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folly

Sun, 14 Sep 2025 15:25:34 +0800

facebook/folly

Folly: Facebook Open-source Library

What is `folly`?

Folly (acronymed loosely after Facebook Open Source Library) is a library of C++17 components designed with practicality and efficiency in mind. Folly contains a variety of core library components used extensively at Facebook. In particular, it’s often a dependency of Facebook’s other open source C++ efforts and place where those projects can share code.

It complements (as opposed to competing against) offerings such as Boost and of course std. In fact, we embark on defining our own component only when something we need is either not available, or does not meet the needed performance profile. We endeavor to remove things from folly if or when std or Boost obsoletes them.

Performance concerns permeate much of Folly, sometimes leading to designs that are more idiosyncratic than they would otherwise be (see e.g. PackedSyncPtr.h, SmallLocks.h). Good performance at large scale is a unifying theme in all of Folly.

Check it out in the intro video

Logical Design

Folly is a collection of relatively independent components, some as simple as a few symbols. There is no restriction on internal dependencies, meaning that a given folly module may use any other folly components.

All symbols are defined in the top-level namespace folly, except of course macros. Macro names are ALL_UPPERCASE and should be prefixed with FOLLY_. Namespace folly defines other internal namespaces such as internal or detail. User code should not depend on symbols in those namespaces.

Physical Design

At the top level Folly uses the classic “stuttering” scheme folly/folly used by Boost and others. The first directory serves as an installation root of the library (with possible versioning a la folly-1.0/), and the second is to distinguish the library when including files, e.g. #include <folly/FBString.h>.

The directory structure is flat (mimicking the namespace structure), i.e. we don’t have an elaborate directory hierarchy (it is possible this will change in future versions). The subdirectory experimental contains files that are used inside folly and possibly at Facebook but not considered stable enough for client use. Your code should not use files in folly/experimental lest it may break when you update Folly.

The folly/folly/test subdirectory includes the unittests for all components, usually named ComponentXyzTest.cpp for each ComponentXyz.*. The folly/folly/docs directory contains documentation.

What’s in it?

Because of folly’s fairly flat structure, the best way to see what’s in it is to look at the headers in top level folly/ directory. You can also check the docs folder for documentation, starting with the overview.

Folly is published on GitHub at https://github.com/facebook/folly.

Build Notes

Because folly does not provide any ABI compatibility guarantees from commit to commit, we generally recommend building folly as a static library.

folly supports gcc (5.1+), clang, or MSVC. It should run on Linux (x86-32, x86-64, and ARM), iOS, macOS, and Windows (x86-64). The CMake build is only tested on some of these platforms; at a minimum, we aim to support macOS and Linux (on the latest Ubuntu LTS release or newer.)

`getdeps.py`

This script is used by many of Meta’s OSS tools. It will download and build all of the necessary dependencies first, and will then invoke cmake etc to build folly. This will help ensure that you build with relevant versions of all of the dependent libraries, taking into account what versions are installed locally on your system.

It’s written in python so you’ll need python3.6 or later on your PATH. It works on Linux, macOS and Windows.

The settings for folly’s cmake build are held in its getdeps manifest build/fbcode_builder/manifests/folly, which you can edit locally if desired.

Dependencies

If on Linux or MacOS (with homebrew installed) you can install system dependencies to save building them:

# Clone the repo
git clone https://github.com/facebook/folly
# Install dependencies
cd folly
sudo ./build/fbcode_builder/getdeps.py install-system-deps --recursive

If you’d like to see the packages before installing them:

./build/fbcode_builder/getdeps.py install-system-deps --dry-run --recursive

On other platforms or if on Linux and without system dependencies getdeps.py will mostly download and build them for you during the build step.

Some of the dependencies getdeps.py uses and installs are:

a version of boost compiled with C++14 support.
googletest is required to build and run folly’s tests.

Build

This script will download and build all of the necessary dependencies first, and will then invoke cmake etc to build folly. This will help ensure that you build with relevant versions of all of the dependent libraries, taking into account what versions are installed locally on your system.

getdeps.py currently requires python 3.6+ to be on your path.

getdeps.py will invoke cmake etc.

# Clone the repo
git clone https://github.com/facebook/folly
cd folly
# Build, using system dependencies if available
python3 ./build/fbcode_builder/getdeps.py --allow-system-packages build

It puts output in its scratch area:

installed/folly/lib/libfolly.a: Library

You can also specify a --scratch-path argument to control the location of the scratch directory used for the build. You can find the default scratch install location from logs or with python3 ./build/fbcode_builder/getdeps.py show-inst-dir.

There are also --install-dir and --install-prefix arguments to provide some more fine-grained control of the installation directories. However, given that folly provides no compatibility guarantees between commits we generally recommend building and installing the libraries to a temporary location, and then pointing your project’s build at this temporary location, rather than installing folly in the traditional system installation directories. e.g., if you are building with CMake you can use the CMAKE_PREFIX_PATH variable to allow CMake to find folly in this temporary installation directory when building your project.

If you want to invoke cmake again to iterate, there is a helpful run_cmake.py script output in the scratch build directory. You can find the scratch build directory from logs or with python3 ./build/fbcode_builder/getdeps.py show-build-dir.

Run tests

By default getdeps.py will build the tests for folly. To run them:

cd folly
python3 ./build/fbcode_builder/getdeps.py --allow-system-packages test

`build.sh`/`build.bat` wrapper

build.sh can be used on Linux and MacOS, on Windows use the build.bat script instead. Its a wrapper around getdeps.py.

Build with cmake directly

If you don’t want to let getdeps invoke cmake for you then by default, building the tests is disabled as part of the CMake all target. To build the tests, specify -DBUILD_TESTS=ON to CMake at configure time.

NB if you want to invoke cmake again to iterate on a getdeps.py build, there is a helpful run_cmake.py script output in the scratch-path build directory. You can find the scratch build directory from logs or with python3 ./build/fbcode_builder/getdeps.py show-build-dir.

