Parse and Analyze Excel Spreadsheets

In this example, learn how to use Parse on 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. Once you do, rename the file to dcf_template.xlsx.

At the end of this example, we will create a mini RAG app with LlamaIndex framework that can answer questions, using OpenAI. You can skip this part, or use another model provider. That final step uses Python.

Setup & Connect to LlamaCloud

You’ll need a LlamaCloud API key. Set it as an environment variable so the SDKs pick it up automatically:

export LLAMA_CLOUD_API_KEY="llx-..."

Install the SDK and construct a client:

Python

pip install "llama-cloud>=2.8"

from llama_cloud import LlamaCloud

client = LlamaCloud()  # reads LLAMA_CLOUD_API_KEY from the environment

TypeScript

npm install @llamaindex/llama-cloud

import LlamaCloud from '@llamaindex/llama-cloud';

const client = new LlamaCloud(); // reads LLAMA_CLOUD_API_KEY from the environment

Go

go get github.com/run-llama/llama-parse-go

package main

import (
  "context"

  llamacloud "github.com/run-llama/llama-parse-go"
)

func main() {
  ctx := context.Background()
  client := llamacloud.NewClient() // reads LLAMA_CLOUD_API_KEY from the environment
  _ = ctx
  _ = client
}

Java

implementation("ai.llamaindex:llama-cloud:1.3.0")

import ai.llamaindex.llamacloud.client.LlamaCloudClient;
import ai.llamaindex.llamacloud.client.okhttp.LlamaCloudOkHttpClient;

// reads LLAMA_CLOUD_API_KEY from the environment
LlamaCloudClient client = LlamaCloudOkHttpClient.fromEnv();

CLI

go install github.com/run-llama/llama-parse-cli/cmd/llp@latest

llp reads LLAMA_CLOUD_API_KEY from the environment (or pass --api-key).

Parse the Spreadsheet

Upload the spreadsheet, parse it with the agentic tier (a strong default for table-heavy documents), and request the per-page markdown. The DCF template’s main table lives on the second sheet, so we read page index 1.

Python

file = client.files.create(file="dcf_template.xlsx", purpose="parse")

result = client.parsing.parse(
    file_id=file.id,
    tier="agentic",
    version="latest",
    expand=["markdown"],
)

# Print the markdown for the second sheet/page
print(result.markdown.pages[1].markdown)

The SDK handles job polling for you—client.parsing.parse() blocks until the job finishes and returns the full result.

TypeScript

import fs from 'fs';

const file = await client.files.create({
  file: fs.createReadStream('dcf_template.xlsx'),
  purpose: 'parse',
});

const result = await client.parsing.parse({
  file_id: file.id,
  tier: 'agentic',
  version: 'latest',
  expand: ['markdown'],
});

// Print the markdown for the second sheet/page
console.log(result.markdown.pages[1].markdown);

The SDK handles job polling for you—client.parsing.parse() awaits until the job finishes and returns the full result.

Go

f, err := os.Open("dcf_template.xlsx")
if err != nil {
  log.Fatal(err)
}
defer f.Close()

file, err := client.Files.New(ctx, llamacloud.FileNewParams{
  File:    f,
  Purpose: "parse",
})
if err != nil {
  log.Fatal(err)
}

job, err := client.Parsing.New(ctx, llamacloud.ParsingNewParams{
  FileID:  llamacloud.String(file.ID),
  Tier:    llamacloud.ParsingNewParamsTierAgentic,
  Version: llamacloud.ParsingNewParamsVersionLatest,
})
if err != nil {
  log.Fatal(err)
}

// expand is a GET parameter in Go, so poll for the result, then request markdown
getParams := llamacloud.ParsingGetParams{Expand: []string{"markdown"}}
result, err := client.Parsing.Get(ctx, job.ID, getParams)
if err != nil {
  log.Fatal(err)
}
for result.Job.Status != "COMPLETED" && result.Job.Status != "FAILED" && result.Job.Status != "CANCELLED" {
  time.Sleep(2 * time.Second)
  result, err = client.Parsing.Get(ctx, job.ID, getParams)
  if err != nil {
    log.Fatal(err)
  }
}
if result.Job.Status != "COMPLETED" {
  log.Fatalf("parse ended as %s", result.Job.Status)
}

// Print the markdown for the second sheet/page
fmt.Println(result.Markdown.Pages[1].Markdown)

Java

FileCreateResponse file = client.files().create(FileCreateParams.builder()
        .file(Paths.get("dcf_template.xlsx"))
        .purpose("parse")
        .build());

ParsingCreateResponse job = client.parsing().create(ParsingCreateParams.builder()
        .fileId(file.id())
        .tier(ParsingCreateParams.Tier.AGENTIC)
        .version(ParsingCreateParams.Version.LATEST)
        .build());

// expand is a query parameter in Java, so poll for the result, then request markdown
ParsingGetParams getParams = ParsingGetParams.builder()
        .jobId(job.id())
        .addExpand("markdown")
        .build();

ParsingGetResponse result = client.parsing().get(getParams);
while (!result.job().status().equals(ParsingGetResponse.Job.Status.COMPLETED)
        && !result.job().status().equals(ParsingGetResponse.Job.Status.FAILED)
        && !result.job().status().equals(ParsingGetResponse.Job.Status.CANCELLED)) {
    Thread.sleep(2000);
    result = client.parsing().get(getParams);
}
if (!result.job().status().equals(ParsingGetResponse.Job.Status.COMPLETED)) {
    throw new RuntimeException("parse ended as " + result.job().status());
}

// Print the markdown for the second sheet/page
System.out.println(result.markdown().get().pages().get(1).asMarkdownResult().markdown());

CLI

# Upload the spreadsheet
FILE_ID=$(llp files create \
  --file dcf_template.xlsx \
  --purpose parse | jq -r '.id')

# Start a parse job
JOB_ID=$(llp parsing create \
  --file-id "$FILE_ID" \
  --tier agentic \
  --version latest | jq -r '.id')

# Poll until the job reaches a terminal status
while true; do
  STATUS=$(llp parsing get --job-id "$JOB_ID" | jq -r '.job.status')
  case "$STATUS" in COMPLETED|FAILED|CANCELLED) break ;; esac
  sleep 2
done

# Print the markdown for the second sheet/page (index 1)
llp parsing get --job-id "$JOB_ID" --expand markdown \
  | jq -r '.markdown.pages[1].markdown'

Expected output:

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

The steps above work from any SDK. The optional RAG walkthrough below uses Python with LlamaIndex and OpenAI to answer questions based on the context provided by the parsed spreadsheet.

Install the extra dependency and set your OpenAI key:

pip install llama-index-llms-openai
export OPENAI_API_KEY="sk-..."

Configure an LLM. In this case, we are using gpt-5-mini:

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

llama_parse_documents = result.markdown.pages

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.markdown for doc in llama_parse_documents])
messages = [
    ChatMessage(
        role="user",
        content=f"Here is some context\n<context>{context}</context>\n\nAnswer the following question: {query_str}",
    )
]

response = llm.chat(messages)
print(response.message.content)

Expected output:

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.