## G4F - File API Documentation with Web Download and Enhanced File Support This document details the enhanced G4F File API, allowing users to upload files, download files from web URLs, and process a wider range of file types for integration with language models. **Key Improvements:** * **Web URL Downloads:** Upload a `downloads.json` file to your bucket containing a list of URLs. The API will download and process these files. Example: `[{"url": "https://example.com/document.pdf"}]` * **Expanded File Support:** Added support for additional plain text file extensions: `.txt`, `.xml`, `.json`, `.js`, `.har`, `.sh`, `.py`, `.php`, `.css`, `.yaml`, `.sql`, `.log`, `.csv`, `.twig`, `.md`. Binary file support remains for `.pdf`, `.html`, `.docx`, `.odt`, `.epub`, `.xlsx`, and `.zip`. * **Server-Sent Events (SSE):** SSE are now used to provide asynchronous updates on file download and processing progress. This improves the user experience, particularly for large files and multiple downloads. **API Endpoints:** * **Upload:** `/v1/files/{bucket_id}` (POST) * **Method:** POST * **Path Parameters:** `bucket_id` (Generated by your own. For example a UUID) * **Body:** Multipart/form-data with files OR a `downloads.json` file containing URLs. * **Response:** JSON object with `bucket_id`, `url`, and a list of uploaded/downloaded filenames. * **Retrieve:** `/v1/files/{bucket_id}` (GET) * **Method:** GET * **Path Parameters:** `bucket_id` * **Query Parameters:** * `delete_files`: (Optional, boolean, default `true`) Delete files after retrieval. * `refine_chunks_with_spacy`: (Optional, boolean, default `false`) Apply spaCy-based refinement. * **Response:** Streaming response with extracted text, separated by ``` markers. SSE updates are sent if the `Accept` header includes `text/event-stream`. **Example Usage (Python):** ```python import requests import uuid import json def upload_and_process(files_or_urls, bucket_id=None): if bucket_id is None: bucket_id = str(uuid.uuid4()) if isinstance(files_or_urls, list): #URLs files = {'files': ('downloads.json', json.dumps(files_or_urls), 'application/json')} elif isinstance(files_or_urls, dict): #Files files = files_or_urls else: raise ValueError("files_or_urls must be a list of URLs or a dictionary of files") upload_response = requests.post(f'http://localhost:1337/v1/files/{bucket_id}', files=files) if upload_response.status_code == 200: upload_data = upload_response.json() print(f"Upload successful. Bucket ID: {upload_data['bucket_id']}") else: print(f"Upload failed: {upload_response.status_code} - {upload_response.text}") response = requests.get(f'http://localhost:1337/v1/files/{bucket_id}', stream=True, headers={'Accept': 'text/event-stream'}) for line in response.iter_lines(): if line: line = line.decode('utf-8') if line.startswith('data:'): try: data = json.loads(line[5:]) #remove data: prefix if "action" in data: print(f"SSE Event: {data}") elif "error" in data: print(f"Error: {data['error']['message']}") else: print(f"File data received: {data}") #Assuming it's file content except json.JSONDecodeError as e: print(f"Error decoding JSON: {e}") else: print(f"Unhandled SSE event: {line}") response.close() # Example with URLs urls = [{"url": "https://github.com/xtekky/gpt4free/issues"}] bucket_id = upload_and_process(urls) #Example with files files = {'files': open('document.pdf', 'rb'), 'files': open('data.json', 'rb')} bucket_id = upload_and_process(files) ``` **Example Usage (JavaScript):** ```javascript function uuid() { return ([1e7]+-1e3+-4e3+-8e3+-1e11).replace(/[018]/g, c => (c ^ crypto.getRandomValues(new Uint8Array(1))[0] & 15 >> c / 4).toString(16) ); } async function upload_files_or_urls(data) { let bucket_id = uuid(); // Use a random generated key for your bucket let formData = new FormData(); if (typeof data === "object" && data.constructor === Array) { //URLs const blob = new Blob([JSON.stringify(data)], { type: 'application/json' }); const file = new File([blob], 'downloads.json', { type: 'application/json' }); // Create File object formData.append('files', file); // Append as a file } else { //Files Array.from(data).forEach(file => { formData.append('files', file); }); } await fetch("/v1/files/" + bucket_id, { method: 'POST', body: formData }); function connectToSSE(url) { const eventSource = new EventSource(url); eventSource.onmessage = (event) => { const data = JSON.parse(event.data); if (data.error) { console.error("Error:", data.error.message); } else if (data.action === "done") { console.log("Files loaded successfully. Bucket ID:", bucket_id); // Use bucket_id in your LLM prompt. const prompt = `Use files from bucket. ${JSON.stringify({"bucket_id": bucket_id})} to answer this: ...your question...`; // ... Send prompt to your language model ... } else { console.log("SSE Event:", data); // Update UI with progress as needed } }; eventSource.onerror = (event) => { console.error("SSE Error:", event); eventSource.close(); }; } connectToSSE(`/v1/files/${bucket_id}`); //Retrieve and refine } // Example with URLs const urls = [{"url": "https://github.com/xtekky/gpt4free/issues"}]; upload_files_or_urls(urls) // Example with files (using a file input element) const fileInput = document.getElementById('fileInput'); fileInput.addEventListener('change', () => { upload_files_or_urls(fileInput.files); }); ``` **Integrating with `ChatCompletion`:** To incorporate file uploads into your client applications, include the `tool_calls` parameter in your chat completion requests, using the `bucket_tool` function. The `bucket_id` is passed as a JSON object within your prompt. ```json { "messages": [ { "role": "user", "content": "Answer this question using the files in the specified bucket: ...your question...\n{\"bucket_id\": \"your_actual_bucket_id\"}" } ], "tool_calls": [ { "function": { "name": "bucket_tool" }, "type": "function" } ] } ``` **Important Considerations:** * **Error Handling:** Implement robust error handling in both Python and JavaScript to gracefully manage potential issues during file uploads, downloads, and API interactions. * **Dependencies:** Ensure all required packages are installed (`pip install -U g4f[files]` for Python). --- [Return to Home](/)