Update app.py

app.py

@@ -10,6 +10,8 @@ from langchain.chains import ConversationalRetrievalChain
 from htmlTemplates import css, bot_template, user_template
 from langchain_community.llms import HuggingFaceHub
 import os
+from sentence_transformers import SentenceTransformer
+
 # from huggingface_hub import login
 
 # Retrieve the Hugging Face token from environment variables
@@ -33,11 +35,20 @@ def get_text_chunks(text):
   return chunks
 
 # token="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1"
+# def get_vectorstore(text_chunks):
+#   # embeddings=HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl",huggingfacehub_token=os.getenv("TOKEN_API2"))
+#   embeddings=HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
+#   vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+#   return vectorstore
+
 def get_vectorstore(text_chunks):
-  # embeddings=HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl",huggingfacehub_token=os.getenv("TOKEN_API2"))
-  embeddings=HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
-  vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
-  return vectorstore
+    # Load a SentenceTransformer model for embeddings
+    embedding_model = SentenceTransformer("hkunlp/instructor-xl")  # Replace with a model of your choice
+    embeddings = [embedding_model.encode(chunk) for chunk in text_chunks]
+
+    # Create a FAISS vectorstore
+    vectorstore = FAISS.from_embeddings(embeddings=embeddings, texts=text_chunks)
+    return vectorstore
 
 def get_conversation_chain(vectorstore):
   llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512})