> ## Documentation Index > Fetch the complete documentation index at: https://docs.dscribeai.com/llms.txt > Use this file to discover all available pages before exploring further. # Analyze > Extract insights and summaries from rich-media content ### Overview The Analyze API extracts meaningful insights, summaries, and custom information from video and audio content across major social media platforms. Simply provide a media URL and a question about the content, and the API will analyze the visual elements (if applicable) and transcriptions to deliver key takeaways and structured insights. ### Features * **Markdown Format**: Receive analysis in formatted markdown, ideal for LLM applications. * **Video Summaries**: Generate concise summaries of video content for quick insights. * **Custom Queries**: Ask specific questions about the content to extract relevant information. * **Brand Detection**: Automatically identify brands displayed or mentioned in the content. * **Full Transcription**: Includes transcription and all its features for deeper analysis. * **Platform Metadata**: Optionally retrieve media metrics such as upload time, like count, comment count,and thumbnail URL. * **Webhook Support**: Get real-time notifications when the analysis is complete. ### Basic Usage To analyze content, simply add the media URL to the `post_url` parameter. You can optionally include a `query` to ask specific questions about the content, and a `callback_url` to receive notifications when the analysis is complete. ```bash cURL theme={null} curl --request POST \ --url https://api.scribesocial.ai/v1/analyze \ --header 'Authorization: Bearer ' \ --header 'Content-Type: application/json' \ --data '{ "post_url": "", "callback_url": "", "query": null }' ``` ```python Python theme={null} import requests url = "https://api.scribesocial.ai/v1/analyze" payload = { "post_url": "", "callback_url": "", "query": None } headers = { "Authorization": "Bearer ", "Content-Type": "application/json" } response = requests.request("POST", url, json=payload, headers=headers) print(response.text) ``` ```javascript JavaScript theme={null} const options = { method: 'POST', headers: {Authorization: 'Bearer ', 'Content-Type': 'application/json'}, body: '{"post_url":"","callback_url":"","query":null}' }; fetch('https://api.scribesocial.ai/v1/analyze', options) .then(response => response.json()) .then(response => console.log(response)) .catch(err => console.error(err)); ``` ```php PHP theme={null} "https://api.scribesocial.ai/v1/analyze", CURLOPT_RETURNTRANSFER => true, CURLOPT_ENCODING => "", CURLOPT_MAXREDIRS => 10, CURLOPT_TIMEOUT => 30, CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1, CURLOPT_CUSTOMREQUEST => "POST", CURLOPT_POSTFIELDS => "{\n \"post_url\": \"\",\n \"callback_url\": \"\",\n \"query\": null\n}", CURLOPT_HTTPHEADER => [ "Authorization: Bearer ", "Content-Type: application/json" ], ]); $response = curl_exec($curl); $err = curl_error($curl); curl_close($curl); if ($err) { echo "cURL Error #:" . $err; } else { echo $response; } ``` ```go Go theme={null} package main import ( "fmt" "strings" "net/http" "io/ioutil" ) func main() { url := "https://api.scribesocial.ai/v1/analyze" payload := strings.NewReader("{\n \"post_url\": \"\",\n \"callback_url\": \"\",\n \"query\": null\n}") req, _ := http.NewRequest("POST", url, payload) req.Header.Add("Authorization", "Bearer ") req.Header.Add("Content-Type", "application/json") res, _ := http.DefaultClient.Do(req) defer res.Body.Close() body, _ := ioutil.ReadAll(res.Body) fmt.Println(res) fmt.Println(string(body)) } ``` ```java Java theme={null} HttpResponse response = Unirest.post("https://api.scribesocial.ai/v1/analyze") .header("Authorization", "Bearer ") .header("Content-Type", "application/json") .body("{\n \"post_url\": \"\",\n \"callback_url\": \"\",\n \"query\": null\n}") .asString(); ``` #### Checking Analysis Status (Async Only) If you're using a `callback_url`, the API response will include a `Location` header containing the operation polling URL, which you can use to check the status of the analysis. Example Operation Polling URL: `https://api.scribesocial.ai/v1/transcribe-result/{operation-id}` When the callback request is sent to your endpoint, it will include a `Scribe-Verification-Token` header. This token corresponds to the API key ID used for the original request, allowing you to verify that the callback is coming from dScribe AI and is legitimate. ### Example Response ```json theme={null} { "status": "completed", "operation_id": "abc123xyz789", "data": [ { "video_summary": "In this video, the speaker provides a comprehensive introduction to machine learning fundamentals, covering basic concepts and practical applications in the healthcare industry...", "query_result": { "answer": "The main topics discussed are: 1) Introduction to ML concepts, 2) Healthcare applications, 3) Implementation challenges, 4) Future trends" }, "detected_brands": [ { "name": "TensorFlow", "description": "An open-source machine learning framework" }, { "name": "PyTorch", "description": "Deep learning framework developed by Meta AI" } ], "video_url": "https://www.youtube.com/watch?v=example1", "post_url": "https://www.youtube.com/watch?v=example1", "description": "Part 1 of the machine learning tutorial series", "transcription": "Welcome to part one of our machine learning series...", "paragraphs": [ { "sentences": [ { "text": "Welcome to part one of our machine learning series.", "start": 0, "end": 3.2 }, { "text": "Today we'll cover the fundamental concepts.", "start": 3.2, "end": 6.5 } ], "num_words": 18, "start": 0, "end": 6.5 } ], "video_id": "example1", "transcribed_duration": 180, "total_duration": 180, "status": "completed", "platform": "youtube", "title": "Machine Learning Fundamentals - Part 1", "metadata": { "upload_time": "2024-01-15T10:30:00Z", "comment_count": 423, "like_count": 892, "thumbnail": "https://i.ytimg.com/vi/example1/maxresdefault.jpg", "retweet_count": null, "quote_count": null } } ], "markdown": "# Machine Learning Fundamentals - Part 1\n\n## Introduction (00:00)\nWelcome to part one of our machine learning series...\n\n## Key Concepts (01:23)\nLet's discuss the fundamental principles..." } ``` For more details about request parameters, response fields, and status codes, check out our [Analyze API Reference](/api-reference/endpoint/analyze). ### Common Use Cases 1. **LLM Applications**: Enrich LLMs with structured visual insights and transcriptions to improve contextual understanding, retrieval, and summarization. 2. **Social Listening & Brand Monitoring**: Track brand mentions, logos, and product placements in both video frames and spoken dialogue, enabling deeper sentiment and trend analysis. 3. **Content Intelligence & Analysis**: Extract structured insights from visual elements and spoken words, categorizing information for better decision-making. 4. **Content Indexing & Searchability**: Make video and audio content searchable by generating transcriptions and tagging visual elements such as objects, faces, and text overlays. 5. **Compliance & Moderation**: Ensure compliance by analyzing both visual and audio content for policy violations, explicit material, and copyright concerns. 6. **Custom Queries & Deep Video Understanding**: Ask specific questions about both the visual and spoken content to extract targeted insights, such as identifying key moments, detecting logos, or summarizing actions.