together 2.11.0

The official Python library for the together API

Together Python API library

The Together Python library provides convenient access to the Together REST API from any Python 3.9+ 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 with Stainless.

Documentation

The REST API documentation can be found on docs.together.ai. The full API of this library can be found in api.md.

Installation

pip install together

uv add together

Usage

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

import os
from together import Together

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

chat_completion = client.chat.completions.create(
    messages=[
        {
            "role": "user",
            "content": "Say this is a test!",
        }
    ],
    model="meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
)
print(chat_completion.choices)

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

Async usage

Simply import AsyncTogether instead of Together and use await with each API call:

import os
import asyncio
from together import AsyncTogether

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


async def main() -> None:
    chat_completion = await client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": "Say this is a test!",
            }
        ],
        model="meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
    )
    print(chat_completion.choices)


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:

# install from PyPI
pip install '--pre together[aiohttp]'

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

import os
import asyncio
from together import DefaultAioHttpClient
from together import AsyncTogether


async def main() -> None:
    async with AsyncTogether(
        api_key=os.environ.get("TOGETHER_API_KEY"),  # This is the default and can be omitted
        http_client=DefaultAioHttpClient(),
    ) as client:
        chat_completion = await client.chat.completions.create(
            messages=[
                {
                    "role": "user",
                    "content": "Say this is a test!",
                }
            ],
            model="meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
        )
        print(chat_completion.choices)


asyncio.run(main())

Streaming responses

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

from together import Together

client = Together()

stream = client.chat.completions.create(
    messages=[
        {
            "role": "user",
            "content": "Say this is a test!",
        }
    ],
    model="meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
    stream=True,
)
for chat_completion in stream:
    print(chat_completion.choices)

The async client uses the exact same interface.

from together import AsyncTogether

client = AsyncTogether()

stream = await client.chat.completions.create(
    messages=[
        {
            "role": "user",
            "content": "Say this is a test!",
        }
    ],
    model="meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
    stream=True,
)
async for chat_completion in stream:
    print(chat_completion.choices)

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.

Nested params

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

from together import Together

client = Together()

chat_completion = client.chat.completions.create(
    messages=[
        {
            "content": "content",
            "role": "system",
        }
    ],
    model="model",
    reasoning={},
)
print(chat_completion.reasoning)

The async client uses the exact same interface. If you pass a PathLike instance, the file contents will be read asynchronously automatically.

Handling errors

When the library is unable to connect to the API (for example, due to network connection problems or a timeout), a subclass of together.APIConnectionError is raised.

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

All errors inherit from together.APIError.

import together
from together import Together

client = Together()

try:
    client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": "Say this is a test",
            }
        ],
        model="meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
    )
except together.APIConnectionError as e:
    print("The server could not be reached")
    print(e.__cause__)  # an underlying Exception, likely raised within httpx.
except together.RateLimitError as e:
    print("A 429 status code was received; we should back off a bit.")
except together.APIStatusError as e:
    print("Another non-200-range status code was received")
    print(e.status_code)
    print(e.response)

Error codes are as follows:

Status Code	Error Type
400	BadRequestError
401	AuthenticationError
403	PermissionDeniedError
404	NotFoundError
422	UnprocessableEntityError
429	RateLimitError
>=500	InternalServerError
N/A	APIConnectionError

Retries

Certain errors are automatically retried 2 times by default, with a short exponential backoff. Connection errors (for example, due to a network connectivity problem), 408 Request Timeout, 409 Conflict, 429 Rate Limit, and >=500 Internal errors are all retried by default.

You can use the max_retries option to configure or disable retry settings:

from together import Together

# Configure the default for all requests:
client = Together(
    # default is 2
    max_retries=0,
)

# Or, configure per-request:
client.with_options(max_retries=5).chat.completions.create(
    messages=[
        {
            "role": "user",
            "content": "Say this is a test",
        }
    ],
    model="meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
)

Timeouts

By default requests time out after 1 minute. You can configure this with a timeout option, which accepts a float or an httpx.Timeout object:

from together import Together

# Configure the default for all requests:
client = Together(
    # 20 seconds (default is 1 minute)
    timeout=20.0,
)

# More granular control:
client = Together(
    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": "Say this is a test",
        }
    ],
    model="meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
)

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 TOGETHER_LOG to info.

