Unify Python API Library

The Unify Python Package provides access to the Unify REST API, allowing you to query Large Language Models (LLMs) from any Python 3.7.1+ application. It includes Synchronous and Asynchronous clients with Streaming responses support.

Just like the REST API, you can:

• 🔑 Use any endpoint with a single key: Access all LLMs at any provider with just one Unify API Key.

• 🚀 Route to the best endpoint: Each prompt is sent to the endpoint that will yield the best throughput, cost or latency.

Note

You can learn more about routing here

Getting started

To use the API, you first need to get Sign In to get an API key. You can then use pip to install the package as follows:

pip install unifyai

Note

At any point, you can pass your key directly in one of the Unify clients as the api_key keyword argument, but we recommend using python-dotenv to add UNIFY_KEY="My API Key" to your .env file for safety. For the rest of the README, we will assume you set your key as an environment variable.

Basic Usage

You can call the Unify API in a couple lines of code by specifying an endpoint Id. Endpoint Ids are a combination of the model Id and provider Id, both of which can be found in the endpoint benchmarks pages.

For e.g, the benchmarks for llama-2-13b show that the model Id for Llama 2 13B is llama-2-13b-chat and the provider Id for Anyscale is anyscale. We can then call:

from unify import Unify
unify = Unify("llama-2-13b-chat@anyscale")
response = unify.generate("Hello Llama! Who was Isaac Newton?")

Changing models and providers

Instead of passing the endpoint, you can also pass the model and provider as separate arguments as shown below:

unify = Unify(
    model="llama-2-13b-chat",
    provider="anyscale"
)

If you want change the endpoint, model or the provider, you can do so using the .set_endpoint, .set_model, .set_provider methods respectively.

unify.set_endpoint("mistral-7b-instruct-v0.1@deepinfra")
unify.set_model("mistral-7b-instruct-v0.1")
unify.set_provider("deepinfra")

Note

Besides the benchmarks, you can also get the model and provider Ids directly in Python using list_models()>, list_providers() and list_endpoints() by using:

models = unify.list_models()
providers = unify.list_providers("mistral-7b-instruct-v0.1")
endpoints = unify.list_endpoints("mistral-7b-instruct-v0.1")

Custom prompting

You can influence the model's persona using the system_prompt argument in the .generate function:

response = unify.generate(
    user_prompt="Hello Llama! Who was Isaac Newton?",  system_prompt="You should always talk in rhymes"
)

If you'd like to send multiple messages using the .generate function, you should use the messages argument as follows:

messages=[
   {"role": "user", "content": "Who won the world series in 2020?"},
   {"role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2020."},
   {"role": "user", "content": "Where was it played?"}
]
res = unify.generate(messages=messages)

Asynchronous Usage

For optimal performance in handling multiple user requests simultaneously, such as in a chatbot application, processing them asynchronously is recommended. To use the AsyncUnify client, simply import AsyncUnify instead of Unify and use await with the .generate function.

from unify import AsyncUnify
import asyncio
async_unify = AsyncUnify("llama-2-13b-chat@anyscale")

async def main():
   responses = await async_unify.generate("Hello Llama! Who was Isaac Newton?")

asyncio.run(main())

Functionality wise, the Async and Sync clients are identical.

Streaming Responses

You can enable streaming responses by setting stream=True in the .generate function.

from unify import Unify
unify = Unify("llama-2-13b-chat@anyscale")
stream = unify.generate("Hello Llama! Who was Isaac Newton?", stream=True)
for chunk in stream:
    print(chunk, end="")

It works in exactly the same way with Async clients.

from unify import AsyncUnify
import asyncio
async_unify = AsyncUnify("llama-2-13b-chat@anyscale")

async def main():
   async_stream = await async_unify.generate("Hello Llama! Who was Isaac Newton?", stream=True)
   async for chunk in async_stream:
       print(chunk, end="")

asyncio.run(main())

Dynamic Routing

As evidenced by our benchmarks, the optimal provider for each model varies by geographic location and time of day due to fluctuating API performances.

With dynamic routing, we automatically direct your requests to the "top-performing provider" at that moment. To enable this feature, simply replace your query's provider with one of the available routing modes.

For e.g, you can query the llama-2-7b-chat endpoint to get the provider with the lowest input-cost as follows:

from unify import Unify
unify = Unify("llama-2-13b-chat@lowest-input-cost")
response = unify.generate("Hello Llama! Who was Isaac Newton?")

You can see the provider chosen by printing the .provider attribute of the client:

print(unify.provider)

Note

Dynamic routing works with both Synchronous and Asynchronous clients!

ChatBot Agent

Our ChatBot allows you to start an interactive chat session with any of our supported llm endpoints with only a few lines of code:

from unify import ChatBot
agent = ChatBot("llama-2-13b-chat@lowest-input-cost")
agent.run()