Update app.py

app.py

@@ -44,14 +44,9 @@ def data_ingestion():
     #create embeddings here
     embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
     vectordb = FAISS.from_documents(splits, embeddings)
-    
-    
-    # #create vector store here
-    # db = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory, client_settings=CHROMA_SETTINGS)
-    # db.persist()
-    # db=None 
-
+    vectordb.save_local("faiss_index")
 
+    
 @st.cache_resource
 def qa_llm():
     pipe = pipeline(
@@ -68,23 +63,24 @@ def qa_llm():
     llm = HuggingFacePipeline(pipeline=pipe)
     embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
     
-    db = Chroma(persist_directory="db", embedding_function = embeddings, client_settings=CHROMA_SETTINGS)
+    vectordb = FAISS.load_local("faiss_index", embeddings)
     retriever = db.as_retriever()
-    qa = RetrievalQA.from_chain_type(
-        llm = llm,
-        chain_type = "stuff",
-        retriever = retriever,
-        return_source_documents=True
+    
+    # Build a QA chain
+    qa_chain = RetrievalQA.from_chain_type(
+        llm=llm,
+        chain_type="stuff",
+        retriever=vectordb.as_retriever(),
     )
-    return qa
+    return qa_chain
 
 def process_answer(instruction):
     response = ''
     instruction = instruction
-    qa = qa_llm()
-    generated_text = qa(instruction)
-    answer = generated_text['result']
-    return answer
+    qa_chain = qa_llm()
+    
+    generated_text = qa_chain.run(instruction)
+    return generated_text
 
 def get_file_size(file):
     file.seek(0, os.SEEK_END)
@@ -162,11 +158,5 @@ def main():
                 display_conversation(st.session_state)
         
 
-        
-
-
-
-
 if __name__ == "__main__":
     main()
-