Add handling multiple PDFs

app.py

@@ -3,31 +3,48 @@ import os
 import time
 
 from langchain.document_loaders import OnlinePDFLoader
-
 from langchain.text_splitter import CharacterTextSplitter
-
-
 from langchain.llms import OpenAI
-
 from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.chains import ConversationalRetrievalChain
+from langchain import PromptTemplate
 
 
-from langchain.vectorstores import Chroma
+_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.
+Chat History:
+{chat_history}
+Follow Up Input: {question}
+Standalone question:"""
 
-from langchain.chains import ConversationalRetrievalChain
+CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
+
+template = """
+You are given the following extracted parts of a long document and a question. Provide a short structured answer.
+If you don't know the answer, look on the web. Don't try to make up an answer.
+Question: {question}
+=========
+{context}
+=========
+Answer in Markdown:"""
 
 def loading_pdf():
     return "Loading..."
 
-def pdf_changes(pdf_doc, open_ai_key):
+
+def pdf_changes(pdf_docs, open_ai_key):
     if openai_key is not None:
         os.environ['OPENAI_API_KEY'] = open_ai_key
-        loader = OnlinePDFLoader(pdf_doc.name)
-        documents = loader.load()
+        documents = []
+        for file in pdf_docs:
+            loader = OnlinePDFLoader(file.name)
+            documents.extend(loader.load_and_split())
+            print(f'{file} loaded')
+            
         text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
         texts = text_splitter.split_documents(documents)
         embeddings = OpenAIEmbeddings()
-        db = Chroma.from_documents(texts, embeddings)
+        db = Chroma.from_documents(documents, embeddings)
         retriever = db.as_retriever()
         global qa 
         qa = ConversationalRetrievalChain.from_llm(