> Source: https://txtfetch.com/docs/quickstarts
> Plain-text twin — every page on txtfetch.com has one. https://txtfetch.com/text

---

# Pick your framework.

Official SDKs and RAG-framework loaders. Install, then extract, in under ten lines.

sdk-js

## @txtfetch/sdk (JavaScript / TypeScript)

Zero runtime dependencies. Node ≥ 20. Dual ESM/CJS with .d.ts.

Install

```install
npm install @txtfetch/sdk
```

Quickstart

```quickstart
import { Txtfetch } from "@txtfetch/sdk";

// apiKey defaults to process.env.TXTFETCH_KEY
const txtfetch = new Txtfetch();

const { extracted_text, metadata } = await txtfetch.extract({ file: "./whitepaper.pdf" });
console.log(extracted_text, metadata.chars);

const byUrl = await txtfetch.extract({ url: "https://example.com/report.docx" });
console.log(byUrl.extracted_text);
```

sdk-python

## txtfetch (Python)

Python 3.9+. The only runtime dependency is httpx.

Install

```install
pip install txtfetch
```

Quickstart

```quickstart
from txtfetch import Txtfetch

# api_key defaults to the TXTFETCH_KEY environment variable
client = Txtfetch(api_key="tf_live_...")

# Extract from a local file (path, bytes, or a file-like object all work)
result = client.extract(file="whitepaper.pdf")
print(result.extracted_text)
print(result.metadata.content_type, result.metadata.bytes, result.metadata.ocr)

# Extract from a URL — txtfetch fetches it server-side
result = client.extract(url="https://example.com/whitepaper.docx")
```

langchain-python

## langchain-txtfetch

A LangChain document loader: a thin adapter over the Python SDK. Each file/URL becomes one Document, ready for a text splitter.

Install

```install
pip install langchain-txtfetch
```

Quickstart

```quickstart
from langchain_text_splitters import RecursiveCharacterTextSplitter

from langchain_txtfetch import TxtfetchLoader

# api_key defaults to the TXTFETCH_KEY environment variable
loader = TxtfetchLoader(
    files=["whitepaper.pdf"],           # single path/URL or a list of them
    urls=["https://example.com/spec.docx"],
)

documents = loader.load()

splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
chunks = splitter.split_documents(documents)
```

langchain-js

## @txtfetch/langchain

A LangChain.js document loader: a thin adapter over @txtfetch/sdk. Requires @langchain/core as a peer dependency.

Install

```install
npm install @txtfetch/langchain @langchain/core
```

Quickstart

```quickstart
import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";
import { TxtfetchLoader } from "@txtfetch/langchain";

// apiKey defaults to the TXTFETCH_KEY environment variable
const loader = new TxtfetchLoader({
  files: ["whitepaper.pdf"], // a single path/URL or an array of them
  urls: ["https://example.com/spec.docx"],
});

const documents = await loader.load();

const splitter = new RecursiveCharacterTextSplitter({ chunkSize: 1000, chunkOverlap: 100 });
const chunks = await splitter.splitDocuments(documents);
```

llamaindex-python

## llama-index-readers-txtfetch

A LlamaIndex reader: a thin adapter over the Python SDK. Each file/URL becomes one Document.

Install

```install
pip install llama-index-readers-txtfetch
```

Quickstart

```quickstart
from llama_index.core import VectorStoreIndex
from llama_index.readers.txtfetch import TxtfetchReader

# api_key defaults to the TXTFETCH_KEY environment variable
reader = TxtfetchReader()

documents = reader.load_data(
    files=["whitepaper.pdf"],           # single path/URL or a list of them
    urls=["https://example.com/spec.docx"],
)

index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
print(query_engine.query("What is this document about?"))
```

next

Turn any of these loaders into a full ingestion pipeline in the [chunk → embed → index recipe](https://txtfetch.com/docs/recipe). Or read the [error reference](https://txtfetch.com/docs/errors) to see how each SDK's typed exceptions map to the wire format. Writing Go, Java, or C#, languages with no official SDK yet? See [extract by language](https://txtfetch.com/for) for the whole HTTP client in each.

## Watch it run, then take a key.

The playground replays a real recorded response for every sample document.

[Open the playground →](https://txtfetch.com/playground)

[Get an API key →](https://app.txtfetch.com/signup)