Running tests with ctests also works if you cd to the build dir, e.g. (cd $(python3 ./build/fbcode_builder/getdeps.py show-build-dir) && ctest)

Finding dependencies in non-default locations

If you have boost, gtest, or other dependencies installed in a non-default location, you can use the CMAKE_INCLUDE_PATH and CMAKE_LIBRARY_PATH variables to make CMAKE look also look for header files and libraries in non-standard locations. For example, to also search the directories /alt/include/path1 and /alt/include/path2 for header files and the directories /alt/lib/path1 and /alt/lib/path2 for libraries, you can invoke cmake as follows:

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cmake \
  -DCMAKE_INCLUDE_PATH=/alt/include/path1:/alt/include/path2 \
  -DCMAKE_LIBRARY_PATH=/alt/lib/path1:/alt/lib/path2 ...

Ubuntu LTS, CentOS Stream, Fedora

Use the getdeps.py approach above. We test in CI on Ubuntu LTS, and occasionally on other distros.

If you find the set of system packages is not quite right for your chosen distro, you can specify distro version specific overrides in the dependency manifests (e.g. https://github.com/facebook/folly/blob/main/build/fbcode_builder/manifests/boost ). You could probably make it work on most recent Ubuntu/Debian or Fedora/Redhat derived distributions.

At time of writing (Dec 2021) there is a build break on GCC 11.x based systems in lang_badge_test. If you don’t need badge functionality you can work around by commenting it out from CMakeLists.txt (unfortunately fbthrift does need it)

Windows (Vcpkg)

Note that many tests are disabled for folly Windows builds, you can see them in the log from the cmake configure step, or by looking for WINDOWS_DISABLED in CMakeLists.txt

That said, getdeps.py builds work on Windows and are tested in CI.

If you prefer, you can try Vcpkg. folly is available in Vcpkg and releases may be built via vcpkg install folly:x64-windows.

You may also use vcpkg install folly:x64-windows --head to build against main.

macOS

getdeps.py builds work on macOS and are tested in CI, however if you prefer, you can try one of the macOS package managers

Homebrew

folly is available as a Formula and releases may be built via brew install folly.

You may also use folly/build/bootstrap-osx-homebrew.sh to build against main:

`1`	`./folly/build/bootstrap-osx-homebrew.sh`

This will create a build directory _build in the top-level.

MacPorts

Install the required packages from MacPorts:

  sudo port install \
    boost \
    cmake \
    gflags \
    git \
    google-glog \
    libevent \
    libtool \
    lz4 \
    lzma \
    openssl \
    snappy \
    xz \
    zlib

Download and install double-conversion:

  git clone https://github.com/google/double-conversion.git
  cd double-conversion
  cmake -DBUILD_SHARED_LIBS=ON .
  make
  sudo make install

Download and install folly with the parameters listed below:

  git clone https://github.com/facebook/folly.git
  cd folly
  mkdir _build
  cd _build
  cmake ..
  make
  sudo make install

go-redis

Sat, 16 Aug 2025 15:27:45 +0800

redis/go-redis

Redis client for Go

go-redis is the official Redis client library for the Go programming language. It offers a straightforward interface for interacting with Redis servers.

Supported versions

In go-redis we are aiming to support the last three releases of Redis. Currently, this means we do support:

Redis 7.2 - using Redis Stack 7.2 for modules support
Redis 7.4 - using Redis Stack 7.4 for modules support
Redis 8.0 - using Redis CE 8.0 where modules are included
Redis 8.2 - using Redis CE 8.2 where modules are included

Although the go.mod states it requires at minimum go 1.18, our CI is configured to run the tests against all three versions of Redis and latest two versions of Go (1.23, 1.24). We observe that some modules related test may not pass with Redis Stack 7.2 and some commands are changed with Redis CE 8.0. Please do refer to the documentation and the tests if you experience any issues. We do plan to update the go version in the go.mod to go 1.24 in one of the next releases.

How do I Redis?

Learn for free at Redis University

Build faster with the Redis Launchpad

Try the Redis Cloud

Dive in developer tutorials

Join the Redis community

Work at Redis

Documentation

Resources

Ecosystem

This client also works with Kvrocks, a distributed key value NoSQL database that uses RocksDB as storage engine and is compatible with Redis protocol.

Features

Redis commands except QUIT and SYNC.
Automatic connection pooling.
StreamingCredentialsProvider (e.g. entra id, oauth) (experimental)
Pub/Sub.
Pipelines and transactions.
Scripting.
Redis Sentinel.
Redis Cluster.
Redis Ring.
Redis Performance Monitoring.
Redis Probabilistic [RedisStack]
Customizable read and write buffers size.

Installation

go-redis supports 2 last Go versions and requires a Go version with modules support. So make sure to initialize a Go module:

`1`	`go mod init github.com/my/repo`

Then install go-redis/v9:

`1`	`go get github.com/redis/go-redis/v9`

Quickstart

import (
    "context"
    "fmt"

    "github.com/redis/go-redis/v9"
)

var ctx = context.Background()

func ExampleClient() {
    rdb := redis.NewClient(&redis.Options{
        Addr:     "localhost:6379",
        Password: "", // no password set
        DB:       0,  // use default DB
    })

    err := rdb.Set(ctx, "key", "value", 0).Err()
    if err != nil {
        panic(err)
    }

    val, err := rdb.Get(ctx, "key").Result()
    if err != nil {
        panic(err)
    }
    fmt.Println("key", val)

    val2, err := rdb.Get(ctx, "key2").Result()
    if err == redis.Nil {
        fmt.Println("key2 does not exist")
    } else if err != nil {
        panic(err)
    } else {
        fmt.Println("key2", val2)
    }
    // Output: key value
    // key2 does not exist
}

Authentication

The Redis client supports multiple ways to provide authentication credentials, with a clear priority order. Here are the available options:

1. Streaming Credentials Provider (Highest Priority) - Experimental feature

The streaming credentials provider allows for dynamic credential updates during the connection lifetime. This is particularly useful for managed identity services and token-based authentication.

type StreamingCredentialsProvider interface {
    Subscribe(listener CredentialsListener) (Credentials, UnsubscribeFunc, error)
}

type CredentialsListener interface {
    OnNext(credentials Credentials)  // Called when credentials are updated
    OnError(err error)              // Called when an error occurs
}

type Credentials interface {
    BasicAuth() (username string, password string)
    RawCredentials() string
}

Example usage:

rdb := redis.NewClient(&redis.Options{
    Addr: "localhost:6379",
    StreamingCredentialsProvider: &MyCredentialsProvider{},
})

Note: The streaming credentials provider can be used with go-redis-entraid to enable Entra ID (formerly Azure AD) authentication. This allows for seamless integration with Azure’s managed identity services and token-based authentication.

