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added llm endpoint calling logic

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This commit is contained in:
SatyamSah5
2025-09-04 11:49:27 +05:30
parent 4ca4f2cc00
commit f1be2a07c6
1 changed files with 31 additions and 38 deletions
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69
repository/src/main/java/org/alfresco/repo/action/executer/AISummaryActionExecuter.java
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@@ -128,7 +128,7 @@ public class AISummaryActionExecuter extends ActionExecuterAbstractBase
ContentReader txtReader = tempWriter.getReader();
try (InputStream is = txtReader.getContentInputStream()) {
String textString = new String(is.readAllBytes());
String aiResult = sendToAIEndpoint(is);
String aiResult = sendToAIEndpoint(textString);
QName AI_SUMMARY_PROP = QName.createQName(CONTENT_MODEL_1_0_URI, "AiSummary");
nodeService.setProperty(actionedUponNodeRef, AI_SUMMARY_PROP, aiResult);
// Optionally, store or log the result
@@ -141,44 +141,37 @@ public class AISummaryActionExecuter extends ActionExecuterAbstractBase
* Placeholder for sending content to an AI endpoint.
* Implement actual HTTP call or integration as needed.
*/
private String sendToAIEndpoint(InputStream txtContent) throws Exception {
// Example: read content, send to AI, return result
// Implement actual HTTP client logic here
private String sendToAIEndpoint(String txtContent) throws Exception {
// Read input stream to string
// String inputText = new String(txtContent.readAllBytes(), StandardCharsets.UTF_8);
//
// // Prepare JSON payload
// String payload = "{ \"inputs\": " + escapeJson(inputText) + " }";
//
// // Create connection
// URL url = new URL("https://api-inference.huggingface.co/models/gpt2");
// HttpURLConnection conn = (HttpURLConnection) url.openConnection();
// conn.setRequestMethod("POST");
// conn.setRequestProperty("Authorization", "Bearer " + HUGGING_FACE_TOKEN);
// conn.setRequestProperty("Content-Type", "application/json");
// conn.setDoOutput(true);
//
// // Send request
// try (OutputStream os = conn.getOutputStream()) {
// os.write(payload.getBytes(StandardCharsets.UTF_8));
// }
//
// // Read response
// int status = conn.getResponseCode();
// InputStream responseStream = (status >= 200 && status < 300) ? conn.getInputStream() : conn.getErrorStream();
// String response = new String(responseStream.readAllBytes(), StandardCharsets.UTF_8);
//
// // Simple extraction of generated text (not robust, for demo)
// int start = response.indexOf("\"generated_text\":\"");
// if (start != -1) {
// start += 18;
// int end = response.indexOf("\"", start);
// if (end != -1) {
// return response.substring(start, end);
// }
// }
// return "AI summary unavailable";
return "AI summary or insights result";
// Build JSON payload
String payload = "{"
+ "\"context\": " + escapeJson(txtContent) + ","
+ "\"prompt\": \"provide concise summary\""
+ "}";
// Create connection
URL url = new URL("http://alfresco-llm-ai:5000/api/respond");
HttpURLConnection conn = (HttpURLConnection) url.openConnection();
conn.setRequestMethod("POST");
conn.setRequestProperty("Content-Type", "application/json");
conn.setDoOutput(true);
// Send request
try (OutputStream os = conn.getOutputStream()) {
os.write(payload.getBytes(StandardCharsets.UTF_8));
}
// Read response
int status = conn.getResponseCode();
InputStream responseStream = (status >= 200 && status < 300) ? conn.getInputStream() : conn.getErrorStream();
String jsonResponse = new String(responseStream.readAllBytes(), StandardCharsets.UTF_8);
// Parse JSON and extract the "response" field
org.json.JSONObject obj = new org.json.JSONObject(jsonResponse);
String summary = obj.optString("response", "");
return summary.trim();
}
// Helper to escape JSON string