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grpc-go

Sun, 14 Sep 2025 15:24:41 +0800

grpc/grpc-go

gRPC-Go

The Go implementation of gRPC: A high performance, open source, general RPC framework that puts mobile and HTTP/2 first. For more information see the Go gRPC docs, or jump directly into the quick start.

Prerequisites

Go: any one of the two latest major releases.

Installation

Simply add the following import to your code, and then go [build|run|test] will automatically fetch the necessary dependencies:

`1`	`import "google.golang.org/grpc"`

Note: If you are trying to access grpc-go from China, see the FAQ below.

Learn more

Go gRPC docs, which include a quick start and API reference among other resources
Low-level technical docs from this repository
Performance benchmark
Examples
Contribution guidelines

FAQ

I/O Timeout Errors

The golang.org domain may be blocked from some countries. go get usually produces an error like the following when this happens:

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2

$ go get -u google.golang.org/grpc
package google.golang.org/grpc: unrecognized import path "google.golang.org/grpc" (https fetch: Get https://google.golang.org/grpc?go-get=1: dial tcp 216.239.37.1:443: i/o timeout)

To build Go code, there are several options:

Set up a VPN and access google.golang.org through that.
With Go module support: it is possible to use the replace feature of go mod to create aliases for golang.org packages. In your project’s directory:
1 2 3 4

go mod edit -replace=google.golang.org/grpc=github.com/grpc/grpc-go@latest go mod tidy go mod vendor go build -mod=vendor
Again, this will need to be done for all transitive dependencies hosted on golang.org as well. For details, refer to golang/go issue #28652.

Compiling error, undefined: grpc.SupportPackageIsVersion

Please update to the latest version of gRPC-Go using go get google.golang.org/grpc.

How to turn on logging

The default logger is controlled by environment variables. Turn everything on like this:

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$ export GRPC_GO_LOG_VERBOSITY_LEVEL=99
$ export GRPC_GO_LOG_SEVERITY_LEVEL=info

The RPC failed with error `"code = Unavailable desc = transport is closing"`

This error means the connection the RPC is using was closed, and there are many possible reasons, including:

mis-configured transport credentials, connection failed on handshaking
bytes disrupted, possibly by a proxy in between
server shutdown
Keepalive parameters caused connection shutdown, for example if you have configured your server to terminate connections regularly to trigger DNS lookups. If this is the case, you may want to increase your MaxConnectionAgeGrace, to allow longer RPC calls to finish.

It can be tricky to debug this because the error happens on the client side but the root cause of the connection being closed is on the server side. Turn on logging on both client and server, and see if there are any transport errors.

openai-python

Sun, 10 Aug 2025 15:28:32 +0800

openai/openai-python

OpenAI Python API library

The OpenAI Python library provides convenient access to the OpenAI REST API from any Python 3.8+ 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.

Documentation

The REST API documentation can be found on platform.openai.com. The full API of this library can be found in api.md.

Installation

1
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# install from PyPI
pip install openai

Usage

The full API of this library can be found in api.md.

The primary API for interacting with OpenAI models is the Responses API. You can generate text from the model with the code below.

import os
from openai import OpenAI

client = OpenAI(
    # This is the default and can be omitted
    api_key=os.environ.get("OPENAI_API_KEY"),
)

response = client.responses.create(
    model="gpt-4o",
    instructions="You are a coding assistant that talks like a pirate.",
    input="How do I check if a Python object is an instance of a class?",
)

print(response.output_text)

The previous standard (supported indefinitely) for generating text is the Chat Completions API. You can use that API to generate text from the model with the code below.

from openai import OpenAI

client = OpenAI()

completion = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "developer", "content": "Talk like a pirate."},
        {
            "role": "user",
            "content": "How do I check if a Python object is an instance of a class?",
        },
    ],
)

print(completion.choices[0].message.content)

While you can provide an api_key keyword argument, we recommend using python-dotenv to add OPENAI_API_KEY="My API Key" to your .env file so that your API key is not stored in source control. Get an API key here.

