# Prompt Foundry Python API library
Prompt Foundry is a comprehensive tool for prompt engineering, management, and evaluation. It is designed to simplify the development and integration process for developers working on Python AI applications utilizing large language models (LLMs).
The Prompt Foundry Python library provides convenient access to the Prompt Foundry REST API from any Python 3.7+
application. The library includes type definitions for all request params and response fields,
and offers both synchronous and asynchronous clients powered by [httpx](https://github.com/encode/httpx).
## Deploy Prompt
To use this SDK, you need a Prompt Foundry account. Sign up at [promptfoundry.ai](https://promptfoundry.ai). Follow the getting started guide in our [documentation](https://docs.promptfoundry.ai/quickstart/getting-started) to get set up.
## Installation
[](https://pypi.org/project/prompt_foundry_python_sdk/)
```sh
# install from PyPI
pip install --pre prompt_foundry_python_sdk
```
## Integration
The full Prompt Foundry documentation can be found at [docs.promptfoundry.ai](https://docs.promptfoundry.ai/libraries/python).
### Option 1 - Completion Proxy
Initiates a completion request to the configured LLM provider using specified parameters and provided variables. This endpoint abstracts the integration with different model providers, enabling seamless switching between models while maintaining a consistent data model for your application.
```python
import os
from prompt_foundry_python_sdk import PromptFoundry
client = PromptFoundry(
# This is the default and can be omitted
api_key=os.environ.get("PROMPT_FOUNDRY_API_KEY"),
)
completion_create_response = client.completion.create(
id="1212121",
append_messages=[{
"role": "user",
"content": [{
"type": "TEXT",
"text": "What is the weather in Seattle, WA?",
}],
}],
)
print(completion_create_response.message)
```
### Option 2 - Direct Provider Integration
Fetches the configured model parameters and messages rendered with the provided variables mapped to the set LLM provider. This endpoint abstracts the need to handle mapping between different providers, while still allowing direct calls to the providers.
#### OpenAI Integration
Install the OpenAI SDK
```sh
pip install openai
```
Import the OpenAI and Prompt Foundry SDKs
```python
import os
from prompt_foundry_python_sdk import PromptFoundry
from openai import OpenAI
# Initialize Prompt Foundry SDK with your API key
pf = PromptFoundry(
api_key=os.environ.get("PROMPT_FOUNDRY_API_KEY"),
)
# Initialize OpenAI SDK with your API key
openai = OpenAI(
api_key=os.environ.get("OPENAI_API_KEY"),
)
def main():
try:
# Retrieve model parameters for the prompt
model_parameters = pf.prompts.get_parameters(
"1212121",
variables={"hello": "world"},
append_messages=[{
"role": "user",
"content": [{
"type": "TEXT",
"text": "What is the weather in Seattle, WA?",
}],
}],
)
# Check if provider is OpenAI
if model_parameters.provider == "openai":
# Use the retrieved parameters to create a chat completion request
model_response = openai.chat.completions.create(
**model_parameters.parameters
)
# Print the response from OpenAI
print(model_response.data)
except Exception as e:
print(f"Error: {e}")
if __name__ == "__main__":
main()
```
#### Anthropic Integration
Install the Anthropic SDK
```sh
pip install anthropic
```
Import the Anthropic and Prompt Foundry SDKs
```python
import os
from prompt_foundry_python_sdk import PromptFoundry
from anthropic import Anthropic
# Initialize Prompt Foundry SDK with your API key
pf = PromptFoundry(
api_key=os.environ.get("PROMPT_FOUNDRY_API_KEY"),
)
# Initialize Anthropic SDK with your API key
anthropic = client = Anthropic(
api_key=os.environ.get("ANTHROPIC_API_KEY"),
)
def main():
try:
# Retrieve model parameters for the prompt
model_parameters = pf.prompts.get_parameters(
"1212121",
variables={"hello": "world"},
append_messages=[{
"role": "user",
"content": [{
"type": "TEXT",
"text": "What is the weather in Seattle, WA?",
}],
}],
)
# Check if provider is Anthropic
if model_parameters.provider == "anthropic":
# Use the retrieved parameters to create a chat request
message = client.messages.create(
**model_parameters.parameters
)
print(message.content)
except Exception as e:
print(f"Error: {e}")
if __name__ == "__main__":
main()
```
While you can provide a `api_key` keyword argument,
we recommend using [python-dotenv](https://pypi.org/project/python-dotenv/)
to add `PROMPT_FOUNDRY_API_KEY="My API Key"` to your `.env` file
so that your API Key is not stored in source control.
