The NeoSpace Python library provides convenient access to the NeoSpace 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.
It is generated from our OpenAPI specification with Stainless.
The REST API documentation can be found on platform.neospace.com. The full API of this library can be found in api.md.
# install from Release
pip install https://github.com/neospace-ai/neospace-python/releases/download/v1.0.0-MTuBbe5M/neospace-1.37.0-py3-none-any.whlThe full API of this library can be found in api.md.
import os
from neospace import NeoSpace
client = NeoSpace(
# This is the default and can be omitted
api_key=os.environ.get("NEOSPACE_API_KEY"),
base_url=os.environ.get("NEOSPACE_BASE_URL")
)
chat_completion = client.chat.completions.create(
messages=[
{
"role": "user",
"content": "Say this is a test",
}
],
model="7b-r16_lora_full_constrained",
)While you can provide a api_key and base_url keyword arguments,
we recommend using python-dotenv
to add NEOSPACE_API_KEY="My API Key" NEOSPACE_BASE_URL="My Inference Endpoint" to your .env file
so that your API Key is not stored in source control.
Important
The SDK was forked from openai-python just to to permit completions in neospace llm's, so the SDK can have some integration errors in the examples below (open issue to solve it).
When interacting with the API some actions such as starting a Run and adding files to vector stores are asynchronous and take time to complete. The SDK includes helper functions which will poll the status until it reaches a terminal state and then return the resulting object. If an API method results in an action that could benefit from polling there will be a corresponding version of the method ending in '_and_poll'.
For instance to create a Run and poll until it reaches a terminal state you can run:
run = client.beta.threads.runs.create_and_poll(
thread_id=thread.id,
assistant_id=assistant.id,
)More information on the lifecycle of a Run can be found in the Run Lifecycle Documentation
When creating and interacting with vector stores, you can use polling helpers to monitor the status of operations. For convenience, we also provide a bulk upload helper to allow you to simultaneously upload several files at once.
sample_files = [Path("sample-paper.pdf"), ...]
batch = await client.vector_stores.file_batches.upload_and_poll(
store.id,
files=sample_files,
)The SDK also includes helpers to process streams and handle incoming events.
with client.beta.threads.runs.stream(
thread_id=thread.id,
assistant_id=assistant.id,
instructions="Please address the user as Jane Doe. The user has a premium account.",
) as stream:
for event in stream:
# Print the text from text delta events
if event.type == "thread.message.delta" and event.data.delta.content:
print(event.data.delta.content[0].text)More information on streaming helpers can be found in the dedicated documentation: helpers.md
Simply import AsyncNeoSpace instead of NeoSpace and use await with each API call:
import os
import asyncio
from neospace import AsyncNeoSpace
client = AsyncNeoSpace(
# This is the default and can be omitted
api_key=os.environ.get("NEOSPACE_API_KEY"),
)
async def main() -> None:
chat_completion = await client.chat.completions.create(
messages=[
{
"role": "user",
"content": "Say this is a test",
}
],
model="7b-r16_lora_full_constrained",
)
asyncio.run(main())Functionality between the synchronous and asynchronous clients is otherwise identical.
We provide support for streaming responses using Server Side Events (SSE).
from neospace import NeoSpace
client = NeoSpace()
stream = client.chat.completions.create(
model="7b-r16_lora_full_constrained",
messages=[{"role": "user", "content": "Say this is a test"}],
stream=True,
)
for chunk in stream:
print(chunk.choices[0].delta.content or "", end="")The async client uses the exact same interface.
from neospace import AsyncNeoSpace
client = AsyncNeoSpace()
async def main():
stream = await client.chat.completions.create(
model="7b-r16_lora_full_constrained",
messages=[{"role": "user", "content": "Say this is a test"}],
stream=True,
)
async for chunk in stream:
print(chunk.choices[0].delta.content or "", end="")
asyncio.run(main())Important
We highly recommend instantiating client instances instead of relying on the global client.
We also expose a global client instance that is accessible in a similar fashion to versions prior to v1.
import neospace
# optional; defaults to `os.environ['NEOSPACE_API_KEY']`
neospace.api_key = '...'
