languru 0.2.0

The general purpose LLM app stacks.

Languru

The general-purpose LLM app stacks deploy AI services quickly and (stupidly) simply.

Getting Started

Install Languru:

pip install languru[server]

Or install all dependencies (include torch, transformers, ...)

pip install languru[all]

Run agent server:

languru server run

Run llm action server:

languru llm run

Query llm service.

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8680/v1")
res = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"},
    ],
)
for choice in res.choices:
    print(f"{choice.message.role}: {choice.message.content}")
# assistant: Hello! How can I assist you today?

Usages

Chat and TextCompletion with customized transformers llm

Inherited from TransformersAction.

# module_path.py
from languru.action.base import ModelDeploy
from languru.action.hf import TransformersAction


class MicrosoftPhiAction(TransformersAction):
    MODEL_NAME = "microsoft/phi-1_5"
    model_deploys = (
        ModelDeploy("microsoft/phi-1_5", "microsoft/phi-1_5"),
        ModelDeploy("phi-1_5", "microsoft/phi-1_5"),
    )

Run agent server and llm action server.

languru server run
languru llm run --action module_path.MicrosoftPhiAction

Query llm chat.

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8680/v1")
res = client.chat.completions.create(
    model="microsoft/phi-1_5",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is the capital of the United States?"},
    ],
)
for choice in res.choices:
    print(f"{choice.message.role}: {choice.message.content}")
# assistant: The capital of the United States is Washington D.C.
#

Query llm text completion.

from textwrap import dedent

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8680/v1")
res = client.completions.create(
    model="microsoft/phi-1_5",
    prompt=dedent(
        """
        Alice: I don't know why, I'm struggling to maintain focus while studying. Any suggestions?
        Bob: Well, have you tried creating a study schedule and sticking to it?
        Alice: Yes, I have, but it doesn't seem to help much.
        """
    ).strip(),
    max_tokens=200,
)
for choice in res.choices:
    print(choice.text)

# Bob: Hmm, maybe you should try studying in a different environment. Sometimes a change of scenery can do wonders for concentration.
# Alice: That's a good idea. I'll give it a try.

# Alice: I'm having trouble understanding this concept in my math class.
# Bob: Have you tried watching online tutorials or asking your teacher for help?
# Alice: Yes, I have, but I still can't grasp it.
# Bob: Well, maybe you should try studying with a study group. Sometimes discussing the material with others can help you understand it better.
# Alice: That's a great suggestion. I'll look into joining a study group.

# Alice: I'm feeling overwhelmed with all

Embeddings with customized transformers model

Inherited from TransformersAction.

# module_path.py
from languru.action.base import ModelDeploy
from languru.action.hf import TransformersAction


class STMiniLML12V2Action(TransformersAction):
    MODEL_NAME = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"

    model_deploys = (
        ModelDeploy(MODEL_NAME, MODEL_NAME),
        ModelDeploy(model_deploy_name="MiniLM-L12-v2", model_name=MODEL_NAME),
    )

Run agent server and llm action server.

languru server run
languru llm run --action module_path.STMiniLML12V2Action

Query text embeddings:

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8680/v1")
res = client.embeddings.create(
    input=[
        "What is your return policy for online purchases?",
        "How long does it usually take for an order to be delivered?",
        "Do you offer international shipping, and what are the rates?",
    ],
    model="MiniLM-L12-v2",
)
print(f"There are {len(res.data)} embeddings.")
print(f"The embeddings length is {len(res.data[0].embedding)}.")
# There are 3 embeddings.
# The embeddings length is 384.