Example with Entra ID:

import (
    "github.com/redis/go-redis/v9"
    "github.com/redis/go-redis-entraid"
)

// Create an Entra ID credentials provider
provider := entraid.NewDefaultAzureIdentityProvider()

// Configure Redis client with Entra ID authentication
rdb := redis.NewClient(&redis.Options{
    Addr: "your-redis-server.redis.cache.windows.net:6380",
    StreamingCredentialsProvider: provider,
    TLSConfig: &tls.Config{
        MinVersion: tls.VersionTLS12,
    },
})

2. Context-based Credentials Provider

The context-based provider allows credentials to be determined at the time of each operation, using the context.

rdb := redis.NewClient(&redis.Options{
    Addr: "localhost:6379",
    CredentialsProviderContext: func(ctx context.Context) (string, string, error) {
        // Return username, password, and any error
        return "user", "pass", nil
    },
})

3. Regular Credentials Provider

A simple function-based provider that returns static credentials.

rdb := redis.NewClient(&redis.Options{
    Addr: "localhost:6379",
    CredentialsProvider: func() (string, string) {
        // Return username and password
        return "user", "pass"
    },
})

4. Username/Password Fields (Lowest Priority)

The most basic way to provide credentials is through the Username and Password fields in the options.

rdb := redis.NewClient(&redis.Options{
    Addr:     "localhost:6379",
    Username: "user",
    Password: "pass",
})

Priority Order

The client will use credentials in the following priority order:

Streaming Credentials Provider (if set)
Context-based Credentials Provider (if set)
Regular Credentials Provider (if set)
Username/Password fields (if set)

If none of these are set, the client will attempt to connect without authentication.

Protocol Version

The client supports both RESP2 and RESP3 protocols. You can specify the protocol version in the options:

rdb := redis.NewClient(&redis.Options{
    Addr:     "localhost:6379",
    Password: "", // no password set
    DB:       0,  // use default DB
    Protocol: 3,  // specify 2 for RESP 2 or 3 for RESP 3
})

Connecting via a redis url

go-redis also supports connecting via the redis uri specification. The example below demonstrates how the connection can easily be configured using a string, adhering to this specification.

import (
    "github.com/redis/go-redis/v9"
)

func ExampleClient() *redis.Client {
    url := "redis://user:password@localhost:6379/0?protocol=3"
    opts, err := redis.ParseURL(url)
    if err != nil {
        panic(err)
    }

    return redis.NewClient(opts)
}

Instrument with OpenTelemetry

import (
    "github.com/redis/go-redis/v9"
    "github.com/redis/go-redis/extra/redisotel/v9"
    "errors"
)

func main() {
    ...
    rdb := redis.NewClient(&redis.Options{...})

    if err := errors.Join(redisotel.InstrumentTracing(rdb), redisotel.InstrumentMetrics(rdb)); err != nil {
        log.Fatal(err)
    }

Buffer Size Configuration

go-redis uses 0.5MiB read and write buffers by default for optimal performance. For high-throughput applications or large pipelines, you can customize buffer sizes:

rdb := redis.NewClient(&redis.Options{
    Addr:            "localhost:6379",
    ReadBufferSize:  1024 * 1024, // 1MiB read buffer
    WriteBufferSize: 1024 * 1024, // 1MiB write buffer
})

Advanced Configuration

go-redis supports extending the client identification phase to allow projects to send their own custom client identification.

Default Client Identification

By default, go-redis automatically sends the client library name and version during the connection process. This feature is available in redis-server as of version 7.2. As a result, the command is “fire and forget”, meaning it should fail silently, in the case that the redis server does not support this feature.

Disabling Identity Verification

When connection identity verification is not required or needs to be explicitly disabled, a DisableIdentity configuration option exists. Initially there was a typo and the option was named DisableIndentity instead of DisableIdentity. The misspelled option is marked as Deprecated and will be removed in V10 of this library. Although both options will work at the moment, the correct option is DisableIdentity. The deprecated option will be removed in V10 of this library, so please use the correct option name to avoid any issues.

To disable verification, set the DisableIdentity option to true in the Redis client options:

rdb := redis.NewClient(&redis.Options{
    Addr:            "localhost:6379",
    Password:        "",
    DB:              0,
    DisableIdentity: true, // Disable set-info on connect
})

Unstable RESP3 Structures for RediSearch Commands

When integrating Redis with application functionalities using RESP3, it’s important to note that some response structures aren’t final yet. This is especially true for more complex structures like search and query results. We recommend using RESP2 when using the search and query capabilities, but we plan to stabilize the RESP3-based API-s in the coming versions. You can find more guidance in the upcoming release notes.

To enable unstable RESP3, set the option in your client configuration:

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redis.NewClient(&redis.Options{
			UnstableResp3: true,
		})

Note: When UnstableResp3 mode is enabled, it’s necessary to use RawResult() and RawVal() to retrieve a raw data. Since, raw response is the only option for unstable search commands Val() and Result() calls wouldn’t have any affect on them:

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res1, err := client.FTSearchWithArgs(ctx, "txt", "foo bar", &redis.FTSearchOptions{}).RawResult()
val1 := client.FTSearchWithArgs(ctx, "txt", "foo bar", &redis.FTSearchOptions{}).RawVal()

Redis-Search Default Dialect

In the Redis-Search module, the default dialect is 2. If needed, you can explicitly specify a different dialect using the appropriate configuration in your queries.

Important: Be aware that the query dialect may impact the results returned. If needed, you can revert to a different dialect version by passing the desired dialect in the arguments of the command you want to execute. For example:

	res2, err := rdb.FTSearchWithArgs(ctx,
		"idx:bicycle",
		"@pickup_zone:[CONTAINS $bike]",
		&redis.FTSearchOptions{
			Params: map[string]interface{}{
				"bike": "POINT(-0.1278 51.5074)",
			},
			DialectVersion: 3,
		},
	).Result()

You can find further details in the query dialect documentation.

Custom buffer sizes

Prior to v9.12, the buffer size was the default go value of 4096 bytes. Starting from v9.12, go-redis uses 256KiB read and write buffers by default for optimal performance. For high-throughput applications or large pipelines, you can customize buffer sizes:

rdb := redis.NewClient(&redis.Options{
    Addr:            "localhost:6379",
    ReadBufferSize:  1024 * 1024, // 1MiB read buffer
    WriteBufferSize: 1024 * 1024, // 1MiB write buffer
})

Important: If you experience any issues with the default buffer sizes, please try setting them to the go default of 4096 bytes.