# all client options can be configured just like the `NeoSpace` instantiation counterpart
neospace.base_url = "https://..."
neospace.default_headers = {"x-foo": "true"}
completion = neospace.chat.completions.create(
model="7b-r16_lora_full_constrained",
messages=[
{
"role": "user",
"content": "How do I output all files in a directory using Python?",
},
],
)
print(completion.choices[0].message.content)The API is the exact same as the standard client instance-based API.
This is intended to be used within REPLs or notebooks for faster iteration, not in application code.
We recommend that you always instantiate a client (e.g., with client = NeoSpace()) in application code because:
- It can be difficult to reason about where client options are configured
- It's not possible to change certain client options without potentially causing race conditions
- It's harder to mock for testing purposes
- It's not possible to control cleanup of network connections
Nested request parameters are TypedDicts. Responses are Pydantic models 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.
List methods in the NeoSpace API are paginated.
This library provides auto-paginating iterators with each list response, so you do not have to request successive pages manually:
from neospace import NeoSpace
client = NeoSpace()
all_jobs = []
# Automatically fetches more pages as needed.
for job in client.fine_tuning.jobs.list(
limit=20,
):
# Do something with job here
all_jobs.append(job)
print(all_jobs)Or, asynchronously:
import asyncio
from neospace import AsyncNeoSpace
client = AsyncNeoSpace()
async def main() -> None:
all_jobs = []
# Iterate through items across all pages, issuing requests as needed.
async for job in client.fine_tuning.jobs.list(
limit=20,
):
all_jobs.append(job)
print(all_jobs)
asyncio.run(main())Alternatively, you can use the .has_next_page(), .next_page_info(), or .get_next_page() methods for more granular control working with pages:
first_page = await client.fine_tuning.jobs.list(
limit=20,
)
if first_page.has_next_page():
print(f"will fetch next page using these details: {first_page.next_page_info()}")
next_page = await first_page.get_next_page()
print(f"number of items we just fetched: {len(next_page.data)}")
# Remove `await` for non-async usage.Or just work directly with the returned data:
first_page = await client.fine_tuning.jobs.list(
limit=20,
)
print(f"next page cursor: {first_page.after}") # => "next page cursor: ..."
for job in first_page.data:
print(job.id)
# Remove `await` for non-async usage.Nested parameters are dictionaries, typed using TypedDict, for example:
from neospace import NeoSpace
client = NeoSpace()
completion = client.chat.completions.create(
messages=[
{
"role": "user",
"content": "Can you generate an example json object describing a fruit?",
}
],
model="7b-r16_lora_full_constrained-1106",
response_format={"type": "json_object"},
)Request parameters that correspond to file uploads can be passed as bytes, a PathLike instance or a tuple of (filename, contents, media type).
from pathlib import Path
from neospace import NeoSpace
client = NeoSpace()
client.files.create(
file=Path("input.jsonl"),
purpose="fine-tune",
)The async client uses the exact same interface. If you pass a PathLike instance, the file contents will be read asynchronously automatically.
When the library is unable to connect to the API (for example, due to network connection problems or a timeout), a subclass of neospace.APIConnectionError is raised.
When the API returns a non-success status code (that is, 4xx or 5xx
response), a subclass of neospace.APIStatusError is raised, containing status_code and response properties.
All errors inherit from neospace.APIError.
import neospace
from neospace import NeoSpace
client = NeoSpace()
try:
client.fine_tuning.jobs.create(
model="7b-r16_lora_full_constrained",
training_file="file-abc123",
)
except neospace.APIConnectionError as e:
print("The server could not be reached")
print(e.__cause__) # an underlying Exception, likely raised within httpx.
except neospace.RateLimitError as e:
print("A 429 status code was received; we should back off a bit.")
