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-->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003ERetrieval-Augmented Generation (RAG) enhances LLM responses by grounding them in your own data. Instead of relying solely on a model's training knowledge, RAG retrieves relevant documents and uses them as context thereby reducing hallucinations and enabling accurate, domain-specific answers.\u003C/p\u003E\n","\u003Cp\u003EIn this quickstart, you'll build a complete RAG pipeline using Snowflake Cortex Search. Starting from raw documents, you'll learn data preparation, text chunking, creating a search service, building a conversational RAG chatbot, and evaluating quality with TruLens.\u003C/p\u003E\n","\u003Cp\u003EThis tutorial distills key lessons from the \u003Ca href=\"https://30daysofai.streamlit.app/\"\u003E#30DaysOfAI\u003C/a\u003E learning challenge into a hands-on guide.\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to prepare and chunk documents for RAG (Days 16-17)\u003C/li\u003E\u003Cli\u003EHow to generate embeddings for semantic search (Day 18)\u003C/li\u003E\u003Cli\u003EHow to create and configure a Cortex Search service (Day 19)\u003C/li\u003E\u003Cli\u003EHow to query Cortex Search for semantic retrieval (Day 20)\u003C/li\u003E\u003Cli\u003EHow to build a RAG chatbot with grounded responses (Day 21)\u003C/li\u003E\u003Cli\u003EHow to add guardrails to keep responses on-topic (Day 22)\u003C/li\u003E\u003Cli\u003EHow to evaluate RAG quality with TruLens and AI Observability (Day 23)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n","\u003Cp\u003EA complete RAG system including a Cortex Search service for customer reviews and a conversational chatbot that answers questions grounded in retrieved documents, with TruLens evaluation for quality metrics.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAccess to a \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake account\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EBasic knowledge of Python and Streamlit\u003C/li\u003E\u003Cli\u003EA Snowflake warehouse with Cortex Search and LLM access\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EGetting Started\u003C/h2\u003E\n","\u003Cp\u003EClone or download the code from the \u003Ca href=\"https://github.com/streamlit/30daysofai\"\u003E30daysofai\u003C/a\u003E GitHub repository:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Egit clone https://github.com/streamlit/30DaysOfAI.git\ncd 30DaysOfAI/app\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe app code for this quickstart:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/streamlit/30DaysOfAI/blob/main/app/day16.py\"\u003EDay 16: Document Extraction\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/streamlit/30DaysOfAI/blob/main/app/day17.py\"\u003EDay 17: Data Preparation & Chunking\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/streamlit/30DaysOfAI/blob/main/app/day18.py\"\u003EDay 18: Generate Embeddings\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/streamlit/30DaysOfAI/blob/main/app/day19.py\"\u003EDay 19: Create Search Service\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/streamlit/30DaysOfAI/blob/main/app/day20.py\"\u003EDay 20: Query Cortex Search\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/streamlit/30DaysOfAI/blob/main/app/day21.py\"\u003EDay 21: RAG Chatbot\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/streamlit/30DaysOfAI/blob/main/app/day22.py\"\u003EDay 22: Chat with Guardrails\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/streamlit/30DaysOfAI/blob/main/app/day23.py\"\u003EDay 23: TruLens Evaluation\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERequirements\u003C/h3\u003E\n","\u003Cp\u003ESave the appropriate requirements content to \u003Ccode\u003Erequirements.txt\u003C/code\u003E in your project directory, or add them to your Streamlit in Snowflake app's package configuration.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDays 16-22\u003C/strong\u003E share the same core dependencies:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-txt\"\u003Estreamlit\nsnowflake-snowpark-python>=1.18.0,<2.0\nsnowflake>=1.0.0\npypdf\npandas\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDay 23 (TruLens Evaluation)\u003C/strong\u003E requires additional packages:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-txt\"\u003Estreamlit\nsnowflake-snowpark-python>=1.18.0,<2.0\nsnowflake>=1.0.0\npypdf\npandas\ntrulens-core\ntrulens-providers-cortex\ntrulens-connectors-snowflake>=2.5.0\nsetuptools<81\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EExtract and Upload Documents\u003C/h2\u003E\n","\u003Cp\u003EThe foundation of any RAG system is well-prepared data. In this section, you'll upload documents, extract their text content, and save to Snowflake.