puyuan-modelhub 1.1.2

A library for managing LLM models

Description

ModelhubClient: A Python client for the Modelhub API

Installation

pip install puyuan_modelhub --user

Usage

OpenAI Client

from openai import OpenAI

client = OpenAI(api_key="xxx", base_url="xxxx")

client.chat.xxxxx

ModelhubClient

Initialize a client

from modelhub import ModelhubClient

client = ModelhubClient(
    host="https://xxxx.com/api/",
    user_name="xxxx",
    user_password="xxxx",
    model="xxx", # Optional
)

get supported models

client.supported_models

Get model supported params

client.get_supported_params("Minimax")

perform a chat query

response = client.chat(
    query,
    model="xxx", # Optional(use model in client construction)
    history=history,
    parameters=dict(
        key1=value1,
        key2=value2
    )
)

Get model embeddings

client.get_embeddings(["你好", "Hello"], model="m3e")

gemini-pro embedding need extra parameters

Use the embed_content method to generate embeddings. The method handles embedding for the following tasks (task_type):

Task Type	Description
RETRIEVAL_QUERY	Specifies the given text is a query in a search/retrieval setting.
RETRIEVAL_DOCUMENT	Specifies the given text is a document in a search/retrieval setting. Using this task type requires a title.
SEMANTIC_SIMILARITY	Specifies the given text will be used for Semantic Textual Similarity (STS).
CLASSIFICATION	Specifies that the embeddings will be used for classification.
CLUSTERING	Specifies that the embeddings will be used for clustering.

Response structure

generated_text: response_text from model
history: generated history
details: generation details. Include tokens used, request duration, ...

History can be only used with ChatGLM3 now.

BaseMessage is the unit of history.

# import some pre-defined message types
from modelhub.common.types import SystemMessage, AIMessage, UserMessage
# construct history of your own
history = [
    SystemMessage(content="xxx", other_value="xxxx"),
    UserMessage(content="xxx", other="xxxx"),
]

VLMClient

Initailize a vlm client

from modelhub import VLMClient
client = VLMClient(...)
client.chat(prompt=..., image_path=..., parameters=...)

Chat with model

VLM Client chat add image_path param to Modelhub Client and other params are same.

client.chat("Hello?", image_path="xxx", model="m3e")

Examples

Use ChatCLM3 for tools calling

from modelhub import ModelhubClient, VLMClient
from modelhub.common.types import SystemMessage

client = ModelhubClient(
    host="https://xxxxx/api/",
    user_name="xxxxx",
    user_password="xxxxx",
)
tools = [
    {
        "name": "track",
        "description": "追踪指定股票的实时价格",
        "parameters": {
            "type": "object",
            "properties": {"symbol": {"description": "需要追踪的股票代码"}},
            "required": ["symbol"],
        },
    },
    {
        "name": "text-to-speech",
        "description": "将文本转换为语音",
        "parameters": {
            "type": "object",
            "properties": {
                "text": {"description": "需要转换成语音的文本"},
                "voice": {"description": "要使用的语音类型（男声、女声等）"},
                "speed": {"description": "语音的速度（快、中等、慢等）"},
            },
            "required": ["text"],
        },
    },
]

# construct system history
history = [
    SystemMessage(
        content="Answer the following questions as best as you can. You have access to the following tools:",
        tools=tools,
    )
]
query = "帮我查询股票10111的价格"

# call ChatGLM3
response = client.chat(query, model="ChatGLM3", history=history)
history = response["history"]
print(response["generated_text"])

Output:
{"name": "track", "parameters": {"symbol": "10111"}}

# generate a fake result for track function call

result = {"price": 1232}

res = client.chat(
    json.dumps(result),
    parameters=dict(role="observation"), # Tell ChatGLM3 this is a function call result
    model="ChatGLM3",
    history=history,
)
print(res["generated_text"])

Output:
根据API调用结果，我得知当前股票的价格为1232。请问您需要我为您做什么？

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