Overview

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HiAgent-SDK is the SDK of the HiAgent product from Volcano Engine. Developers can use this SDK to quickly develop functions and improve development efficiency. HiAgent-SDK provides a complete AI native application development suite, including a rich set of development components and application example code.

Architecture

Documentation

Access the document at document address to obtain the documentation.

Quick Start

import os

from dotenv import load_dotenv
from hiagent_api.chat import ChatService
from hiagent_api.knowledgebase import KnowledgebaseService
from hiagent_api.tool import ToolService
from hiagent_components.agent import Agent
from hiagent_components.integrations.langchain import LangChainTool
from hiagent_components.retriever import KnowledgeRetriever
from hiagent_components.tool import Tool
from langchain.agents import AgentExecutor, create_structured_chat_agent
from langchain.callbacks import StdOutCallbackHandler
from langchain_openai import ChatOpenAI
from langsmith import Client

load_dotenv()

def get_tool_svc() -> ToolService:
    svc = ToolService(
        endpoint=os.getenv("HIAGENT_TOP_ENDPOINT") or "", region="cn-north-1"
    )
    return svc

def get_chat_svc() -> ChatService:
    svc = ChatService(
        endpoint=os.getenv("HIAGENT_TOP_ENDPOINT") or "", region="cn-north-1"
    )
    svc.set_app_base_url(os.getenv("HIAGENT_APP_BASE_URL") or "")

    return svc

def get_knowledgebase_svc() -> KnowledgebaseService:
    svc = KnowledgebaseService(
        endpoint=os.getenv("HIAGENT_TOP_ENDPOINT") or "", region="cn-north-1"
    )

    return svc

if __name__ == "__main__":
    tool = Tool.init(
        svc=get_tool_svc(),
        workspace_id="cuq0pp9s7366bfl0cns0",
        tool_id="5njoa3j2m2t5cpaotjlg"
    )

    app_key = os.getenv("HIAGENT_AGENT_APP_KEY") or ""
    agent = Agent.init(
        svc=get_chat_svc(),
        app_key=app_key,
        user_id="test",
        variables={"name": "weather_assistant"},
    )

    retriever = KnowledgeRetriever(
        svc=get_knowledgebase_svc(),
        name="knowledge_tool",
        description="knowledge retriever, used to search knowledge about pandas",
        workspace_id="cuq0pp9s7366bfl0cns0",
        dataset_ids=["019613e3-f37b-7b80-8e0a-579435bb9870"],
        top_k=3,
        score_threshold=0.4,
        retrieval_search_method=0,
    )

    ocr_tool = LangChainTool.from_tool(tool)
    agent_tool = LangChainTool.from_tool(agent.as_tool())
    retriever_tool = LangChainTool.from_tool(retriever.as_tool())
    tools = [ocr_tool, agent_tool, retriever_tool]


    # Pull the prompt template from the hub
    # ReAct = Reason and Action
    # https://smith.langchain.com/hub/hwchase17/react
    client = Client()
    prompt = client.pull_prompt("hwchase17/structured-chat-agent")
    prompt.messages[0].prompt.template += "\n\n## Notice\n\n tool's action_input must be json object rather than string"

    callbacks = [StdOutCallbackHandler()]

    # export OPENAI_API_KEY=xxxx
    # Initialize a ChatOpenAI model
    llm = ChatOpenAI(
        model=os.getenv("OPENAI_MODEL"),  # pro
        base_url="https://ark.cn-beijing.volces.com/api/v3",
        callbacks=callbacks,
    )
    agent = create_structured_chat_agent(
        llm=llm,
        tools=tools,
        prompt=prompt,
    )
    # Create an agent executor from the agent and tools
    agent_executor = AgentExecutor.from_agent_and_tools(
        agent=agent,
        tools=tools,
        verbose=False,
        callbacks=callbacks,
    )
    # Run the agent with a test query
    response = agent_executor.invoke(
        {"input": "Is the weather in Shenzhen suitable for going out today?"},
    )

    # Print the response from the agent
    print("response:", response)

Code of Conduct

Please check Code of Conduct for more details.

Security and privacy

This project takes security seriously. For vulnerability reporting and supported versions, see SECURITY.md

License

This project is licensed under the Apache-2.0 License.