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote: This section is repurposed from Day 16 of the \u003Ca href=\"https://30daysofai.streamlit.app/?day=16\"\u003E#30DaysOfAI learning challenge\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ESnowflake Connection\u003C/h3\u003E\n","\u003Cp\u003EThe app establishes a Snowflake connection that works both in Streamlit in Snowflake (SiS) and locally. In SiS, it uses the active session; locally, it reads credentials from \u003Ccode\u003Est.secrets\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Etry:\n    from snowflake.snowpark.context import get_active_session\n    session = get_active_session()\nexcept:\n    from snowflake.snowpark import Session\n    session = Session.builder.configs(st.secrets["connections"]["snowflake"]).create()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EDatabase Configuration\u003C/h3\u003E\n","\u003Cp\u003EUsers configure the target database, schema, and table name through text inputs. Session state persists these values across reruns:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eif 'database' not in st.session_state:\n    st.session_state.database = "RAG_DB"\nif 'schema' not in st.session_state:\n    st.session_state.schema = "RAG_SCHEMA"\nif 'table_name' not in st.session_state:\n    st.session_state.table_name = "EXTRACTED_DOCUMENTS"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EFile Upload and Text Extraction\u003C/h3\u003E\n","\u003Cp\u003EThe file uploader accepts TXT, Markdown, and PDF files. For PDFs, \u003Ccode\u003Epypdf.PdfReader\u003C/code\u003E extracts text from each page. A progress bar shows extraction status for batch uploads:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Euploaded_files = st.file_uploader(\n    "Choose file(s)",\n    type=["txt", "md", "pdf"],\n    accept_multiple_files=True\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EReplace vs Append Mode\u003C/h3\u003E\n","\u003Cp\u003EA checkbox lets users choose between replacing existing data or appending to it. The default is based on whether the table already contains records:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Ereplace_mode = st.checkbox(\n    f":material/sync: Replace Table Mode for `{st.session_state.table_name}`",\n    value=table_exists\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESave to Snowflake\u003C/h3\u003E\n","\u003Cp\u003EThe app creates the database, schema, and table if they don't exist, then inserts each document with metadata including word count and character count.\u003C/p\u003E\n","\u003Ch3\u003EFull Code\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport streamlit as st\nfrom pypdf import PdfReader\nimport io\nimport pandas as pd\nfrom datetime import datetime\n\ntry:\n    from snowflake.snowpark.context import get_active_session\n    session = get_active_session()\nexcept:\n    from snowflake.snowpark import Session\n    session = Session.builder.configs(st.secrets["connections"]["snowflake"]).create()\n\nst.title(":material/description: Batch Document Text Extractor")\nst.write("Upload multiple documents at once to extract text and save to Snowflake for RAG applications.")\n\nif 'database' not in st.session_state:\n    st.session_state.database = "RAG_DB"\nif 'schema' not in st.session_state:\n    st.session_state.schema = "RAG_SCHEMA"\nif 'table_name' not in st.session_state:\n    st.session_state.table_name = "EXTRACTED_DOCUMENTS"\n\nwith st.container(border=True):\n    st.subheader(":material/analytics: Database Setup")\n\n    col1, col2, col3 = st.columns(3)\n    with col1:\n        st.session_state.database = st.text_input("Database", value=st.session_state.database, key="db_input")\n    with col2:\n        st.session_state.schema = st.text_input("Schema", value=st.session_state.schema, key="schema_input")\n    with col3:\n        st.session_state.table_name = st.text_input("Table Name", value=st.session_state.table_name, key="table_input")\n    \n    st.info(f":material/location_on: Target location: `{st.session_state.database}.{st.session_state.schema}.{st.session_state.table_name}`")\n    st.caption(":material/lightbulb: Database will be created automatically when you save documents")\n    \n    st.divider()\n    \n    st.subheader(":material/download: Download Review Data")\n    st.write("To get started quickly, download our sample dataset of 100 customer reviews from Avalanche winter sports equipment.")\n    \n    col1, col2 = st.columns([2, 1])\n    with col1:\n        st.info(":material/info: **Sample Dataset**: 100 customer review files (TXT format) with product feedback, sentiment scores, and order information.")\n    with col2:\n        st.link_button(\n            ":material/download: Download review.zip",\n            "https://github.com/streamlit/30DaysOfAI/raw/refs/heads/main/assets/review.zip",\n            use_container_width=True\n        )\n    \n    with st.expander(":material/help: How to use the sample data"):\n        st.markdown("""\n        **Steps:**\n        1. Click the **Download review.zip** button above\n        2. Unzip the downloaded file on your computer\n        3. Use the **Upload Documents** section below to select all 100 review files\n        4. Click **Extract Text** to process and save to Snowflake\n        \n        **What's included:**\n        - 100 customer review files (`review-001.txt` to `review-100.txt`)\n        - Each review contains: product name, date, review summary, sentiment score, and order ID\n        - Perfect for testing batch processing and building RAG applications\n        \n        **Tip:** You can upload all 100 files at once for optimal batch processing!