tencent 1.0.5

Contributed API Collections of Tencent Product and Services, such as Wechat Public Account Backend Automatic Reply, Calling OpenAI Agent to Use Tencent Service,etc.

package: tencent

*** IMPORTANT LEGAL DISCLAIMER ***

"tencent" python package is not affiliated, endorsed, or vetted by Tencent Corporation. It's an open-source pakcage and crowd contributed API Wrapper of public available tencent products and services to help developers deploy and use these product or services easier. This package name is originally intended to serve as "ten cent(s)" or "one dime", quoted from one ex-Tencent-er.

Contributing API Wrapper to tencent pypi package, Visit Github tencent package Github repo and follow the guidelines on forum Tencent Pypi Developing Forum deepnlp.org.

Public Available API Wrappers

API NAME	FUNCTIONS	Status
greeting	Greeting when import package	Prod
stock_price	Fetch Tencent Stock Price (HKEX: 700) Realtime Quote	Prod
WechatServerVeriAPI	Wechat Server Verification 微信公众号订阅号服务器验证	Prod
WechatTextReplyBaseAPI	Wechat Message Text Reply 处理微信用户文本输入API的基类	Prod
WechatImageReplyBaseAPI	Wechat Message Image Reply 处理微信用户图像输入基类API 基类	Prod

Install

pip install tencent

Usage

Package Import Greeting

import tencent

tencent.greeting()

• note: if you want to display the greeting, just set variable START_SCREEN_ENABLE in constants.py to False

Tencent(HKEX: 700) Stock Price Quote

import tencent

stock_dict = tencent.api("stock_price")
keys=["symbol", "avg_price", "high", "low", "change", "update_time", "market_capitalization", "source"]
print ("#### Tencent Stock Price #### ")
for key in keys:
    if key in stock_dict:
        print (key + "|" + stock_dict[key])
    else:
    	print (key + "|" + "")

Output

    symbol|700
    avg_price|420.400 HKD
    high|424.600 HKD
    low|412.600 HKD
    change|+3.400
    update_time|16 Oct 2024 09:36
    market_capitalization|3,901.15 B HKD
    source|HKEX, https://www.hkex.com.hk/Market-Data/Securities-Prices/Equities/Equities-Quote?sym=700&sc_lang=en

Wechat Public Account Backend Automatic Reply

微信公众号后台，python服务自动回复(文本/图像/语音)，一键验证和服务部署，依赖Flask的python框架

see /examples/wechat/main.py

    # start flask server backend
    python ./examples/wechat/main.py

执行效果

Open AI Completions APIs and pass Tencent APIs as function Schema

see /tests/test_agent_api_tools.py for more details

import json
import tencent
from tencent.utils.agent_utils import function_to_schema

def tencent_api_base(arg1, arg2, arg3, arg4="value4", arg5 = "value5"):
    result = tencent.api("api_base", arg1, arg2, arg3, arg4=arg4, arg5 = arg5)
    print ("DEBUG: tencent_api_base result %s" % str(result))
    return result

def prepare_agent_api_schema():
    tools = [tencent_api_base]

    tool_schemas = [function_to_schema(tool) for tool in tools]
    print ("DEBUG: Agent API Schema")
    [print(json.dumps(schema, indent=2)) for schema in tool_schemas]
    
    return tool_schemas

prepare_agent_api_schema()


def call_openai_api_tools():
    """
        swarm, need openai keys
    """
    tool_schemas = prepare_agent_api_schema()

    response = client.chat.completions.create(
                model="gpt-4o-mini",
                messages=[{"role": "user", "content": "Calling Tencent Service and Return Results"}],
                tools=tool_schemas,
            )
    message = response.choices[0].message
    message.tool_calls[0].function


call_openai_api_tools()

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