| Name | anthropic JSON |
| Version |
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| Summary | The official Python library for the anthropic API |
| upload_time | 2024-05-08 00:06:37 |
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| requires_python | >=3.7 |
| license | None |
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# Anthropic Python API library
[](https://pypi.org/project/anthropic/)
The Anthropic Python library provides convenient access to the Anthropic REST API from any Python 3.7+
application. It 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).
## Documentation
The REST API documentation can be found [on docs.anthropic.com](https://docs.anthropic.com/claude/reference/). The full API of this library can be found in [api.md](https://github.com/anthropics/anthropic-sdk-python/tree/main/api.md).
## Installation
```sh
# install from PyPI
pip install anthropic
```
## Usage
The full API of this library can be found in [api.md](https://github.com/anthropics/anthropic-sdk-python/tree/main/api.md).
```python
import os
from anthropic import Anthropic
client = Anthropic(
# This is the default and can be omitted
api_key=os.environ.get("ANTHROPIC_API_KEY"),
)
message = client.messages.create(
max_tokens=1024,
messages=[
{
"role": "user",
"content": "Hello, Claude",
}
],
model="claude-3-opus-20240229",
)
print(message.content)
```
While you can provide an `api_key` keyword argument,
we recommend using [python-dotenv](https://pypi.org/project/python-dotenv/)
to add `ANTHROPIC_API_KEY="my-anthropic-api-key"` to your `.env` file
so that your API Key is not stored in source control.
## Async usage
Simply import `AsyncAnthropic` instead of `Anthropic` and use `await` with each API call:
```python
import os
import asyncio
from anthropic import AsyncAnthropic
client = AsyncAnthropic(
# This is the default and can be omitted
api_key=os.environ.get("ANTHROPIC_API_KEY"),
)
async def main() -> None:
message = await client.messages.create(
max_tokens=1024,
messages=[
{
"role": "user",
"content": "Hello, Claude",
}
],
model="claude-3-opus-20240229",
)
print(message.content)
asyncio.run(main())
```
Functionality between the synchronous and asynchronous clients is otherwise identical.
## Streaming responses
We provide support for streaming responses using Server Side Events (SSE).
```python
from anthropic import Anthropic
client = Anthropic()
stream = client.messages.create(
max_tokens=1024,
messages=[
{
"role": "user",
"content": "Hello, Claude",
}
],
model="claude-3-opus-20240229",
stream=True,
)
for event in stream:
print(event.type)
```
The async client uses the exact same interface.
```python
from anthropic import AsyncAnthropic
client = AsyncAnthropic()
stream = await client.messages.create(
max_tokens=1024,
messages=[
{
"role": "user",
"content": "Hello, Claude",
}
],
model="claude-3-opus-20240229",
stream=True,
)
async for event in stream:
print(event.type)
```
### Streaming Helpers
This library provides several conveniences for streaming messages, for example:
```py
import asyncio
from anthropic import AsyncAnthropic
client = AsyncAnthropic()
async def main() -> None:
async with client.messages.stream(
max_tokens=1024,
messages=[
{
"role": "user",
"content": "Say hello there!",
}
],
model="claude-3-opus-20240229",
) as stream:
async for text in stream.text_stream:
print(text, end="", flush=True)
print()
message = await stream.get_final_message()
print(message.to_json())
asyncio.run(main())
```
Streaming with `client.messages.stream(...)` exposes [various helpers for your convenience](https://github.com/anthropics/anthropic-sdk-python/tree/main/helpers.md) including event handlers and accumulation.
Alternatively, you can use `client.messages.create(..., stream=True)` which only returns an async iterable of the events in the stream and thus uses less memory (it does not build up a final message object for you).
## Token counting
You can see the exact usage for a given request through the `usage` response property, e.g.
```py
message = client.messages.create(...)
message.usage
# Usage(input_tokens=25, output_tokens=13)
```
## Tool use beta
This SDK provides beta support for tool use, aka function calling. More details can be found in [the documentation](https://docs.anthropic.com/claude/docs/tool-use).