\n        """)\n    \n    st.divider()\n    \n    st.subheader(":material/upload: Upload Documents")\n    uploaded_files = st.file_uploader(\n        "Choose file(s)",\n        type=["txt", "md", "pdf"],\n        accept_multiple_files=True,\n        help="Supported formats: TXT, MD, PDF. Upload multiple files at once!"\n    )\n\n    table_exists = False\n    try:\n        check_result = session.sql(f"""\n            SELECT COUNT(*) as CNT FROM {st.session_state.database}.{st.session_state.schema}.{st.session_state.table_name}\n        """).collect()\n        table_exists = True\n    except:\n        table_exists = False\n    \n    replace_mode = st.checkbox(\n        f":material/sync: Replace Table Mode for `{st.session_state.table_name}`",\n        value=table_exists,\n        help=f"When enabled, clears all existing data in {st.session_state.database}.{st.session_state.schema}.{st.session_state.table_name} before saving new documents"\n    )\n    \n    if replace_mode:\n        st.warning(f":material/warning: **Replace Mode Enabled** - All existing documents in `{st.session_state.table_name}` will be deleted before saving new ones.")\n    else:\n        st.info(f":material/add: **Append Mode** - New documents will be added to `{st.session_state.table_name}`.")\n\ndatabase = st.session_state.database\nschema = st.session_state.schema\ntable_name = st.session_state.table_name\n\nif uploaded_files:\n    with st.container(border=True):\n        st.subheader(":material/upload: Uploaded Documents")\n        st.success(f":material/folder: {len(uploaded_files)} file(s) uploaded")\n        \n        with st.expander(":material/assignment: View Selected Files", expanded=False):\n            file_list_df = pd.DataFrame([\n                {\n                    "File Name": f.name,\n                    "Size": f"{f.size:,} bytes",\n                    "Type": "TXT" if f.name.lower().endswith('.txt') \n                           else "Markdown" if f.name.lower().endswith('.md')\n                           else "PDF" if f.name.lower().endswith('.pdf')\n                           else "Unknown"\n                }\n                for f in uploaded_files\n            ])\n            st.dataframe(file_list_df, use_container_width=True)\n        \n        process_button = st.button(\n            f":material/sync: Extract Text from {len(uploaded_files)} File(s)",\n            type="primary",\n            use_container_width=True\n        )\n    \n    if process_button:\n        success_count = 0\n        error_count = 0\n        extracted_data = []\n        \n        progress_bar = st.progress(0, text="Starting extraction...")\n        status_container = st.empty()\n        \n        for idx, uploaded_file in enumerate(uploaded_files):\n            progress_pct = (idx + 1) / len(uploaded_files)\n            progress_bar.progress(progress_pct, text=f"Processing {idx+1}/{len(uploaded_files)}: {uploaded_file.name}")\n            \n            try:\n                if uploaded_file.name.lower().endswith('.txt'):\n                    file_type = "TXT"\n                elif uploaded_file.name.lower().endswith('.md'):\n                    file_type = "Markdown"\n                elif uploaded_file.name.lower().endswith('.pdf'):\n                    file_type = "PDF"\n                else:\n                    file_type = "Unknown"\n                \n                uploaded_file.seek(0)\n                extracted_text = ""\n                \n                if uploaded_file.name.lower().endswith(('.txt', '.md')):\n                    extracted_text = uploaded_file.read().decode("utf-8")\n                elif uploaded_file.name.lower().endswith('.pdf'):\n                    pdf_reader = PdfReader(io.BytesIO(uploaded_file.read()))\n                    for page in pdf_reader.pages:\n                        page_text = page.extract_text()\n                        if page_text:\n                            extracted_text += page_text + "\\n\\n"\n                \n                if extracted_text and extracted_text.strip():\n                    word_count = len(extracted_text.split())\n                    char_count = len(extracted_text)\n                    \n                    extracted_data.append({\n                        'file_name': uploaded_file.name,\n                        'file_type': file_type,\n                        'file_size': uploaded_file.size,\n                        'extracted_text': extracted_text,\n                        'word_count': word_count,\n                        'char_count': char_count\n                    })\n                    success_count += 1\n                else:\n                    error_count += 1\n                    status_container.warning(f":material/warning: No text extracted from: {uploaded_file.name}")\n                    \n            except Exception as e:\n                error_count += 1\n                status_container.error(f":material/cancel: Error processing {uploaded_file.name}: {str(e)}")\n        \n        progress_bar.empty()\n        status_container.empty()\n        \n        with st.container(border=True):\n            st.subheader(":material/analytics: Documents Written to a Database Table")\n            \n            col1, col2, col3 = st.columns(3)\n            with col1:\n                st.metric(":material/check_circle: Successful", success_count)\n            with col2:\n                st.metric(":material/cancel: Failed", error_count)\n            with col3:\n                st.metric(":material/analytics: Total Words", f"{sum(d['word_count'] for d in extracted_data):,}")\n            \n            if extracted_data:\n                st.session_state.extracted_data = extracted_data\n                st.success(f":material/check_circle: Successfully extracted text from {success_count} file(s)!")