Vision

With an image URL:

prompt = "What is in this image?"
img_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/d5/2023_06_08_Raccoon1.jpg/1599px-2023_06_08_Raccoon1.jpg"

response = client.responses.create(
    model="gpt-4o-mini",
    input=[
        {
            "role": "user",
            "content": [
                {"type": "input_text", "text": prompt},
                {"type": "input_image", "image_url": f"{img_url}"},
            ],
        }
    ],
)

With the image as a base64 encoded string:

import base64
from openai import OpenAI

client = OpenAI()

prompt = "What is in this image?"
with open("path/to/image.png", "rb") as image_file:
    b64_image = base64.b64encode(image_file.read()).decode("utf-8")

response = client.responses.create(
    model="gpt-4o-mini",
    input=[
        {
            "role": "user",
            "content": [
                {"type": "input_text", "text": prompt},
                {"type": "input_image", "image_url": f"data:image/png;base64,{b64_image}"},
            ],
        }
    ],
)

Async usage

Simply import AsyncOpenAI instead of OpenAI and use await with each API call:

import os
import asyncio
from openai import AsyncOpenAI

client = AsyncOpenAI(
    # This is the default and can be omitted
    api_key=os.environ.get("OPENAI_API_KEY"),
)


async def main() -> None:
    response = await client.responses.create(
        model="gpt-4o", input="Explain disestablishmentarianism to a smart five year old."
    )
    print(response.output_text)


asyncio.run(main())

Functionality between the synchronous and asynchronous clients is otherwise identical.

With aiohttp

By default, the async client uses httpx for HTTP requests. However, for improved concurrency performance you may also use aiohttp as the HTTP backend.

You can enable this by installing aiohttp:

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# install from PyPI
pip install openai[aiohttp]

Then you can enable it by instantiating the client with http_client=DefaultAioHttpClient():

import asyncio
from openai import DefaultAioHttpClient
from openai import AsyncOpenAI


async def main() -> None:
    async with AsyncOpenAI(
        api_key="My API Key",
        http_client=DefaultAioHttpClient(),
    ) as client:
        chat_completion = await client.chat.completions.create(
            messages=[
                {
                    "role": "user",
                    "content": "Say this is a test",
                }
            ],
            model="gpt-4o",
        )


asyncio.run(main())

Streaming responses

We provide support for streaming responses using Server Side Events (SSE).

from openai import OpenAI

client = OpenAI()

stream = client.responses.create(
    model="gpt-4o",
    input="Write a one-sentence bedtime story about a unicorn.",
    stream=True,
)

for event in stream:
    print(event)

The async client uses the exact same interface.

import asyncio
from openai import AsyncOpenAI

client = AsyncOpenAI()


async def main():
    stream = await client.responses.create(
        model="gpt-4o",
        input="Write a one-sentence bedtime story about a unicorn.",
        stream=True,
    )

    async for event in stream:
        print(event)


asyncio.run(main())

Realtime API beta

The Realtime API enables you to build low-latency, multi-modal conversational experiences. It currently supports text and audio as both input and output, as well as function calling through a WebSocket connection.

Under the hood the SDK uses the websockets library to manage connections.

The Realtime API works through a combination of client-sent events and server-sent events. Clients can send events to do things like update session configuration or send text and audio inputs. Server events confirm when audio responses have completed, or when a text response from the model has been received. A full event reference can be found here and a guide can be found here.

Basic text based example:

import asyncio
from openai import AsyncOpenAI

async def main():
    client = AsyncOpenAI()

    async with client.beta.realtime.connect(model="gpt-4o-realtime-preview") as connection:
        await connection.session.update(session={'modalities': ['text']})

        await connection.conversation.item.create(
            item={
                "type": "message",
                "role": "user",
                "content": [{"type": "input_text", "text": "Say hello!"}],
            }
        )
        await connection.response.create()

        async for event in connection:
            if event.type == 'response.text.delta':
                print(event.delta, flush=True, end="")

            elif event.type == 'response.text.done':
                print()

            elif event.type == "response.done":
                break

asyncio.run(main())

However the real magic of the Realtime API is handling audio inputs / outputs, see this example TUI script for a fully fledged example.

