--- title: Parse and Analyze Excel Spreadsheets with LlamaParse | Developer Documentation --- In this example, learn how to use LlamaParse to parse Excel spreadsheets, and (optionally) use that as the basis for a RAG app that can answer questions about the data within the table. For this example, we’ll be using a simple DCF template, which you can [download here](https://www.google.com/url?q=https%3A%2F%2Feqvista.com%2Fwp-content%2Fuploads%2F2020%2F09%2FEqvista_DCF-Excel-Template.xlsx). Once you do, rename the file to `dcf_template.xlsx`. ![](/_astro/example_spreadsheet.BmKC2Yqw_Z1qYBCC.png) > At the end of this example, we will create a mini RAG app with [LlamaIndex framework](https://docs.llamaindex.ai/) that can answer questions, using OpenAI. You can skip this part, or use another [model provider](https://docs.llamaindex.ai/en/stable/module_guides/models/llms/modules/) ## Setup & Connect to LlamaCloud To get started, we’ll install `llama-cloud-services` and (optionally) `llama-index`: Terminal window ``` !pip install llama-index !pip install llama-cloud-services ``` ``` import os from getpass import getpass os.environ["LLAMA_CLOUD_API_KEY"] = getpass("Llama Cloud API Key: ") os.environ["OPENAI_API_KEY"] = getpass("OpenAI API Key: ") ``` Once you have your API keys, you can initialize `LlamaParse`: ``` from llama_cloud_services import LlamaParse parser = LlamaParse( parse_mode="parse_page_with_agent", model="openai-gpt-4o-mini", high_res_ocr=True, adaptive_long_table=True, outlined_table_extraction=True, output_tables_as_HTML=True, ) ``` ## Parse the Spreadsheet Once you have LlamaParse set up, you can parse the example spreadsheet, which should result in the following output: ``` result = await parser.aparse("dcf_template.xlsx") llama_parse_documents = result.get_text_documents(split_by_page=True) print(llama_parse_documents[1].text) ``` ``` Discounted Cash Flow Excel Template Here is a simple discounted cash flow excel template for estimating your company value based on this income valuation approach Instructions: 1) Fill out the two assumptions in yellow highlight 2) Fill in either the 5 year or 3 year weighted average figures in yellow highlight Assumptions Tax Rate 20% Discount Rate 15% 5 Year Weighted Moving Average Indication of Company Value $242,995.43 3 Year Weighted Moving Average Indication of Company Value $158,651.07 5 Year Weighted Moving Average Past Years Forecasted Future Years Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Year 9 Year 10 Terminal Value Pre-tax income 50,000.00 55,000.00 45,000.00 52,000.00 60,000.00 Income Taxes 10,000.00 11,000.00 9,000.00 10,400.00 12,000.00 Net Income 40,000.00 44,000.00 36,000.00 41,600.00 48,000.00 Depreciation Expense 5,000.00 4,000.00 3,000.00 2,000.00 1,000.00 Capital Expenditures 10,000.00 8,000.00 5,000.00 5,000.00 7,000.00 Debt Repayments 5,000.00 5,000.00 5,000.00 5,000.00 5,000.00 Net Cash Flow 20,000.00 27,000.00 23,000.00 29,600.00 35,000.00 29,093.33 29,817.78 30,177.48 30,469.23 30,379.74 287,188.00 Discounting Factor 0.8696 0.7561 0.6575 0.5718 0.4972 0.4972 Present Value of Future Cash Flow 25,298.55 22,546.52 19,842.18 17,420.88 15,104.10 142,783.19 3 Year Weighted Moving Average Past Years Forecasted Future Years Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Terminal Value Pre-tax income 50,000.00 55,000.00 45,000.00 Income Taxes 10,000.00 11,000.00 9,000.00 Net Income 40,000.00 44,000.00 36,000.00 Depreciation Expense 5,000.00 4,000.00 3,000.00 Capital Expenditures 10,000.00 8,000.00 5,000.00 Debt Repayments 5,000.00 5,000.00 5,000.00 Net Cash Flow 20,000.00 27,000.00 23,000.00 23,833.33 24,083.33 23,819.44 158,253.59 Discounting Factor 0.8696 0.7561 0.6575 0.6575 Present Value of Future Cash Flow 20,724.64 18,210.46 15,661.67 104,054.30 Notes: -We based this simple discounted cash flow excel model based on the weighted moving averages (5 year or 3 year) for simplicity, in case a constant growth rate cannot be easily determined. -The factors such as Depreciation Expense, Capital Expense and Debt Repayments remain constant, so consider this when looking at the forecasted figures. -For the terminal value constant growth rate, we make the assumption of the growth from the last forecasted year compared to the first forecasted year. Adjust in the formula as needed. ``` ## (Optional) Ask Questions Over the Data Now, you can create a system that is able to answer questions based on the context provided by the parsed excel spreadsheet. For this step, we’ll be using `LlamaIndex`. We start by configuring an LLM. In this case, we are using `gpt-5`: ``` from llama_index.llms.openai import OpenAI llm = OpenAI(model="gpt-5-mini") ``` The easiest way to do this is to augment a prompt with the contents of the parsed spreadsheet: ``` from llama_index.core.llms import ChatMessage query_str = "Tell me about the income taxes in the past years (year 3-5) for the 5 year WMA table" context = "\n\n".join([doc.text for doc in llama_parse_documents]) messages = [ ChatMessage( role="user", content=f"Here is some context\n{context}\n\nAnswer the following question: {query_str}", ) ] response = await llm.achat(messages) print(response.message.content) ``` ``` In the 5‑year WMA table the income tax amounts for past years 3–5 are: - Year 3: $9,000.00 - Year 4: $10,400.00 - Year 5: $12,000.00 These equal 20% of the respective pre‑tax incomes (45,000; 52,000; 60,000), consistent with the 20% tax rate assumption. The taxes rise each year as pre‑tax income increases. ```