Update DocuChat.py

DocuChat.py

@@ -14,14 +14,13 @@ pn.config.sizing_mode = 'stretch_width'
 # Panel extension
 pn.extension()
 
-
-
 class LangchainConversation:
     def __init__(self):
         self.file_input = pn.widgets.FileInput(height=45)
         self.openaikey = pn.widgets.PasswordInput(value="", placeholder="Enter your OpenAI API Key here...", height=45)
         self.chatbox = pn.widgets.ChatBox(height=300, primary_name="User")
         self.chatbox.param.watch(self._chat, 'value')
+        self.chat_history = []  # Chat history to store previous queries and responses
 
     def _chat(self, event):
         user_message = event.new[-1]
@@ -42,15 +41,23 @@ class LangchainConversation:
         return '\n'.join([line.strip() for line in lines if line.strip()])
 
     def qa(self, file, query):
+        # Consider chat history when processing new queries
+        chat_history_str = "\n".join([f"User: {q}\nAI: {a}" for q, a in self.chat_history])
+
+        # Load, split, and analyze the entire document
         loader = PyPDFLoader(file)
         documents = loader.load()
-        text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+        text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0, context_aware=True)  # Context-aware splitting
         texts = text_splitter.split_documents(documents)
         embeddings = OpenAIEmbeddings()
         db = Chroma.from_documents(texts, embeddings)
         retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 3})
         qa = RetrievalQA.from_chain_type(llm=LangchainOpenAI(), chain_type="stuff", retriever=retriever, return_source_documents=True)
-        result = qa({"query": query})
+        result = qa({"query": query + "\n" + chat_history_str})
+
+        # Update chat history
+        self.chat_history.append((query, result['result']))
+
         return result['result']
 
     def view(self):