Create app.py

app.py

@@ -0,0 +1,306 @@
+import gradio as gr
+import os
+from typing import List, Dict
+from langchain.text_splitter import (
+    RecursiveCharacterTextSplitter,
+    CharacterTextSplitter,
+    TokenTextSplitter
+)
+from langchain_community.vectorstores import FAISS, Chroma, Qdrant
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.chains import ConversationalRetrievalChain
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_huggingface import HuggingFaceEndpoint
+from langchain.memory import ConversationBufferMemory
+
+list_llm = ["meta-llama/Meta-Llama-3-8B-Instruct", "mistralai/Mistral-7B-Instruct-v0.2"]
+list_llm_simple = [os.path.basename(llm) for llm in list_llm]
+api_token = os.getenv("HF_TOKEN")
+
+CHUNK_SIZES = {
+    "small": {"recursive": 512, "fixed": 512, "token": 256},
+    "medium": {"recursive": 1024, "fixed": 1024, "token": 512}
+}
+
+def get_text_splitter(strategy: str, chunk_size: int = 1024, chunk_overlap: int = 64):
+    splitters = {
+        "recursive": RecursiveCharacterTextSplitter(
+            chunk_size=chunk_size,
+            chunk_overlap=chunk_overlap
+        ),
+        "fixed": CharacterTextSplitter(
+            chunk_size=chunk_size,
+            chunk_overlap=chunk_overlap
+        ),
+        "token": TokenTextSplitter(
+            chunk_size=chunk_size,
+            chunk_overlap=chunk_overlap
+        )
+    }
+    return splitters.get(strategy)
+
+def load_doc(list_file_path: List[str], splitting_strategy: str, chunk_size: str):
+    chunk_size_value = CHUNK_SIZES[chunk_size][splitting_strategy]
+    loaders = [PyPDFLoader(x) for x in list_file_path]
+    pages = []
+    for loader in loaders:
+        pages.extend(loader.load())
+    
+    text_splitter = get_text_splitter(splitting_strategy, chunk_size_value)
+    doc_splits = text_splitter.split_documents(pages)
+    return doc_splits
+
+def create_db(splits, db_choice: str = "faiss"):
+    embeddings = HuggingFaceEmbeddings()
+    db_creators = {
+        "faiss": lambda: FAISS.from_documents(splits, embeddings),
+        "chroma": lambda: Chroma.from_documents(splits, embeddings),
+        "qdrant": lambda: Qdrant.from_documents(
+            splits,
+            embeddings,
+            location=":memory:",
+            collection_name="pdf_docs"
+        )
+    }
+    return db_creators[db_choice]()
+
+def initialize_database(list_file_obj, splitting_strategy, chunk_size, db_choice, progress=gr.Progress()):
+    """Initialize vector database with error handling"""
+    try:
+        if not list_file_obj:
+            return None, "No files uploaded. Please upload PDF documents first."
+            
+        list_file_path = [x.name for x in list_file_obj if x is not None]
+        if not list_file_path:
+            return None, "No valid files found. Please upload PDF documents."
+            
+        doc_splits = load_doc(list_file_path, splitting_strategy, chunk_size)
+        if not doc_splits:
+            return None, "No content extracted from documents."
+            
+        vector_db = create_db(doc_splits, db_choice)
+        return vector_db, f"Database created successfully using {splitting_strategy} splitting and {db_choice} vector database!"
+        
+    except Exception as e:
+        return None, f"Error creating database: {str(e)}"
+
+def initialize_llmchain(llm_choice, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
+    """Initialize LLM chain with error handling"""
+    try:
+        if vector_db is None:
+            return None, "Please create vector database first."
+            
+        llm_model = list_llm[llm_choice]
+        
+        llm = HuggingFaceEndpoint(
+            repo_id=llm_model,
+            huggingfacehub_api_token=api_token,
+            temperature=temperature,
+            max_new_tokens=max_tokens,
+            top_k=top_k
+        )
+        
+        memory = ConversationBufferMemory(
+            memory_key="chat_history",
+            output_key='answer',
+            return_messages=True
+        )
+
+        retriever = vector_db.as_retriever()
+        qa_chain = ConversationalRetrievalChain.from_llm(
+            llm,
+            retriever=retriever,
+            memory=memory,
+            return_source_documents=True
+        )
+        return qa_chain, "LLM initialized successfully!"