## Async usage
Simply import `AsyncPromptFoundry` instead of `PromptFoundry` and use `await` with each API call:
```python
import os
import asyncio
from prompt_foundry_python_sdk import AsyncPromptFoundry
client = AsyncPromptFoundry(
# This is the default and can be omitted
api_key=os.environ.get("PROMPT_FOUNDRY_API_KEY"),
)
async def main() -> None:
completion_create_response = await client.completion.create(
id="1212121",
append_messages=[
{
"role": "user",
"content": [
{
"type": "TEXT",
"text": "What is the weather in Seattle, WA?",
}
],
}
],
)
print(completion_create_response.message)
asyncio.run(main())
```
Functionality between the synchronous and asynchronous clients is otherwise identical.
## Using types
Nested request parameters are [TypedDicts](https://docs.python.org/3/library/typing.html#typing.TypedDict). Responses are [Pydantic models](https://docs.pydantic.dev) which also provide helper methods for things like:
- Serializing back into JSON, `model.to_json()`
- Converting to a dictionary, `model.to_dict()`
Typed requests and responses provide autocomplete and documentation within your editor. If you would like to see type errors in VS Code to help catch bugs earlier, set `python.analysis.typeCheckingMode` to `basic`.
## Handling errors
When the library is unable to connect to the API (for example, due to network connection problems or a timeout), a subclass of `prompt_foundry_python_sdk.APIConnectionError` is raised.
When the API returns a non-success status code (that is, 4xx or 5xx
response), a subclass of `prompt_foundry_python_sdk.APIStatusError` is raised, containing `status_code` and `response` properties.
All errors inherit from `prompt_foundry_python_sdk.APIError`.
```python
import prompt_foundry_python_sdk
from prompt_foundry_python_sdk import PromptFoundry
client = PromptFoundry()
try:
client.completion.create(
id="1212121",
)
except prompt_foundry_python_sdk.APIConnectionError as e:
print("The server could not be reached")
print(e.__cause__) # an underlying Exception, likely raised within httpx.
except prompt_foundry_python_sdk.RateLimitError as e:
print("A 429 status code was received; we should back off a bit.")
except prompt_foundry_python_sdk.APIStatusError as e:
print("Another non-200-range status code was received")
print(e.status_code)
print(e.response)
```
Error codes are as followed:
| Status Code | Error Type |
| ----------- | -------------------------- |
| 400 | `BadRequestError` |
| 401 | `AuthenticationError` |
| 403 | `PermissionDeniedError` |
| 404 | `NotFoundError` |
| 422 | `UnprocessableEntityError` |
| 429 | `RateLimitError` |
| >=500 | `InternalServerError` |
| N/A | `APIConnectionError` |
### Retries
Certain errors are automatically retried 2 times by default, with a short exponential backoff.
Connection errors (for example, due to a network connectivity problem), 408 Request Timeout, 409 Conflict,
429 Rate Limit, and >=500 Internal errors are all retried by default.
You can use the `max_retries` option to configure or disable retry settings:
```python
from prompt_foundry_python_sdk import PromptFoundry
# Configure the default for all requests:
client = PromptFoundry(
# default is 2
max_retries=0,
)
# Or, configure per-request:
client.with_options(max_retries=5).completion.create(
id="1212121",
)
```
### Timeouts
By default requests time out after 1 minute. You can configure this with a `timeout` option,
which accepts a float or an [`httpx.Timeout`](https://www.python-httpx.org/advanced/#fine-tuning-the-configuration) object:
```python
from prompt_foundry_python_sdk import PromptFoundry
# Configure the default for all requests:
client = PromptFoundry(
# 20 seconds (default is 1 minute)
timeout=20.0,
)
# More granular control:
client = PromptFoundry(
timeout=httpx.Timeout(60.0, read=5.0, write=10.0, connect=2.0),
)
# Override per-request:
client.with_options(timeout=5.0).completion.create(
id="1212121",
)
```
On timeout, an `APITimeoutError` is thrown.
