Create app.py

app.py

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+import os
+import shutil
+import streamlit as st
+from io import BytesIO
+
+# Importing LlamaIndex components
+from llama_index.llms.openai import OpenAI
+from llama_index.embeddings.openai import OpenAIEmbedding
+from llama_index.core import Settings, SimpleDirectoryReader, VectorStoreIndex, StorageContext
+from llama_index.vector_stores.qdrant import QdrantVectorStore
+from llama_index.core.memory import ChatMemoryBuffer
+import qdrant_client
+
+# =============================================================================
+# Configuration and Global Initialization
+# =============================================================================
+
+# Ensure that the OpenAI API key is available
+openai_api_key = os.getenv("OPENAI_API_KEY")
+if not openai_api_key:
+    raise ValueError("Please set your OPENAI_API_KEY environment variable.")
+
+# System prompt for the chat engine
+SYSTEM_PROMPT = (
+    "You are an AI assistant who answers the user questions, "
+    "use the schema fields to generate appropriate and valid json queries"
+)
+
+# Configure the LLM and embedding models
+Settings.llm = OpenAI(model="gpt-3.5-turbo", temperature=0.4)
+Settings.embed_model = OpenAIEmbedding(model="text-embedding-ada-002")
+
+# Load initial documents from a directory called "new_file"
+if os.path.exists("new_file"):
+    documents = SimpleDirectoryReader("new_file").load_data()
+else:
+    documents = []
+
+# Set up the Qdrant vector store (using an in-memory collection for simplicity)
+client = qdrant_client.QdrantClient(location=":memory:")
+vector_store = QdrantVectorStore(
+    collection_name="paper",
+    client=client,
+    enable_hybrid=True,
+    batch_size=20,
+)
+storage_context = StorageContext.from_defaults(vector_store=vector_store)
+
+# Build the initial index and chat engine
+index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)
+chat_memory = ChatMemoryBuffer.from_defaults(token_limit=3000)
+chat_engine = index.as_chat_engine(
+    chat_mode="context",
+    memory=chat_memory,
+    system_prompt=SYSTEM_PROMPT,
+)
+
+# =============================================================================
+# Helper Functions
+# =============================================================================
+
+def process_uploaded_file(uploaded_file: BytesIO) -> str:
+    """
+    Process the uploaded file:
+      1. Save the file to an "uploads" folder.
+      2. Copy it to a temporary folder ("temp_upload") for reading.
+      3. Update the global documents list and rebuild the index and chat engine.
+    """
+    if uploaded_file is None:
+        return "No file uploaded."
+
+    # Ensure the uploads directory exists
+    uploads_dir = "uploads"
+    os.makedirs(uploads_dir, exist_ok=True)
+
+    # Save the uploaded file locally
+    file_name = uploaded_file.name
+    dest_path = os.path.join(uploads_dir, file_name)
+    with open(dest_path, "wb") as f:
+        f.write(uploaded_file.getbuffer())
+
+    # Prepare a temporary directory for processing the file
+    temp_dir = "temp_upload"
+    os.makedirs(temp_dir, exist_ok=True)
+    # Clear any existing file in temp_upload directory
+    for f_name in os.listdir(temp_dir):
+        os.remove(os.path.join(temp_dir, f_name))
+    shutil.copy(dest_path, temp_dir)
+
+    # Load new document(s) from the temporary folder using SimpleDirectoryReader
+    new_docs = SimpleDirectoryReader(temp_dir).load_data()
+
+    # Update global documents and rebuild the index and chat engine
+    global documents, index, chat_engine
+    documents.extend(new_docs)
+    index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)
+    chat_engine = index.as_chat_engine(
+        chat_mode="context",
+        memory=chat_memory,
+        system_prompt=SYSTEM_PROMPT,
+    )
+
+    return f"File '{file_name}' processed and added to the index."
+
+def chat_with_ai(user_input: str) -> str:
+    """
+    Send user input to the chat engine and return the response.
+    """
+    response = chat_engine.chat(user_input)
+    # Extract references from the response (if any)
+    references = response.source_nodes
+    ref = []
+    for node in references:
+        if "file_name" in node.metadata and node.metadata["file_name"] not in ref:
+            ref.append(node.metadata["file_name"])
+    complete_response = str(response)
+    if ref:
+        complete_response += "\n\nReferences: " + ", ".join(ref)
+    return complete_response
+
+# =============================================================================
+# Streamlit App Layout
+# =============================================================================
+
+st.set_page_config(page_title="LlamaIndex Chat & File Upload", layout="wide")
+st.title("Chat Interface for LlamaIndex with File Upload")
+
+# Use Streamlit tabs for separate Chat and Upload functionalities
+tab1, tab2 = st.tabs(["Chat", "Upload"])
+
+# -----------------------------------------------------------------------------
+# Chat Tab
+# -----------------------------------------------------------------------------
+with tab1:
+    st.header("Chat with the AI")
+    # Initialize chat history in session state if it does not exist
+    if "chat_history" not in st.session_state:
+        st.session_state["chat_history"] = []
+
+    # Display conversation history
+    for chat in st.session_state["chat_history"]:
+        st.markdown(f"**User:** {chat[0]}")
+        st.markdown(f"**AI:** {chat[1]}")
+        st.markdown("---")
+
+    # Input text for user query
+    user_input = st.text_input("Enter your question:")
+
+    # When the "Send" button is clicked, process the chat
+    if st.button("Send") and user_input:
+        with st.spinner("Processing..."):
+            response = chat_with_ai(user_input)
+        st.session_state["chat_history"].append((user_input, response))
+        st.experimental_rerun()  # Refresh the page to show updated history
+
+    # Button to clear the conversation history
+    if st.button("Clear History"):
+        st.session_state["chat_history"] = []
+        st.experimental_rerun()
+
+# -----------------------------------------------------------------------------
+# Upload Tab
+# -----------------------------------------------------------------------------
+with tab2:
+    st.header("Upload a File")
+    uploaded_file = st.file_uploader("Choose a file to upload", type=["txt", "pdf", "doc", "docx", "csv", "xlsx"])
+    if st.button("Upload and Process"):
+        if uploaded_file is not None:
+            with st.spinner("Uploading and processing file..."):
+                status = process_uploaded_file(uploaded_file)
+            st.success(status)
+        else:
+            st.error("No file uploaded.")
+