Create rag_deep.py

rag_deep.py

@@ -0,0 +1,147 @@
+import streamlit as st
+from langchain_community.document_loaders import PDFPlumberLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_core.vectorstores import InMemoryVectorStore
+from langchain_ollama import OllamaEmbeddings
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_ollama.llms import OllamaLLM
+
+st.markdown("""
+    <style>
+    .stApp {
+        background-color: #0E1117;
+        color: #FFFFFF;
+    }
+    
+    /* Chat Input Styling */
+    .stChatInput input {
+        background-color: #1E1E1E !important;
+        color: #FFFFFF !important;
+        border: 1px solid #3A3A3A !important;
+    }
+    
+    /* User Message Styling */
+    .stChatMessage[data-testid="stChatMessage"]:nth-child(odd) {
+        background-color: #1E1E1E !important;
+        border: 1px solid #3A3A3A !important;
+        color: #E0E0E0 !important;
+        border-radius: 10px;
+        padding: 15px;
+        margin: 10px 0;
+    }
+    
+    /* Assistant Message Styling */
+    .stChatMessage[data-testid="stChatMessage"]:nth-child(even) {
+        background-color: #2A2A2A !important;
+        border: 1px solid #404040 !important;
+        color: #F0F0F0 !important;
+        border-radius: 10px;
+        padding: 15px;
+        margin: 10px 0;
+    }
+    
+    /* Avatar Styling */
+    .stChatMessage .avatar {
+        background-color: #00FFAA !important;
+        color: #000000 !important;
+    }
+    
+    /* Text Color Fix */
+    .stChatMessage p, .stChatMessage div {
+        color: #FFFFFF !important;
+    }
+    
+    .stFileUploader {
+        background-color: #1E1E1E;
+        border: 1px solid #3A3A3A;
+        border-radius: 5px;
+        padding: 15px;
+    }
+    
+    h1, h2, h3 {
+        color: #00FFAA !important;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+PROMPT_TEMPLATE = """
+You are an expert research assistant. Use the provided context to answer the query. 
+If unsure, state that you don't know. Be concise and factual (max 3 sentences).
+
+Query: {user_query} 
+Context: {document_context} 
+Answer:
+"""
+PDF_STORAGE_PATH = 'document_store/pdfs/'
+EMBEDDING_MODEL = OllamaEmbeddings(model="deepseek-r1:1.5b")
+DOCUMENT_VECTOR_DB = InMemoryVectorStore(EMBEDDING_MODEL)
+LANGUAGE_MODEL = OllamaLLM(model="deepseek-r1:1.5b")
+
+
+def save_uploaded_file(uploaded_file):
+    file_path = PDF_STORAGE_PATH + uploaded_file.name
+    with open(file_path, "wb") as file:
+        file.write(uploaded_file.getbuffer())
+    return file_path
+
+def load_pdf_documents(file_path):
+    document_loader = PDFPlumberLoader(file_path)
+    return document_loader.load()
+
+def chunk_documents(raw_documents):
+    text_processor = RecursiveCharacterTextSplitter(
+        chunk_size=1000,
+        chunk_overlap=200,
+        add_start_index=True
+    )
+    return text_processor.split_documents(raw_documents)
+
+def index_documents(document_chunks):
+    DOCUMENT_VECTOR_DB.add_documents(document_chunks)
+
+def find_related_documents(query):
+    return DOCUMENT_VECTOR_DB.similarity_search(query)
+
+def generate_answer(user_query, context_documents):
+    context_text = "\n\n".join([doc.page_content for doc in context_documents])
+    conversation_prompt = ChatPromptTemplate.from_template(PROMPT_TEMPLATE)
+    response_chain = conversation_prompt | LANGUAGE_MODEL
+    return response_chain.invoke({"user_query": user_query, "document_context": context_text})
+
+
+# UI Configuration
+
+
+st.title("📘 DocuMind AI")
+st.markdown("### Your Intelligent Document Assistant")
+st.markdown("---")
+
+# File Upload Section
+uploaded_pdf = st.file_uploader(
+    "Upload Research Document (PDF)",
+    type="pdf",
+    help="Select a PDF document for analysis",
+    accept_multiple_files=False
+
+)
+
+if uploaded_pdf:
+    saved_path = save_uploaded_file(uploaded_pdf)
+    raw_docs = load_pdf_documents(saved_path)
+    processed_chunks = chunk_documents(raw_docs)
+    index_documents(processed_chunks)
+    
+    st.success("✅ Document processed successfully! Ask your questions below.")
+    
+    user_input = st.chat_input("Enter your question about the document...")
+    
+    if user_input:
+        with st.chat_message("user"):
+            st.write(user_input)
+        
+        with st.spinner("Analyzing document..."):
+            relevant_docs = find_related_documents(user_input)
+            ai_response = generate_answer(user_input, relevant_docs)
+            
+        with st.chat_message("assistant", avatar="🤖"):
+            st.write(ai_response)