Contributing

We welcome contributions to the go-redis library! If you have a bug fix, feature request, or improvement, please open an issue or pull request on GitHub. We appreciate your help in making go-redis better for everyone. If you are interested in contributing to the go-redis library, please check out our contributing guidelines for more information on how to get started.

Look and feel

Some corner cases:

// SET key value EX 10 NX
set, err := rdb.SetNX(ctx, "key", "value", 10*time.Second).Result()

// SET key value keepttl NX
set, err := rdb.SetNX(ctx, "key", "value", redis.KeepTTL).Result()

// SORT list LIMIT 0 2 ASC
vals, err := rdb.Sort(ctx, "list", &redis.Sort{Offset: 0, Count: 2, Order: "ASC"}).Result()

// ZRANGEBYSCORE zset -inf +inf WITHSCORES LIMIT 0 2
vals, err := rdb.ZRangeByScoreWithScores(ctx, "zset", &redis.ZRangeBy{
    Min: "-inf",
    Max: "+inf",
    Offset: 0,
    Count: 2,
}).Result()

// ZINTERSTORE out 2 zset1 zset2 WEIGHTS 2 3 AGGREGATE SUM
vals, err := rdb.ZInterStore(ctx, "out", &redis.ZStore{
    Keys: []string{"zset1", "zset2"},
    Weights: []int64{2, 3}
}).Result()

// EVAL "return {KEYS[1],ARGV[1]}" 1 "key" "hello"
vals, err := rdb.Eval(ctx, "return {KEYS[1],ARGV[1]}", []string{"key"}, "hello").Result()

// custom command
res, err := rdb.Do(ctx, "set", "key", "value").Result()

Run the test

go-redis will start a redis-server and run the test cases.

The paths of redis-server bin file and redis config file are defined in main_test.go:

var (
	redisServerBin, _  = filepath.Abs(filepath.Join("testdata", "redis", "src", "redis-server"))
	redisServerConf, _ = filepath.Abs(filepath.Join("testdata", "redis", "redis.conf"))
)

For local testing, you can change the variables to refer to your local files, or create a soft link to the corresponding folder for redis-server and copy the config file to testdata/redis/:

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ln -s /usr/bin/redis-server ./go-redis/testdata/redis/src
cp ./go-redis/testdata/redis.conf ./go-redis/testdata/redis/

Lastly, run:

go test

Another option is to run your specific tests with an already running redis. The example below, tests against a redis running on port 9999.:

`1`	`REDIS_PORT=9999 go test <your options>`

Contributors

The go-redis project was originally initiated by :star: uptrace/uptrace. Uptrace is an open-source APM tool that supports distributed tracing, metrics, and logs. You can use it to monitor applications and set up automatic alerts to receive notifications via email, Slack, Telegram, and others.

See OpenTelemetry example which demonstrates how you can use Uptrace to monitor go-redis.

Thanks to all the people who already contributed!

openai-node

Mon, 11 Aug 2025 15:35:37 +0800

openai/openai-node

OpenAI TypeScript and JavaScript API Library

This library provides convenient access to the OpenAI REST API from TypeScript or JavaScript.

It is generated from our OpenAPI specification with Stainless.

To learn how to use the OpenAI API, check out our API Reference and Documentation.

Installation

`1`	`npm install openai`

Installation from JSR

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deno add jsr:@openai/openai
npx jsr add @openai/openai

These commands will make the module importable from the @openai/openai scope. You can also import directly from JSR without an install step if you’re using the Deno JavaScript runtime:

`1`	`import OpenAI from 'jsr:@openai/openai';`

Usage

The full API of this library can be found in api.md file along with many code examples.

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

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env['OPENAI_API_KEY'], // This is the default and can be omitted
});

const response = await client.responses.create({
  model: 'gpt-4o',
  instructions: 'You are a coding assistant that talks like a pirate',
  input: 'Are semicolons optional in JavaScript?',
});

console.log(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.

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env['OPENAI_API_KEY'], // This is the default and can be omitted
});

const completion = await client.chat.completions.create({
  model: 'gpt-4o',
  messages: [
    { role: 'developer', content: 'Talk like a pirate.' },
    { role: 'user', content: 'Are semicolons optional in JavaScript?' },
  ],
});

console.log(completion.choices[0].message.content);

Streaming responses

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

import OpenAI from 'openai';

const client = new OpenAI();

const stream = await client.responses.create({
  model: 'gpt-4o',
  input: 'Say "Sheep sleep deep" ten times fast!',
  stream: true,
});

for await (const event of stream) {
  console.log(event);
}

File uploads

Request parameters that correspond to file uploads can be passed in many different forms:

File (or an object with the same structure)
a fetch Response (or an object with the same structure)
an fs.ReadStream
the return value of our toFile helper

import fs from 'fs';
import OpenAI, { toFile } from 'openai';

const client = new OpenAI();

// If you have access to Node `fs` we recommend using `fs.createReadStream()`:
await client.files.create({ file: fs.createReadStream('input.jsonl'), purpose: 'fine-tune' });

// Or if you have the web `File` API you can pass a `File` instance:
await client.files.create({ file: new File(['my bytes'], 'input.jsonl'), purpose: 'fine-tune' });

// You can also pass a `fetch` `Response`:
await client.files.create({ file: await fetch('https://somesite/input.jsonl'), purpose: 'fine-tune' });

// Finally, if none of the above are convenient, you can use our `toFile` helper:
await client.files.create({
  file: await toFile(Buffer.from('my bytes'), 'input.jsonl'),
  purpose: 'fine-tune',
});
await client.files.create({
  file: await toFile(new Uint8Array([0, 1, 2]), 'input.jsonl'),
  purpose: 'fine-tune',
});

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 throw 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.

import { headers } from 'next/headers';
import OpenAI from 'openai';

const client = new OpenAI({
  webhookSecret: process.env.OPENAI_WEBHOOK_SECRET, // env var used by default; explicit here.
});

export async function webhook(request: Request) {
  const headersList = headers();
  const body = await request.text();

  try {
    const event = client.webhooks.unwrap(body, headersList);

    switch (event.type) {
      case 'response.completed':
        console.log('Response completed:', event.data);
        break;
      case 'response.failed':
        console.log('Response failed:', event.data);
        break;
      default:
        console.log('Unhandled event type:', event.type);
    }

    return Response.json({ message: 'ok' });
  } catch (error) {
    console.error('Invalid webhook signature:', error);
    return new Response('Invalid signature', { status: 400 });
  }
}

