Update app.py

app.py

@@ -1,113 +1,114 @@
-import PyPDF2
-from langchain_community.embeddings import OllamaEmbeddings
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import Chroma
-from langchain.chains import ConversationalRetrievalChain
-from langchain_community.chat_models import ChatOllama
-from langchain.memory import ChatMessageHistory, ConversationBufferMemory
-import chainlit as cl
-
-
-
-@cl.on_chat_start
-async def on_chat_start():
-    files = None #Initialize variable to store uploaded files
-
-    # Wait for the user to upload a file
-    while files is None:
-        files = await cl.AskFileMessage(
-            content="Please upload a pdf file to begin!",
-            accept=["application/pdf"],
-            max_size_mb=100,# Optionally limit the file size
-            timeout=180, # Set a timeout for user response,
-        ).send()
-
-    file = files[0] # Get the first uploaded file
-    print(file) # Print the file object for debugging
-    
-     # Sending an image with the local file path
-    elements = [
-    cl.Image(name="image", display="inline", path="pic.jpg")
-    ]
-    # Inform the user that processing has started
-    msg = cl.Message(content=f"Processing `{file.name}`...",elements=elements)
-    await msg.send()
-
-    # Read the PDF file
-    pdf = PyPDF2.PdfReader(file.path)
-    pdf_text = ""
-    for page in pdf.pages:
-        pdf_text += page.extract_text()
-        
-
-    # Split the text into chunks
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1200, chunk_overlap=50)
-    texts = text_splitter.split_text(pdf_text)
-
-    # Create a metadata for each chunk
-    metadatas = [{"source": f"{i}-pl"} for i in range(len(texts))]
-
-    # Create a Chroma vector store
-    embeddings = OllamaEmbeddings(model="nomic-embed-text")
-    docsearch = await cl.make_async(Chroma.from_texts)(
-        texts, embeddings, metadatas=metadatas
-    )
-    
-    # Initialize message history for conversation
-    message_history = ChatMessageHistory()
-    
-    # Memory for conversational context
-    memory = ConversationBufferMemory(
-        memory_key="chat_history",
-        output_key="answer",
-        chat_memory=message_history,
-        return_messages=True,
-    )
-
-    # Create a chain that uses the Chroma vector store
-    chain = ConversationalRetrievalChain.from_llm(
-        ChatOllama(model="gemma:7b"),
-        chain_type="stuff",
-        retriever=docsearch.as_retriever(),
-        memory=memory,
-        return_source_documents=True,
-    )
-
-    # Let the user know that the system is ready
-    msg.content = f"Processing `{file.name}` done. You can now ask questions!"
-    await msg.update()
-    #store the chain in user session
-    cl.user_session.set("chain", chain)
-
-
-@cl.on_message
-async def main(message: cl.Message):
-     # Retrieve the chain from user session
-    chain = cl.user_session.get("chain") 
-    #call backs happens asynchronously/parallel 
-    cb = cl.AsyncLangchainCallbackHandler()
-    
-    # call the chain with user's message content
-    res = await chain.ainvoke(message.content, callbacks=[cb])
-    answer = res["answer"]
-    source_documents = res["source_documents"] 
-
-    text_elements = [] # Initialize list to store text elements
-    
-    # Process source documents if available
-    if source_documents:
-        for source_idx, source_doc in enumerate(source_documents):
-            source_name = f"source_{source_idx}"
-            # Create the text element referenced in the message
-            text_elements.append(
-                cl.Text(content=source_doc.page_content, name=source_name)
-            )
-        source_names = [text_el.name for text_el in text_elements]
-        
-         # Add source references to the answer
-        if source_names:
-            answer += f"\nSources: {', '.join(source_names)}"
-        else:
-            answer += "\nNo sources found"
-    #return results
+import PyPDF2
+from langchain_community.embeddings import OllamaEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+from langchain.chains import ConversationalRetrievalChain
+from langchain_community.chat_models import ChatOllama
+from langchain_community.chat_message_histories import ChatMessageHistory, ConversationBufferMemory
+
+import chainlit as cl
+
+
+
+@cl.on_chat_start
+async def on_chat_start():
+    files = None #Initialize variable to store uploaded files
+
+    # Wait for the user to upload a file
+    while files is None:
+        files = await cl.AskFileMessage(
+            content="Please upload a pdf file to begin!",
+            accept=["application/pdf"],
+            max_size_mb=100,# Optionally limit the file size
+            timeout=180, # Set a timeout for user response,
+        ).send()
+
+    file = files[0] # Get the first uploaded file
+    print(file) # Print the file object for debugging
+    
+     # Sending an image with the local file path
+    elements = [
+    cl.Image(name="image", display="inline", path="pic.jpg")
+    ]
+    # Inform the user that processing has started
+    msg = cl.Message(content=f"Processing `{file.name}`...",elements=elements)
+    await msg.send()
+
+    # Read the PDF file
+    pdf = PyPDF2.PdfReader(file.path)
+    pdf_text = ""
+    for page in pdf.pages:
+        pdf_text += page.extract_text()
+        
+
+    # Split the text into chunks
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1200, chunk_overlap=50)
+    texts = text_splitter.split_text(pdf_text)
+
+    # Create a metadata for each chunk
+    metadatas = [{"source": f"{i}-pl"} for i in range(len(texts))]
+
+    # Create a Chroma vector store
+    embeddings = OllamaEmbeddings(model="nomic-embed-text")
+    docsearch = await cl.make_async(Chroma.from_texts)(
+        texts, embeddings, metadatas=metadatas
+    )
+    
+    # Initialize message history for conversation
+    message_history = ChatMessageHistory()
+    
+    # Memory for conversational context
+    memory = ConversationBufferMemory(
+        memory_key="chat_history",
+        output_key="answer",
+        chat_memory=message_history,
+        return_messages=True,
+    )
+
+    # Create a chain that uses the Chroma vector store
+    chain = ConversationalRetrievalChain.from_llm(
+        ChatOllama(model="gemma:7b"),
+        chain_type="stuff",
+        retriever=docsearch.as_retriever(),
+        memory=memory,
+        return_source_documents=True,
+    )
+
+    # Let the user know that the system is ready
+    msg.content = f"Processing `{file.name}` done. You can now ask questions!"
+    await msg.update()
+    #store the chain in user session
+    cl.user_session.set("chain", chain)
+
+
+@cl.on_message
+async def main(message: cl.Message):
+     # Retrieve the chain from user session
+    chain = cl.user_session.get("chain") 
+    #call backs happens asynchronously/parallel 
+    cb = cl.AsyncLangchainCallbackHandler()
+    
+    # call the chain with user's message content
+    res = await chain.ainvoke(message.content, callbacks=[cb])
+    answer = res["answer"]
+    source_documents = res["source_documents"] 
+
+    text_elements = [] # Initialize list to store text elements
+    
+    # Process source documents if available
+    if source_documents:
+        for source_idx, source_doc in enumerate(source_documents):
+            source_name = f"source_{source_idx}"
+            # Create the text element referenced in the message
+            text_elements.append(
+                cl.Text(content=source_doc.page_content, name=source_name)
+            )
+        source_names = [text_el.name for text_el in text_elements]
+        
+         # Add source references to the answer
+        if source_names:
+            answer += f"\nSources: {', '.join(source_names)}"
+        else:
+            answer += "\nNo sources found"
+    #return results
     await cl.Message(content=answer, elements=text_elements).send()