Update app.py

app.py

@@ -1,14 +1,12 @@
-# Importing necessary modules for embeddings and vector database storage.
-#from langchain_chroma import Chroma
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_chroma import Chroma
 # Importing RunnableLambda for wrapping functions as runnable components.
-#from langchain_core.runnables import RunnableLambda
-#from langchain_core.prompts import PromptTemplate                                    # To format prompts
-#from langchain_core.output_parsers import StrOutputParser                            # to transform the output of an LLM into a more usable format
-#from langchain.schema.runnable import RunnableParallel, RunnablePassthrough 
+from langchain_core.runnables import RunnableLambda
+from langchain_core.prompts import PromptTemplate                                    # To format prompts
+from langchain_core.output_parsers import StrOutputParser                            # to transform the output of an LLM into a more usable format
+from langchain.schema.runnable import RunnableParallel, RunnablePassthrough 
 # importing HuggingFace model abstraction class from langchain
-from langchain_huggingface.llms import HuggingFacePipeline
-from langchain.llms import HuggingFaceEndpoint
-from sentence_transformers import SentenceTransformer
+from langchain_huggingface import HuggingFaceEndpoint
 
 import torch
 import gradio
@@ -28,9 +26,14 @@ llm = HuggingFaceEndpoint(
     repetition_penalty=1.03,  # Slightly penalizes repeated tokens to reduce redundancy in responses.
 )
 
-# Load the embedding model
+# Initializing the Hugging Face embedding model.
+embedding =  HuggingFaceEmbeddings(
+    model_name="mixedbread-ai/mxbai-embed-large-v1",  # Using a pre-trained embedding model for text vectorization.
 
-embedding = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1")
+    model_kwargs={'device': "cuda" if torch.cuda.is_available() else "cpu"},  # Configuring the model to use GPU if available.
+
+    encode_kwargs={'normalize_embeddings': False}  # Disabling normalization to preserve the original embedding scale.
+)
 
 # Creating a Chroma vector database instance.
 vectordb = Chroma(