added global embedding

backend/score.py

@@ -446,7 +446,7 @@ async def generate():
                     graph = create_graph_database_connection(uri, userName, decoded_password, database)
                     graphDb_data_Access = graphDBdataAccess(graph)
                     result = graphDb_data_Access.get_current_status_document_node(file_name)
-                    print(f'Result of document status in SSE : {result}')
+                    # print(f'Result of document status in SSE : {result}')
                     if len(result) > 0:
                         status = json.dumps({'fileName':file_name, 
                         'status':result[0]['Status'],

backend/src/make_relationships.py

@@ -9,6 +9,9 @@
 
 logging.basicConfig(format='%(asctime)s - %(message)s',level='INFO')
 
+EMBEDDING_MODEL = os.getenv('EMBEDDING_MODEL')
+EMBEDDING_FUNCTION , EMBEDDING_DIMENSION = load_embedding_model(EMBEDDING_MODEL)
+
 def merge_relationship_between_chunk_and_entites(graph: Neo4jGraph, graph_documents_chunk_chunk_Id : list):
     batch_data = []
     logging.info("Create HAS_ENTITY relationship between chunks and entities")
@@ -39,9 +42,9 @@ def merge_relationship_between_chunk_and_entites(graph: Neo4jGraph, graph_docume
 def update_embedding_create_vector_index(graph, chunkId_chunkDoc_list, file_name):
     #create embedding
     isEmbedding = os.getenv('IS_EMBEDDING')
-    embedding_model = os.getenv('EMBEDDING_MODEL')
+    # embedding_model = os.getenv('EMBEDDING_MODEL')
 
-    embeddings, dimension = load_embedding_model(embedding_model)
+    embeddings, dimension = EMBEDDING_FUNCTION , EMBEDDING_DIMENSION
     logging.info(f'embedding model:{embeddings} and dimesion:{dimension}')
     data_for_query = []
     logging.info(f"update embedding and vector index for chunks")