Note that requests that time out are [retried twice by default](https://github.com/prompt-foundry/python-sdk/tree/main/#retries).
## Advanced
### Logging
We use the standard library [`logging`](https://docs.python.org/3/library/logging.html) module.
You can enable logging by setting the environment variable `PROMPT_FOUNDRY_LOG` to `debug`.
```shell
export PROMPT_FOUNDRY_LOG=debug
```
### How to tell whether `None` means `null` or missing
In an API response, a field may be explicitly `null`, or missing entirely; in either case, its value is `None` in this library. You can differentiate the two cases with `.model_fields_set`:
```py
if response.my_field is None:
if 'my_field' not in response.model_fields_set:
print('Got json like {}, without a "my_field" key present at all.')
else:
print('Got json like {"my_field": null}.')
```
### Accessing raw response data (e.g. headers)
The "raw" Response object can be accessed by prefixing `.with_raw_response.` to any HTTP method call, e.g.,
```py
from prompt_foundry_python_sdk import PromptFoundry
client = PromptFoundry()
response = client.completion.with_raw_response.create(
id="1212121",
)
print(response.headers.get('X-My-Header'))
completion = response.parse() # get the object that `completion.create()` would have returned
print(completion.provider)
```
These methods return an [`APIResponse`](https://github.com/prompt-foundry/python-sdk/tree/main/src/prompt_foundry_python_sdk/_response.py) object.
The async client returns an [`AsyncAPIResponse`](https://github.com/prompt-foundry/python-sdk/tree/main/src/prompt_foundry_python_sdk/_response.py) with the same structure, the only difference being `await`able methods for reading the response content.
#### `.with_streaming_response`
The above interface eagerly reads the full response body when you make the request, which may not always be what you want.
To stream the response body, use `.with_streaming_response` instead, which requires a context manager and only reads the response body once you call `.read()`, `.text()`, `.json()`, `.iter_bytes()`, `.iter_text()`, `.iter_lines()` or `.parse()`. In the async client, these are async methods.
```python
with client.completion.with_streaming_response.create(
id="1212121",
) as response:
print(response.headers.get("X-My-Header"))
for line in response.iter_lines():
print(line)
```
The context manager is required so that the response will reliably be closed.
### Making custom/undocumented requests
This library is typed for convenient access to the documented API.
If you need to access undocumented endpoints, params, or response properties, the library can still be used.
#### Undocumented endpoints
To make requests to undocumented endpoints, you can make requests using `client.get`, `client.post`, and other
http verbs. Options on the client will be respected (such as retries) will be respected when making this
request.
```py
import httpx
response = client.post(
"/foo",
cast_to=httpx.Response,
body={"my_param": True},
)
print(response.headers.get("x-foo"))
```
#### Undocumented request params
If you want to explicitly send an extra param, you can do so with the `extra_query`, `extra_body`, and `extra_headers` request
options.
#### Undocumented response properties
To access undocumented response properties, you can access the extra fields like `response.unknown_prop`. You
can also get all the extra fields on the Pydantic model as a dict with
[`response.model_extra`](https://docs.pydantic.dev/latest/api/base_model/#pydantic.BaseModel.model_extra).
### Configuring the HTTP client
You can directly override the [httpx client](https://www.python-httpx.org/api/#client) to customize it for your use case, including:
- Support for proxies
- Custom transports
- Additional [advanced](https://www.python-httpx.org/advanced/clients/) functionality
```python
from prompt_foundry_python_sdk import PromptFoundry, DefaultHttpxClient
client = PromptFoundry(
# Or use the `PROMPT_FOUNDRY_BASE_URL` env var
base_url="http://my.test.server.example.com:8083",
http_client=DefaultHttpxClient(
proxies="http://my.test.proxy.example.com",
transport=httpx.HTTPTransport(local_address="0.0.0.0"),
),
)
```
You can also customize the client on a per-request basis by using `with_options()`:
```python
client.with_options(http_client=DefaultHttpxClient(...))