\n                \n                with st.expander(":material/visibility: Preview First 3 Files"):\n                    for data in extracted_data[:3]:\n                        with st.container(border=True):\n                            st.markdown(f"**{data['file_name']}**")\n                            st.caption(f"{data['word_count']:,} words")\n                            preview_text = data['extracted_text'][:200]\n                            if len(data['extracted_text']) > 200:\n                                preview_text += "..."\n                            st.text(preview_text)\n                    \n                    if len(extracted_data) > 3:\n                        st.caption(f"... and {len(extracted_data) - 3} more")\n                \n                with st.status("Saving to Snowflake...", expanded=True) as status:\n                    try:\n                        st.write(":material/looks_one: Setting up database structure...")\n                        session.sql(f"CREATE DATABASE IF NOT EXISTS {database}").collect()\n                        session.sql(f"CREATE SCHEMA IF NOT EXISTS {database}.{schema}").collect()\n                        \n                        st.write(":material/looks_two: Creating table if needed...")\n                        create_table_sql = f"""\n                        CREATE TABLE IF NOT EXISTS {database}.{schema}.{table_name} (\n                            DOC_ID NUMBER AUTOINCREMENT,\n                            FILE_NAME VARCHAR,\n                            FILE_TYPE VARCHAR,\n                            FILE_SIZE NUMBER,\n                            EXTRACTED_TEXT VARCHAR,\n                            UPLOAD_TIMESTAMP TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP(),\n                            WORD_COUNT NUMBER,\n                            CHAR_COUNT NUMBER\n                        )\n                        """\n                        session.sql(create_table_sql).collect()\n                        \n                        if replace_mode:\n                            st.write(":material/sync: Replace mode: Clearing existing data...")\n                            try:\n                                session.sql(f"TRUNCATE TABLE {database}.{schema}.{table_name}").collect()\n                                st.write("   :material/check_circle: Existing data cleared")\n                            except Exception as e:\n                                st.write(f"   :material/warning: No existing data to clear")\n                        \n                        st.write(f":material/looks_3: Inserting {len(extracted_data)} document(s)...")\n                        \n                        for idx, data in enumerate(extracted_data, 1):\n                            st.caption(f"Saving {idx}/{len(extracted_data)}: {data['file_name']}")\n                            safe_text = data['extracted_text'].replace("'", "''")\n                            insert_sql = f"""\n                            INSERT INTO {database}.{schema}.{table_name}\n                            (FILE_NAME, FILE_TYPE, FILE_SIZE, EXTRACTED_TEXT, WORD_COUNT, CHAR_COUNT)\n                            VALUES ('{data['file_name']}', '{data['file_type']}', {data['file_size']}, \n                                    '{safe_text}', {data['word_count']}, {data['char_count']})\n                            """\n                            session.sql(insert_sql).collect()\n                        \n                        status.update(label=":material/check_circle: All documents saved!", state="complete", expanded=False)\n                        \n                        mode_msg = "replaced in" if replace_mode else "saved to"\n                        st.success(f":material/check_circle: Successfully {mode_msg} `{database}.{schema}.{table_name}`\\n\\n:material/description: {len(extracted_data)} document(s) now in table")\n                        \n                        st.session_state.rag_source_table = f"{database}.{schema}.{table_name}"\n                        st.session_state.rag_source_database = database\n                        st.session_state.rag_source_schema = schema\n                        \n                        st.balloons()\n                    \n                    except Exception as e:\n                        st.error(f"Error saving to Snowflake: {str(e)}")\n            else:\n                st.warning("No text was successfully extracted from any file.")\n\nst.divider()\n\nwith st.container(border=True):\n    st.subheader(":material/search: View Saved Documents")\n    \n    try:\n        count_result = session.sql(f"""\n            SELECT COUNT(*) as CNT FROM {database}.{schema}.{table_name}\n        """).collect()\n        \n        if count_result:\n            record_count = count_result[0]['CNT']\n            if record_count > 0:\n                st.warning(f":material/warning: **{record_count} record(s)** currently in table `{database}.