> Source: https://txtfetch.com/integrations/make > Plain-text twin — every page on txtfetch.com has one. https://txtfetch.com/text --- # Extract text in Make, no dedicated module needed. Make's module directory has no txtfetch entry. The generic HTTP app's Make a request module calls the same API, and a document link is a cleaner input than a binary bundle. the-problem A Make scenario that ingests documents usually pulls a file from a watched folder, an email module, or a form trigger. Forwarding that file as a binary bundle into a generic HTTP module works, but it adds a data-mapping step first. It also carries a real risk of getting the content type wrong. Make also caps how long a single module can run, so a scenario that waits on slow OCR can fail before the module returns anything. how-to-wire-it-up txtfetch has no dedicated Make module — no app to add, no OAuth connection to authorize. The HTTP app's Make a request module reaches the same REST endpoint a curl command would. Most Make triggers already expose a file's public or signed URL alongside its bundle. Pass that as the url query string parameter, instead of mapping the binary field. For a document that's large or slow, add async and webhook\_url to the same request. Then let a second scenario start from a Webhooks module when the result lands. - Pass ?url= with the file's URL from the trigger bundle, so the HTTP module never maps a binary field. - Automatic OCR runs on scanned and photographed documents, in the same module call as any other format. - Async mode returns a job\_id right away, so a slow extraction never exceeds the module's run limit. - A Webhooks module on a second scenario catches the webhook\_url callback and continues from there. - Idempotency-Key support stops a re-run scenario from re-billing an extraction it already completed. pass-a-link-not-a-file curl ```curl curl -X POST "https://api.txtfetch.com/v1/extract?url=https://example.com/report.pdf" \ -H "Authorization: Bearer $TXTFETCH_KEY" ``` Python ```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/report.pdf"}, ) print(r.json()["extracted_text"]) ``` JavaScript ```javascript const endpoint = new URL("https://api.txtfetch.com/v1/extract"); endpoint.searchParams.set("url", "https://example.com/report.pdf"); 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 ```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/report.pdf") 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": "..." } ``` large-or-slow-documents `POST /v1/extract` returns `202` with a `job_id` whenever the document is large or slow to process, or whenever `async=true` is set. Poll `GET /v1/extract/{job_id}` for the result, or set `webhook_url` and have txtfetch push it instead. No single request on this page is safe to assume will always finish synchronously. Submit (async) ```submit curl -X POST "https://api.txtfetch.com/v1/extract?url=https://example.com/report.pdf&async=true" \ -H "Authorization: Bearer $TXTFETCH_KEY" # {"status": "processing", "job_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6"} ``` Poll ```poll curl "https://api.txtfetch.com/v1/extract/3fa85f64-5717-4562-b3fc-2c963f66afa6" \ -H "Authorization: Bearer $TXTFETCH_KEY" # {"status": "processing", "job_id": "..."} while running, then the same # {"status": "success", "extracted_text": "...", "metadata": {...}} shape # POST /v1/extract would have returned synchronously. ``` curl ```curl curl -X POST "https://api.txtfetch.com/v1/extract?url=https://example.com/report.pdf" \ -H "Authorization: Bearer $TXTFETCH_KEY" \ --data-urlencode "webhook_url=https://example.com/webhooks/txtfetch" ``` setup-steps 1. Add an HTTP module and choose Make a request. 2. Set Method to POST. 3. Set URL to https://api.txtfetch.com/v1/extract, and add url as a query string parameter mapped to the document's link. 4. Under Headers, add Authorization with the value Bearer, followed by your API key. 5. For a large document, add async set to true and webhook\_url set to a second scenario's Webhooks module address. 6. Run the scenario once. The module's output bundle carries extracted\_text for the next module to use. faq **Does Make have a built-in txtfetch module?**: No. There is no txtfetch app in the Make module library. The generic HTTP app's Make a request module calls the same REST API with no extra app to add. **How do I avoid mapping a binary field into the HTTP module?**: Pass the file's URL from the trigger bundle as the url query string parameter instead. txtfetch fetches the document server-side, so the module never handles raw bytes. **What happens if a scenario's run limit is shorter than the OCR job?**: Add async=true and webhook_url to the request. The module returns a job_id immediately, and a second scenario's Webhooks module picks up the finished result. **Can a Make scenario branch on a failed extraction?**: Yes. Add a filter after the HTTP module that checks the status field. A failed request, marked by an error.code, routes down a different path than a success. related-reading - [Batch and large-document ingestion →](https://txtfetch.com/blog/batch-and-large-document-ingestion) - [OCR scanned documents through one API →](https://txtfetch.com/blog/ocr-scanned-documents-api) - [Async jobs & webhooks →](https://txtfetch.com/docs/async) - [Idempotency →](https://txtfetch.com/docs/idempotency) - [Get an API key →](https://app.txtfetch.com/signup) other-integrations - [n8n →](https://txtfetch.com/integrations/n8n) - [Zapier →](https://txtfetch.com/integrations/zapier) - [Apache Airflow →](https://txtfetch.com/integrations/airflow) - [S3 and Lambda →](https://txtfetch.com/integrations/aws-s3-lambda) ## Add the step to your Make flow. One HTTP node with your key does the whole job. [Get an API key →](https://app.txtfetch.com/signup) [See every integration →](https://txtfetch.com/integrations)