basic_agent.py

from llama_index.core.agent.workflow import AgentWorkflow
from llama_index.core.workflow import Context
from llama_index.core.tools import FunctionTool
from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
from llama_index.tools.duckduckgo import DuckDuckGoSearchToolSpec
from llama_index.tools.wikipedia import WikipediaToolSpec
from llama_index.core.tools.tool_spec.load_and_search import LoadAndSearchToolSpec
from llama_index.readers.web import SimpleWebPageReader
from llama_index.core.tools.ondemand_loader_tool import OnDemandLoaderTool

class BasicAgent:
    def __init__(self):
        llm = HuggingFaceInferenceAPI(model_name="Qwen/Qwen2.5-Coder-32B-Instruct")

        # Initialize tools
        tool_spec = DuckDuckGoSearchToolSpec()
        search_tool = FunctionTool.from_defaults(tool_spec.duckduckgo_full_search)

        wiki_spec = WikipediaToolSpec()
        wiki_search_tool = wiki_spec.to_tool_list()[1]

        # Convert into a LoadAndSearchToolSpec because the wikipedia search tool returns
        # entire Wikipedia pages and this can pollute the context window of the LLM
        wiki_spec = WikipediaToolSpec()
        wiki_search_tool = wiki_spec.to_tool_list()[1]

        # Convert into a LoadAndSearchToolSpec because the wikipedia search tool returns
        # entire Wikipedia pages and this can pollute the context window of the LLM
        wiki_search_tool_las = LoadAndSearchToolSpec.from_defaults(wiki_search_tool).to_tool_list()

        webpage_tool = OnDemandLoaderTool.from_defaults(
            SimpleWebPageReader(html_to_text=True),
            name="Webpage search tool",
            description="A tool for loading the content of a webpage and querying it for information",
        )

        # Create Alfred with all the tools
        self.agent = AgentWorkflow.from_tools_or_functions(
            [search_tool, webpage_tool] + wiki_search_tool_las,
            llm=llm,
            verbose=True
        )

        # self.ctx = Context(self.agent)

    async def __call__(self, question: str) -> str:
        response = await self.agent.run(user_msg=question) # ctx=self.ctx)
        return response.response.content