seekrai 1.0.0

Python client for SeekrAI

The Seekr Python Library is the official Python client for SeekrFlow's API platform, providing a convenient way for interacting with the REST APIs and enables easy integrations with Python 3.9+ applications with easy to use synchronous and asynchronous clients.

Installation

To install Seekr Python Library from PyPi, simply run:

pip install --upgrade seekrai

Setting up API Key

🚧 You will need to create an account with Seekr.com to obtain a SeekrFlow API Key.

Setting environment variable

export SEEKR_API_KEY=xxxxx

Using the client

from seekrai import SeekrFlow

client = SeekrFlow(api_key="xxxxx")

RBAC Team Routing

Every API key currently resolves to a personal team. We do not currently have application-level or shared team-level API keys.

The SDK can send the RBAC context header x-team-id when provided. This header does not grant access by itself.

Authorization is enforced server-side using the authenticated identity (API key/JWT). A request is only allowed if that identity has access to the requested team. If no team context is provided, the backend defaults to the personal team resolved from authentication.

You can set team context in either of these ways:

1. Set SEEKR_TEAM_ID and let the SDK populate x-team-id automatically.
2. Pass supplied_headers={"x-team-id": "..."} explicitly.

If both are provided, supplied_headers["x-team-id"] takes precedence.

import os
from seekrai import SeekrFlow

client = SeekrFlow(
    api_key=os.environ.get("SEEKR_API_KEY"),
)

import os
from seekrai import AsyncSeekrFlow

async_client = AsyncSeekrFlow(
    api_key=os.environ.get("SEEKR_API_KEY"),
    supplied_headers={"x-team-id": os.environ.get("SEEKR_TEAM_ID")},
)

Usage – Python Client

Chat Completions

import os
from seekrai import SeekrFlow

client = SeekrFlow(api_key=os.environ.get("SEEKR_API_KEY"))

response = client.chat.completions.create(
    model="meta-llama/Llama-3.1-8B-Instruct",
    messages=[{"role": "user", "content": "tell me about new york"}],
)
print(response.choices[0].message.content)

Streaming

import os
from seekrai import SeekrFlow

client = SeekrFlow(api_key=os.environ.get("SEEKR_API_KEY"))
stream = client.chat.completions.create(
    model="meta-llama/Llama-3.1-8B-Instruct",
    messages=[{"role": "user", "content": "tell me about new york"}],
    stream=True,
)

for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="", flush=True)

Async usage

import os, asyncio
from seekrai import AsyncSeekrFlow

async_client = AsyncSeekrFlow(api_key=os.environ.get("SEEKR_API_KEY"))
messages = [
    "What are the top things to do in San Francisco?",
    "What country is Paris in?",
]


async def async_chat_completion(messages):
    async_client = AsyncSeekrFlow(api_key=os.environ.get("SEEKR_API_KEY"))
    tasks = [
        async_client.chat.completions.create(
            model="meta-llama/Llama-3.1-8B-Instruct",
            messages=[{"role": "user", "content": message}],
        )
        for message in messages
    ]
    responses = await asyncio.gather(*tasks)

    for response in responses:
        print(response.choices[0].message.content)


asyncio.run(async_chat_completion(messages))

Files

The files API is used for fine-tuning and allows developers to upload data to fine-tune on. It also has several methods to list all files, retrieve files, and delete files

import os
from seekrai import SeekrFlow

client = SeekrFlow(api_key=os.environ.get("SEEKR_API_KEY"))

client.files.upload(file="somedata.parquet")  # uploads a file
client.files.list()  # lists all uploaded files
client.files.delete(id="file-d0d318cb-b7d9-493a-bd70-1cfe089d3815")  # deletes a file

Fine-tunes

The finetune API is used for fine-tuning and allows developers to create finetuning jobs. It also has several methods to list all jobs, retrieve statuses and get checkpoints.

import os
from seekrai import SeekrFlow

client = SeekrFlow(api_key=os.environ.get("SEEKR_API_KEY"))

client.fine_tuning.create(
    training_file='file-d0d318cb-b7d9-493a-bd70-1cfe089d3815',
    model='meta-llama/Llama-3.1-8B-Instruct',
    n_epochs=3,
    n_checkpoints=1,
    batch_size=4,
    learning_rate=1e-5,
    suffix='my-demo-finetune',
)
client.fine_tuning.list()  # lists all fine-tuned jobs
client.fine_tuning.retrieve(id="ft-c66a5c18-1d6d-43c9-94bd-32d756425b4b")  # retrieves information on finetune event