## AWS Bedrock
This library also provides support for the [Anthropic Bedrock API](https://aws.amazon.com/bedrock/claude/) if you install this library with the `bedrock` extra, e.g. `pip install -U anthropic[bedrock]`.
You can then import and instantiate a separate `AnthropicBedrock` class, the rest of the API is the same.
```py
from anthropic import AnthropicBedrock
client = AnthropicBedrock()
message = client.messages.create(
max_tokens=1024,
messages=[
{
"role": "user",
"content": "Hello!",
}
],
model="anthropic.claude-3-sonnet-20240229-v1:0",
)
print(message)
```
For a more fully fledged example see [`examples/bedrock.py`](https://github.com/anthropics/anthropic-sdk-python/blob/main/examples/bedrock.py).
## Google Vertex
This library also provides support for the [Anthropic Vertex API](https://cloud.google.com/vertex-ai?hl=en) if you install this library with the `vertex` extra, e.g. `pip install -U anthropic[vertex]`.
You can then import and instantiate a separate `AnthropicVertex`/`AsyncAnthropicVertexAsync` class, which has the same API as the base `Anthropic`/`AsyncAnthropic` class.
```py
from anthropic import AnthropicVertex
client = AnthropicVertex()
message = client.messages.create(
model="claude-3-sonnet@20240229",
max_tokens=100,
messages=[
{
"role": "user",
"content": "Hello!",
}
],
)
print(message)
```
For a more complete example see [`examples/vertex.py`](https://github.com/anthropics/anthropic-sdk-python/blob/main/examples/vertex.py).
## 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 `anthropic.APIConnectionError` is raised.
When the API returns a non-success status code (that is, 4xx or 5xx
response), a subclass of `anthropic.APIStatusError` is raised, containing `status_code` and `response` properties.
All errors inherit from `anthropic.APIError`.
```python
import anthropic
from anthropic import Anthropic
client = Anthropic()
try:
client.messages.create(
max_tokens=1024,
messages=[
{
"role": "user",
"content": "Hello, Claude",
}
],
model="claude-3-opus-20240229",
)
except anthropic.APIConnectionError as e:
print("The server could not be reached")
print(e.__cause__) # an underlying Exception, likely raised within httpx.
except anthropic.RateLimitError as e:
print("A 429 status code was received; we should back off a bit.")
except anthropic.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 anthropic import Anthropic
# Configure the default for all requests:
client = Anthropic(
# default is 2
max_retries=0,
)
# Or, configure per-request:
client.with_options(max_retries=5).messages.create(
max_tokens=1024,
messages=[
{
"role": "user",
"content": "Hello, Claude",
}
],
model="claude-3-opus-20240229",
)
```
### Timeouts
By default requests time out after 10 minutes. 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 anthropic import Anthropic
# Configure the default for all requests:
client = Anthropic(
# 20 seconds (default is 10 minutes)
timeout=20.0,
)
# More granular control:
client = Anthropic(
timeout=httpx.Timeout(60.0, read=5.0, write=10.0, connect=2.0),
)
# Override per-request:
client.with_options(timeout=5.0).messages.create(
max_tokens=1024,
messages=[
{
"role": "user",
"content": "Hello, Claude",
}
],
model="claude-3-opus-20240229",
)
```
On timeout, an `APITimeoutError` is thrown.
Note that requests that time out are [retried twice by default](https://github.com/anthropics/anthropic-sdk-python/tree/main/#retries).
## Default Headers
We automatically send the `anthropic-version` header set to `2023-06-01`.
If you need to, you can override it by setting default headers per-request or on the client object.
Be aware that doing so may result in incorrect types and other unexpected or undefined behavior in the SDK.