$ export TOGETHER_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 together import Together

client = Together()
response = client.chat.completions.with_raw_response.create(
    messages=[{
        "role": "user",
        "content": "Say this is a test",
    }],
    model="meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
)
print(response.headers.get('X-My-Header'))

completion = response.parse()  # get the object that `chat.completions.create()` would have returned
print(completion.choices)

These methods return an APIResponse object.

The async client returns an AsyncAPIResponse with the same structure, the only difference being awaitable methods for reading the response content.

.with_streaming_response

The above interface eagerly reads the full response body when you make the request, which may not always be what you want.

To stream the response body, use .with_streaming_response instead, which requires a context manager and only reads the response body once you call .read(), .text(), .json(), .iter_bytes(), .iter_text(), .iter_lines() or .parse(). In the async client, these are async methods.

with client.chat.completions.with_streaming_response.create(
    messages=[
        {
            "role": "user",
            "content": "Say this is a test",
        }
    ],
    model="meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
) 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 together import Together, DefaultHttpxClient

client = Together(
    # Or use the `TOGETHER_BASE_URL` env var
    base_url="http://my.test.server.example.com:8083",
    http_client=DefaultHttpxClient(
        proxy="http://my.test.proxy.example.com",
        transport=httpx.HTTPTransport(local_address="0.0.0.0"),
    ),
)

You can also customize the client on a per-request basis by using with_options():

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 together import Together

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

# HTTP client is now closed

Usage – CLI

Files

# Help
tg files --help

# Check file
tg files check example.jsonl

# Upload file
tg files upload example.jsonl

# List files
tg files list

# Retrieve file metadata
tg files retrieve file-6f50f9d1-5b95-416c-9040-0799b2b4b894

# Retrieve file content
tg files retrieve-content file-6f50f9d1-5b95-416c-9040-0799b2b4b894

# Delete remote file
tg files delete file-6f50f9d1-5b95-416c-9040-0799b2b4b894

Fine-tuning

# `tg ft` and `tg fine-tuning` are equivalent

# Help
tg ft --help

# Create fine-tune job
tg ft create \
  --model togethercomputer/llama-2-7b-chat \
  --training-file file-711d8724-b3e3-4ae2-b516-94841958117d

# List fine-tune jobs
tg ft list

# Retrieve fine-tune job details
tg ft retrieve ft-c66a5c18-1d6d-43c9-94bd-32d756425b4b

# List fine-tune job events
tg ft list-events ft-c66a5c18-1d6d-43c9-94bd-32d756425b4b

# Cancel running job
tg ft cancel ft-c66a5c18-1d6d-43c9-94bd-32d756425b4b

# Download fine-tuned model weights
tg ft download ft-c66a5c18-1d6d-43c9-94bd-32d756425b4b

Models

# Help
tg models --help

# List models
tg models list

Versioning

This package generally follows SemVer conventions, though certain backwards-incompatible changes may be released as minor versions:

1. Changes that only affect static types, without breaking runtime behavior.
2. Changes to library internals which are technically public but not intended or documented for external use. (Please open a GitHub issue to let us know if you are relying on such internals.)
3. Changes that we do not expect to impact the vast majority of users in practice.

We take backwards-compatibility seriously and work hard to ensure you can rely on a smooth upgrade experience.

We are keen for your feedback; please open an issue 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:

import together
print(together.__version__)

Requirements

Python 3.9 or higher.

