app.py

app.py

@@ -10,10 +10,8 @@ from aimakerspace.openai_utils.prompts import (
 from aimakerspace.openai_utils.embedding import EmbeddingModel
 from aimakerspace.vectordatabase import VectorDatabase
 from aimakerspace.openai_utils.chatmodel import ChatOpenAI
-from langchain_experimental.text_splitter import SemanticChunker
-from langchain_openai.embeddings import OpenAIEmbeddings
 import chainlit as cl
-from langchain_community.document_loaders.pdf import PyPDFLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
 
 system_template = """\
 Use the following context to answer a users question. If you cannot find the answer in the context, say you don't know the answer."""
@@ -22,7 +20,6 @@ system_role_prompt = SystemRolePrompt(system_template)
 user_prompt_template = """\
 Context:
 {context}
-
 Question:
 {question}
 """
@@ -50,11 +47,12 @@ class RetrievalAugmentedQAPipeline:
 
         return {"response": generate_response(), "context": context_list}
 
-# text_splitter = CharacterTextSplitter()
-text_splitter = SemanticChunker(OpenAIEmbeddings(), breakpoint_threshold_type="standard_deviation")
+text_splitter = RecursiveCharacterTextSplitter()
+
 
 def process_text_file(file: AskFileResponse):
     import tempfile
+    from langchain_community.document_loaders.pdf import PyPDFLoader
 
     with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=file.name) as temp_file:
         temp_file_path = temp_file.name
@@ -82,7 +80,7 @@ async def on_chat_start():
     # Wait for the user to upload a file
     while files == None:
         files = await cl.AskFileMessage(
-            content="Please upload a Text File or PDF to begin!",
+            content="Please upload a Text file or a PDF to begin!",
             accept=["text/plain", "application/pdf"],
             max_size_mb=2,
             timeout=180,