Realtime error handling

Whenever an error occurs, the Realtime API will send an error event and the connection will stay open and remain usable. This means you need to handle it yourself, as no errors are raised directly by the SDK when an error event comes in.

client = AsyncOpenAI()

async with client.beta.realtime.connect(model="gpt-4o-realtime-preview") as connection:
    ...
    async for event in connection:
        if event.type == 'error':
            print(event.error.type)
            print(event.error.code)
            print(event.error.event_id)
            print(event.error.message)

Using types

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.

Pagination

List methods in the OpenAI API are paginated.

This library provides auto-paginating iterators with each list response, so you do not have to request successive pages manually:

from openai import OpenAI

client = OpenAI()

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 openai import AsyncOpenAI

client = AsyncOpenAI()


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 params

Nested parameters are dictionaries, typed using TypedDict, for example:

from openai import OpenAI

client = OpenAI()

response = client.chat.responses.create(
    input=[
        {
            "role": "user",
            "content": "How much ?",
        }
    ],
    model="gpt-4o",
    response_format={"type": "json_object"},
)

File uploads

Request parameters that correspond to file uploads can be passed as bytes, or a PathLike instance or a tuple of (filename, contents, media type).

from pathlib import Path
from openai import OpenAI

client = OpenAI()

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.

Webhook Verification

Verifying webhook signatures is optional but encouraged.

For more information about webhooks, see the API docs.

Parsing webhook payloads

For most use cases, you will likely want to verify the webhook and parse the payload at the same time. To achieve this, we provide the method client.webhooks.unwrap(), which parses a webhook request and verifies that it was sent by OpenAI. This method will raise an error if the signature is invalid.

Note that the body parameter must be the raw JSON string sent from the server (do not parse it first). The .unwrap() method will parse this JSON for you into an event object after verifying the webhook was sent from OpenAI.

from openai import OpenAI
from flask import Flask, request

app = Flask(__name__)
client = OpenAI()  # OPENAI_WEBHOOK_SECRET environment variable is used by default


@app.route("/webhook", methods=["POST"])
def webhook():
    request_body = request.get_data(as_text=True)

    try:
        event = client.webhooks.unwrap(request_body, request.headers)

        if event.type == "response.completed":
            print("Response completed:", event.data)
        elif event.type == "response.failed":
            print("Response failed:", event.data)
        else:
            print("Unhandled event type:", event.type)

        return "ok"
    except Exception as e:
        print("Invalid signature:", e)
        return "Invalid signature", 400


if __name__ == "__main__":
    app.run(port=8000)

Verifying webhook payloads directly

In some cases, you may want to verify the webhook separately from parsing the payload. If you prefer to handle these steps separately, we provide the method client.webhooks.verify_signature() to only verify the signature of a webhook request. Like .unwrap(), this method will raise an error if the signature is invalid.

Note that the body parameter must be the raw JSON string sent from the server (do not parse it first). You will then need to parse the body after verifying the signature.

import json
from openai import OpenAI
from flask import Flask, request

app = Flask(__name__)
client = OpenAI()  # OPENAI_WEBHOOK_SECRET environment variable is used by default


@app.route("/webhook", methods=["POST"])
def webhook():
    request_body = request.get_data(as_text=True)

    try:
        client.webhooks.verify_signature(request_body, request.headers)

        # Parse the body after verification
        event = json.loads(request_body)
        print("Verified event:", event)

        return "ok"
    except Exception as e:
        print("Invalid signature:", e)
        return "Invalid signature", 400


if __name__ == "__main__":
    app.run(port=8000)

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 openai.APIConnectionError is raised.

When the API returns a non-success status code (that is, 4xx or 5xx response), a subclass of openai.APIStatusError is raised, containing status_code and response properties.