{schema}.{table_name}`")\n            else:\n                st.info(":material/inbox: **Table is empty** - No documents uploaded yet.")\n    except:\n        st.info(":material/inbox: **Table doesn't exist yet** - Upload and save documents to create it.")\n    \n    query_button = st.button("Query Table", type="secondary", use_container_width=True)\n    \n    if query_button:\n        try:\n            full_table_name = f"{database}.{schema}.{table_name}"\n            \n            query_sql = f"""\n            SELECT DOC_ID, FILE_NAME, FILE_TYPE, FILE_SIZE, UPLOAD_TIMESTAMP, WORD_COUNT, CHAR_COUNT\n            FROM {full_table_name}\n            ORDER BY UPLOAD_TIMESTAMP DESC\n            """\n            df = session.sql(query_sql).to_pandas()\n        \n            st.session_state.queried_docs = df\n            st.session_state.full_table_name = full_table_name\n            st.rerun()\n                \n        except Exception as e:\n            st.error(f"Error: {str(e)}")\n            st.info(":material/lightbulb: Table may not exist yet. Upload and save documents first!")\n    \n    if 'queried_docs' in st.session_state and 'full_table_name' in st.session_state:\n        current_full_table_name = f"{st.session_state.database}.{st.session_state.schema}.{st.session_state.table_name}"\n        \n        if st.session_state.full_table_name == current_full_table_name:\n            df = st.session_state.queried_docs\n            \n            if len(df) > 0:\n                st.code(f"{current_full_table_name}", language="sql")\n                \n                col1, col2, col3 = st.columns(3)\n                with col1:\n                    st.metric("Documents", len(df))\n                with col2:\n                    st.metric("Words", f"{df['WORD_COUNT'].sum():,}")\n                with col3:\n                    st.metric("Characters", f"{df['CHAR_COUNT'].sum():,}")\n                \n                st.divider()\n                \n                st.dataframe(\n                    df[['DOC_ID', 'FILE_NAME', 'FILE_TYPE', 'WORD_COUNT', 'UPLOAD_TIMESTAMP']],\n                    use_container_width=True\n                )\n                \n                with st.expander(":material/menu_book: View Full Document Text"):\n                    doc_id = st.selectbox(\n                        "Select Document ID:",\n                        options=df['DOC_ID'].tolist(),\n                        format_func=lambda x: f"Doc #{x} - {df[df['DOC_ID']==x]['FILE_NAME'].values[0]}"\n                    )\n                    \n                    if st.button("Load Text"):\n                        text_sql = f"SELECT EXTRACTED_TEXT, FILE_NAME FROM {current_full_table_name} WHERE DOC_ID = {doc_id}"\n                        text_result = session.sql(text_sql).to_pandas()\n                        if len(text_result) > 0:\n                            doc = text_result.iloc[0]\n                            st.session_state.loaded_doc_text = doc['EXTRACTED_TEXT']\n                            st.session_state.loaded_doc_name = doc['FILE_NAME']\n                    \n                    if 'loaded_doc_text' in st.session_state:\n                        st.text_area(\n                            st.session_state.loaded_doc_name,\n                            value=st.session_state.loaded_doc_text,\n                            height=400\n                        )\n            else:\n                st.info(":material/inbox: Table is empty. Upload files above!")\n        else:\n            st.info(f":material/sync: Showing results for a different table. Click 'Query Table' to refresh.")\n    else:\n        st.info(":material/inbox: No documents queried yet. Click 'Query Table' to view saved documents.")\n\nst.divider()\nst.caption("Day 16: Batch Document Text Extractor for RAG | 30 Days of AI")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHere's the document extraction app in action:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-rag-app-with-streamlit-and-snowflake-cortex/day16.png\" alt=\"Day 16: Batch document text extractor\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPrepare and Chunk Documents\u003C/h2\u003E\n","\u003Cp\u003EText chunking is crucial for effective retrieval. In this section, you'll load the extracted documents from Day 16 and process them into searchable chunks.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote: This section is repurposed from Day 17 of the \u003Ca href=\"https://30daysofai.streamlit.app/?day=17\"\u003E#30DaysOfAI learning challenge\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ESession State Initialization\u003C/h3\u003E\n","\u003Cp\u003EThe app checks if database configuration was passed from Day 16 via session state. If available, it inherits those values; otherwise, it uses defaults:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eif 'day17_database' not in st.session_state:\n    if 'rag_source_database' in st.session_state:\n        st.session_state.day17_database = st.session_state.rag_source_database\n        st.session_state.day17_schema = st.session_state.rag_source_schema\n        st.session_state.day17_table_name = "EXTRACTED_DOCUMENTS"\n    else:\n        st.session_state.day17_database = "RAG_DB"\n        st.session_state.day17_schema = "RAG_SCHEMA"\n        st.session_state.day17_table_name = "EXTRACTED_DOCUMENTS"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ELoading Documents\u003C/h3\u003E\n","\u003Cp\u003EDocuments are loaded from Snowflake with a status indicator showing progress. The \u003Ccode\u003Est.rerun()\u003C/code\u003E call ensures the UI updates after loading:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eif st.button(":material/folder_open: Load Reviews", type="primary", use_container_width=True):\n    with st.status("Loading reviews from Snowflake...", expanded=True) as status:\n        query = f"""SELECT DOC_ID, FILE_NAME, EXTRACTED_TEXT, WORD_COUNT\n                    FROM {st.session_state.day17_database}.