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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
from langfuse.llama_index import LlamaIndexInstrumentor
from llama_index.llms.ollama import Ollama
class BasicAgent:
def __init__(self, ollama=False, langfuse=True):
if not ollama:
llm = HuggingFaceInferenceAPI(model_name="Qwen/Qwen2.5-Coder-32B-Instruct")
else:
llm = Ollama(model="mistral:latest", request_timeout=120.0)
# Langfuse
self.langfuse = langfuse
if self.langfuse:
self.instrumentor = LlamaIndexInstrumentor()
self.instrumentor.start()
# 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
# TODO this does not work so well. We need to make the retriever return the top 5 chunks or sth.
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(
wiki_search_tool_las + [search_tool], # webpage_tool does not work properly - cookies etc
llm=llm,
verbose=True,
system_prompt=("You are a helpful agent that can search Wikipedia and the Internet for answers. "
"Please be concise when answering questions and make sure your final outputs are relevant to the question.")
)
# self.ctx = Context(self.agent)
async def __call__(self, question: str) -> str:
response = await self.agent.run(user_msg=question) # ctx=self.ctx)
if self.langfuse:
self.instrumentor.flush()
return response.response.content
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