> For the complete documentation index, see [llms.txt](https://docs.roboflow.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.roboflow.com/roboflow/roboflow-hi/deploy/serverless/instance-segmentation.md). # Instance Segmentation {% tabs %} {% tab title="cURL" %} **Linux या MacOS** नाम की local file के लिए JSON predictions प्राप्त करना `YOUR_IMAGE.jpg`: ```bash base64 YOUR_IMAGE.jpg | curl -d @- \\ "https://serverless.roboflow.com/your-model/42?api_key=YOUR_KEY" ``` वेब पर कहीं और hosted image पर उसके URL के माध्यम से inference करना (ना भूलें कि [इसे URL encode करें](https://www.urlencoder.org/)): ```bash curl -X POST "https://serverless.roboflow.com/your-model/42?\ api_key=YOUR_KEY&\ image=https%3A%2F%2Fi.imgur.com%2FPEEvqPN.png" ``` **Windows** आपको install करना होगा [Windows के लिए curl](https://curl.se/windows/) और [Windows के लिए GNU का base64 tool](http://gnuwin32.sourceforge.net/packages/coreutils.htm). ऐसा करने का सबसे आसान तरीका है उपयोग करना [git for Windows installer](https://git-scm.com/downloads) जिसमें यह भी शामिल है `curl` और `base64` command line tools जब आप installation के दौरान "Use Git and optional Unix tools from the Command Prompt" चुनते हैं। फिर आप ऊपर वाले same commands का उपयोग कर सकते हैं। {% endtab %} {% tab title="Python" %} **स्थानीय और Hosted Images पर Inference करें** निर्भरताएँ install करने के लिए, `pip install inference-sdk`. ``` from inference_sdk import InferenceHTTPClient CLIENT = InferenceHTTPClient( api_url="https://serverless.roboflow.com", api_key="API_KEY" ) result = CLIENT.infer(your_image.jpg, model_id="football-pitch-segmentation/1") ``` {% endtab %} {% tab title="JavaScript" %} **Node.js** इस उदाहरण में POST request करने के लिए हम [axios](https://github.com/axios/axios) का उपयोग कर रहे हैं, इसलिए पहले चलाएँ `npm install axios` निर्भरता install करने के लिए। **Local Image पर inference करना** ```javascript const axios = require("axios"); const fs = require("fs"); const image = fs.readFileSync("YOUR_IMAGE.jpg", { encoding: "base64" }); axios({ method: "POST", url: "https://serverless.roboflow.com/your-model/42", params: { api_key: "YOUR_KEY" }, data: image, headers: { "Content-Type": "application/x-www-form-urlencoded" } }) .then(function(response) { console.log(response.data); }) .catch(function(error) { console.log(error.message); }); ``` **URL के माध्यम से कहीं और hosted image पर inference करना** ```javascript const axios = require("axios"); axios({ method: "POST", url: "https://serverless.roboflow.com/your-model/42", params: { api_key: "YOUR_KEY", image: "https://i.imgur.com/PEEvqPN.png" } }) .then(function(response) { console.log(response.data); }) .catch(function(error) { console.log(error.message); }); ``` **वेब** हमारे पास roboflow\.js के माध्यम से real-time on-device inference उपलब्ध है `roboflow.js`; देखें [दस्तावेज़ यहाँ](/roboflow/roboflow-hi/deploy/sdks/web-browser.md). {% endtab %} {% tab title="Swift/iOS" %} **Swift** **Local Image पर inference करना** ```swift import UIKit // Image लोड करें और Base64 में बदलें let image = UIImage(named: "your-image-path") // अपलोड करने के लिए image का path, जैसे: image.jpg let imageData = image?.jpegData(compressionQuality: 1) let fileContent = imageData?.base64EncodedString() let postData = fileContent!.data(using: .utf8) // API_KEY, Model, और Model Version के साथ Inference Server Request प्रारंभ करें var request = URLRequest(url: URL(string: "https://serverless.roboflow.com/your-model/your-model-version?api_key=YOUR_APIKEY&name=YOUR_IMAGE.jpg")!,timeoutInterval: Double.infinity) request.addValue("application/x-www-form-urlencoded", forHTTPHeaderField: "Content-Type") request.httpMethod = "POST" request.httpBody = postData // Post Request निष्पादित करें URLSession.shared.dataTask(with: request, completionHandler: { data, response, error in // Response को String में parse करें guard let data = data else { print(String(describing: error)) return } // Response String को Dictionary में बदलें do { let dict = try JSONSerialization.jsonObject(with: data, options: []) as? [String: Any] } catch { print(error.localizedDescription) } // String Response print करें print(String(data: data, encoding: .utf8)!) }).resume() ``` **Objective C** [Objective-C snippet का अनुरोध करने के लिए यहाँ क्लिक करें।](https://app.roboflow.com/request/snippet.inference-objc) {% endtab %} {% tab title="Android" %} **Kotlin** **Local Image पर inference करना** ```kotlin import java.io.* import java.net.HttpURLConnection import java.net.URL import java.nio.charset.StandardCharsets import java.util.* fun main() { // Image path प्राप्त करें val filePath = System.getProperty("user.dir") + System.getProperty("file.separator") + "YOUR_IMAGE.jpg" val file = File(filePath) // Base64 encode करें val encodedFile: String val fileInputStreamReader = FileInputStream(file) val bytes = ByteArray(file.length().toInt()) fileInputStreamReader.read(bytes) encodedFile = String(Base64.getEncoder().encode(bytes), StandardCharsets.US_ASCII) val API_KEY = "" // आपकी API Key val MODEL_ENDPOINT = "dataset/v" // model endpoint सेट करें (Dataset URL में मिलता है) // URL बनाएं val uploadURL ="https://serverless.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&name=YOUR_IMAGE.jpg"; // Http Request var connection: HttpURLConnection? = null try { // URL के लिए connection configure करें val url = URL(uploadURL) connection = url.openConnection() as HttpURLConnection connection.requestMethod = "POST" connection.setRequestProperty("Content-Type", "application/x-www-form-urlencoded") connection.setRequestProperty("Content-Length", Integer.toString(encodedFile.toByteArray().size)) connection.setRequestProperty("Content-Language", "en-US") connection.useCaches = false connection.doOutput = true // Request भेजें val wr = DataOutputStream( connection.outputStream) wr.writeBytes(encodedFile) wr.close() // Response प्राप्त करें val stream = connection.inputStream val reader = BufferedReader(InputStreamReader(stream)) var line: String? while (reader.readLine().also { line = it } != null) { println(line) } reader.close() } catch (e: Exception) { e.printStackTrace() } finally { connection?.disconnect() } } main() ``` **URL के माध्यम से कहीं और hosted image पर inference करना** ```kotlin import java.io.BufferedReader import java.io.DataOutputStream import java.io.InputStreamReader import java.net.HttpURLConnection import java.net.URL import java.net.URLEncoder fun main() { val imageURL = "https://i.imgur.com/PEEvqPN.png" // Image URL बदलें val API_KEY = "" // आपकी API Key val MODEL_ENDPOINT = "dataset/v" // model endpoint सेट करें // अपलोड URL val uploadURL = "https://serverless.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&image=" + URLEncoder.encode(imageURL, "utf-8"); // Http Request var connection: HttpURLConnection? = null try { // URL के लिए connection configure करें val url = URL(uploadURL) connection = url.openConnection() as HttpURLConnection connection.requestMethod = "POST" connection.setRequestProperty("Content-Type", "application/x-www-form-urlencoded") connection.setRequestProperty("Content-Length", Integer.toString(uploadURL.toByteArray().size)) connection.setRequestProperty("Content-Language", "en-US") connection.useCaches = false connection.doOutput = true // Request भेजें val wr = DataOutputStream(connection.outputStream) wr.writeBytes(uploadURL) wr.close() // Response प्राप्त करें val stream = URL(uploadURL).openStream() val reader = BufferedReader(InputStreamReader(stream)) var line: String? while (reader.readLine().also { line = it } != null) { println(line) } reader.close() } catch (e: Exception) { e.printStackTrace() } finally { connection?.disconnect() } } main() ``` **Java** **Local Image पर inference करना** ```java import java.io.