Add configurable NVIDIA model support

nvidia/txt2kg/assets/frontend/app/api/extract-triples/route.ts

@@ -29,7 +29,8 @@ export async function POST(req: NextRequest) {
       ollamaModel,
       ollamaBaseUrl,
       vllmModel,
-      vllmBaseUrl
+      vllmBaseUrl,
+      nvidiaModel
     } = body;
 
     if (!text || typeof text !== 'string') {
@@ -136,7 +137,8 @@ export async function POST(req: NextRequest) {
         ollamaModel: ollamaModel,
         ollamaBaseUrl: ollamaBaseUrl,
         vllmModel: vllmModel,
-        vllmBaseUrl: vllmBaseUrl
+        vllmBaseUrl: vllmBaseUrl,
+        nvidiaModel: nvidiaModel
       });
     }
 

nvidia/txt2kg/assets/frontend/contexts/document-context.tsx

@@ -379,7 +379,8 @@ export function DocumentProvider({ children }: { children: React.ReactNode }) {
           console.log(`🦙 Using Ollama model: ${requestBody.ollamaModel}`);
         } else if (model.id === "nvidia-nemotron" || model.id === "nvidia-nemotron-nano") {
           requestBody.llmProvider = "nvidia";
-          console.log(`🖥️ Using NVIDIA model: ${model.id}`);
+          requestBody.nvidiaModel = model.model; // Pass the actual model name
+          console.log(`🖥️ Using NVIDIA model: ${model.model}`);
         }
       } catch (e) {
         // Ignore parsing errors, will use default
@@ -498,8 +499,51 @@ export function DocumentProvider({ children }: { children: React.ReactNode }) {
     setIsProcessing(true);
     
     try {
+      // Check which documents are already processed in ArangoDB
+      console.log('🔍 Checking which documents are already processed in ArangoDB...');
+      let alreadyProcessedDocs: Set<string> = new Set();
+      
+      try {
+        const response = await fetch('/api/graph-db/check-document', {
+          method: 'POST',
+          headers: { 'Content-Type': 'application/json' },
+          body: JSON.stringify({
+            documentNames: docsToProcess.map(d => d.name)
+          })
+        });
+        
+        if (response.ok) {
+          const result = await response.json();
+          if (result.processedDocuments) {
+            Object.entries(result.processedDocuments).forEach(([docName, isProcessed]) => {
+              if (isProcessed) {
+                alreadyProcessedDocs.add(docName);
+              }
+            });
+            console.log(`✅ Found ${alreadyProcessedDocs.size} documents already processed in ArangoDB:`, Array.from(alreadyProcessedDocs));
+          }
+        }
+      } catch (checkError) {
+        console.warn('⚠️ Could not check for already processed documents, continuing anyway:', checkError);
+      }
+
       // Process each document sequentially
       for (const doc of docsToProcess) {
+        // Skip if document is already processed in ArangoDB
+        if (alreadyProcessedDocs.has(doc.name)) {
+          console.log(`⏭️ Skipping document "${doc.name}" - already processed in ArangoDB`);
+          updateDocumentStatus(doc.id, "Processed", {
+            triples: doc.triples || [],
+            graph: doc.graph,
+            error: undefined
+          });
+          toast({
+            title: "Document Skipped",
+            description: `"${doc.name}" is already stored in ArangoDB`,
+            duration: 3000,
+          });
+          continue;
+        }
         // Update status to Processing before we begin
         updateDocumentStatus(doc.id, "Processing");
         

nvidia/txt2kg/assets/frontend/lib/text-processor.ts

@@ -76,6 +76,7 @@ export class TextProcessor {
   private ollamaBaseUrl: string = 'http://localhost:11434/v1';
   private vllmModel: string = 'meta-llama/Llama-3.2-3B-Instruct';
   private vllmBaseUrl: string = 'http://localhost:8001/v1';
+  private nvidiaModel: string = 'nvidia/llama-3.3-nemotron-super-49b-v1.5'; // Default NVIDIA model
 
   private constructor() {
     this.sentenceTransformerUrl = process.env.SENTENCE_TRANSFORMER_URL || "http://localhost:8000";
@@ -343,6 +344,7 @@ Text: ${chunk}
 ${formatInstructions}`;
 
         // Call NVIDIA API directly using fetch
+        console.log(`🖥️ Calling NVIDIA API with model: ${this.nvidiaModel}`);
         const response = await fetch('https://integrate.api.nvidia.com/v1/chat/completions', {
           method: 'POST',
           headers: {
@@ -350,7 +352,7 @@ ${formatInstructions}`;
             'Authorization': `Bearer ${apiKey}`
           },
           body: JSON.stringify({
-            model: 'nvidia/llama-3.3-nemotron-super-49b-v1.5',
+            model: this.nvidiaModel, // Use the configured model
             messages: [
               {
                 role: 'user',
@@ -561,11 +563,15 @@ ${formatInstructions}`;
     ollamaBaseUrl?: string;
     vllmModel?: string;
     vllmBaseUrl?: string;
+    nvidiaModel?: string;
   }): void {
     this.selectedLLMProvider = provider;
     if (provider === 'ollama') {
       this.ollamaModel = options?.ollamaModel || this.ollamaModel;
       this.ollamaBaseUrl = options?.ollamaBaseUrl || this.ollamaBaseUrl;
+    } else if (provider === 'nvidia') {
+      this.nvidiaModel = options?.nvidiaModel || this.nvidiaModel;
+      console.log(`🖥️ TextProcessor: NVIDIA model set to: ${this.nvidiaModel}`);
     } else if (provider === 'vllm') {
       this.vllmModel = options?.vllmModel || this.vllmModel;
       this.vllmBaseUrl = options?.vllmBaseUrl || this.vllmBaseUrl;