Update app.py

app.py

@@ -41,15 +41,15 @@ recursive_framework_document = metadata_generator(ai_framework_document, "AI Fra
 recursive_blueprint_document = metadata_generator(ai_blueprint_document, "AI Blueprint")
 combined_documents = recursive_framework_document + recursive_blueprint_document
 
-"""
-#from transformers import AutoTokenizer, AutoModel
-#import torch
-#embedding = AutoModel.from_pretrained("Cheselle/finetuned-arctic-sentence")
-#tokenizer = AutoTokenizer.from_pretrained("Cheselle/finetuned-arctic-sentence")
 
-from sentence_transformers import SentenceTransformer
-embedding_model = SentenceTransformer("Cheselle/finetuned-arctic-sentence")
-embeddings = embedding_model.encode(ai_framework_document + ai_blueprint_document)
+from transformers import AutoTokenizer, AutoModel
+import torch
+embeddings = AutoModel.from_pretrained("Cheselle/finetuned-arctic-sentence")
+tokenizer = AutoTokenizer.from_pretrained("Cheselle/finetuned-arctic-sentence")
+
+#from sentence_transformers import SentenceTransformer
+#embedding_model = SentenceTransformer("Cheselle/finetuned-arctic-sentence")
+#embeddings = embedding_model.encode(ai_framework_document + ai_blueprint_document)
 
 vectorstore = Qdrant.from_documents(
     documents=combined_documents,
@@ -57,41 +57,7 @@ vectorstore = Qdrant.from_documents(
     location=":memory:",
     collection_name="ai_policy"
 )
-"""
-
-from transformers import AutoTokenizer, AutoModel
-import torch
-from qdrant_client import QdrantClient
-
-# Load the tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained("Cheselle/finetuned-arctic-sentence")
-model = AutoModel.from_pretrained("Cheselle/finetuned-arctic-sentence")
-
-# Define a wrapper function for embedding documents
-def embed(documents):
-    inputs = tokenizer(documents, return_tensors="pt", padding=True, truncation=True)
-    with torch.no_grad():
-        outputs = model(**inputs)
-        return outputs.last_hidden_state.mean(dim=1).numpy()  # Return embeddings
-
-# Initialize Qdrant client (in-memory for testing)
-qdrant_client = QdrantClient(":memory:")
-
-# Create the Qdrant collection
-qdrant_client.recreate_collection(
-    collection_name="ai_policy",
-    vectors_config={"size": 768, "distance": "Cosine"}  # Adjust size based on embedding dimensions
-)
-
 
-# Create vectorstore (with embedding function)
-vectorstore = qdrant_client.upsert(
-    collection_name="ai_policy",
-    points=[
-        {"id": i, "vector": embed([doc])[0], "payload": {"document": doc}}
-        for i, doc in enumerate(combined_documents)
-    ]
-)
 retriever = vectorstore.as_retriever()
 
 ## Generation LLM