All errors inherit from openai.APIError.

import openai
from openai import OpenAI

client = OpenAI()

try:
    client.fine_tuning.jobs.create(
        model="gpt-4o",
        training_file="file-abc123",
    )
except openai.APIConnectionError as e:
    print("The server could not be reached")
    print(e.__cause__)  # an underlying Exception, likely raised within httpx.
except openai.RateLimitError as e:
    print("A 429 status code was received; we should back off a bit.")
except openai.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`

Request IDs

For more information on debugging requests, see these docs

All object responses in the SDK provide a _request_id property which is added from the x-request-id response header so that you can quickly log failing requests and report them back to OpenAI.

response = await client.responses.create(
    model="gpt-4o-mini",
    input="Say 'this is a test'.",
)
print(response._request_id)  # req_123

Note that unlike other properties that use an _ prefix, the _request_id property is public. Unless documented otherwise, all other _ prefix properties, methods and modules are private.

[!IMPORTANT]
If you need to access request IDs for failed requests you must catch the APIStatusError exception

import openai

try:
    completion = await client.chat.completions.create(
        messages=[{"role": "user", "content": "Say this is a test"}], model="gpt-4"
    )
except openai.APIStatusError as exc:
    print(exc.request_id)  # req_123
    raise exc

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:

from openai import OpenAI

# Configure the default for all requests:
client = OpenAI(
    # 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 JavaScript?",
        }
    ],
    model="gpt-4o",
)

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 object:

from openai import OpenAI

# Configure the default for all requests:
client = OpenAI(
    # 20 seconds (default is 10 minutes)
    timeout=20.0,
)

# More granular control:
client = OpenAI(
    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="gpt-4o",
)

On timeout, an APITimeoutError is thrown.

Note that requests that time out are retried twice by default.

Advanced

Logging

We use the standard library logging module.

You can enable logging by setting the environment variable OPENAI_LOG to info.

`1`	`$ export OPENAI_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:

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.,

from openai import OpenAI

client = OpenAI()
response = client.chat.completions.with_raw_response.create(
    messages=[{
        "role": "user",
        "content": "Say this is a test",
    }],
    model="gpt-4o",
)
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 a 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.

`.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 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="gpt-4o",
) 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.

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.

Configuring the HTTP client

You can directly override the httpx client to customize it for your use case, including:

Support for proxies
Custom transports
Additional advanced functionality

import httpx
from openai import OpenAI, DefaultHttpxClient

client = OpenAI(
    # Or use the `OPENAI_BASE_URL` env var
    base_url="http://my.test.server.example.com:8083/v1",
    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():

`1`	`client.with_options(http_client=DefaultHttpxClient(...))`

Managing HTTP resources

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.

from openai import OpenAI

with OpenAI() as client:
  # make requests here
  ...

# HTTP client is now closed

Microsoft Azure OpenAI

To use this library with Azure OpenAI, use the AzureOpenAI class instead of the OpenAI 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 openai import AzureOpenAI

# gets the API Key from environment variable AZURE_OPENAI_API_KEY
client = AzureOpenAI(
    # https://learn.microsoft.com/azure/ai-services/openai/reference#rest-api-versioning
    api_version="2023-07-01-preview",
    # https://learn.microsoft.com/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource
    azure_endpoint="https://example-endpoint.openai.azure.com",
)

completion = client.chat.completions.create(
    model="deployment-name",  # e.g. gpt-35-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 OpenAI client, the following options are provided:

azure_endpoint (or the AZURE_OPENAI_ENDPOINT environment variable)
azure_deployment
api_version (or the OPENAI_API_VERSION environment variable)
azure_ad_token (or the AZURE_OPENAI_AD_TOKEN environment 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.

Versioning

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.

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:

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2

import openai
print(openai.__version__)

Requirements

Python 3.8 or higher.

Contributing

See the contributing documentation.

spdlog

Sat, 21 Jun 2025 15:28:00 +0800

gabime/spdlog

spdlog

Very fast, header only, C++ logging library.

Install

Just copy the headers:

Copy the source folder to your build tree and use a C++11 compiler.

