Bud Serve Python SDK

A client package to directly integrate Bud Serve engine to your python application. The package support different prompting libraries like Guidance, langchain etc.

Installation

pip install git+https://github.com/BudEcosystem/bud-serve-sdk.git

Usage

The inference engine can be accessed via OpenAI api format and the options available in the OpenAI chat and completion can be used from our client.

from budserve import BudServe

client = BudServe(base_url="http://x.x.x.x:xxx/v1")

completion = client.chat.completions.create(
    model="meta-llama/Llama-2-7b-chat-hf",
    messages=[{"role": "user", "content": "write an essay about history of cricket"}],
    max_tokens=200
)

print(completion.choices[0].message.content)

You will need add the api key, BUDSERVE_API_KEY=XXXXXXXX in your env to authenticate.

Streaming responses:

The streaming response support is provided using Server Side Events.

from budserve import BudServe

client = BudServe(base_url="http://x.x.x.x:xxx/v1")

stream = client.chat.completions.create(
    model="meta-llama/Llama-2-7b-chat-hf",
    messages=[{"role": "user", "content": "write an essay about history of cricket"}],
    stream=True,
    max_tokens=200
)

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

Supported integrations:

• Guidance
• LangChain
• LlamaIndex
• Haystack
• LMQL

Guidance example

Add the API key to the enviornament

export BUDSERVE_API_KEY=XXXXXXXX

Sample code to connect to bud serve remote server using guidance

from guidance import gen, user, system, assistant
from budserve.models.guidance import BudServeClient

llama2 = BudServeClient("meta-llama/Llama-2-7b-chat-hf", echo=False, base_url="http://localhost9000/v1")

with user():
    llama2 += f'what is your name? '

with assistant():
    llama2 += gen("answer", stop='.')


print(llama2["answer"])

Tasks

We have extended the package with some off the shelf fuctions for specific task.

Summarization

from budserve import BudServe
from budserve.tasks import Summerization

client = BudServe(base_url="x.x.x.x:xxxx/v1", model_name="microsoft/Phi-3-mini-4k-instruct")


article = "You call transcription here"

summerizer = Summerization(client=client, num_sen=3, num_words=40)
summary = summerizer.summarize_text(text=article, topic="Support call transcription")
print(summary)

Here are the option available to for summarization task

client: BudServe client which connect with the server for inference

num_sen: Required number of sentences for the summary. Default value: 4

num_words: Total number of required words in the summary. Default value: 80

topic: The type of content which provided for summarisation. This helps to align the model generation to specific domain. eg: Article, paper, documentation, call transcription etc