```
### Managing HTTP resources
By default the library closes underlying HTTP connections whenever the client is [garbage collected](https://docs.python.org/3/reference/datamodel.html#object.__del__). You can manually close the client using the `.close()` method if desired, or with a context manager that closes when exiting.
## Versioning
This package generally follows [SemVer](https://semver.org/spec/v2.0.0.html) conventions, though certain backwards-incompatible changes may be released as minor versions:
1. Changes that only affect static types, without breaking runtime behavior.
2. Changes to library internals which are technically public but not intended or documented for external use. _(Please open a GitHub issue to let us know if you are relying on such internals)_.
3. Changes that we do not expect to impact the vast majority of users in practice.
We take backwards-compatibility seriously and work hard to ensure you can rely on a smooth upgrade experience.
We are keen for your feedback; please open an [issue](https://www.github.com/prompt-foundry/python-sdk/issues) with questions, bugs, or suggestions.
## Requirements
Python 3.7 or higher.
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"description": "# Prompt Foundry Python API library\n\nPrompt Foundry is a comprehensive tool for prompt engineering, management, and evaluation. It is designed to simplify the development and integration process for developers working on Python AI applications utilizing large language models (LLMs).\n\nThe Prompt Foundry Python library provides convenient access to the Prompt Foundry REST API from any Python 3.7+\napplication. The library includes type definitions for all request params and response fields,\nand offers both synchronous and asynchronous clients powered by [httpx](https://github.com/encode/httpx).\n\n## Deploy Prompt\n\nTo use this SDK, you need a Prompt Foundry account. Sign up at [promptfoundry.ai](https://promptfoundry.ai). Follow the getting started guide in our [documentation](https://docs.promptfoundry.ai/quickstart/getting-started) to get set up.\n\n\n\n## Installation\n\n[](https://pypi.org/project/prompt_foundry_python_sdk/)\n\n```sh\n# install from PyPI\npip install --pre prompt_foundry_python_sdk\n```\n\n## Integration\n\nThe full Prompt Foundry documentation can be found at [docs.promptfoundry.ai](https://docs.promptfoundry.ai/libraries/python).\n\n### Option 1 - Completion Proxy\n\nInitiates a completion request to the configured LLM provider using specified parameters and provided variables. This endpoint abstracts the integration with different model providers, enabling seamless switching between models while maintaining a consistent data model for your application.\n\n```python\nimport os\nfrom prompt_foundry_python_sdk import PromptFoundry\n\nclient = PromptFoundry(\n # This is the default and can be omitted\n api_key=os.environ.get(\"PROMPT_FOUNDRY_API_KEY\"),\n)\n\ncompletion_create_response = client.completion.create(\n id=\"1212121\",\n append_messages=[{\n \"role\": \"user\",\n \"content\": [{\n \"type\": \"TEXT\",\n \"text\": \"What is the weather in Seattle, WA?\",\n }],\n }],\n)\n\nprint(completion_create_response.message)\n```\n\n### Option 2 - Direct Provider Integration\n\nFetches the configured model parameters and messages rendered with the provided variables mapped to the set LLM provider. This endpoint abstracts the need to handle mapping between different providers, while still allowing direct calls to the providers.\n\n#### OpenAI Integration\n\nInstall the OpenAI SDK\n\n```sh\npip install openai\n```\n\nImport the OpenAI and Prompt Foundry SDKs\n\n```python\nimport os\nfrom prompt_foundry_python_sdk import PromptFoundry\nfrom openai import OpenAI\n\n# Initialize Prompt Foundry SDK with your API key\npf = PromptFoundry(\n api_key=os.environ.get(\"PROMPT_FOUNDRY_API_KEY\"),\n)\n\n# Initialize OpenAI SDK with your API key\nopenai = OpenAI(\n api_key=os.environ.get(\"OPENAI_API_KEY\"),\n)\n\ndef main():\n try:\n # Retrieve model parameters for the prompt\n model_parameters = pf.prompts.get_parameters(\n \"1212121\",\n variables={\"hello\": \"world\"},\n append_messages=[{\n \"role\": \"user\",\n \"content\": [{\n \"type\": \"TEXT\",\n \"text\": \"What is the weather in Seattle, WA?