We're on a mission to simplify the LLM landscape, Unify lets you:
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🔑 Use any LLM from any Provider: With a single interface, you can use all LLMs from all providers by simply changing one string. No need to manage several API keys or handle different input-output formats. Unify handles all of that for you!
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📊 Improve LLM Performance: Add your own custom tests and evals, and benchmark your own prompts on all models and providers. Comparing quality, cost and speed, and iterate on your system prompt until all test cases pass, and you can deploy your app!
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🔀 Route to the Best LLM: Improve quality, cost and speed by routing to the perfect model and provider for each individual prompt.
Simply install the package:
pip install unifyaiThen sign up to get your API key, then you're ready to go! 🚀
import unify
client = unify.Unify("gpt-4o@openai", api_key=<your_key>)
client.generate("hello world!")Note
We recommend using python-dotenv
to add UNIFY_KEY="My API Key" to your .env file, avoiding the need to use the api_key argument as above.
For the rest of the README, we will assume you set your key as an environment variable.
You can list all models, providers and endpoints (<model>@<provider> pair) as follows:
models = unify.utils.list_models()
providers = unify.utils.list_providers()
endpoints = unify.utils.list_endpoints()You can also filter within these functions as follows:
import random
anthropic_models = unify.utils.list_models("anthropic")
client.set_endpoint(random.choice(anthropic_models) + "@anthropic")
latest_llama3p1_providers = unify.utils.list_providers("llama-3.1-405b-chat")
client.set_endpoint("llama-3.1-405b-chat@" + random.choice(latest_llama3p1_providers))
openai_endpoints = unify.utils.list_endpoints("openai")
client.set_endpoint(random.choice(openai_endpoints))
mixtral8x7b_endpoints = unify.utils.list_endpoints("mixtral-8x7b-instruct-v0.1")
client.set_endpoint(random.choice(mixtral8x7b_endpoints))If you want change the endpoint, model or the provider, you can do so using the .set_endpoint, .set_model, .set_provider methods respectively.
client.set_endpoint("mistral-7b-instruct-v0.3@deepinfra")
client.set_model("mistral-7b-instruct-v0.3")
client.set_provider("deepinfra")You can influence the model's persona using the system_prompt argument in the .generate function:
response = client.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 = client.generate(messages=messages)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.
import unify
import asyncio
async_client = unify.AsyncUnify("llama-3-8b-chat@anyscale")
async def main():
responses = await async_client.generate("Hello Llama! Who was Isaac Newton?")
asyncio.run(main())Functionality wise, the Async and Sync clients are identical.
You can enable streaming responses by setting stream=True in the .generate function.
import unify
client = unify.Unify("llama-3-8b-chat@anyscale")
stream = client.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.
import unify
import asyncio
async_client = unify.AsyncUnify("llama-3-8b-chat@anyscale")
async def main():
async_stream = await async_client.generate("Hello Llama! Who was Isaac Newton?", stream=True)
async for chunk in async_stream:
print(chunk, end="")
asyncio.run(main())To learn more about our more advanced API features, benchmarking, and LLM routing, go check out our comprehensive docs!
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