*; import java.net.HttpURLConnection; import java.net.URL; import java.nio.charset.StandardCharsets; import java.util.Base64; public class InferenceLocal { public static void main(String[] args) throws IOException { // Image path प्राप्त करें String filePath = System.getProperty("user.dir") + System.getProperty("file.separator") + "YOUR_IMAGE.jpg"; File file = new File(filePath); // Base64 encode करें String encodedFile; FileInputStream fileInputStreamReader = new FileInputStream(file); byte[] bytes = new byte[(int) file.length()]; fileInputStreamReader.read(bytes); encodedFile = new String(Base64.getEncoder().encode(bytes), StandardCharsets.US_ASCII); String API_KEY = ""; // आपकी API Key String MODEL_ENDPOINT = "dataset/v"; // model endpoint सेट करें // URL बनाएं String uploadURL = "https://serverless.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&name=YOUR_IMAGE.jpg"; // Http Request HttpURLConnection connection = null; try { // URL के लिए connection configure करें URL url = new URL(uploadURL); connection = (HttpURLConnection) url.openConnection(); connection.setRequestMethod("POST"); connection.setRequestProperty("Content-Type", "application/x-www-form-urlencoded"); connection.setRequestProperty("Content-Length", Integer.toString(encodedFile.getBytes().length)); connection.setRequestProperty("Content-Language", "en-US"); connection.setUseCaches(false); connection.setDoOutput(true); // Request भेजें DataOutputStream wr = new DataOutputStream(connection.getOutputStream()); wr.writeBytes(encodedFile); wr.close(); // Response प्राप्त करें InputStream stream = connection.getInputStream(); BufferedReader reader = new BufferedReader(new InputStreamReader(stream)); String line; while ((line = reader.readLine()) != null) { System.out.println(line); } reader.close(); } catch (Exception e) { e.printStackTrace(); } finally { if (connection != null) { connection.disconnect(); } } } } ``` **URL के माध्यम से कहीं और hosted image पर inference करना** ```java import java.io.BufferedReader; import java.io.DataOutputStream; import java.io.InputStream; import java.io.InputStreamReader; import java.net.HttpURLConnection; import java.net.URL; import java.net.URLEncoder; import java.nio.charset.StandardCharsets; public class InferenceHosted { public static void main(String[] args) { String imageURL = "https://i.imgur.com/PEEvqPN.png"; // Image URL बदलें String API_KEY = ""; // आपकी API Key String MODEL_ENDPOINT = "dataset/v"; // model endpoint सेट करें // अपलोड URL String uploadURL = "https://serverless.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&image=" + URLEncoder.encode(imageURL, StandardCharsets.UTF_8); // Http Request HttpURLConnection connection = null; try { // URL के लिए connection configure करें URL url = new URL(uploadURL); connection = (HttpURLConnection) url.openConnection(); connection.setRequestMethod("POST"); connection.setRequestProperty("Content-Type", "application/x-www-form-urlencoded"); connection.setRequestProperty("Content-Length", Integer.toString(uploadURL.getBytes().length)); connection.setRequestProperty("Content-Language", "en-US"); connection.setUseCaches(false); connection.setDoOutput(true); // Request