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.verifySignature() to only verify the signature of a webhook request. Like .unwrap(), this method will throw 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 { headers } from 'next/headers';
import OpenAI from 'openai';

const client = new OpenAI({
  webhookSecret: process.env.OPENAI_WEBHOOK_SECRET, // env var used by default; explicit here.
});

export async function webhook(request: Request) {
  const headersList = headers();
  const body = await request.text();

  try {
    client.webhooks.verifySignature(body, headersList);

    // Parse the body after verification
    const event = JSON.parse(body);
    console.log('Verified event:', event);

    return Response.json({ message: 'ok' });
  } catch (error) {
    console.error('Invalid webhook signature:', error);
    return new Response('Invalid signature', { status: 400 });
  }
}

Handling errors

When the library is unable to connect to the API, or if the API returns a non-success status code (i.e., 4xx or 5xx response), a subclass of APIError will be thrown:

const job = await client.fineTuning.jobs
  .create({ model: 'gpt-4o', training_file: 'file-abc123' })
  .catch(async (err) => {
    if (err instanceof OpenAI.APIError) {
      console.log(err.request_id);
      console.log(err.status); // 400
      console.log(err.name); // BadRequestError
      console.log(err.headers); // {server: 'nginx', ...}
    } else {
      throw err;
    }
  });

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.

const completion = await client.chat.completions.create({
  messages: [{ role: 'user', content: 'Say this is a test' }],
  model: 'gpt-4o',
});
console.log(completion._request_id); // req_123

You can also access the Request ID using the .withResponse() method:

const { data: stream, request_id } = await openai.chat.completions
  .create({
    model: 'gpt-4',
    messages: [{ role: 'user', content: 'Say this is a test' }],
    stream: true,
  })
  .withResponse();

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.

import { OpenAIRealtimeWebSocket } from 'openai/beta/realtime/websocket';

const rt = new OpenAIRealtimeWebSocket({ model: 'gpt-4o-realtime-preview-2024-12-17' });

rt.on('response.text.delta', (event) => process.stdout.write(event.delta));

For more information see realtime.md.

Microsoft Azure OpenAI

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

[!IMPORTANT] The Azure API shape slightly differs from the core API shape which means that the static types for responses / params won’t always be correct.

import { AzureOpenAI } from 'openai';
import { getBearerTokenProvider, DefaultAzureCredential } from '@azure/identity';

const credential = new DefaultAzureCredential();
const scope = 'https://cognitiveservices.azure.com/.default';
const azureADTokenProvider = getBearerTokenProvider(credential, scope);

const openai = new AzureOpenAI({ azureADTokenProvider });

const result = await openai.chat.completions.create({
  model: 'gpt-4o',
  messages: [{ role: 'user', content: 'Say hello!' }],
});

console.log(result.choices[0]!.message?.content);

Retries

Certain errors will be 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 will all be retried by default.

You can use the maxRetries option to configure or disable this:

// Configure the default for all requests:
const client = new OpenAI({
  maxRetries: 0, // default is 2
});

// Or, configure per-request:
await client.chat.completions.create({ messages: [{ role: 'user', content: 'How can I get the name of the current day in JavaScript?' }], model: 'gpt-4o' }, {
  maxRetries: 5,
});

Timeouts

Requests time out after 10 minutes by default. You can configure this with a timeout option:

// Configure the default for all requests:
const client = new OpenAI({
  timeout: 20 * 1000, // 20 seconds (default is 10 minutes)
});

// Override per-request:
await client.chat.completions.create({ messages: [{ role: 'user', content: 'How can I list all files in a directory using Python?' }], model: 'gpt-4o' }, {
  timeout: 5 * 1000,
});

On timeout, an APIConnectionTimeoutError is thrown.

Note that requests which time out will be retried twice by default.

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.

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2

const response = await client.responses.create({ model: 'gpt-4o', input: 'testing 123' });
console.log(response._request_id); // req_123

You can also access the Request ID using the .withResponse() method:

const { data: stream, request_id } = await openai.responses
  .create({
    model: 'gpt-4o',
    input: 'Say this is a test',
    stream: true,
  })
  .withResponse();

Auto-pagination

List methods in the OpenAI API are paginated. You can use the for await … of syntax to iterate through items across all pages:

async function fetchAllFineTuningJobs(params) {
  const allFineTuningJobs = [];
  // Automatically fetches more pages as needed.
  for await (const fineTuningJob of client.fineTuning.jobs.list({ limit: 20 })) {
    allFineTuningJobs.push(fineTuningJob);
  }
  return allFineTuningJobs;
}

Alternatively, you can request a single page at a time:

let page = await client.fineTuning.jobs.list({ limit: 20 });
for (const fineTuningJob of page.data) {
  console.log(fineTuningJob);
}

// Convenience methods are provided for manually paginating:
while (page.hasNextPage()) {
  page = await page.getNextPage();
  // ...
}

Realtime API Beta

import { OpenAIRealtimeWebSocket } from 'openai/beta/realtime/websocket';

const rt = new OpenAIRealtimeWebSocket({ model: 'gpt-4o-realtime-preview-2024-12-17' });

rt.on('response.text.delta', (event) => process.stdout.write(event.delta));

For more information see realtime.md.

Microsoft Azure OpenAI

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

[!IMPORTANT] The Azure API shape slightly differs from the core API shape which means that the static types for responses / params won’t always be correct.

import { AzureOpenAI } from 'openai';
import { getBearerTokenProvider, DefaultAzureCredential } from '@azure/identity';

const credential = new DefaultAzureCredential();
const scope = 'https://cognitiveservices.azure.com/.default';
const azureADTokenProvider = getBearerTokenProvider(credential, scope);

const openai = new AzureOpenAI({
  azureADTokenProvider,
  apiVersion: '<The API version, e.g. 2024-10-01-preview>',
});

const result = await openai.chat.completions.create({
  model: 'gpt-4o',
  messages: [{ role: 'user', content: 'Say hello!' }],
});

console.log(result.choices[0]!.message?.content);

For more information on support for the Azure API, see azure.md.