\",\n }],\n }],\n )\n\n # Check if provider is OpenAI\n if model_parameters.provider == \"openai\":\n # Use the retrieved parameters to create a chat completion request\n model_response = openai.chat.completions.create(\n **model_parameters.parameters\n )\n\n # Print the response from OpenAI\n print(model_response.data)\n\n except Exception as e:\n print(f\"Error: {e}\")\n\nif __name__ == \"__main__\":\n main()\n```\n\n#### Anthropic Integration\n\nInstall the Anthropic SDK\n\n```sh\npip install anthropic\n```\n\nImport the Anthropic and Prompt Foundry SDKs\n\n```python\nimport os\nfrom prompt_foundry_python_sdk import PromptFoundry\nfrom anthropic import Anthropic\n\n# Initialize Prompt Foundry SDK with your API key\npf = PromptFoundry(\n api_key=os.environ.get(\"PROMPT_FOUNDRY_API_KEY\"),\n)\n\n# Initialize Anthropic SDK with your API key\nanthropic = client = Anthropic(\n api_key=os.environ.get(\"ANTHROPIC_API_KEY\"),\n)\n\ndef main():\n try:\n # Retrieve model parameters for the prompt\n model_parameters = pf.prompts.get_parameters(\n \"1212121\",\n variables={\"hello\": \"world\"},\n append_messages=[{\n \"role\": \"user\",\n \"content\": [{\n \"type\": \"TEXT\",\n \"text\": \"What is the weather in Seattle, WA?\",\n }],\n }],\n )\n\n # Check if provider is Anthropic\n if model_parameters.provider == \"anthropic\":\n # Use the retrieved parameters to create a chat request\n message = client.messages.create(\n **model_parameters.parameters\n )\n print(message.content)\n\n except Exception as e:\n print(f\"Error: {e}\")\n\nif __name__ == \"__main__\":\n main()\n```\n\nWhile you can provide a `api_key` keyword argument,\nwe recommend using [python-dotenv](https://pypi.org/project/python-dotenv/)\nto add `PROMPT_FOUNDRY_API_KEY=\"My API Key\"` to your `.env` file\nso that your API Key is not stored in source control.\n\n## Async usage\n\nSimply import `AsyncPromptFoundry` instead of `PromptFoundry` and use `await` with each API call:\n\n```python\nimport os\nimport asyncio\nfrom prompt_foundry_python_sdk import AsyncPromptFoundry\n\nclient = AsyncPromptFoundry(\n # This is the default and can be omitted\n api_key=os.environ.get(\"PROMPT_FOUNDRY_API_KEY\"),\n)\n\n\nasync def main() -> None:\n completion_create_response = await client.completion.create(\n id=\"1212121\",\n append_messages=[\n {\n \"role\": \"user\",\n \"content\": [\n {\n \"type\": \"TEXT\",\n \"text\": \"What is the weather in Seattle, WA?\",\n }\n ],\n }\n ],\n )\n print(completion_create_response.message)\n\n\nasyncio.run(main())\n```\n\nFunctionality between the synchronous and asynchronous clients is otherwise identical.\n\n## Using types\n\nNested request parameters are [TypedDicts](https://docs.python.org/3/library/typing.html#typing.TypedDict). Responses are [Pydantic models](https://docs.pydantic.dev) which also provide helper methods for things like:\n\n- Serializing back into JSON, `model.to_json()`\n- Converting to a dictionary, `model.to_dict()`\n\nTyped requests and responses provide autocomplete and documentation within your editor. If you would like to see type errors in VS Code to help catch bugs earlier, set `python.analysis.typeCheckingMode` to `basic`.\n\n## Handling errors\n\nWhen the library is unable to connect to the API (for example, due to network connection problems or a timeout), a subclass of `prompt_foundry_python_sdk.APIConnectionError` is raised.\n\nWhen the API returns a non-success status code (that is, 4xx or 5xx\nresponse), a subclass of `prompt_foundry_python_sdk.APIStatusError` is raised, containing `status_code` and `response` properties.\n\nAll errors inherit from `prompt_foundry_python_sdk.APIError`.