भेजें DataOutputStream wr = new DataOutputStream(connection.getOutputStream()); wr.writeBytes(uploadURL); wr.close(); // Response प्राप्त करें InputStream stream = new URL(uploadURL).openStream(); BufferedReader reader = new BufferedReader(new InputStreamReader(stream)); String line; while ((line = reader.readLine()) != null) { System.out.println(line); } reader.close(); } catch (Exception e) { e.printStackTrace(); } finally { if (connection != null) { connection.disconnect(); } } } } ``` {% endtab %} {% tab title="Ruby" %} **Gemfile** {% code title="Gemfile" %} ```ruby source "https://rubygems.org" gem "httparty", "~> 0.18.1" gem "base64", "~> 0.1.0" gem "cgi", "~> 0.2.1" ``` {% endcode %} **Gemfile.lock** {% code title="Gemfile.lock" %} ```ruby GEM remote: https://rubygems.org/ specs: base64 (0.1.0) cgi (0.2.1) httparty (0.18.1) mime-types (~> 3.0) multi_xml (>= 0.5.2) mime-types (3.3.1) mime-types-data (~> 3.2015) mime-types-data (3.2021.0225) multi_xml (0.6.0) PLATFORMS x64-mingw32 x86_64-linux DEPENDENCIES base64 (~> 0.1.0) cgi (~> 0.2.1) httparty (~> 0.18.1) BUNDLED WITH 2.2.15 ``` {% endcode %} **Local Image पर inference करना** ```ruby require 'base64' require 'httparty' encoded = Base64.encode64(File.open("YOUR_IMAGE.jpg", "rb").read) model_endpoint = "dataset/v" # model endpoint सेट करें api_key = "" # आपकी API KEY यहाँ params = "?api_key=" + api_key + "&name=YOUR_IMAGE.jpg" response = HTTParty.post( "https://serverless.roboflow.com/" + model_endpoint + params, body: encoded, headers: { 'Content-Type' => 'application/x-www-form-urlencoded', 'charset' => 'utf-8' }) puts response ``` **URL के माध्यम से कहीं और hosted image पर inference करना** ```ruby require 'httparty' require 'cgi' model_endpoint = "dataset/v" # model endpoint सेट करें api_key = "" # आपकी API KEY यहाँ img_url = "https://i.imgur.com/PEEvqPN.png" # URL बनाएं img_url = CGI::escape(img_url) params = "?api_key=" + api_key + "&image=" + img_url response = HTTParty.post( "https://serverless.roboflow.com/" + model_endpoint + params, headers: { 'Content-Type' => 'application/x-www-form-urlencoded', 'charset' => 'utf-8' }) puts response ``` {% endtab %} {% tab title="PHP" %} **Local Image पर inference करना** ```php array ( 'header' => "Content-type: application/x-www-form-urlencoded\r\n", 'method' => 'POST', 'content' => $data )); $context = stream_context_create($options); $result = file_get_contents($url, false, $context); echo $result; ?> ``` **URL के माध्यम से कहीं और hosted image पर inference करना** ```php array ( 'header' => "Content-type: application/x-www-form-urlencoded\r\n", 'method' => 'POST' )); $context = stream_context_create($options); $result = file_get_contents($url, false, $context); echo $result; ?> ``` {% endtab %} {% tab title="Go" %} **Local Image पर inference करना** ```go package main import ( "bufio" "encoding/base64" "fmt" "io/ioutil" "os" "net/http" "strings" ) func main() { api_key := "" // आपकी API Key model_endpoint := "dataset/v" // model endpoint सेट करें // disk पर file खोलें। f, _ := os.Open("YOUR_IMAGE.jpg") // पूरे JPG को byte slice में पढ़ें। reader := bufio.NewReader(f) content, _ := ioutil.ReadAll(reader) // base64 के रूप में encode करें। data := base64.StdEncoding.EncodeToString(content) uploadURL := "https://serverless.roboflow.com/" + model_endpoint + "?api_key=" + api_key + "&name=YOUR_IMAGE.jpg" req, _ := http.NewRequest("POST", uploadURL, strings.NewReader(data)) req.Header.Set("Accept", "application/json") client := &http.Client{} resp, _ := client.Do(req) defer resp.Body.Close() bytes, _ := ioutil.ReadAll(resp.Body) fmt.Println(string(bytes)) } ``` **URL