Create app.py

app.py

@@ -0,0 +1,77 @@
+import os
+import gradio as gr
+from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from llama_index.llms.llama_cpp import LlamaCPP
+from llama_index.llms.llama_cpp.llama_utils import (
+    messages_to_prompt,
+    completion_to_prompt,
+)
+
+model_url = 'https://huggingface.co/bartowski/Llama-3.2-3B-Instruct-GGUF/resolve/main/Llama-3.2-3B-Instruct-Q4_K_M.gguf'
+llm = LlamaCPP(
+    # You can pass in the URL to a GGML model to download it automatically
+    model_url=model_url,
+    temperature=0.1,
+    max_new_tokens=256,
+    context_window=2048,
+    # kwargs to pass to __call__()
+    generate_kwargs={},
+    # kwargs to pass to __init__()
+    # set to at least 1 to use GPU
+    model_kwargs={"n_gpu_layers": 1},
+    # transform inputs into Llama2 format
+    messages_to_prompt=messages_to_prompt,
+    completion_to_prompt=completion_to_prompt,
+    verbose=True,
+)
+# Initialize embeddings and LLM
+embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-small-en-v1.5")
+
+def initialize_index():
+    """Initialize the vector store index from PDF files in the data directory"""
+    # Load documents from the data directory
+    loader = SimpleDirectoryReader(
+        input_dir="data",
+        required_exts=[".pdf"]
+    )
+    documents = loader.load_data()
+    
+    # Create index
+    index = VectorStoreIndex.from_documents(
+        documents,
+        embed_model=embeddings,
+    )
+    
+    # Return query engine with Llama
+    return index.as_query_engine(llm=llm)
+
+# Initialize the query engine at startup
+query_engine = initialize_index()
+
+def process_query(
+    message: str,
+    history: list[tuple[str, str]],
+) -> str:
+    """Process a query using the RAG system"""
+    try:
+        # Get response from the query engine
+        response = query_engine.query(
+            message,
+            #streaming=True
+        )
+        return str(response)
+    except Exception as e:
+        return f"Error processing query: {str(e)}"
+
+# Create the Gradio interface
+demo = gr.ChatInterface(
+    process_query,
+    title="PDF Question Answering with RAG + Llama",
+    description="Ask questions about the content of the loaded PDF documents using Llama model",
+    #undo_btn="Delete Previous",
+    #clear_btn="Clear",
+)
+
+if __name__ == "__main__":
+    demo.launch(debug=True)