{st.session_state.day17_schema}.{st.session_state.day17_table_name}"""\n        df = session.sql(query).to_pandas()\n        st.session_state.loaded_data = df\n        st.rerun()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EProcessing Strategy\u003C/h3\u003E\n","\u003Cp\u003EUsers choose between keeping each review as a single chunk (recommended for short reviews) or splitting longer reviews with configurable chunk size and overlap:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eprocessing_option = st.radio(\n    "Select processing strategy:",\n    ["Keep each review as a single chunk (Recommended)", \n     "Chunk reviews longer than threshold"]\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EChunking Logic\u003C/h3\u003E\n","\u003Cp\u003EFor chunking, the app uses a sliding window approach with overlap to maintain context between chunks:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efor i in range(0, len(words), chunk_size - overlap):\n    chunk_words = words[i:i + chunk_size]\n    chunk_text = ' '.join(chunk_words)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EBulk Insert with write_pandas\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ccode\u003Esession.write_pandas()\u003C/code\u003E method efficiently inserts all chunks in a single operation, with options for replace or append mode.\u003C/p\u003E\n","\u003Ch3\u003EFull Code\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport streamlit as st\nimport pandas as pd\nimport re\n\ntry:\n    from snowflake.snowpark.context import get_active_session\n    session = get_active_session()\nexcept:\n    from snowflake.snowpark import Session\n    session = Session.builder.configs(st.secrets["connections"]["snowflake"]).create()\n\nst.title(":material/sync: Prepare and Chunk Data for RAG")\nst.write("Load customer reviews from Day 16, process them, and prepare searchable chunks for RAG.")\n\nif 'day17_database' not in st.session_state:\n    if 'rag_source_database' in st.session_state:\n        st.session_state.day17_database = st.session_state.rag_source_database\n        st.session_state.day17_schema = st.session_state.rag_source_schema\n        st.session_state.day17_table_name = "EXTRACTED_DOCUMENTS"\n    else:\n        st.session_state.day17_database = "RAG_DB"\n        st.session_state.day17_schema = "RAG_SCHEMA"\n        st.session_state.day17_table_name = "EXTRACTED_DOCUMENTS"\n\nif 'day17_chunk_table' not in st.session_state:\n    st.session_state.day17_chunk_table = "REVIEW_CHUNKS"\n\nwith st.container(border=True):\n    st.subheader(":material/analytics: Source Data Configuration")\n    \n    col1, col2, col3 = st.columns(3)\n    with col1:\n        st.session_state.day17_database = st.text_input(\n            "Database", \n            value=st.session_state.day17_database, \n            key="day17_db_input"\n        )\n    with col2:\n        st.session_state.day17_schema = st.text_input(\n            "Schema", \n            value=st.session_state.day17_schema, \n            key="day17_schema_input"\n        )\n    with col3:\n        st.session_state.day17_table_name = st.text_input(\n            "Source Table", \n            value=st.session_state.day17_table_name, \n            key="day17_table_input"\n        )\n    \n    st.info(f":material/location_on: Loading from: `{st.session_state.day17_database}.{st.session_state.day17_schema}.{st.session_state.day17_table_name}`")\n    st.caption(":material/lightbulb: This should point to the EXTRACTED_DOCUMENTS table from Day 16")\n    \n    if 'loaded_data' in st.session_state:\n        st.success(f":material/check_circle: **{len(st.session_state.loaded_data)} document(s)** already loaded")\n\n    if st.button(":material/folder_open: Load Reviews", type="primary", use_container_width=True):\n        try:\n            with st.status("Loading reviews from Snowflake...", expanded=True) as status:\n                st.write(":material/wifi: Querying database...")\n                \n                query = f"""\n                SELECT \n                    DOC_ID,\n                    FILE_NAME,\n                    FILE_TYPE,\n                    EXTRACTED_TEXT,\n                    UPLOAD_TIMESTAMP,\n                    WORD_COUNT,\n                    CHAR_COUNT\n                FROM {st.session_state.day17_database}.{st.session_state.day17_schema}.