Update app.py

app.py

@@ -13,6 +13,7 @@ from langchain_chroma import Chroma
 from langchain_community.document_loaders import TextLoader
 from langchain_community.embeddings.sentence_transformer import (SentenceTransformerEmbeddings,)
 from langchain_text_splitters import CharacterTextSplitter
+from langchain.chains import RetrievalQA
 
 #import vertexai
 #from langchain.llms import VertexAI
@@ -25,7 +26,7 @@ from langchain_text_splitters import CharacterTextSplitter
 #Text models can create include document summaries, answers to questions, and labels that classify content.
 
 llm = HuggingFaceEndpoint(repo_id="mistralai/Mistral-7B-Instruct-v0.2", Temperature=0.9)
-model = SentenceTransformer("all-MiniLM-L6-v2")
+#model = SentenceTransformer("all-MiniLM-L6-v2")
 
 #llm = VertexAI(model_name="text-bison@001",max_output_tokens=256,temperature=0.1,top_p=0.8,top_k=40,verbose=True,)
     
@@ -59,7 +60,6 @@ def create_langchain_index(input_text):
     # split it into chunks
     text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
     docs = text_splitter.split_documents(documents)
-    print(docs)
     # create the open-source embedding function
     embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
     # load it into Chroma
@@ -84,7 +84,9 @@ def create_langchain_index(input_text):
 @st.cache_data
 def get_response(input_text,query):
     print(f"--querying---{query}")
-    response = index.query(query,llm=llm)
+    retrieval_chain = RetrievalQA.from_chain_type(llm, chain_type="stuff", retriever=new_db.as_retriever())
+    response = retrieval_chain.run(query)
+    #response = index.query(query,llm=llm)
     return response
 
 #The below code is a simple flow to accept the webpage link and process the queries