Adding Langchain QA

app.py

@@ -6,9 +6,10 @@ from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.embeddings import SentenceTransformerEmbeddings
 from langchain.vectorstores import FAISS
 from langchain import HuggingFaceHub
+from langchain.chains import RetrievalQA
+from langchain.prompts import PromptTemplate
 
-
-DEVICE = 'cpu '
+DEVICE = 'cpu'
 FILE_EXT = ['pdf','text','csv','word','wav']
 
 
@@ -36,14 +37,14 @@ def get_hugging_face_model(model_id,API_key,temperature=0.1):
                                       model_kwargs={"temperature": temperature, "max_new_tokens": 2048})
     return chat_llm
 
-def chat_application(llm_model,key):
+def chat_application(llm_service,key):
     if llm_model == 'HuggingFace':
         llm = get_hugging_face_model(model_id='tiiuae/falcon-7b-instruct',API_key=key)
     else:
         llm_model = get_openai_chat_model(API_key=key)
 
 
-def document_loader(file_data,doc_type='pdf',key=None):
+def document_loader(file_data,api_key,doc_type='pdf',llm='Huggingface'):
     embedding_model = SentenceTransformerEmbeddings(model_name='all-mpnet-base-v2',model_kwargs={"device": DEVICE})
     document = None
     if doc_type == 'pdf':
@@ -54,15 +55,21 @@ def document_loader(file_data,doc_type='pdf',key=None):
         document = process_csv_document(document_file_name=file_data)
     elif doc_type == 'word':
         document = process_word_document(document_file_name=file_data)
-    
+    print(document)
     if document:
         texts = process_documents(documents=document)
-        global vectordb
-        vectordb = FAISS.from_documents(documents=texts, embedding= embedding_model)
+        vector_db = FAISS.from_documents(documents=texts, embedding= embedding_model)
+        global qa 
+        qa = RetrievalQA.from_chain_type(llm=chat_application(llm_service=llm,key=api_key),
+                                     chain_type='stuff',
+                                     retriever=vector_db.as_retriever(),
+                                    #  chain_type_kwargs=chain_type_kwargs,
+                                     return_source_documents=True
+                                     )
     else:
         return "Error in loading Documents "
     
-    return "Document loaded - Embeddings ready "
+    return "Ready..."
 
         
 def process_text_document(document_file_name):
@@ -125,6 +132,7 @@ with gr.Blocks(css=css) as demo:
         submit_button = gr.Button("Send Message")
     load_pdf.click(loading_file, None, langchain_status, queue=False)    
     load_pdf.click(document_loader, inputs=[pdf_doc,file_extension,API_key], outputs=[langchain_status], queue=False)
+    
     # question.submit(add_text, [chatbot, question], [chatbot, question]).then(
     #     bot, chatbot, chatbot
     # )