Update app.py

app.py

@@ -12,13 +12,12 @@ from langchain.prompts.chat import (
     HumanMessagePromptTemplate,
 )
 
+# Initialize text splitter and other settings
 text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
-
 system_template = """
 Use the following pieces of context to answer the user's question. If the question cannot be answered with the supplied context, simply answer "I cannot determine this based on the provided context."  
 ----------------
 {context}"""
-
 messages = [
     SystemMessagePromptTemplate.from_template(system_template),
     HumanMessagePromptTemplate.from_template("{question}"),
@@ -28,31 +27,25 @@ chain_type_kwargs = {"prompt": prompt}
 
 @cl.author_rename
 def rename(orig_author: str):
-    rename_dict = {"RetrievalQA": "Consulting PageTurn"}
+    rename_dict = {"RetrievalQA": "PageTurn"}
     return rename_dict.get(orig_author, orig_author)
 
+# Initialize the index and other setup
 @cl.on_chat_start
 async def init():
     msg = cl.Message(content=f"Building Index...")
     await msg.send()
 
-    # Read text from a .txt file
     with open('./data/aerodynamic_drag.txt', 'r', encoding='Windows-1252') as f:
         aerodynamic_drag_data = f.read()
 
-    # Split the text into smaller chunks
     documents = text_splitter.create_documents([aerodynamic_drag_data])
-
-    # Create a local file store for caching
     store = LocalFileStore("./cache/")
     core_embeddings_model = OpenAIEmbeddings()
     embedder = CacheBackedEmbeddings.from_bytes_store(
         core_embeddings_model, store, namespace=core_embeddings_model.model
     )
-
-    # Make async docsearch
     docsearch = await cl.make_async(FAISS.from_documents)(documents, embedder)
-
     chain = RetrievalQA.from_chain_type(
         ChatOpenAI(model="gpt-4", temperature=0, streaming=True),
         chain_type="stuff",
@@ -63,9 +56,9 @@ async def init():
 
     msg.content = f"Index built!"
     await msg.send()
-
     cl.user_session.set("chain", chain)
 
+# Main function to handle incoming queries
 @cl.on_message
 async def main(message):
     chain = cl.user_session.get("chain")
@@ -74,28 +67,5 @@ async def main(message):
     )
     cb.answer_reached = True
     res = await chain.acall(message, callbacks=[cb], )
-
     answer = res["result"]
-    source_elements = []
-    visited_sources = set()
-
-    # Get the documents from the user session
-    docs = res["source_documents"]
-    metadatas = [doc.metadata for doc in docs]
-    all_sources = [m["source"] for m in metadatas]
-
-    for source in all_sources:
-        if source in visited_sources:
-            continue
-        visited_sources.add(source)
-        # Create the text element referenced in the message
-        source_elements.append(
-            cl.Text(content="https://www.imdb.com" + source, name="Review URL")
-        )
-
-    if source_elements:
-        answer += f"\nSources: {', '.join([e.content.decode('utf-8') for e in source_elements])}"
-    else:
-        answer += "\nNo sources found"
-
-    await cl.Message(content=answer, elements=source_elements).send()
+    await cl.Message(content=answer).send()