```python
from anthropic import Anthropic
client = Anthropic(
default_headers={"anthropic-version": "My-Custom-Value"},
)
```
## 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 `ANTHROPIC_LOG` to `debug`.
```shell
$ export ANTHROPIC_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 anthropic import Anthropic
client = Anthropic()
response = client.messages.with_raw_response.create(
max_tokens=1024,
messages=[{
"role": "user",
"content": "Hello, Claude",
}],
model="claude-3-opus-20240229",
)
print(response.headers.get('X-My-Header'))
message = response.parse() # get the object that `messages.create()` would have returned
print(message.content)
```
These methods return an [`LegacyAPIResponse`](https://github.com/anthropics/anthropic-sdk-python/tree/main/src/anthropic/_legacy_response.py) object. This is a legacy class as we're changing it slightly in the next major version.
For the sync client this will mostly be the same with the exception
of `content` & `text` will be methods instead of properties. In the
async client, all methods will be async.
A migration script will be provided & the migration in general should
be smooth.
#### `.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.
As such, `.with_streaming_response` methods return a different [`APIResponse`](https://github.com/anthropics/anthropic-sdk-python/tree/main/src/anthropic/_response.py) object, and the async client returns an [`AsyncAPIResponse`](https://github.com/anthropics/anthropic-sdk-python/tree/main/src/anthropic/_response.py) object.
```python
with client.messages.with_streaming_response.create(
max_tokens=1024,
messages=[
{
"role": "user",
"content": "Hello, Claude",
}
],
model="claude-3-opus-20240229",
) 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/#client-instances) functionality
```python
from anthropic import Anthropic, DefaultHttpxClient
client = Anthropic(
# Or use the `ANTHROPIC_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"),
),
)
```
### 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/anthropics/anthropic-sdk-python/issues) with questions, bugs, or suggestions.
## Requirements
Python 3.7 or higher.
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"description": "# Anthropic Python API library\n\n[](https://pypi.org/project/anthropic/)\n\nThe Anthropic Python library provides convenient access to the Anthropic REST API from any Python 3.7+\napplication. It 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## Documentation\n\nThe REST API documentation can be found [on docs.anthropic.com](https://docs.anthropic.com/claude/reference/). The full API of this library can be found in [api.md](https://github.com/anthropics/anthropic-sdk-python/tree/main/api.md).\n\n## Installation\n\n```sh\n# install from PyPI\npip install anthropic\n```\n\n## Usage\n\nThe full API of this library can be found in [api.md](https://github.com/anthropics/anthropic-sdk-python/tree/main/api.md).\n\n```python\nimport os\nfrom anthropic import Anthropic\n\nclient = Anthropic(\n # This is the default and can be omitted\n api_key=os.environ.get(\"ANTHROPIC_API_KEY\"),\n)\n\nmessage = client.messages.create(\n max_tokens=1024,\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Hello, Claude\",\n }\n ],\n model=\"claude-3-opus-20240229\",\n)\nprint(message.content)\n```\n\nWhile you can provide an `api_key` keyword argument,\nwe recommend using [python-dotenv](https://pypi.org/project/python-dotenv/)\nto add `ANTHROPIC_API_KEY=\"my-anthropic-api-key\"` to your `.env` file\nso that your API Key is not stored in source control.\n\n## Async usage\n\nSimply import `AsyncAnthropic` instead of `Anthropic` and use `await` with each API call:\n\n```python\nimport os\nimport asyncio\nfrom anthropic import AsyncAnthropic\n\nclient = AsyncAnthropic(\n # This is the default and can be omitted\n api_key=os.environ.get(\"ANTHROPIC_API_KEY\"),\n)\n\n\nasync def main() -> None:\n message = await client.messages.create(\n max_tokens=1024,\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Hello, Claude\",\n }\n ],\n model=\"claude-3-opus-20240229\",\n )\n print(message.content)\n\n\nasyncio.run(main())\n```\n\nFunctionality between the synchronous and asynchronous clients is otherwise identical.