Or use your favorite package manager:

Ubuntu: apt-get install libspdlog-dev
Homebrew: brew install spdlog
FreeBSD: cd /usr/ports/devel/spdlog/ && make install clean
Fedora: yum install spdlog
Gentoo: emerge dev-libs/spdlog
Arch Linux: yaourt -S spdlog-git
vcpkg: vcpkg install spdlog

Platforms

Linux, FreeBSD, Solaris, AIX
Windows (vc 2013+, cygwin)
Mac OSX (clang 3.5+)
Android

Features

Very fast - performance is the primary goal (see benchmarks below).
Headers only, just copy and use.
Feature rich call style using the excellent fmt library.
Optional printf syntax support.
Asynchronous mode (optional)
Custom formatting.
Conditional Logging
Multi/Single threaded loggers.
Various log targets:
- Rotating log files.
- Daily log files.
- Console logging (colors supported).
- syslog.
- Windows debugger (OutputDebugString(..))
- Easily extendable with custom log targets (just implement a single function in the sink interface).
Severity based filtering - threshold levels can be modified in runtime as well as in compile time.

Benchmarks

Below are some benchmarks comparing popular log libraries under Ubuntu 64 bit, Intel i7-4770 CPU @ 3.40GHz

Synchronous mode

Time needed to log 1,000,000 lines in synchronous mode (in seconds, the best of 3 runs):

threads	boost log 1.54	glog	easylogging	spdlog
1	4.169s	1.066s	0.975s	0.392s
10	6.180s	3.032s	2.857s	0.773s
100	5.981s	1.139s	4.512s	0.587s

Asynchronous mode

Time needed to log 1,000,000 lines in asynchronous mode, i.e. the time it takes to put them in the async queue (in seconds, the best of 3 runs):

threads	g2log ^{async logger}	spdlog ^{async mode}
1	1.850s	0.39s
10	0.943s	0.416s
100	0.959s	0.413s

Usage Example


#include "spdlog/spdlog.h"

#include <iostream>
#include <memory>

void async_example();
void syslog_example();
void user_defined_example();
void err_handler_example();

namespace spd = spdlog;
int main(int, char*[])
{
    try
    {
        // Console logger with color
        auto console = spd::stdout_color_mt("console");
        console->info("Welcome to spdlog!");
        console->error("Some error message with arg{}..", 1);
	
        // Formatting examples
        console->warn("Easy padding in numbers like {:08d}", 12);
        console->critical("Support for int: {0:d};  hex: {0:x};  oct: {0:o}; bin: {0:b}", 42);
        console->info("Support for floats {:03.2f}", 1.23456);
        console->info("Positional args are {1} {0}..", "too", "supported");
        console->info("{:<30}", "left aligned");
	
	// Use global registry to retrieve loggers
        spd::get("console")->info("loggers can be retrieved from a global registry using the spdlog::get(logger_name) function");
        
        // Create basic file logger (not rotated)
        auto my_logger = spd::basic_logger_mt("basic_logger", "logs/basic.txt");
        my_logger->info("Some log message");

        // Create a file rotating logger with 5mb size max and 3 rotated files
        auto rotating_logger = spd::rotating_logger_mt("some_logger_name", "logs/mylogfile.txt", 1048576 * 5, 3);
        for (int i = 0; i < 10; ++i)
            rotating_logger->info("{} * {} equals {:>10}", i, i, i*i);

        // Create a daily logger - a new file is created every day on 2:30am
        auto daily_logger = spd::daily_logger_mt("daily_logger", "logs/daily.txt", 2, 30);
        // trigger flush if the log severity is error or higher
        daily_logger->flush_on(spd::level::err);
        daily_logger->info(123.44);

        // Customize msg format for all messages
        spd::set_pattern("[%^+++%$] [%H:%M:%S %z] [thread %t] %v");
        console->info("This an info message with custom format (and custom color range between the '%^' and '%$')");
	console->error("This an error message with custom format (and custom color range between the '%^' and '%$')");