Usage – CLI

Files

# Help
tg files --help

# Check file
tg files check example.jsonl

# Upload file
tg files upload example.jsonl

# List files
tg files list

# Retrieve file metadata
tg files retrieve file-6f50f9d1-5b95-416c-9040-0799b2b4b894

# Retrieve file content
tg files retrieve-content file-6f50f9d1-5b95-416c-9040-0799b2b4b894

# Delete remote file
tg files delete file-6f50f9d1-5b95-416c-9040-0799b2b4b894

Fine-tuning

# `tg ft` and `tg fine-tuning` are equivalent

# Help
tg ft --help

# Create fine-tune job
tg ft create \
  --model togethercomputer/llama-2-7b-chat \
  --training-file file-711d8724-b3e3-4ae2-b516-94841958117d

# List fine-tune jobs
tg ft list

# Retrieve fine-tune job details
tg ft retrieve ft-c66a5c18-1d6d-43c9-94bd-32d756425b4b

# List fine-tune job events
tg ft list-events ft-c66a5c18-1d6d-43c9-94bd-32d756425b4b

# List fine-tune checkpoints
tg ft list-checkpoints ft-c66a5c18-1d6d-43c9-94bd-32d756425b4b

# Cancel running job
tg ft cancel ft-c66a5c18-1d6d-43c9-94bd-32d756425b4b

# Download fine-tuned model weights
tg ft download ft-c66a5c18-1d6d-43c9-94bd-32d756425b4b

# Delete fine-tuned model weights
tg ft delete ft-c66a5c18-1d6d-43c9-94bd-32d756425b4b

Models

# Help
tg models --help

# List models
tg models list

# Upload a model
tg models upload --model-name my-org/my-model --model-source s3-or-hugging-face

Clusters

# Help
tg beta clusters --help

# Create a cluster
tg beta clusters create

# List clusters
tg beta clusters list

# Retrieve cluster details
tg beta clusters retrieve [cluster-id]

# Update a cluster
tg beta clusters update [cluster-id]

# Retrieve Together cluster configuration options such as regions, gpu types and drivers available
tg beta clusters list-regions

Cluster Storage

# Help
tg beta clusters storage --help

# Create cluster storage volume
tg beta clusters storage create

# List storage volumes
tg beta clusters storage list

# Retrieve storage volume
tg beta clusters storage retrieve [storage-id]

# Delete storage volume
tg beta clusters storage delete [storage-id]

Jig (Container Deployments)

# Help
tg beta jig --help

# Initialize jig configuration (creates pyproject.toml)
tg beta jig init

# Generate Dockerfile from config
tg beta jig dockerfile

# Build container image
tg beta jig build
tg beta jig build --tag v1.0 --warmup

# Push image to registry
tg beta jig push
tg beta jig push --tag v1.0

# Deploy model (builds, pushes, and deploys)
tg beta jig deploy
tg beta jig deploy --build-only
tg beta jig deploy --image existing-image:tag

# Get deployment status
tg beta jig status

# Get deployment endpoint URL
tg beta jig endpoint

# View deployment logs
tg beta jig logs
tg beta jig logs --follow

# Destroy deployment
tg beta jig destroy

# Get queue metrics
tg beta jig queue-status

# List all deployments
tg beta jig list

Jig Secrets

# Help
tg beta jig secrets --help

# Set a secret (creates or updates)
tg beta jig secrets set --name MY_SECRET --value "secret-value"

# Remove a secret from local state
tg beta jig secrets unset --name MY_SECRET

# List all secrets with sync status
tg beta jig secrets list

Jig Volumes

# Help
tg beta jig volumes --help

# Create a volume and upload files from directory
tg beta jig volumes create --name my-volume --source ./data

# Update a volume with new files
tg beta jig volumes update --name my-volume --source ./data

# Set volume mount path for deployment
tg beta jig volumes set --name my-volume --mount-path /app/data

# Remove volume from deployment config (does not delete remote volume)
tg beta jig volumes unset --name my-volume

# Delete a volume
tg beta jig volumes delete --name my-volume

# Describe a volume
tg beta jig volumes describe --name my-volume

# List all volumes
tg beta jig volumes list

Contributing

See the contributing documentation.