के माध्यम से कहीं और hosted image पर inference करना** ```go package main import ( "fmt" "net/http" "net/url" "io/ioutil" ) func main() { api_key := "" // आपकी API Key model_endpoint := "dataset/v" // model endpoint सेट करें img_url := "https://i.ibb.co/jzr27x0/YOUR-IMAGE.jpg" uploadURL := "https://serverless.roboflow.com/" + model_endpoint + "?api_key=" + api_key + "&image=" + url.QueryEscape(img_url) req, _ := http.NewRequest("POST", uploadURL, nil) req.Header.Set("Accept", "application/json") client := &http.Client{} resp, _ := client.Do(req) defer resp.Body.Close() bytes, _ := ioutil.ReadAll(resp.Body) fmt.Println(string(bytes)) } ``` {% endtab %} {% tab title=".NET" %} **Local Image पर inference करना** ```csharp using System; using System.IO; using System.Net; using System.Text; namespace InferenceLocal { class InferenceLocal { static void Main(string[] args) { byte[] imageArray = System.IO.File.ReadAllBytes(@"YOUR_IMAGE.jpg"); string encoded = Convert.ToBase64String(imageArray); byte[] data = Encoding.ASCII.GetBytes(encoded); string API_KEY = ""; // आपकी API कुंजी string MODEL_ENDPOINT = "dataset/v"; // model endpoint सेट करें // URL बनाएं string uploadURL = "https://serverless.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&name=YOUR_IMAGE.jpg"; // सेवा अनुरोध कॉन्फ़िगरेशन ServicePointManager.Expect100Continue = true; ServicePointManager.SecurityProtocol = SecurityProtocolType.Tls12; // अनुरोध कॉन्फ़िगर करें WebRequest request = WebRequest.Create(uploadURL); request.Method = "POST"; request.ContentType = "application/x-www-form-urlencoded"; request.ContentLength = data.Length; // डेटा लिखें using (Stream stream = request.GetRequestStream()) { stream.Write(data, 0, data.Length); } // Response प्राप्त करें string responseContent = null; using (WebResponse response = request.GetResponse()) { using (Stream stream = response.GetResponseStream()) { using (StreamReader sr99 = new StreamReader(stream)) { responseContent = sr99.ReadToEnd(); } } } Console.WriteLine(responseContent); } } } ``` **URL के माध्यम से कहीं और hosted image पर inference करना** ```csharp using System; using System.IO; using System.Net; using System.Web; namespace InferenceHosted { class InferenceHosted { static void Main(string[] args) { string API_KEY = ""; // आपकी API कुंजी string imageURL = "https://i.ibb.co/jzr27x0/YOUR-IMAGE.jpg"; string MODEL_ENDPOINT = "dataset/v"; // model endpoint सेट करें // URL बनाएं string uploadURL = "https://serverless.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&image=" + HttpUtility.UrlEncode(imageURL); // सेवा बिंदु कॉन्फ़िगरेशन ServicePointManager.Expect100Continue = true; ServicePointManager.SecurityProtocol = SecurityProtocolType.Tls12; // HTTP अनुरोध कॉन्फ़िगर करें WebRequest request = WebRequest.Create(uploadURL); request.Method = "POST"; request.ContentType = "application/x-www-form-urlencoded"; request.ContentLength = 0; // Response प्राप्त करें string responseContent = null; using (WebResponse response = request.GetResponse()) { using (Stream stream = response.GetResponseStream()) { using (StreamReader sr99 = new StreamReader(stream)) { responseContent = sr99.ReadToEnd(); } } } Console.WriteLine(responseContent); } } } ``` {% endtab %} {% tab title="Elixer" %} हम उपयोगकर्ताओं के अनुरोध के अनुसार code snippets जोड़ रहे हैं। यदि आप inference API को अपने Elixir app में integrate करना चाहते हैं, तो कृपया [अपना upvote दर्ज करने के लिए यहाँ क्लिक करें](https://app.roboflow.com/request/snippet.upload-elixir). {% endtab %} {% endtabs %} ## Response Object Format Hosted API inference route एक लौटाता है `JSON` object लौटाते हैं जिसमें predictions