        // Runtime log levels
	spd::set_level(spd::level::info); //Set global log level to info
	console->debug("This message should not be displayed!");
	console->set_level(spd::level::debug); // Set specific logger's log level
	console->debug("This message should be displayed..");

        // Compile time log levels
        // define SPDLOG_DEBUG_ON or SPDLOG_TRACE_ON
        SPDLOG_TRACE(console, "Enabled only #ifdef SPDLOG_TRACE_ON..{} ,{}", 1, 3.23);
        SPDLOG_DEBUG(console, "Enabled only #ifdef SPDLOG_DEBUG_ON.. {} ,{}", 1, 3.23);

        // Asynchronous logging is very fast..
        // Just call spdlog::set_async_mode(q_size) and all created loggers from now on will be asynchronous..
        async_example();

        // syslog example. linux/osx only
        syslog_example();

        // android example. compile with NDK
        android_example();

        // Log user-defined types example
        user_defined_example();

        // Change default log error handler
        err_handler_example();

        // Apply a function on all registered loggers
        spd::apply_all([&](std::shared_ptr<spd::logger> l)
        {
            l->info("End of example.");
        });

        // Release and close all loggers
        spd::drop_all();
    }
    // Exceptions will only be thrown upon failed logger or sink construction (not during logging)
    catch (const spd::spdlog_ex& ex)
    {
        std::cout << "Log init failed: " << ex.what() << std::endl;
        return 1;
    }
}

void async_example()
{
    size_t q_size = 4096; 
    spd::set_async_mode(q_size);
    auto async_file = spd::daily_logger_st("async_file_logger", "logs/async_log.txt");
    for (int i = 0; i < 100; ++i)
        async_file->info("Async message #{}", i);
}

//syslog example
void syslog_example()
{
#ifdef SPDLOG_ENABLE_SYSLOG 
    std::string ident = "spdlog-example";
    auto syslog_logger = spd::syslog_logger("syslog", ident, LOG_PID);
    syslog_logger->warn("This is warning that will end up in syslog..");
#endif
}

// user defined types logging by implementing operator<<
struct my_type
{
    int i;
    template<typename OStream>
    friend OStream& operator<<(OStream& os, const my_type &c)
    {
        return os << "[my_type i="<<c.i << "]";
    }
};

#include <spdlog/fmt/ostr.h> // must be included
void user_defined_example()
{
    spd::get("console")->info("user defined type: {}", my_type { 14 });
}

//
//custom error handler
//
void err_handler_example()
{	
	spd::set_error_handler([](const std::string& msg) {
		std::cerr << "my err handler: " << msg << std::endl;
	}); 
	// (or logger->set_error_handler(..) to set for specific logger)
}

Documentation

Documentation can be found in the wiki pages.

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grpc-go

grpc/grpc-go

gRPC-Go

Prerequisites

Installation

Learn more

FAQ

I/O Timeout Errors

Compiling error, undefined: grpc.SupportPackageIsVersion

How to turn on logging

The RPC failed with error "code = Unavailable desc = transport is closing"

openai-python

openai/openai-python

OpenAI Python API library

Documentation

Installation

Usage

Vision

Async usage

With aiohttp

Streaming responses

Realtime API beta

Realtime error handling

Using types

Pagination

Nested params

File uploads

Webhook Verification

Parsing webhook payloads

Verifying webhook payloads directly

Handling errors

Request IDs

Retries

Timeouts

Advanced

Logging

How to tell whether None means null or missing

Accessing raw response data (e.g. headers)

.with_streaming_response

Making custom/undocumented requests

Undocumented endpoints

Undocumented request params

Undocumented response properties

Configuring the HTTP client

Managing HTTP resources

Microsoft Azure OpenAI

Versioning

Determining the installed version

Requirements

Contributing

spdlog

gabime/spdlog

spdlog

Install

Just copy the headers:

Or use your favorite package manager:

Platforms

Features

Benchmarks

Synchronous mode

Asynchronous mode

Usage Example

Documentation

The RPC failed with error `"code = Unavailable desc = transport is closing"`

How to tell whether `None` means `null` or missing

`.with_streaming_response`