{st.session_state.day17_table_name}\n                ORDER BY FILE_NAME\n                """\n                df = session.sql(query).to_pandas()\n                \n                st.write(f":material/check_circle: Loaded {len(df)} review(s)")\n                status.update(label="Reviews loaded successfully!", state="complete", expanded=False)\n                \n                st.session_state.loaded_data = df\n                st.session_state.source_table = f"{st.session_state.day17_database}.{st.session_state.day17_schema}.{st.session_state.day17_table_name}"\n                st.rerun()\n                \n        except Exception as e:\n            st.error(f"Error loading reviews: {str(e)}")\n            st.info(":material/lightbulb: Make sure you've uploaded review files in Day 16 first!")\n\nif 'loaded_data' in st.session_state:\n    with st.container(border=True):\n        st.subheader(":material/looks_one: Review Summary")\n        \n        df = st.session_state.loaded_data\n                \n        col1, col2, col3 = st.columns(3)\n        with col1:\n            st.metric("Total Reviews", len(df))\n        with col2:\n            st.metric("Total Words", f"{df['WORD_COUNT'].sum():,}")\n        with col3:\n            st.metric("Avg Words/Review", f"{df['WORD_COUNT'].mean():.0f}")\n            \n        st.dataframe(df[['DOC_ID', 'FILE_NAME', 'FILE_TYPE', 'UPLOAD_TIMESTAMP', 'WORD_COUNT']], \n                    use_container_width=True)\n                \n    with st.container(border=True):\n        st.subheader(":material/looks_two: Choose Processing Strategy")\n        \n        st.info("""\n        **Customer Review Processing Options:**\n        \n        Since customer reviews are typically short (~150 words each), you have two options:\n        - **Option 1**: Use each review as-is (Recommended for reviews)\n        - **Option 2**: Chunk longer reviews (For reviews >200 words)\n        """)\n        \n        processing_option = st.radio(\n            "Select processing strategy:",\n            ["Keep each review as a single chunk (Recommended)", \n             "Chunk reviews longer than threshold"],\n            index=0\n        )\n        \n        if "Chunk reviews" in processing_option:\n            col1, col2 = st.columns(2)\n            with col1:\n                chunk_size = st.slider(\n                    "Chunk Size (words):",\n                    min_value=50,\n                    max_value=500,\n                    value=200,\n                    step=50,\n                    help="Maximum number of words per chunk"\n                )\n            with col2:\n                overlap = st.slider(\n                    "Overlap (words):",\n                    min_value=0,\n                    max_value=100,\n                    value=50,\n                    step=10,\n                    help="Number of overlapping words between chunks"\n                )\n            st.caption(f"Reviews with >{chunk_size} words will be split into chunks of {chunk_size} words with {overlap} word overlap")\n        else:\n            chunk_size = 200\n            overlap = 50\n        \n        if st.button(":material/flash_on: Process Reviews", type="primary", use_container_width=True):\n            chunks = []\n            \n            with st.status("Processing reviews...", expanded=True) as status:\n                if "Keep each review" in processing_option:\n                    st.write(":material/edit_note: Creating one chunk per review...")\n                    \n                    for idx, row in df.iterrows():\n                        chunks.append({\n                            'doc_id': row['DOC_ID'],\n                            'file_name': row['FILE_NAME'],\n                            'chunk_id': idx + 1,\n                            'chunk_text': row['EXTRACTED_TEXT'],\n                            'chunk_size': row['WORD_COUNT'],\n                            'chunk_type': 'full_review'\n                        })\n                    \n                    st.write(f":material/check_circle: Created {len(chunks)} chunks (1 per review)")\n                    \n                else:\n                    st.write(f":material/edit_note: Chunking reviews longer than {chunk_size} words...")\n                    chunk_id = 1\n                    \n                    for idx, row in df.iterrows():\n                        text = row['EXTRACTED_TEXT']\n                        words = text.split()\n                        \n                        if len(words) <= chunk_size:\n                            chunks.append({\n                                'doc_id': row['DOC_ID'],\n                                'file_name': row['FILE_NAME'],\n                                'chunk_id': chunk_id,\n                                'chunk_text': text,\n                                'chunk_size': len(words),\n                                'chunk_type': 'full_review'\n                            })\n                            chunk_id += 1\n                        else:\n                            for i in range(0, len(words), chunk_size - overlap):\n                                chunk_words = words[i:i + chunk_size]\n                                chunk_text = ' '.join(chunk_words)\n                                \n                                chunks.append({\n                                    'doc_id': row['DOC_ID'],\n                                    'file_name': row['FILE_NAME'],\n                                    