की एक array होती है। प्रत्येक prediction में निम्न properties होती हैं: * `x` = detected object का horizontal center point * `y` = detected object का vertical center point * `width` = bounding box की चौड़ाई * `height` = bounding box की ऊंचाई * `class` = detected object का class label * `confidence` = model का confidence कि detected object का सही label और position coordinates हैं * `points` = उन बिंदुओं की सूची जो object की polygon outline बनाते हैं - सूची का प्रत्येक item keys वाले एक object होता है `x` और `y` क्रमशः बिंदु के horizontal और vertical coordinate के लिए ```json // एक उदाहरण JSON object { "predictions": [ { "x": 179.2, "y": 247, "width": 231, "height": 147, "class": "A", "confidence": 0.98, "points": [ { "x": 134, "y": 314 }, { "x": 116, "y": 313 }, { "x": 103, "y": 310.1 }, { "x": 72.7, "y": 282 }, { "x": 66.8, "y": 273 }, ] } ] } ``` ## API संदर्भ ## Inference API का उपयोग करना `POST` `https://serverless.roboflow.com/:datasetSlug/:versionNumber` आप सीधे अपने model endpoint पर base64 encoded image POST कर सकते हैं। या आप query string में URL को `image` पैरामीटर के रूप में पास कर सकते हैं, यदि आपकी image पहले से कहीं और hosted है। #### Path Parameters | Name | प्रकार | विवरण | | ----------- | ------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | datasetSlug | string | डेटासेट नाम का URL-सुरक्षित संस्करण। आप इसे मुख्य project view के URL को देखकर, या अपना model train करने के बाद अपने dataset version के train results section में "Get curl command" बटन पर क्लिक करके web UI में पा सकते हैं। | | version | number | आपके dataset के version की पहचान करने वाला version number | #### क्वेरी पैरामीटर्स | Name | प्रकार | विवरण | | ---------- | ------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | image | string |

जोड़ने के लिए image का URL। इसका उपयोग तब करें जब आपकी image कहीं और hosted हो। (जब आप request body में base64 encoded image POST नहीं करते हैं, तब यह आवश्यक है.)

नोट: इसे URL-encode करना न भूलें।

| | overlap | number |

0-100 के पैमाने पर वह अधिकतम प्रतिशत, जितना एक ही class के bounding box predictions को एक single box में combine किए जाने से पहले overlap करने की अनुमति है।

डिफ़ॉल्ट: 30

इस parameter का RF-DETR models पर कोई प्रभाव नहीं पड़ता।

| | confidence | number |

0-100 के पैमाने पर लौटाए गए predictions के लिए एक threshold। कम संख्या अधिक predictions लौटाएगी। अधिक संख्या कम, लेकिन अधिक निश्चित predictions लौटाएगी।

डिफ़ॉल्ट: 40

| | api\_key | string | आपकी API key (workspace API settings page के माध्यम से प्राप्त) | #### अनुरोध बॉडी | Name | प्रकार | विवरण | | ---- | ------ | ----------------------------------------------------------------------------------------------- | | | string | एक base64 encoded image। (जब आप query parameters में image URL नहीं देते हैं, तब यह आवश्यक है.) | {% tabs %} {% tab title="200 JSON प्रारूप की भविष्यवाणियाँ। (x,y) बॉक्स के" %} ``` { "predictions": [{ "x": 234.0, "y": 363.5, "width": 160, "height": 197, "class": "hand", "confidence": 0.943 }, { "x": 504.5, "y": 363.0, "width": 215, "height": 172, "class": "hand", "confidence": 0.917 }, { "x": 1112.5, "y": 691.0, "width": 139, "height": 52, "class": "hand", "confidence": 0.87 }, { "x": 78.5, "y": 700.0, "width": 139, "height": 34, "class": "hand", "confidence": 0.404 }] } ``` {% endtab %} {% tab title="403 यदि आपका api\_key model तक पहुँचने के लिए अधिकृत नहीं है।" %} ``` { "Message": "उपयोगकर्ता को इस संसाधन तक पहुँचने की अनुमति नहीं है" } ``` {% endtab %} {% endtabs %} --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.roboflow.com/roboflow/roboflow-hi/deploy/serverless/instance-segmentation.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.