4_llamaparse.py

from dotenv import load_dotenv
load_dotenv()
from llama_index.core.agent import ReActAgent
from llama_index.llms.openai import OpenAI
from llama_index.core.tools import FunctionTool
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex, Settings
from llama_parse import LlamaParse

# settings
Settings.llm = OpenAI(model="gpt-3.5-turbo",temperature=0)

# function tools
def multiply(a: float, b: float) -> float:
    """Multiply two numbers and returns the product"""
    return a * b

multiply_tool = FunctionTool.from_defaults(fn=multiply)

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

add_tool = FunctionTool.from_defaults(fn=add)

# rag pipeline
documents = SimpleDirectoryReader("./data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()

response = query_engine.query("How much exactly was allocated to a tax credit to promote investment in green technologies in the 2023 Canadian federal budget?")
print(response)

documents2 = LlamaParse(result_type="markdown").load_data("./data/2023_canadian_budget.pdf")
index2 = VectorStoreIndex.from_documents(documents2)
query_engine2 = index2.as_query_engine()

response2 = query_engine2.query("How much exactly was allocated to a tax credit to promote investment in green technologies in the 2023 Canadian federal budget?")
print(response2)