added langchain app for simplicity

langchainapp.py

@@ -78,7 +78,8 @@ class MyEmbeddingFunction(EmbeddingFunction):
         embeddings_flattened = [emb for sublist in embeddings for emb in sublist]  
         metadata_flattened = [meta for sublist in metadata for meta in sublist]  
         return embeddings_flattened, metadata_flattened  
-  
+    
+    @spaces.GPU  
     def compute_embeddings(self, input_text: str):  
         escaped_input_text = escape_special_characters(input_text)  
           
@@ -94,14 +95,14 @@ class MyEmbeddingFunction(EmbeddingFunction):
         parsed_task = parse_and_route(intention_output)  
         selected_task = parsed_task if parsed_task in tasks else "DEFAULT"  
         task_description = tasks[selected_task]  
-          
+        # query_prefix = "Instruct: " +tasks[selected_task] +"\nQuery: "  
         # Construct the embed_instruction and query_instruction dynamically  
-        embed_instruction = f"Represent the document for retrieval: {task_description}"  
-        query_instruction = f"Represent the query for retrieval: {task_description}"  
+        embed_instruction = f"Instruct: {task_description}" +"\nQuery:"  
+        query_instruction = f""  
           
         # Update the hf_embeddings object with the new instructions  
         self.hf_embeddings.embed_instruction = embed_instruction  
-        self.hf_embeddings.query_instruction = query_instruction  
+        # self.hf_embeddings.query_instruction = query_instruction  
           
         # Get the metadata  
         metadata_completion = self.intention_client.chat.completions.create(