Advanced Usage

Accessing raw Response data (e.g., headers)

The “raw” Response returned by fetch() can be accessed through the .asResponse() method on the APIPromise type that all methods return. This method returns as soon as the headers for a successful response are received and does not consume the response body, so you are free to write custom parsing or streaming logic.

You can also use the .withResponse() method to get the raw Response along with the parsed data. Unlike .asResponse() this method consumes the body, returning once it is parsed.

const client = new OpenAI();

const httpResponse = await client.responses
  .create({ model: 'gpt-4o', input: 'say this is a test.' })
  .asResponse();

// access the underlying web standard Response object
console.log(httpResponse.headers.get('X-My-Header'));
console.log(httpResponse.statusText);

const { data: modelResponse, response: raw } = await client.responses
  .create({ model: 'gpt-4o', input: 'say this is a test.' })
  .withResponse();
console.log(raw.headers.get('X-My-Header'));
console.log(modelResponse);

Logging

[!IMPORTANT] All log messages are intended for debugging only. The format and content of log messages may change between releases.

Log levels

The log level can be configured in two ways:

Via the OPENAI_LOG environment variable
Using the logLevel client option (overrides the environment variable if set)

import OpenAI from 'openai';

const client = new OpenAI({
  logLevel: 'debug', // Show all log messages
});

Available log levels, from most to least verbose:

'debug' - Show debug messages, info, warnings, and errors
'info' - Show info messages, warnings, and errors
'warn' - Show warnings and errors (default)
'error' - Show only errors
'off' - Disable all logging

At the 'debug' level, all HTTP requests and responses are logged, including headers and bodies. Some authentication-related headers are redacted, but sensitive data in request and response bodies may still be visible.

Custom logger

By default, this library logs to globalThis.console. You can also provide a custom logger. Most logging libraries are supported, including pino, winston, bunyan, consola, signale, and @std/log. If your logger doesn’t work, please open an issue.

When providing a custom logger, the logLevel option still controls which messages are emitted, messages below the configured level will not be sent to your logger.

import OpenAI from 'openai';
import pino from 'pino';

const logger = pino();

const client = new OpenAI({
  logger: logger.child({ name: 'OpenAI' }),
  logLevel: 'debug', // Send all messages to pino, allowing it to filter
});

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 use client.get, client.post, and other HTTP verbs. Options on the client, such as retries, will be respected when making these requests.

await client.post('/some/path', {
  body: { some_prop: 'foo' },
  query: { some_query_arg: 'bar' },
});

Undocumented request params

To make requests using undocumented parameters, you may use // @ts-expect-error on the undocumented parameter. This library doesn’t validate at runtime that the request matches the type, so any extra values you send will be sent as-is.

client.chat.completions.create({
  // ...
  // @ts-expect-error baz is not yet public
  baz: 'undocumented option',
});

For requests with the GET verb, any extra params will be in the query, all other requests will send the extra param in the body.

If you want to explicitly send an extra argument, you can do so with the query, body, and headers request options.

Undocumented response properties

To access undocumented response properties, you may access the response object with // @ts-expect-error on the response object, or cast the response object to the requisite type. Like the request params, we do not validate or strip extra properties from the response from the API.

Customizing the fetch client

If you want to use a different fetch function, you can either polyfill the global:

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2
3

import fetch from 'my-fetch';

globalThis.fetch = fetch;

Or pass it to the client:

import OpenAI from 'openai';
import fetch from 'my-fetch';

const client = new OpenAI({ fetch });

Fetch options

If you want to set custom fetch options without overriding the fetch function, you can provide a fetchOptions object when instantiating the client or making a request. (Request-specific options override client options.)

import OpenAI from 'openai';

const client = new OpenAI({
  fetchOptions: {
    // `RequestInit` options
  },
});

Configuring proxies

To modify proxy behavior, you can provide custom fetchOptions that add runtime-specific proxy options to requests:

Node ^[docs]

import OpenAI from 'openai';
import * as undici from 'undici';

const proxyAgent = new undici.ProxyAgent('http://localhost:8888');
const client = new OpenAI({
  fetchOptions: {
    dispatcher: proxyAgent,
  },
});

Bun ^[docs]

import OpenAI from 'openai';

const client = new OpenAI({
  fetchOptions: {
    proxy: 'http://localhost:8888',
  },
});

Deno ^[docs]

import OpenAI from 'npm:openai';

const httpClient = Deno.createHttpClient({ proxy: { url: 'http://localhost:8888' } });
const client = new OpenAI({
  fetchOptions: {
    client: httpClient,
  },
});

Frequently Asked Questions

Semantic 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.

Requirements

TypeScript >= 4.9 is supported.

The following runtimes are supported:

Node.js 20 LTS or later (non-EOL) versions.
Deno v1.28.0 or higher.
Bun 1.0 or later.
Cloudflare Workers.
Vercel Edge Runtime.
Jest 28 or greater with the "node" environment ("jsdom" is not supported at this time).
Nitro v2.6 or greater.
Web browsers: disabled by default to avoid exposing your secret API credentials. Enable browser support by explicitly setting dangerouslyAllowBrowser to true'.
More explanation

Why is this dangerous?
Enabling the dangerouslyAllowBrowser option can be dangerous because it exposes your secret API credentials in the client-side code. Web browsers are inherently less secure than server environments, any user with access to the browser can potentially inspect, extract, and misuse these credentials. This could lead to unauthorized access using your credentials and potentially compromise sensitive data or functionality.

When might this not be dangerous?
In certain scenarios where enabling browser support might not pose significant risks:
- Internal Tools: If the application is used solely within a controlled internal environment where the users are trusted, the risk of credential exposure can be mitigated.
- Public APIs with Limited Scope: If your API has very limited scope and the exposed credentials do not grant access to sensitive data or critical operations, the potential impact of exposure is reduced.
- Development or debugging purpose: Enabling this feature temporarily might be acceptable, provided the credentials are short-lived, aren’t also used in production environments, or are frequently rotated.

Note that React Native is not supported at this time.

If you are interested in other runtime environments, please open or upvote an issue on GitHub.

Contributing

See the contributing documentation.

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

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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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2

# 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

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

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:

1
2

import openai
print(openai.__version__)

Requirements

Python 3.8 or higher.

