0b52424e454846beed8621a12b85db88c6c17049f65c2f417211372c20dc9e"},
{file = "pytest_examples-0.0.10.tar.gz", hash = "sha256:5d34d22e689aca2bbad8dd6b7cdcc9d0107e2942853b3154f3a3c68d145d91c5"},
]
[package.dependencies]
black = ">=23"
pytest = ">=7"
ruff = ">=0.0.258"
[[package]]
name = "python-dateutil"
version = "2.8.2"
@@ -1384,6 +1492,24 @@ files = [
[package.dependencies]
pyyaml = "*"
[[package]]
name = "redis"
version = "5.0.1"
description = "Python client for Redis database and key-value store"
optional = false
python-versions = ">=3.7"
files = [
{file = "redis-5.0.1-py3-none-any.whl", hash = "sha256:ed4802971884ae19d640775ba3b03aa2e7bd5e8fb8dfaed2decce4d0fc48391f"},
{file = "redis-5.0.1.tar.gz", hash = "sha256:0dab495cd5753069d3bc650a0dde8a8f9edde16fc5691b689a566eda58100d0f"},
]
[package.dependencies]
async-timeout = {version = ">=4.0.2", markers = "python_full_version <= \"3.11.2\""}
[package.extras]
hiredis = ["hiredis (>=1.0.0)"]
ocsp = ["cryptography (>=36.0.1)", "pyopenssl (==20.0.1)", "requests (>=2.26.0)"]
[[package]]
name = "regex"
version = "2023.12.25"
@@ -1525,6 +1651,32 @@ pygments = ">=2.13.0,<3.0.0"
[package.extras]
jupyter = ["ipywidgets (>=7.5.1,<9)"]
[[package]]
name = "ruff"
version = "0.2.0"
description = "An extremely fast Python linter and code formatter, written in Rust."
optional = false
python-versions = ">=3.7"
files = [
{file = "ruff-0.2.0-py3-none-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:638ea3294f800d18bae84a492cb5a245c8d29c90d19a91d8e338937a4c27fca0"},
{file = "ruff-0.2.0-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:3ff35433fcf4dff6d610738712152df6b7d92351a1bde8e00bd405b08b3d5759"},
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{file = "ruff-0.2.0.tar.gz", hash = "sha256:63856b91837606c673537d2889989733d7dffde553828d3b0f0bacfa6def54be"},
]
[[package]]
name = "six"
version = "1.16.0"
@@ -1547,6 +1699,23 @@ files = [
{file = "sniffio-1.3.0.tar.gz", hash = "sha256:e60305c5e5d314f5389259b7f22aaa33d8f7dee49763119234af3755c55b9101"},
]
[[package]]
name = "starlette"
version = "0.36.3"
description = "The little ASGI library that shines."
optional = false
python-versions = ">=3.8"
files = [
{file = "starlette-0.36.3-py3-none-any.whl", hash = "sha256:13d429aa93a61dc40bf503e8c801db1f1bca3dc706b10ef2434a36123568f044"},
{file = "starlette-0.36.3.tar.gz", hash = "sha256:90a671733cfb35771d8cc605e0b679d23b992f8dcfad48cc60b38cb29aeb7080"},
]
[package.dependencies]
anyio = ">=3.4.0,<5"
[package.extras]
full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.7)", "pyyaml"]
[[package]]
name = "tenacity"
version = "8.2.3"
@@ -1786,4 +1955,4 @@ multidict = ">=4.0"
[metadata]
lock-version = "2.0"
python-versions = "^3.10"
content-hash = "612f87060d75d66a49e0a1256950f472768411dd7545c343b58c7265b8aaf44f"
content-hash = "c32017c38256d401cecf4162f927215b2b3760b6f3ec0fcc2bc54ffce7a688fa"
pyproject.toml
+7
View File
@@ -30,6 +30,13 @@ mkdocstrings-python = "^1.1.2"
pytest-asyncio = "^0.21.1"
coverage = "^7.3.2"
mypy = "^1.7.1"
pytest-examples = "^0.0.10"
[tool.poetry.group.test-docs.dependencies]
fastapi = "^0.109.2"
redis = "^5.0.1"
diskcache = "^5.6.3"
[build-system]
requires = ["poetry-core"]
requirements-doc.txt
+1 -1
View File
@@ -11,4 +11,4 @@ aiohttp==3.9.1
yarl==1.8.1
frozenlist==1.3.1
mkdocs-minify-plugin
mkdocs-rss-plugin
mkdocs-rss-plugin
tests/docs/test_docs.py
-14
View File
@@ -1,14 +0,0 @@
import pytest
from pytest_examples import find_examples, CodeExample, EvalExample
@pytest.mark.parametrize("example", find_examples("README.md"), ids=str)
def test_readme(example: CodeExample, eval_example: EvalExample):
eval_example.format(example)
eval_example.run_print_update(example)
@pytest.mark.parametrize("example", find_examples("docs/"), ids=str)
def test_readme(example: CodeExample, eval_example: EvalExample):
eval_example.format(example)
eval_example.run_print_update(example)
tests/openai/docs/test_docs.py
+54
View File
@@ -0,0 +1,54 @@