except neospace.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 |
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:
from neospace import NeoSpace
# Configure the default for all requests:
client = NeoSpace(
# default is 2
max_retries=0,
)
# Or, configure per-request:
client.with_options(max_retries=5).chat.completions.create(
messages=[
{
"role": "user",
"content": "How can I get the name of the current day in Node.js?",
}
],
model="7b-r16_lora_full_constrained",
)By default requests time out after 10 minutes. You can configure this with a timeout option,
which accepts a float or an httpx.Timeout object:
from neospace import NeoSpace
# Configure the default for all requests:
client = NeoSpace(
# 20 seconds (default is 10 minutes)
timeout=20.0,
)
# More granular control:
client = NeoSpace(
timeout=httpx.Timeout(60.0, read=5.0, write=10.0, connect=2.0),
)
# Override per-request:
client.with_options(timeout=5.0).chat.completions.create(
messages=[
{
"role": "user",
"content": "How can I list all files in a directory using Python?",
}
],
model="7b-r16_lora_full_constrained",
)On timeout, an APITimeoutError is thrown.
Note that requests that time out are retried twice by default.
We use the standard library logging module.
You can enable logging by setting the environment variable NEOSPACE_LOG to debug.
$ export NEOSPACE_LOG=debugIn 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:
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}.')The "raw" Response object can be accessed by prefixing .with_raw_response. to any HTTP method call, e.g.,
from neospace import NeoSpace
client = NeoSpace()
response = client.chat.completions.with_raw_response.create(
messages=[{
"role": "user",
"content": "Say this is a test",
}],
model="7b-r16_lora_full_constrained",
)
print(response.headers.get('X-My-Header'))
completion = response.parse() # get the object that `chat.completions.create()` would have returned
print(completion)These methods return an LegacyAPIResponse 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.
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 object, and the async client returns an AsyncAPIResponse object.
with client.chat.completions.with_streaming_response.create(
messages=[
{
"role": "user",
"content": "Say this is a test",
}
],
model="7b-r16_lora_full_constrained",
) 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.
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.
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.
import httpx
response = client.post(
"/foo",
cast_to=httpx.Response,
body={"my_param": True},
)
print(response.headers.get("x-foo"))If you want to explicitly send an extra param, you can do so with the extra_query, extra_body, and extra_headers request
options.
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.
You can directly override the httpx client to customize it for your use case, including:
- Support for proxies
- Custom transports
- Additional advanced functionality
from neospace import NeoSpace, DefaultHttpxClient
client = NeoSpace(
# Or use the `NEOSPACE_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():
client.with_options(http_client=DefaultHttpxClient(...))By default the library closes underlying HTTP connections whenever the client is garbage collected. You can manually close the client using the .close() method if desired, or with a context manager that closes when exiting.
To use this library with Azure NeoSpace, use the AzureNeoSpace
class instead of the NeoSpace class.
Important
The Azure API shape differs from the core API shape which means that the static types for responses / params won't always be correct.
from neospace import AzureNeoSpace
# gets the API Key from environment variable AZURE_NEOSPACE_API_KEY
client = AzureNeoSpace(
# https://learn.microsoft.com/azure/ai-services/neospace/reference#rest-api-versioning
api_version="2023-07-01-preview",
# https://learn.microsoft.com/azure/cognitive-services/neospace/how-to/create-resource?pivots=web-portal#create-a-resource
azure_endpoint="https://example-endpoint.neospace.azure.com",
)
completion = client.chat.completions.create(
model="deployment-name", # e.g. 7b-r16_lora_full_constrained-instant
messages=[
{
"role": "user",
"content": "How do I output all files in a directory using Python?",
},
],
)
print(completion.to_json())In addition to the options provided in the base NeoSpace client, the following options are provided:
azure_endpoint(or theAZURE_NEOSPACE_ENDPOINTenvironment variable)azure_deploymentapi_version(or theNEOSPACE_API_VERSIONenvironment variable)azure_ad_token(or theAZURE_NEOSPACE_AD_TOKENenvironment variable)azure_ad_token_provider
An example of using the client with Microsoft Entra ID (formerly known as Azure Active Directory) can be found here.
This package generally follows SemVer conventions, though certain backwards-incompatible changes may be released as minor versions:
- Changes that only affect static types, without breaking runtime behavior.
- 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).
- 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 with questions, bugs, or suggestions.
Python 3.7 or higher.