Added the app file

app.py

@@ -0,0 +1,159 @@
+# Import modules and classes
+from llama_index.core import VectorStoreIndex, StorageContext, load_index_from_storage
+from llama_index.llms.nvidia import NVIDIA
+from llama_index.embeddings.nvidia import NVIDIAEmbedding
+from llama_index.core.llms import ChatMessage, MessageRole
+from langchain_nvidia_ai_endpoints import NVIDIARerank
+from langchain_core.documents import Document as LangDocument
+from llama_index.core import Document as LlamaDocument
+from llama_index.core import Settings
+from llama_parse import LlamaParse
+import streamlit as st
+import os
+
+# Set environmental variables
+nvidia_api_key = os.getenv("NVIDIA_KEY")
+llamaparse_api_key = os.getenv("PARSE_KEY")
+
+# Initialize ChatNVIDIA, NVIDIARerank, and NVIDIAEmbeddings
+client = NVIDIA(
+    model="meta/llama-3.1-8b-instruct",
+    api_key=nvidia_api_key,
+    temperature=0.2,
+    top_p=0.7,
+    max_tokens=1024
+)
+
+embed_model = NVIDIAEmbedding(
+    model="nvidia/nv-embedqa-e5-v5", 
+    api_key=nvidia_api_key, 
+    truncate="NONE"
+)
+
+reranker = NVIDIARerank(
+  model="nvidia/nv-rerankqa-mistral-4b-v3", 
+  api_key=nvidia_api_key,
+)
+
+# Set the NVIDIA models globally
+Settings.embed_model = embed_model
+Settings.llm = client
+
+# Parse the local PDF document
+parser = LlamaParse(
+    api_key=llamaparse_api_key,
+    result_type="markdown",
+    verbose=True
+)
+
+# Get the absolute path of the script's directory
+script_dir = os.path.dirname(os.path.abspath(__file__))
+data_file = os.path.join(script_dir, "PhilDataset.pdf")
+
+# Load the PDF document using the relative path
+documents = parser.load_data(data_file)
+print("Document Parsed")
+
+# Split parsed text into chunks for embedding model
+def split_text(text, max_tokens=512):
+    words = text.split()
+    chunks = []
+    current_chunk = []
+    current_length = 0
+
+    for word in words:
+        word_length = len(word)
+        if current_length + word_length + 1 > max_tokens:
+            chunks.append(" ".join(current_chunk))
+            current_chunk = [word]
+            current_length = word_length + 1
+        else:
+            current_chunk.append(word)
+            current_length += word_length + 1
+
+    if current_chunk:
+        chunks.append(" ".join(current_chunk))
+
+    return chunks
+
+# Generate embeddings for document chunks
+all_embeddings = []
+all_documents = []
+
+for doc in documents:
+    text_chunks = split_text(doc.text)
+    for chunk in text_chunks:
+        embedding = embed_model.get_text_embedding(chunk)
+        all_embeddings.append(embedding)
+        all_documents.append(LlamaDocument(text=chunk))
+print("Embeddings generated")
+
+# Create and persist index with NVIDIAEmbeddings
+index = VectorStoreIndex.from_documents(all_documents, embeddings=all_embeddings, embed_model=embed_model)
+index.set_index_id("vector_index")
+index.storage_context.persist("./storage")
+print("Index created")
+
+# Load index from storage
+storage_context = StorageContext.from_defaults(persist_dir="storage")
+index = load_index_from_storage(storage_context, index_id="vector_index")
+print("Index loaded")
+
+# Query the index and use output as LLM context
+def query_model_with_context(question):
+
+    retriever = index.as_retriever(similarity_top_k=3)
+    nodes = retriever.retrieve(question)
+
+    for node in nodes:
+        print(node)
+
+    # Rerank the nodes
+    ranked_documents = reranker.compress_documents(
+        query=question,
+        documents = [LangDocument(page_content=node.text) for node in nodes]
+    )
+
+    # Print the most relevant and least relevant node
+    print(f"Most relevant node: {ranked_documents[0].page_content}")
+
+    # Use the most relevant node as context
+    context = ranked_documents[0].page_content
+
+    # Construct the messages using the ChatMessage class
+    messages = [
+        ChatMessage(role=MessageRole.SYSTEM, content=context),
+        ChatMessage(role=MessageRole.USER, content=str(question))
+    ]
+
+    completion = client.chat(messages)
+    
+    # Process response - assuming completion is a single string or a tuple containing a string
+    response_text = ""
+
+    if isinstance(completion, (list, tuple)):
+        # Join elements of tuple/list if it's in such format
+        response_text = ' '.join(completion)
+    elif isinstance(completion, str):
+        # Directly assign if it's a string
+        response_text = completion
+    else:
+        # Fallback for unexpected types, convert to string
+        response_text = str(completion)
+    
+    response_text = response_text.replace("assistant:", "Final Response:").strip()
+
+    return response_text
+
+
+# Streamlit UI
+st.title("Chat with this Rerank RAG App")
+question = st.text_input("Enter a relevant question to chat with the attached PhilDataset PDF file:")
+
+if st.button("Submit"):
+    if question:
+        st.write("**RAG Response:**")
+        response = query_model_with_context(question)
+        st.write(response)
+    else:
+        st.warning("Please enter a question.")