+        
+    except Exception as e:
+        return None, f"Error initializing LLM: {str(e)}"
+
+def conversation(qa_chain, message, history):
+    """Conversation function returning all required outputs"""
+    response = qa_chain.invoke({
+        "question": message,
+        "chat_history": [(hist[0], hist[1]) for hist in history]
+    })
+    
+    response_answer = response["answer"]
+    if "Helpful Answer:" in response_answer:
+        response_answer = response_answer.split("Helpful Answer:")[-1]
+    
+    sources = response["source_documents"][:3]
+    source_contents = []
+    source_pages = []
+    
+    for source in sources:
+        source_contents.append(source.page_content.strip())
+        source_pages.append(source.metadata.get("page", 0) + 1)
+    
+    while len(source_contents) < 3:
+        source_contents.append("")
+        source_pages.append(0)
+    
+    return (
+        qa_chain,  
+        gr.update(value=""), 
+        history + [(message, response_answer)], 
+        source_contents[0],  
+        source_pages[0],   
+        source_contents[1],  
+        source_pages[1],    
+        source_contents[2], 
+        source_pages[2]     
+    )
+
+def demo():
+    with gr.Blocks(theme=gr.themes.Default(primary_hue="red", secondary_hue="pink", neutral_hue="sky")) as demo:
+        vector_db = gr.State()
+        qa_chain = gr.State()
+        
+        gr.HTML("<center><h1>RAG PDF Chatbot</h1></center>")
+        
+        with gr.Column(scale=86):
+            gr.Markdown("<b>Step 1 - Configure and Initialize RAG Pipeline</b>")
+            with gr.Row():
+                document = gr.Files(
+                    height=300,
+                    file_count="multiple",
+                    file_types=["pdf"],
+                    interactive=True,
+                    label="Upload PDF documents"
+                )
+            
+            with gr.Row():
+                splitting_strategy = gr.Radio(
+                    ["recursive", "fixed", "token"],
+                    label="Text Splitting Strategy",
+                    value="recursive"
+                )
+                db_choice = gr.Radio(
+                    ["faiss", "chroma", "qdrant"],
+                    label="Vector Database",
+                    value="faiss"
+                )
+                chunk_size = gr.Radio(
+                    ["small", "medium"],
+                    label="Chunk Size",
+                    value="medium"
+                )
+            
+            with gr.Row():
+                db_btn = gr.Button("Create vector database")
+                db_progress = gr.Textbox(
+                    value="Not initialized",
+                    show_label=False
+                )
+            
+            gr.Markdown("<b>Step 2 - Configure LLM</b>")
+            with gr.Row():
+                llm_choice = gr.Radio(
+                    list_llm_simple,
+                    label="Available LLMs",
+                    value=list_llm_simple[0],
+                    type="index"
+                )
+            
+            with gr.Row():
+                with gr.Accordion("LLM Parameters", open=False):
+                    temperature = gr.Slider(
+                        minimum=0.01,
+                        maximum=1.0,
+                        value=0.5,
+                        step=0.1,
+                        label="Temperature"
+                    )
+                    max_tokens = gr.Slider(
+                        minimum=128,
+                        maximum=4096,
+                        value=2048,
+                        step=128,
+                        label="Max Tokens"
+                    )
+                    top_k = gr.Slider(
+                        minimum=1,
+                        maximum=10,
+                        value=3,
+                        step=1,
+                        label="Top K"
+                    )
+            
+            with gr.Row():
+                init_llm_btn = gr.Button("Initialize LLM")
+                llm_progress = gr.Textbox(
+                    value="Not initialized",
+                    show_label=False
+                )
+        
+        with gr.Column(scale=200):
+            gr.Markdown("<b>Step 3 - Chat with Documents</b>")
+            chatbot = gr.Chatbot(height=505)
+            
+            with gr.Accordion("Source References", open=False):
+                with gr.Row():
+                    source1 = gr.Textbox(label="Source 1", lines=2)
+                    page1 = gr.Number(label="Page")
+                with gr.Row():
+                    source2 = gr.Textbox(label="Source 2", lines=2)
+                    page2 = gr.Number(label="Page")
+                with gr.Row():
+                    source3 = gr.Textbox(label="Source 3", lines=2)
+                    page3 = gr.Number(label="Page")
+            
+            with gr.Row():
+                msg = gr.Textbox(
+                    placeholder="Ask a question",
+                    show_label=False
+                )
+            with gr.Row():
+                submit_btn = gr.Button("Submit")
+                clear_btn = gr.ClearButton(
+                    [msg, chatbot],
+                    value="Clear Chat"
+                )
+
+        # Event handlers
+        db_btn.click(
+            initialize_database,
+            inputs=[document, splitting_strategy, chunk_size, db_choice],
+            outputs=[vector_db, db_progress]
+        ).then(
+            lambda x: gr.update(interactive=True) if x[0] is not None else gr.update(interactive=False),
+            inputs=[vector_db],
+            outputs=[init_llm_btn]
+        )
+        
+        init_llm_btn.click(
+            initialize_llmchain,
+            inputs=[llm_choice, temperature, max_tokens, top_k, vector_db],
+            outputs=[qa_chain, llm_progress]
+        ).then(
+            lambda x: gr.update(interactive=True) if x[0] is not None else gr.update(interactive=False),
+            inputs=[qa_chain],
+            outputs=[msg]
+        )
+        
+        msg.submit(
+            conversation,
+            inputs=[qa_chain, msg, chatbot],
+            outputs=[qa_chain, msg, chatbot, source1, page1, source2, page2, source3, page3]
+        )
+        
+        submit_btn.click(
+            conversation,
+            inputs=[qa_chain, msg, chatbot],
+            outputs=[qa_chain, msg, chatbot, source1, page1, source2, page2, source3, page3]
+        )
+
+        clear_btn.click(
+            lambda: [None, "", 0, "", 0, "", 0],
+            outputs=[chatbot, source1, page1, source2, page2, source3, page3]
+        )
+
+    demo.queue().launch(debug=True)
+
+if __name__ == "__main__":
+    demo()