> ## Documentation Index
> Fetch the complete documentation index at: https://docs.anannas.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# PDF Inputs
> Send PDF files to any model on Anannas
Anannas supports **PDF file processing** through the `/chat/completions` API. \
PDFs can be sent as **direct URLs** or **base64-encoded data URLs** in the messages array, using the `file` content type. This works on **all models**, even if the model doesnโt natively support PDFs.
URLs are preferred for large files since they avoid payload bloat.
Use base64 encoding when working with local or private files.
### Using PDF URLs
For publicly accessible PDFs, send the file URL directly:
```json JSON theme={null}
import requests
url = "https://api.anannas.ai/v1/chat/completions"
headers = {
"Authorization": f"Bearer {API_KEY_REF}",
"Content-Type": "application/json",
}
messages = [
{
"role": "user",
"content": [
{ "type": "text", "text": "Summarize this PDF." },
{
"type": "file",
"file": {
"filename": "sample.pdf",
"file_data": "https://example.com/sample.pdf",
},
},
],
}
]
payload = { "model": "openai/gpt-5-mini", "messages": messages }
response = requests.post(url, headers=headers, json=payload)
print(response.json())
```
```python Python theme={null}
const response = await fetch("https://api.anannas.ai/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${API_KEY_REF}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
model: "openai/gpt-5-mini",
messages: [
{
role: "user",
content: [
{ type: "text", text: "Summarize this PDF." },
{
type: "file",
file: {
filename: "sample.pdf",
file_data: "https://example.com/sample.pdf",
},
},
],
},
],
}),
});
const data = await response.json();
console.log(data);
```
### Using Base64-Encoded PDFs
For local or private PDFs, encode to base64 and send as a data URL:
```python Python theme={null}
import base64
import requests
def encode_file_to_base64(file_path):
with open(file_path, "rb") as f:
return base64.b64encode(f.read()).decode("utf-8")
pdf_path = "document.pdf"
base64_pdf = encode_file_to_base64(pdf_path)
data_url = f"data:application/pdf;base64,{base64_pdf}"
url = "https://api.anannas.ai/v1/chat/completions"
headers = {
"Authorization": f"Bearer {API_KEY_REF}",
"Content-Type": "application/json",
}
messages = [
{
"role": "user",
"content": [
{ "type": "text", "text": "Extract key points from this PDF." },
{
"type": "file",
"file": {
"filename": "document.pdf",
"file_data": data_url,
},
},
],
}
]
payload = { "model": "anthropic/claude-3-5-sonnet-20241022", "messages": messages }
response = requests.post(url, headers=headers, json=payload)
print(response.json())
```
```Typescript theme={null}
import fs from "fs";
async function encodeFileToBase64(filePath: string): Promise {
const buffer = await fs.promises.readFile(filePath);
return `data:application/pdf;base64,${buffer.toString("base64")}`;
}
const base64File = await encodeFileToBase64("document.pdf");
const response = await fetch("https://api.anannas.ai/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${API_KEY_REF}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
model: "anthropic/claude-3-5-sonnet-20241022",
messages: [
{
role: "user",
content: [
{ type: "text", text: "Extract key points from this PDF." },
{
type: "file",
file: {
filename: "document.pdf",
file_data: base64File,
},
},
],
},
],
}),
});
const data = await response.json();
console.log(data);
```
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