import pytest
from pytest_examples import find_examples, CodeExample, EvalExample
@pytest.mark.parametrize("example", find_examples("README.md"), ids=str)
def test_readme(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/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:
eval_example.format(example)
eval_example.run_print_update(example)
else:
eval_example.lint(example)
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):
if eval_example.update_examples:
eval_example.format(example)
eval_example.run_print_update(example)
else:
eval_example.lint(example)
eval_example.run(example)
tests/test_patch.py
+1 -134
View File
@@ -1,16 +1,9 @@
import functools
import pytest
from openai import AsyncOpenAI, OpenAI
from openai.types.chat import ChatCompletionMessage, ChatCompletionMessageParam
from openai.types.chat.chat_completion_message import FunctionCall
from openai.types.chat.chat_completion_message_tool_call import (
ChatCompletionMessageToolCall,
Function,
)
import instructor
from instructor.patch import OVERRIDE_DOCS, dump_message, is_async
from instructor.patch import OVERRIDE_DOCS, is_async
def test_patch_completes_successfully():
@@ -50,129 +43,3 @@ def test_override_docs():
assert (
"response_model" in OVERRIDE_DOCS
), "response_model should be in OVERRIDE_DOCS"
@pytest.mark.parametrize(
"name_of_test, message, expected",
[
(
"tool_calls and content and no function_call",
ChatCompletionMessage(
role="assistant",
content="Hello, world!",
tool_calls=[
ChatCompletionMessageToolCall(
id="test_tool",
function=Function(arguments="", name="test_tool"),
type="function",
)
],
),
{
"role": "assistant",
"content": 'Hello, world![{"id": "test_tool", "function": {"arguments": "", "name": "test_tool"}, "type": "function"}]',
"tool_calls": [
{
"id": "test_tool",
"function": {"arguments": "", "name": "test_tool"},
"type": "function",
}
],
},
),
(
"tool_calls and no content and no function_call",
ChatCompletionMessage(
role="assistant",
content=None,
tool_calls=[
ChatCompletionMessageToolCall(
id="test_tool",
function=Function(arguments="", name="test_tool"),
type="function",
)
],
),
{
"role": "assistant",
"content": '[{"id": "test_tool", "function": {"arguments": "", "name": "test_tool"}, "type": "function"}]',
"tool_calls": [
{
"id": "test_tool",
"function": {"arguments": "", "name": "test_tool"},
"type": "function",
}
],
},
),
(
"no tool_calls and no content no function_call",
ChatCompletionMessage(
role="assistant",
content=None,
),
{
"role": "assistant",
"content": "",
},
),
(
"no tool_calls and content and function_call",
ChatCompletionMessage(
role="assistant",
content="Hello, world!",
function_call=FunctionCall(arguments="", name="test_tool"),
),
{
"role": "assistant",
"content": 'Hello, world!{"arguments": "", "name": "test_tool"}',
},
),
(
"no tool_calls and no content and function_call",
ChatCompletionMessage(
role="assistant",
content=None,
function_call=FunctionCall(arguments="", name="test_tool"),
),
{
"role": "assistant",
"content": '{"arguments": "", "name": "test_tool"}',
},
),
(
"tool_calls and no content and function_call",
ChatCompletionMessage(
role="assistant",
content="",
function_call=FunctionCall(arguments="", name="test_tool"),
tool_calls=[
ChatCompletionMessageToolCall(
id="test_tool",
function=Function(arguments="", name="test_tool"),
type="function",
)
],
),
{
"role": "assistant",
"content": '[{"id": "test_tool", "function": {"arguments": "", "name": "test_tool"}, "type": "function"}]{"arguments": "", "name": "test_tool"}',
"tool_calls": [
{
"id": "test_tool",
"function": {"arguments": "", "name": "test_tool"},
"type": "function",
}
],
},
),
],
)
@pytest.mark.skip("New changes to tools and functions")
def test_dump_message(
name_of_test: str,
message: ChatCompletionMessage,
expected: ChatCompletionMessageParam,
):
#! Something is going on right now, but I don't have time to figure it out @jxnlco
assert dump_message(message) == expected, name_of_test