使用 LlamaIndex 构建简单的 RAG 代理

pip install llama-index python-dotenv

from llama_index.core import simpledirectoryreader, vectorstoreindex, settingsfrom llama_index.llms.openai import openaifrom dotenv import load_dotenv# load environment variables (e.g., openai_api_key)load_dotenv()# configure openai modelsettings.llm = openai(model="gpt-4o-mini")# load documents from the local directorydocuments = simpledirectoryreader("./data").load_data()# create an index from documents for queryingindex = vectorstoreindex.from_documents(documents)query_engine = index.as_query_engine()

def multiply(a: float, b: float) -> float:    """multiply two numbers and returns the product"""    return a * bdef add(a: float, b: float) -> float:    """add two numbers and returns the sum"""    return a + b

from llama_index.core.tools import functiontool, queryenginetool# wrap functions as toolsadd_tool = functiontool.from_defaults(fn=add)multiply_tool = functiontool.from_defaults(fn=multiply)# create a query engine tool for document retrievalspace_facts_tool = queryenginetool.from_defaults(    query_engine,    name="space_facts_tool",    description="a rag engine with information about fun space facts.")

from llama_index.core.agent import reactagent# create the agent with the toolsagent = reactagent.from_tools(    [multiply_tool, add_tool, space_facts_tool], verbose=true)

while True:    query = input("Query: ")    if query == "/bye":        exit()    response = agent.chat(query)    print(response)    print("-" * 10)