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Every Office format, one endpoint.

Word, PowerPoint, Excel, RTF, OpenDocument. Per-format libraries and screenshot-reading VLMs both struggle to cover that breadth in one pipeline.

the-problem

Office documents aren't one format. They're a family: .docx and the legacy .doc binary format, .pptx and .ppt, .xlsx and .xls, plus the OpenDocument siblings (.odt/.ods/.odp) and .rtf. A 'parse Office docs' feature usually means five or six separate libraries, each with its own quirks, versioning, and failure modes. Vision-based extractors treat every page as an image. That loses slide speaker notes, spreadsheet formulas, and the structural distinction between a table and a wall of text. Breadth across office formats is exactly where they're weakest.

one-request-solution

txtfetch wraps Apache Tika's Office parsers behind one request. POST any file in the family, or point it at a URL. You get the same { status, extracted_text } response, with slide notes, sheet contents, and body text included.

curl
curl -X POST https://api.txtfetch.com/v1/extract \
  -H "Authorization: Bearer $TXTFETCH_KEY" \
  -F file=@board-deck.pptx
Python
import os
import requests

with open("board-deck.pptx", "rb") as f:
    r = requests.post(
        "https://api.txtfetch.com/v1/extract",
        headers={"Authorization": f"Bearer {os.environ['TXTFETCH_KEY']}"},
        files={"file": f},
    )

print(r.json()["extracted_text"])
JavaScript
import { readFile } from "node:fs/promises";

const file = new Blob([await readFile("board-deck.pptx")]);
const form = new FormData();
form.append("file", file, "board-deck.pptx");

const res = await fetch("https://api.txtfetch.com/v1/extract", {
  method: "POST",
  headers: { Authorization: `Bearer ${process.env.TXTFETCH_KEY}` },
  body: form,
});

const { extracted_text } = await res.json();
console.log(extracted_text);
Go
package main

import (
	"bytes"
	"encoding/json"
	"fmt"
	"io"
	"mime/multipart"
	"net/http"
	"os"
)

type extractResponse struct {
	Status        string `json:"status"`
	ExtractedText string `json:"extracted_text"`
}

func main() {
	f, err := os.Open("board-deck.pptx")
	if err != nil {
		panic(err)
	}
	defer f.Close()

	var body bytes.Buffer
	writer := multipart.NewWriter(&body)
	part, err := writer.CreateFormFile("file", "board-deck.pptx")
	if err != nil {
		panic(err)
	}
	if _, err := io.Copy(part, f); err != nil {
		panic(err)
	}
	writer.Close()

	req, err := http.NewRequest("POST", "https://api.txtfetch.com/v1/extract", &body)
	if err != nil {
		panic(err)
	}
	req.Header.Set("Authorization", "Bearer "+os.Getenv("TXTFETCH_KEY"))
	req.Header.Set("Content-Type", writer.FormDataContentType())

	resp, err := http.DefaultClient.Do(req)
	if err != nil {
		panic(err)
	}
	defer resp.Body.Close()

	var result extractResponse
	if err := json.NewDecoder(resp.Body).Decode(&result); err != nil {
		panic(err)
	}
	fmt.Println(result.ExtractedText)
}

Or skip the download. Pass a url parameter and txtfetch fetches the document server-side:

curl
curl -X POST "https://api.txtfetch.com/v1/extract?url=https://example.com/sales-workbook.xlsx" \
  -H "Authorization: Bearer $TXTFETCH_KEY"
Python
import os
import requests

r = requests.post(
    "https://api.txtfetch.com/v1/extract",
    headers={"Authorization": f"Bearer {os.environ['TXTFETCH_KEY']}"},
    params={"url": "https://example.com/sales-workbook.xlsx"},
)

print(r.json()["extracted_text"])
JavaScript
const endpoint = new URL("https://api.txtfetch.com/v1/extract");
endpoint.searchParams.set("url", "https://example.com/sales-workbook.xlsx");

const res = await fetch(endpoint, {
  method: "POST",
  headers: { Authorization: `Bearer ${process.env.TXTFETCH_KEY}` },
});

const { extracted_text } = await res.json();
console.log(extracted_text);
Go
package main

import (
	"encoding/json"
	"fmt"
	"net/http"
	"net/url"
	"os"
)

type extractResponse struct {
	Status        string `json:"status"`
	ExtractedText string `json:"extracted_text"`
}

func main() {
	endpoint, err := url.Parse("https://api.txtfetch.com/v1/extract")
	if err != nil {
		panic(err)
	}
	q := endpoint.Query()
	q.Set("url", "https://example.com/sales-workbook.xlsx")
	endpoint.RawQuery = q.Encode()

	req, err := http.NewRequest("POST", endpoint.String(), nil)
	if err != nil {
		panic(err)
	}
	req.Header.Set("Authorization", "Bearer "+os.Getenv("TXTFETCH_KEY"))

	resp, err := http.DefaultClient.Do(req)
	if err != nil {
		panic(err)
	}
	defer resp.Body.Close()

	var result extractResponse
	if err := json.NewDecoder(resp.Body).Decode(&result); err != nil {
		panic(err)
	}
	fmt.Println(result.ExtractedText)
}
{
  "status": "success",
  "extracted_text": "..."
}

what-comes-back

That is the shape. This is the text. A real .docx from the benchmark corpus, word-diffed against the raw parser output. Nothing here was written for this page. Every character comes from a committed fixture.

Q3 2026 Board Update (DOCX)

board-update-q3-2026.docx · application/vnd.openxmlformats-officedocument.wordprocessingml.document · left pane read via Apache Tika, right pane: the human-checked expected.json

raw parser output 

Q3 2026 Board Update

This update covers Q3 2026 performance across all product lines.

Highlights

Annual recurring revenue grew 14% quarter over quarter
Shipped the structured-output beta
Markdown mode
Element JSON mode
Support headcount increased to 6 engineers

Revenue by Plan

Plan	MRR	Growth
Hobby	$0	n/a
Developer	$18,200	11%
Scale	$41,900	19%

Full detail is available in the appendix & supporting spreadsheets.

the human-checked ideal 

Q3 2026 Board Update

This update covers Q3 2026 performance across all product lines.

Highlights

Annual recurring revenue grew 14% quarter over quarter
Shipped the structured-output beta

Markdown mode
Element JSON mode
Support headcount increased to 6 engineers

Revenue by Plan

Plan	MRR	Growth
Hobby	$0	n/a
Developer	$18,200	11%
Scale	$41,900	19%

Full detail is available in the appendix & supporting spreadsheets.

See all ten documents, with the full explanation →

formats-covered

  • .docx
  • .doc
  • .pptx
  • .ppt
  • .xlsx
  • .xls
  • .odt
  • .ods
  • .odp
  • .rtf

faq

How do I extract text from a .docx file?
POST it as multipart form data to https://api.txtfetch.com/v1/extract with your API key in the Authorization header. The response is { "status": "success", "extracted_text": "..." }.
Can it read PowerPoint speaker notes, not just slide text?
Yes. Apache Tika extracts slide body text and speaker notes from .pptx/.ppt files. Both are included in the returned text.
Does it handle the legacy .doc and .xls binary formats too?
Yes. txtfetch covers both the modern XML-based Office formats and their legacy binary predecessors through the same endpoint.
What about OpenDocument files (.odt, .ods, .odp)?
Supported the same way. POST the file or its URL, and get back the same JSON response shape as any other format.

go-further

Office

Send a real .docx through it.

One HTTP call returns the text. Read one in your browser first, for free.

Get an API key →

Open the free .docx reader →