Merge pull request #29 from kamyabnazari/kn-trying-deployment

Adding deployed functions

backend/README.md

@@ -8,12 +8,12 @@ Create a `.env` file in the root of the backend directory with the following var
 
 ```
 OPENAI_API_KEY={your openai api key}
-QDRANT_API_KEY={your qdrant api key}
 POCKETBASE_ADMIN_EMAIL={your pocketbase admin email}
 POCKETBASE_ADMIN_PASSWORD={your pocketbase admin password}
 PUBLIC_POCKETBASE_URL=http://localhost:8090
 PUBLIC_FRONTEND_URL=http://localhost:5173
 PUBLIC_QDRANT_URL=http://localhost:6333
+QDRANT__SERVICE_API_KEY={Only for Deployed Qdrant}
 ```
 
 ### Running the backend

backend/chat_bot_function.py

@@ -18,7 +18,14 @@ def make_chain(documentId:str):
 
     embeddings = OpenAIEmbeddings()
 
-    client = QdrantClient(os.getenv('PUBLIC_QDRANT_URL'))
+    url = os.getenv('PUBLIC_QDRANT_URL')
+    api_key = os.getenv('QDRANT__SERVICE_API_KEY')   
+
+    if api_key:
+        client = QdrantClient(url=url, prefer_grpc=True, api_key=api_key)
+    else:
+        client = QdrantClient(url=url)
+
     qdrant = Qdrant(client, documentId, embeddings)
 
     return ConversationalRetrievalChain.from_llm(

backend/ingest.py

@@ -124,16 +124,27 @@ def create_embeddings_from_pdf_file(file_path: str, documentId: str):
 
     # Step 3 + 4: Generate embeddings and store them in DB
     embeddings = OpenAIEmbeddings()
-    
+
     url = os.getenv('PUBLIC_QDRANT_URL')
-    api_key = os.getenv('QDRANT__SERVICE_API_KEY')
-
-    qdrant = Qdrant.from_documents(
-        document_chunks,
-        embedding=embeddings,
-        url=url,
-        prefer_grpc=True,
-        api_key=api_key,
-        collection_name=documentId,
-        timeout=120
-    )
+    api_key = os.getenv('QDRANT__SERVICE_API_KEY')   
+
+    if api_key:
+        Qdrant.from_documents(
+            document_chunks,
+            embedding=embeddings,
+            url=url,
+            prefer_grpc=True,
+            api_key=api_key,
+            collection_name=documentId,
+            timeout=120
+        )
+    else:
+        Qdrant.from_documents(
+            document_chunks,
+            embedding=embeddings,
+            url=url,
+            prefer_grpc=False,
+            collection_name=documentId,
+            timeout=120
+        )
+

backend/main.py

@@ -2,6 +2,7 @@
 import pdfkit
 import io
 import os
+from qdrant_client import QdrantClient
 import requests
 
 # Python package for PDF parsing
@@ -80,6 +81,20 @@ async def read_favicon():
 async def read_api_root():
     return {"message": "Welcome to the EE API!"}
 
+@app.post("/api/documents/{document_id}/delete_vector_file")
+async def delete_api_vector_file(document_id: str):
+
+    url = os.getenv('PUBLIC_QDRANT_URL')
+    api_key = os.getenv('QDRANT__SERVICE_API_KEY')   
+
+    if api_key:
+        client = QdrantClient(url=url, prefer_grpc=True, api_key=api_key)
+    else:
+        client = QdrantClient(url=url)
+
+    client.delete_collection(document_id)
+    return {"message": "Vector file deleted!"}
+
 @app.post("/api/documents/{document_id}/send_new_message/{user_id}")
 async def read_api_documents_send_new_message(document_id: str, user_id: str, request: Request):
     # Accessing Request body and converting it to a dictionary

frontend/src/lib/components/FileTable.svelte

@@ -34,8 +34,8 @@
 		try {
 			await pb.collection('documents').delete(documentID);
 			await axios({
-				url: `${env.PUBLIC_QDRANT_URL}/collections/${documentID}`,
-				method: 'delete',
+				url: `${env.PUBLIC_BACKEND_URL}/api/documents/${documentID}/delete_vector_file`,
+				method: 'post',
 				headers: { 'Content-Type': 'application/json' }
 			});
 			documentList = documentList.filter((document) => document.id !== documentID);

frontend/src/lib/components/PDFViewer.svelte

@@ -21,11 +21,15 @@
 
 	let isLoading = true;
 
-	let pdfjsWorkerPromise = import('pdfjs-dist/build/pdf.worker.entry').then((worker) => {
-		let pdfjsWorker = URL.createObjectURL(new Blob([worker], { type: 'application/javascript' }));
-		GlobalWorkerOptions.workerSrc = pdfjsWorker;
-		return pdfjsWorker;
-	});
+	let pdfjsWorkerPromise = import('pdfjs-dist/build/pdf.worker.entry')
+		.then((worker) => {
+			let pdfjsWorker = URL.createObjectURL(new Blob([worker], { type: 'application/javascript' }));
+			GlobalWorkerOptions.workerSrc = pdfjsWorker;
+			return pdfjsWorker;
+		})
+		.catch((error) => {
+			console.error("Error importing 'pdfjs-dist/build/pdf.worker.entry':", error);
+		});
 
 	afterUpdate(() => {
 		previousDocumentUrl = generatedDocumentURL;

frontend/src/routes/(auth)/profile/+page.server.ts

@@ -39,8 +39,8 @@ export const actions: Actions = {
 			documentList.forEach(async (document: Record) => {
 				// delete all user embeddings from Qdrant
 				await axios({
-					url: `${env.PUBLIC_QDRANT_URL}/collections/${document.id}`,
-					method: 'delete',
+					url: `${env.PUBLIC_BACKEND_URL}/api/documents/${document.id}/delete_vector_file`,
+					method: 'post',
 					headers: { 'Content-Type': 'application/json' },
 					httpAgent: new http.Agent({ family: 4 }), // Force IPv4
 					httpsAgent: new https.Agent({ family: 4 }) // Force IPv4