Update appChatbot.py

appChatbot.py

@@ -6,8 +6,9 @@ import sys
 import gradio as gr
 from huggingface_hub import InferenceClient
 
+from langchain_huggingface import HuggingFaceEmbeddings
 #from chromadb.utils import embedding_functions
-from langchain_community.embeddings import SentenceTransformerEmbeddings
+#from langchain_community.embeddings import SentenceTransformerEmbeddings
 
 from langchain.text_splitter import CharacterTextSplitter
 from langchain.embeddings import OpenAIEmbeddings
@@ -20,6 +21,13 @@ For more information on `huggingface_hub` Inference API support, please check th
 """
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
+# Select which embeddings we want to use
+#embeddings = OpenAIEmbeddings()
+#embeddings = SentenceTransformerEmbeddings(model_name="nomic-ai/nomic-embed-text-v1", model_kwargs={"trust_remote_code":True}) 
+
+embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+
+
 ABS_PATH = os.path.dirname(os.path.abspath(__file__))
 DB_DIR = os.path.join(ABS_PATH, "db")
 
@@ -50,9 +58,6 @@ def init_chromadb():
     # Split the documents into chunks
     text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
     texts = text_splitter.split_documents(documents)
-    # Select which embeddings we want to use
-    #embeddings = OpenAIEmbeddings()
-    embeddings = SentenceTransformerEmbeddings(model_name="nomic-ai/nomic-embed-text-v1", model_kwargs={"trust_remote_code":True}) 
     #query_chromadb()
 
     # Create the vectorestore to use as the index
@@ -65,9 +70,6 @@ def query_chromadb(ASK):
     if not os.path.exists(DB_DIR):
         raise Exception(f"{DB_DIR} does not exist, nothing can be queried")
 
-    # Select which embeddings we want to use
-    #embeddings = OpenAIEmbeddings()
-    embeddings = SentenceTransformerEmbeddings(model_name="nomic-ai/nomic-embed-text-v1", model_kwargs={"trust_remote_code":True}) 
     # Load Vector store from local disk
     vectorstore = Chroma(persist_directory=DB_DIR, embedding_function=embeddings)