\n\n## Streaming responses\n\nWe provide support for streaming responses using Server Side Events (SSE).\n\n```python\nfrom anthropic import Anthropic\n\nclient = Anthropic()\n\nstream = client.messages.create(\n max_tokens=1024,\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Hello, Claude\",\n }\n ],\n model=\"claude-3-opus-20240229\",\n stream=True,\n)\nfor event in stream:\n print(event.type)\n```\n\nThe async client uses the exact same interface.\n\n```python\nfrom anthropic import AsyncAnthropic\n\nclient = AsyncAnthropic()\n\nstream = await client.messages.create(\n max_tokens=1024,\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Hello, Claude\",\n }\n ],\n model=\"claude-3-opus-20240229\",\n stream=True,\n)\nasync for event in stream:\n print(event.type)\n```\n\n### Streaming Helpers\n\nThis library provides several conveniences for streaming messages, for example:\n\n```py\nimport asyncio\nfrom anthropic import AsyncAnthropic\n\nclient = AsyncAnthropic()\n\nasync def main() -> None:\n async with client.messages.stream(\n max_tokens=1024,\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Say hello there!\",\n }\n ],\n model=\"claude-3-opus-20240229\",\n ) as stream:\n async for text in stream.text_stream:\n print(text, end=\"\", flush=True)\n print()\n\n message = await stream.get_final_message()\n print(message.to_json())\n\nasyncio.run(main())\n```\n\nStreaming with `client.messages.stream(...)` exposes [various helpers for your convenience](https://github.com/anthropics/anthropic-sdk-python/tree/main/helpers.md) including event handlers and accumulation.\n\nAlternatively, you can use `client.messages.create(..., stream=True)` which only returns an async iterable of the events in the stream and thus uses less memory (it does not build up a final message object for you).\n\n## Token counting\n\nYou can see the exact usage for a given request through the `usage` response property, e.g.\n\n```py\nmessage = client.messages.create(...)\nmessage.usage\n# Usage(input_tokens=25, output_tokens=13)\n```\n\n## Tool use beta\n\nThis SDK provides beta support for tool use, aka function calling. More details can be found in [the documentation](https://docs.anthropic.com/claude/docs/tool-use).\n\n## AWS Bedrock\n\nThis library also provides support for the [Anthropic Bedrock API](https://aws.amazon.com/bedrock/claude/) if you install this library with the `bedrock` extra, e.g. `pip install -U anthropic[bedrock]`.\n\nYou can then import and instantiate a separate `AnthropicBedrock` class, the rest of the API is the same.\n\n```py\nfrom anthropic import AnthropicBedrock\n\nclient = AnthropicBedrock()\n\nmessage = client.messages.create(\n max_tokens=1024,\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Hello!\",\n }\n ],\n model=\"anthropic.claude-3-sonnet-20240229-v1:0\",\n)\nprint(message)\n```\n\nFor a more fully fledged example see [`examples/bedrock.py`](https://github.com/anthropics/anthropic-sdk-python/blob/main/examples/bedrock.py).\n\n## Google Vertex\n\nThis library also provides support for the [Anthropic Vertex API](https://cloud.google.com/vertex-ai?hl=en) if you install this library with the `vertex` extra, e.g. `pip install -U anthropic[vertex]`.\n\nYou can then import and instantiate a separate `AnthropicVertex`/`AsyncAnthropicVertexAsync` class, which has the same API as the base `Anthropic`/`AsyncAnthropic` class.\n\n```py\nfrom anthropic import AnthropicVertex\n\nclient = AnthropicVertex()\n\nmessage = client.messages.create(\n model=\"claude-3-sonnet@20240229\",\n max_tokens=100,\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Hello!\",\n }\n ],\n)\nprint(message)\n```\n\nFor a more complete example see [`examples/vertex.py`](https://github.com/anthropics/anthropic-sdk-python/blob/main/examples/vertex.py).\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 `anthropic.APIConnectionError` is raised.\n\nWhen the API returns a non-success status code (that is, 4xx or 5xx\nresponse), a subclass of `anthropic.APIStatusError` is raised, containing `status_code` and `response` properties.\n\nAll errors inherit from `anthropic.APIError`.