\n\n```python\nimport prompt_foundry_python_sdk\nfrom prompt_foundry_python_sdk import PromptFoundry\n\nclient = PromptFoundry()\n\ntry:\n client.completion.create(\n id=\"1212121\",\n )\nexcept prompt_foundry_python_sdk.APIConnectionError as e:\n print(\"The server could not be reached\")\n print(e.__cause__) # an underlying Exception, likely raised within httpx.\nexcept prompt_foundry_python_sdk.RateLimitError as e:\n print(\"A 429 status code was received; we should back off a bit.\")\nexcept prompt_foundry_python_sdk.APIStatusError as e:\n print(\"Another non-200-range status code was received\")\n print(e.status_code)\n print(e.response)\n```\n\nError codes are as followed:\n\n| Status Code | Error Type |\n| ----------- | -------------------------- |\n| 400 | `BadRequestError` |\n| 401 | `AuthenticationError` |\n| 403 | `PermissionDeniedError` |\n| 404 | `NotFoundError` |\n| 422 | `UnprocessableEntityError` |\n| 429 | `RateLimitError` |\n| >=500 | `InternalServerError` |\n| N/A | `APIConnectionError` |\n\n### Retries\n\nCertain errors are automatically retried 2 times by default, with a short exponential backoff.\nConnection errors (for example, due to a network connectivity problem), 408 Request Timeout, 409 Conflict,\n429 Rate Limit, and >=500 Internal errors are all retried by default.\n\nYou can use the `max_retries` option to configure or disable retry settings:\n\n```python\nfrom prompt_foundry_python_sdk import PromptFoundry\n\n# Configure the default for all requests:\nclient = PromptFoundry(\n # default is 2\n max_retries=0,\n)\n\n# Or, configure per-request:\nclient.with_options(max_retries=5).completion.create(\n id=\"1212121\",\n)\n```\n\n### Timeouts\n\nBy default requests time out after 1 minute. You can configure this with a `timeout` option,\nwhich accepts a float or an [`httpx.Timeout`](https://www.python-httpx.org/advanced/#fine-tuning-the-configuration) object:\n\n```python\nfrom prompt_foundry_python_sdk import PromptFoundry\n\n# Configure the default for all requests:\nclient = PromptFoundry(\n # 20 seconds (default is 1 minute)\n timeout=20.0,\n)\n\n# More granular control:\nclient = PromptFoundry(\n timeout=httpx.Timeout(60.0, read=5.0, write=10.0, connect=2.0),\n)\n\n# Override per-request:\nclient.with_options(timeout=5.0).completion.create(\n id=\"1212121\",\n)\n```\n\nOn timeout, an `APITimeoutError` is thrown.\n\nNote that requests that time out are [retried twice by default](https://github.com/prompt-foundry/python-sdk/tree/main/#retries).\n\n## Advanced\n\n### Logging\n\nWe use the standard library [`logging`](https://docs.python.org/3/library/logging.html) module.\n\nYou can enable logging by setting the environment variable `PROMPT_FOUNDRY_LOG` to `debug`.\n\n```shell\nexport PROMPT_FOUNDRY_LOG=debug\n```\n\n### How to tell whether `None` means `null` or missing\n\nIn an API response, a field may be explicitly `null`, or missing entirely; in either case, its value is `None` in this library. You can differentiate the two cases with `.model_fields_set`:\n\n```py\nif response.my_field is None:\n if 'my_field' not in response.model_fields_set:\n print('Got json like {}, without a \"my_field\" key present at all.')\n else:\n print('Got json like {\"my_field\": null}.')\n```\n\n### Accessing raw response data (e.g. headers)\n\nThe \"raw\" Response object can be accessed by prefixing `.with_raw_response.` to any HTTP method call, e.g.,\n\n```py\nfrom prompt_foundry_python_sdk import PromptFoundry\n\nclient = PromptFoundry()\nresponse = client.completion.with_raw_response.create(\n id=\"1212121\",\n)\nprint(response.headers.get('X-My-Header'))\n\ncompletion = response.parse() # get the object that `completion.create()` would have returned\nprint(completion.provider)\n```\n\nThese methods return an [`APIResponse`](https://github.com/prompt-foundry/python-sdk/tree/main/src/prompt_foundry_python_sdk/_response.py) object.\n\nThe async client returns an [`AsyncAPIResponse`](https://github.com/prompt-foundry/python-sdk/tree/main/src/prompt_foundry_python_sdk/_response.py) with the same structure, the only difference being `await`able methods for reading the response content.