Contributing

See the contributing documentation.

better-auth

Wed, 23 Jul 2025 15:34:42 +0800

better-auth/better-auth

Better Auth

The most comprehensive authentication library for TypeScript
Learn more »

Discord · Website · Issues

About the Project

Better Auth is framework-agnostic authentication (and authorization) library for TypeScript. It provides a comprehensive set of features out of the box and includes a plugin ecosystem that simplifies adding advanced functionalities with minimal code in short amount of time. Whether you need 2FA, multi-tenant support, or other complex features. It lets you focus on building your actual application instead of reinventing the wheel.

Why Better Auth

Authentication in the TypeScript ecosystem is a half-solved problem. Other open-source libraries often requires a lot of additional code for anything beyond basic authentication. Rather than just pushing third-party services as the solution, I believe we can do better as a community—hence, Better Auth.

Contribution

Better Auth is free and open source project licensed under the MIT License. You are free to do whatever you want with it.

You could help continuing its development by:

Security

If you discover a security vulnerability within Better Auth, please send an e-mail to security@better-auth.com.

All reports will be promptly addressed, and you’ll be credited accordingly.

BookLore

Mon, 30 Jun 2025 15:32:16 +0800

adityachandelgit/BookLore

BookLore

BookLore is a self-hosted web app for organizing and managing your personal book collection. It provides an intuitive interface to browse, read, and track your progress across PDFs and eBooks. With robust metadata management, multi-user support, and a sleek, modern UI, BookLore makes it easy to build and explore your personal library.

✨ Key Features

📚 Organized Book Management - Categorize books with Libraries and Shelves for easy discovery and structured organization.
🧠 Smart Metadata Handling - Automatically fetch book details from Goodreads, Amazon, and Google Books, or edit them manually with fine-grained control.
👥 Multi-User Support - Admins can create accounts, assign libraries, and manage permissions for metadata edits, uploads, and downloads.
📖 Built-in PDF & ePub Reader - A fast, feature-rich reader for PDFs and ePubs, with customizable reading settings and a clean UI.
🌐 OPDS 1.2 Support - Browse and download books through the Open Publication Distribution System – compatible with many reading apps.
🔐 Optional OIDC Authentication - Secure access with OpenID Connect, supporting both local JWT authentication and external providers like Authentik.
📤 Multi-Book Uploads - Upload multiple books at once with metadata auto-detection and file organization.
📧 Send Books via Email - Share books directly with others by sending them via email – quick and easy.
🚀 Continuous Improvements - Frequent updates with new features, performance enhancements, and UI improvements. BookLore is perfect for self-hosters who want complete control over their digital library. Stay tuned for updates!

🎥 Video Guides & Tutorials

For a step-by-step walkthrough, check out the official BookLore video guides on YouTube:

📺 BookLore Tutorials – YouTube

These videos cover deployment, configuration, and feature highlights to help you get started quickly.

🐳 Deploy with Docker

You can quickly set up and run BookLore using Docker.

1️⃣ Install Docker & Docker Compose

Ensure you have Docker and Docker Compose installed.

2️⃣ Create docker-compose.yml

Create a docker-compose.yml file with content:

services:
  booklore:
    image: ghcr.io/adityachandelgit/booklore-app:latest
    container_name: booklore
    environment:
      - PUID=1000
      - PGID=1000
      - TZ=Etc/UTC
      - DATABASE_URL=jdbc:mariadb://mariadb:3306/booklore # Only modify this if you're familiar with JDBC and your database setup
      - DATABASE_USERNAME=booklore # Must match MYSQL_USER defined in the mariadb container
      - DATABASE_PASSWORD=your_secure_password # Use a strong password; must match MYSQL_PASSWORD defined in the mariadb container 
      - SWAGGER_ENABLED=false # Enable or disable Swagger UI (API docs). Set to 'true' to allow access; 'false' to block access (recommended for production).
    depends_on:
      mariadb:
        condition: service_healthy
    ports:
      - "6060:6060"
    volumes:
      - /your/local/path/to/booklore/data:/app/data
      - /your/local/path/to/booklore/books:/books
    restart: unless-stopped

  mariadb:
    image: lscr.io/linuxserver/mariadb:11.4.5
    container_name: mariadb
    environment:
      - PUID=1000
      - PGID=1000
      - TZ=Etc/UTC
      - MYSQL_ROOT_PASSWORD=super_secure_password # Use a strong password for the database's root user, should be different from MYSQL_PASSWORD
      - MYSQL_DATABASE=booklore
      - MYSQL_USER=booklore # Must match DATABASE_USERNAME defined in the booklore container
      - MYSQL_PASSWORD=your_secure_password # Use a strong password; must match DATABASE_PASSWORD defined in the booklore container
    volumes:
      - /your/local/path/to/mariadb/config:/config
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "mariadb-admin", "ping", "-h", "localhost"]
      interval: 5s
      timeout: 5s
      retries: 10

Note: You can find the latest BookLore image tag BOOKLORE_IMAGE_TAG (e.g. v.0.x.x) from the Releases section: 📦 Latest Image Tag – GitHub Releases

3️⃣ Start the Containers

Run the following command to start the services:

`1`	`docker compose up -d`

4️⃣ Access BookLore

Once the containers are up, access BookLore in your browser at:

`1`	`http://localhost:6060`

🔑 OIDC/OAuth2 Authentication (Authentik, Pocket ID, etc.)

BookLore supports optional OIDC/OAuth2 authentication for secure access. This feature allows you to integrate external authentication providers for a seamless login experience.

While the integration has been tested with Authentik and Pocket ID, it should work with other OIDC providers like Authelia as well. The setup allows you to use either JWT-based local authentication or external providers, giving users the flexibility to choose their preferred method.

For detailed instructions on setting up OIDC authentication:

🔐 Remote Authentication (Trusted Header SSO, Forward Auth)

If you run BookLore behind a reverse proxy with remote authentication (middleware), you can enable automatic login by setting REMOTE_AUTH_ENABLED to true.

This allows you to use your existing authentication system (e.g., OAuth, SAML) to log in to BookLore.