\n\n```python\nimport anthropic\nfrom anthropic import Anthropic\n\nclient = Anthropic()\n\ntry:\n client.messages.create(\n max_tokens=1024,\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Hello, Claude\",\n }\n ],\n model=\"claude-3-opus-20240229\",\n )\nexcept anthropic.APIConnectionError as e:\n print(\"The server could not be reached\")\n print(e.__cause__) # an underlying Exception, likely raised within httpx.\nexcept anthropic.RateLimitError as e:\n print(\"A 429 status code was received; we should back off a bit.\")\nexcept anthropic.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 anthropic import Anthropic\n\n# Configure the default for all requests:\nclient = Anthropic(\n # default is 2\n max_retries=0,\n)\n\n# Or, configure per-request:\nclient.with_options(max_retries=5).messages.create(\n max_tokens=1024,\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Hello, Claude\",\n }\n ],\n model=\"claude-3-opus-20240229\",\n)\n```\n\n### Timeouts\n\nBy default requests time out after 10 minutes. 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 anthropic import Anthropic\n\n# Configure the default for all requests:\nclient = Anthropic(\n # 20 seconds (default is 10 minutes)\n timeout=20.0,\n)\n\n# More granular control:\nclient = Anthropic(\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).messages.create(\n max_tokens=1024,\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Hello, Claude\",\n }\n ],\n model=\"claude-3-opus-20240229\",\n)\n```\n\nOn timeout, an `APITimeoutError` is thrown.\n\nNote that requests that time out are [retried twice by default](https://github.com/anthropics/anthropic-sdk-python/tree/main/#retries).\n\n## Default Headers\n\nWe automatically send the `anthropic-version` header set to `2023-06-01`.\n\nIf you need to, you can override it by setting default headers per-request or on the client object.\n\nBe aware that doing so may result in incorrect types and other unexpected or undefined behavior in the SDK.\n\n```python\nfrom anthropic import Anthropic\n\nclient = Anthropic(\n default_headers={\"anthropic-version\": \"My-Custom-Value\"},\n)\n```\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 `ANTHROPIC_LOG` to `debug`.\n\n```shell\n$ export ANTHROPIC_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 anthropic import Anthropic\n\nclient = Anthropic()\nresponse = client.messages.with_raw_response.create(\n max_tokens=1024,\n messages=[{\n \"role\": \"user\",\n \"content\": \"Hello, Claude\",\n }],\n model=\"claude-3-opus-20240229\",\n)\nprint(response.headers.get('X-My-Header'))\n\nmessage = response.parse() # get the object that `messages.create()` would have returned\nprint(message.content)\n```\n\nThese methods return an [`LegacyAPIResponse`](https://github.com/anthropics/anthropic-sdk-python/tree/main/src/anthropic/_legacy_response.py) object. This is a legacy class as we're changing it slightly in the next major version.\n\nFor the sync client this will mostly be the same with the exception\nof `content` & `text` will be methods instead of properties. In the\nasync client, all methods will be async.\n\nA migration script will be provided & the migration in general should\nbe smooth.\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\nAs such, `.with_streaming_response` methods return a different [`APIResponse`](https://github.com/anthropics/anthropic-sdk-python/tree/main/src/anthropic/_response.py) object, and the async client returns an [`AsyncAPIResponse`](https://github.com/anthropics/anthropic-sdk-python/tree/main/src/anthropic/_response.py) object.\n\n```python\nwith client.messages.with_streaming_response.create(\n max_tokens=1024,\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Hello, Claude\",\n }\n ],\n model=\"claude-3-opus-20240229\",\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/#client-instances) functionality\n\n```python\nfrom anthropic import Anthropic, DefaultHttpxClient\n\nclient = Anthropic(\n # Or use the `ANTHROPIC_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\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/anthropics/anthropic-sdk-python/issues) with questions, bugs, or suggestions.\n\n## Requirements\n\nPython 3.7 or higher.\n",
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