\n\n#### `.with_streaming_response`\n\nThe above interface eagerly reads the full response body when you make the request, which may not always be what you want.\n\nTo stream the response body, use `.with_streaming_response` instead, which requires a context manager and only reads the response body once you call `.read()`, `.text()`, `.json()`, `.iter_bytes()`, `.iter_text()`, `.iter_lines()` or `.parse()`. In the async client, these are async methods.\n\n```python\nwith client.completion.with_streaming_response.create(\n id=\"1212121\",\n) as response:\n print(response.headers.get(\"X-My-Header\"))\n\n for line in response.iter_lines():\n print(line)\n```\n\nThe context manager is required so that the response will reliably be closed.\n\n### Making custom/undocumented requests\n\nThis library is typed for convenient access to the documented API.\n\nIf you need to access undocumented endpoints, params, or response properties, the library can still be used.\n\n#### Undocumented endpoints\n\nTo make requests to undocumented endpoints, you can make requests using `client.get`, `client.post`, and other\nhttp verbs. Options on the client will be respected (such as retries) will be respected when making this\nrequest.\n\n```py\nimport httpx\n\nresponse = client.post(\n \"/foo\",\n cast_to=httpx.Response,\n body={\"my_param\": True},\n)\n\nprint(response.headers.get(\"x-foo\"))\n```\n\n#### Undocumented request params\n\nIf you want to explicitly send an extra param, you can do so with the `extra_query`, `extra_body`, and `extra_headers` request\noptions.\n\n#### Undocumented response properties\n\nTo access undocumented response properties, you can access the extra fields like `response.unknown_prop`. You\ncan also get all the extra fields on the Pydantic model as a dict with\n[`response.model_extra`](https://docs.pydantic.dev/latest/api/base_model/#pydantic.BaseModel.model_extra).\n\n### Configuring the HTTP client\n\nYou can directly override the [httpx client](https://www.python-httpx.org/api/#client) to customize it for your use case, including:\n\n- Support for proxies\n- Custom transports\n- Additional [advanced](https://www.python-httpx.org/advanced/clients/) functionality\n\n```python\nfrom prompt_foundry_python_sdk import PromptFoundry, DefaultHttpxClient\n\nclient = PromptFoundry(\n # Or use the `PROMPT_FOUNDRY_BASE_URL` env var\n base_url=\"http://my.test.server.example.com:8083\",\n http_client=DefaultHttpxClient(\n proxies=\"http://my.test.proxy.example.com\",\n transport=httpx.HTTPTransport(local_address=\"0.0.0.0\"),\n ),\n)\n```\n\nYou can also customize the client on a per-request basis by using `with_options()`:\n\n```python\nclient.with_options(http_client=DefaultHttpxClient(...))\n```\n\n### Managing HTTP resources\n\nBy default the library closes underlying HTTP connections whenever the client is [garbage collected](https://docs.python.org/3/reference/datamodel.html#object.__del__). You can manually close the client using the `.close()` method if desired, or with a context manager that closes when exiting.\n\n## Versioning\n\nThis package generally follows [SemVer](https://semver.org/spec/v2.0.0.html) conventions, though certain backwards-incompatible changes may be released as minor versions:\n\n1. Changes that only affect static types, without breaking runtime behavior.\n2. Changes to library internals which are technically public but not intended or documented for external use. _(Please open a GitHub issue to let us know if you are relying on such internals)_.\n3. Changes that we do not expect to impact the vast majority of users in practice.\n\nWe take backwards-compatibility seriously and work hard to ensure you can rely on a smooth upgrade experience.\n\nWe are keen for your feedback; please open an [issue](https://www.github.com/prompt-foundry/python-sdk/issues) with questions, bugs, or suggestions.\n\n## Requirements\n\nPython 3.7 or higher.\n",
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