Enhancing with LlamaParse

In the previous example we asked a very basic question of our document, about the total amount of the budget. Let's instead ask a more complicated question about a specific fact in the document:

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)

We unfortunately get an unhelpful answer:

The budget allocated funds to a new green investments tax credit, but the exact amount was not specified in the provided context information.

This is bad, because we happen to know the exact number is in the document! But the PDF is complicated, with tables and multi-column layout, and the LLM is missing the answer. Luckily, we can use LlamaParse to help us out.

First, you need a LlamaCloud API key. You can get one for free by signing up for LlamaCloud. Then put it in your .env file just like your OpenAI key:

LLAMA_CLOUD_API_KEY=llx-xxxxx

Now you're ready to use LlamaParse in your code. Let's bring it in as as import:

from llama_parse import LlamaParse

And let's put in a second attempt to parse and query the file (note that this uses documents2, index2, etc.) and see if we get a better answer to the exact same question:

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)

We do!

$20 billion was allocated to a tax credit to promote investment in green technologies in the 2023 Canadian federal budget.

You can always check the repo to what this code looks like.

As you can see, parsing quality makes a big difference to what the LLM can understand, even for relatively simple questions. Next let's see how memory can help us with more complex questions.