The following remote auth environment variables can be configured:

Variable Name	Description	Default Value
REMOTE_AUTH_ENABLED	Enable remote authentication	`false`
REMOTE_AUTH_CREATE_NEW_USERS	Auto-create users from remote auth	`true`
REMOTE_AUTH_HEADER_NAME	HTTP header containing user’s name	`Remote-Name`
REMOTE_AUTH_HEADER_USER	HTTP header containing username	`Remote-User`
REMOTE_AUTH_HEADER_EMAIL	HTTP header containing user’s email	`Remote-Email`
REMOTE_AUTH_HEADER_GROUPS	HTTP header containing user’s groups	`Remote-Groups`
REMOTE_AUTH_ADMIN_GROUP	Group name that grants admin privileges	-

Example implementations:

🤝 Community & Support

🐞 Found a bug? Open an issue
✨ Want to contribute? Check out CONTRIBUTING.md
💬 Ask questions or share feedback: Discussions
💬 Join our Discord: Click here to chat with the community

👨‍💻 Contributors & Developers

Thanks to all the amazing people who contribute to Booklore.

⭐ Star History

💖 Support the Project

If you find BookLore helpful, consider ⭐ starring the repo!

Or support the project via Venmo:

🧰 Tech Stack

⚖️ License

GNU GPL v3

base-ui

Sat, 28 Jun 2025 15:28:55 +0800

mui/base-ui

Base UI

From the creators of Radix, Floating UI, and Material UI, Base UI is an unstyled UI component library for building accessible user interfaces.

Documentation

To get started, check out the Base UI documentation.

Contributing

Read our contributing guide to learn about our development process, how to propose bug fixes and improvements, and how to build and test your changes.

Releases

To see the latest updates, check out the releases.

Community

Discord For community support, questions, and tips, join our Discord.
X To stay up-to-date on new releases and announcements follow Base UI on X.
Bluesky We’re also on Bluesky.

Team

Colm Tuite (Radix) @colmtuite
Vlad Moroz (Radix) @vladyslavmoroz
James Nelson (Floating UI) @atomiksdev
Michał Dudak (Material UI) @michaldudak
Marija Najdova (Material UI + Fluent UI) @marijanajdova
Albert Yu (Material UI) @mj12albert

License

This project is licensed under the terms of the MIT license.

microui

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

rxi/microui

A tiny, portable, immediate-mode UI library written in ANSI C

Features

Tiny: around 1100 sloc of ANSI C
Works within a fixed-sized memory region: no additional memory is allocated
Built-in controls: window, scrollable panel, button, slider, textbox, label, checkbox, wordwrapped text
Works with any rendering system that can draw rectangles and text
Designed to allow the user to easily add custom controls
Simple layout system

Example

if (mu_begin_window(ctx, "My Window", mu_rect(10, 10, 140, 86))) {
  mu_layout_row(ctx, 2, (int[]) { 60, -1 }, 0);

  mu_label(ctx, "First:");
  if (mu_button(ctx, "Button1")) {
    printf("Button1 pressed\n");
  }

  mu_label(ctx, "Second:");
  if (mu_button(ctx, "Button2")) {
    mu_open_popup(ctx, "My Popup");
  }

  if (mu_begin_popup(ctx, "My Popup")) {
    mu_label(ctx, "Hello world!");
    mu_end_popup(ctx);
  }

  mu_end_window(ctx);
}

Screenshot

Browser Demo

Usage

See doc/usage.md for usage instructions
See the demo directory for a usage example

Notes

The library expects the user to provide input and handle the resultant drawing commands, it does not do any drawing itself.

Contributing

The library is designed to be lightweight, providing a foundation to which you can easily add custom controls and UI elements; pull requests adding additional features will likely not be merged. Bug reports are welcome.

License

This library is free software; you can redistribute it and/or modify it under the terms of the MIT license. See LICENSE for details.

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.

react-bits

Sun, 08 Jun 2025 15:27:19 +0800

DavidHDev/react-bits

The largest & most creative library of animated React components.

Documentation

Go to reactbits.dev to view the documentation.

About

React Bits is a large collection of animated React components made to spice up your web creations. We’ve got animations, components, backgrounds, and awesome stuff that you won’t be able to find anywhere else - all free for you to use! These components are all enhanced with customization options as props, to make it easy for you to get exactly what you need.

Key Features

60 total components (text animations, animations, components, backgrounds), growing every day
All components are lightweight, with minimal dependencies, and highly customizable
Designed to integrate seamlessly with any modern React project
Each component comes in 4 variants, to keep everyone happy:
- JS + CSS
- JS + Tailwind CSS
- TS + CSS
- TS + Tailwind CSS

CLI ()

React Bits uses jsrepo for installing components via CLI.
The setup steps can be found on each component’s page in the documentation.

How To Contribute?

Contributions are welcome! Check the Open Issues to see how you can help or submit ideas using the Feature Request template.
Please review the Contribution Guide and follow our standards. Thanks for your time!

Library on Producthunt daily

folly

facebook/folly

Folly: Facebook Open-source Library

What is folly?

Check it out in the intro video

Logical Design

Physical Design

What’s in it?

Build Notes

getdeps.py

Dependencies

Build

Run tests

build.sh/build.bat wrapper

Build with cmake directly

Finding dependencies in non-default locations

Ubuntu LTS, CentOS Stream, Fedora

Windows (Vcpkg)

macOS

Homebrew

MacPorts

go-redis

redis/go-redis

Redis client for Go

Supported versions

How do I Redis?

Documentation

Resources

Ecosystem

Features

Installation

Quickstart

Authentication

1. Streaming Credentials Provider (Highest Priority) - Experimental feature

2. Context-based Credentials Provider

3. Regular Credentials Provider

4. Username/Password Fields (Lowest Priority)

Priority Order

Protocol Version

Connecting via a redis url

Instrument with OpenTelemetry

Buffer Size Configuration

Advanced Configuration

Default Client Identification

Disabling Identity Verification

Unstable RESP3 Structures for RediSearch Commands

Redis-Search Default Dialect

Custom buffer sizes

Contributing

Look and feel

Run the test

See also

Contributors

openai-node

openai/openai-node

OpenAI TypeScript and JavaScript API Library

Installation

Installation from JSR

Usage

Streaming responses

File uploads

Webhook Verification

Parsing webhook payloads

Verifying webhook payloads directly

Handling errors

Request IDs

Realtime API Beta

Microsoft Azure OpenAI

Retries

Timeouts

Request IDs

Auto-pagination

Realtime API Beta

Microsoft Azure OpenAI

Advanced Usage

Accessing raw Response data (e.g., headers)

Logging

Log levels

Custom logger

What is `folly`?

`getdeps.py`

`build.sh`/`build.bat` wrapper

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

`.with_streaming_response`