From 90fd19f2156e28845d9288ea8ffc2d7d9573b77a Mon Sep 17 00:00:00 2001
From: Dhravya Shah <dhravya@supermemory.com>
Date: Sat, 13 Sep 2025 22:09:40 -0700
Subject: update: Readme

---
 apps/docs/memory-api/sdks/python.mdx | 349 -----------------------------------
 1 file changed, 349 deletions(-)
 delete mode 100644 apps/docs/memory-api/sdks/python.mdx

(limited to 'apps/docs/memory-api/sdks/python.mdx')

diff --git a/apps/docs/memory-api/sdks/python.mdx b/apps/docs/memory-api/sdks/python.mdx
deleted file mode 100644
index 2b1f56fc..00000000
--- a/apps/docs/memory-api/sdks/python.mdx
+++ /dev/null
@@ -1,349 +0,0 @@
----
-title: 'Python SDK'
-sidebarTitle: "Python"
-description: 'Learn how to use supermemory with Python'
----
-
-## Installation
-
-```sh
-# install from PyPI
-pip install --pre supermemory
-```
-
-## Usage
-
-
-```python
-import os
-from supermemory import Supermemory
-
-client = supermemory(
-    api_key=os.environ.get("SUPERMEMORY_API_KEY"),  # This is the default and can be omitted
-)
-
-response = client.search.execute(
-    q="documents related to python",
-)
-print(response.results)
-```
-
-While you can provide an `api_key` keyword argument,
-we recommend using [python-dotenv](https://pypi.org/project/python-dotenv/)
-to add `SUPERMEMORY_API_KEY="My API Key"` to your `.env` file
-so that your API Key is not stored in source control.
-
-## Async usage
-
-Simply import `AsyncSupermemory` instead of `supermemory` and use `await` with each API call:
-
-```python
-import os
-import asyncio
-from supermemory import AsyncSupermemory
-
-client = AsyncSupermemory(
-    api_key=os.environ.get("SUPERMEMORY_API_KEY"),  # This is the default and can be omitted
-)
-
-
-async def main() -> None:
-    response = await client.search.execute(
-        q="documents related to python",
-    )
-    print(response.results)
-
-
-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`.
-
-## File uploads
-
-Request parameters that correspond to file uploads can be passed as `bytes`, or a [`PathLike`](https://docs.python.org/3/library/os.html#os.PathLike) instance or a tuple of `(filename, contents, media type)`.
-
-```python
-from pathlib import Path
-from supermemory import Supermemory
-
-client = supermemory()
-
-client.memories.upload_file(
-    file=Path("/path/to/file"),
-)
-```
-
-The async client uses the exact same interface. If you pass a [`PathLike`](https://docs.python.org/3/library/os.html#os.PathLike) instance, the file contents will be read asynchronously automatically.
-
-## 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 `supermemory.APIConnectionError` is raised.
-
-When the API returns a non-success status code (that is, 4xx or 5xx
-response), a subclass of `supermemory.APIStatusError` is raised, containing `status_code` and `response` properties.
-
-All errors inherit from `supermemory.APIError`.
-
-```python
-import supermemory
-from supermemory import Supermemory
-
-client = supermemory()
-
-try:
-    client.memories.add(
-        content="This is a detailed article about machine learning concepts...",
-    )
-except supermemory.APIConnectionError as e:
-    print("The server could not be reached")
-    print(e.__cause__)  # an underlying Exception, likely raised within httpx.
-except supermemory.RateLimitError as e:
-    print("A 429 status code was received; we should back off a bit.")
-except supermemory.APIStatusError as e:
-    print("Another non-200-range status code was received")
-    print(e.status_code)
-    print(e.response)
-```
-
-Error codes are as follows:
-
-| 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 supermemory import Supermemory
-
-# Configure the default for all requests:
-client = supermemory(
-    # default is 2
-    max_retries=0,
-)
-
-# Or, configure per-request:
-client.with_options(max_retries=5).memories.add(
-    content="This is a detailed article about machine learning concepts...",
-)
-```
-
-### 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 supermemory import Supermemory
-
-# Configure the default for all requests:
-client = supermemory(
-    # 20 seconds (default is 1 minute)
-    timeout=20.0,
-)
-
-# More granular control:
-client = supermemory(
-    timeout=httpx.Timeout(60.0, read=5.0, write=10.0, connect=2.0),
-)
-
-# Override per-request:
-client.with_options(timeout=5.0).memories.add(
-    content="This is a detailed article about machine learning concepts...",
-)
-```
-
-On timeout, an `APITimeoutError` is thrown.
-
-Note that requests that time out are [retried twice by default](#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 `SUPERMEMORY_LOG` to `info`.
-
-```shell
-$ export SUPERMEMORY_LOG=info
-```
-
-Or to `debug` for more verbose logging.
-
-### 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 supermemory import Supermemory
-
-client = supermemory()
-response = client.memories.with_raw_response.add(
-    content="This is a detailed article about machine learning concepts...",
-)
-print(response.headers.get('X-My-Header'))
-
-memory = response.parse()  # get the object that `memories.add()` would have returned
-print(memory.id)
-```
-
-These methods return an [`APIResponse`](https://github.com/supermemoryai/python-sdk/tree/main/src/supermemory/_response.py) object.
-
-The async client returns an [`AsyncAPIResponse`](https://github.com/supermemoryai/python-sdk/tree/main/src/supermemory/_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.memories.with_streaming_response.add(
-    content="This is a detailed article about machine learning concepts...",
-) 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) 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](https://www.python-httpx.org/advanced/proxies/)
-- Custom [transports](https://www.python-httpx.org/advanced/transports/)
-- Additional [advanced](https://www.python-httpx.org/advanced/clients/) functionality
-
-```python
-import httpx
-from supermemory import Supermemory, DefaultHttpxClient
-
-client = supermemory(
-    # Or use the `SUPERMEMORY_BASE_URL` env var
-    base_url="http://my.test.server.example.com:8083",
-    http_client=DefaultHttpxClient(
-        proxy="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.
-
-```py
-from supermemory import Supermemory
-
-with supermemory() as client:
-  # make requests here
-  ...
-
-# HTTP client is now closed
-```
-
-## 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/supermemoryai/python-sdk/issues) with questions, bugs, or suggestions.
-
-### Determining the installed version
-
-If you've upgraded to the latest version but aren't seeing any new features you were expecting then your python environment is likely still using an older version.
-
-You can determine the version that is being used at runtime with:
-
-```py
-import supermemory
-print(supermemory.__version__)
-```
-
-## Requirements
-
-Python 3.8 or higher.
\ No newline at end of file
-- 
cgit v1.2.3