'chunk_id': chunk_id,\n                                    'chunk_text': chunk_text,\n                                    'chunk_size': len(chunk_words),\n                                    'chunk_type': 'chunked_review'\n                                })\n                                chunk_id += 1\n                    \n                    st.write(f":material/check_circle: Created {len(chunks)} chunks from {len(df)} reviews")\n                \n                status.update(label="Processing complete!", state="complete", expanded=False)\n                    \n            st.session_state.review_chunks = chunks\n            st.session_state.processing_option = processing_option\n            \n            st.success(f":material/check_circle: Processed {len(df)} reviews into {len(chunks)} searchable chunks!")\n    \n    if 'review_chunks' in st.session_state:\n        with st.container(border=True):\n            st.subheader(":material/looks_3: Processed Review Chunks")\n            \n            chunks = st.session_state.review_chunks\n            \n            col1, col2, col3 = st.columns(3)\n            with col1:\n                st.metric("Total Chunks", len(chunks))\n            with col2:\n                full_reviews = len([c for c in chunks if c['chunk_type'] == 'full_review'])\n                st.metric("Full Reviews", full_reviews)\n            with col3:\n                split_reviews = len([c for c in chunks if c['chunk_type'] == 'chunked_review'])\n                st.metric("Split Reviews", split_reviews)\n            \n            with st.expander(":material/description: View Chunks"):\n                chunks_df = pd.DataFrame(chunks)\n                st.dataframe(chunks_df[['chunk_id', 'file_name', 'chunk_size', 'chunk_type', 'chunk_text']], \n                            use_container_width=True)\n        \n        with st.container(border=True):\n            st.subheader(":material/looks_4: Save Chunks to Snowflake")\n            \n            chunks = st.session_state.review_chunks\n            \n            col1, col2 = st.columns([2, 1])\n            with col1:\n                st.session_state.day17_chunk_table = st.text_input(\n                    "Chunk Table Name",\n                    value=st.session_state.day17_chunk_table,\n                    help="Table name for storing review chunks",\n                    key="day17_chunk_table_input"\n                )\n            \n            full_chunk_table = f"{st.session_state.day17_database}.{st.session_state.day17_schema}.{st.session_state.day17_chunk_table}"\n            st.code(full_chunk_table, language="sql")\n            \n            chunk_table_exists = False\n            try:\n                count_result = session.sql(f"""\n                    SELECT COUNT(*) as CNT FROM {full_chunk_table}\n                """).collect()\n                \n                if count_result:\n                    record_count = count_result[0]['CNT']\n                    if record_count > 0:\n                        st.warning(f":material/warning: **{record_count} chunk(s)** currently in table `{full_chunk_table}`")\n                        chunk_table_exists = True\n                    else:\n                        st.info(":material/inbox: **Chunk table is empty** - No chunks saved yet.")\n                        chunk_table_exists = False\n            except:\n                st.info(":material/inbox: **Chunk table doesn't exist yet** - Will be created when you save chunks.")\n                chunk_table_exists = False\n            \n            if 'day17_replace_mode' not in st.session_state:\n                st.session_state.day17_replace_mode = chunk_table_exists\n            else:\n                if 'day17_last_chunk_table' not in st.session_state or st.session_state.day17_last_chunk_table != full_chunk_table:\n                    st.session_state.day17_replace_mode = chunk_table_exists\n                    st.session_state.day17_last_chunk_table = full_chunk_table\n            \n            replace_mode = st.checkbox(\n                f":material/sync: Replace Table Mode for `{st.session_state.day17_chunk_table}`",\n                help=f"When enabled, clears all existing data in {full_chunk_table} before saving new chunks",\n                key="day17_replace_mode"\n            )\n            \n            if replace_mode:\n                st.warning("**Replace Mode Active**: Existing chunks will be deleted before saving new ones.")\n            else:\n                st.success("**Append Mode Active**: New chunks will be added to existing data.")\n            \n            if st.button(":material/save: Save Chunks to Snowflake", type="primary", use_container_width=True):\n                try:\n                    with st.status("Saving chunks to Snowflake...", expanded=True) as status:\n                        st.write(":material/looks_one: Checking table...")\n                        create_table_sql = f"""\n                        CREATE TABLE IF NOT EXISTS {full_chunk_table} (\n                            CHUNK_ID NUMBER,\n                            DOC_ID NUMBER,\n                            FILE_NAME VARCHAR,\n                            CHUNK_TEXT VARCHAR,\n                            CHUNK_SIZE NUMBER,\n                            CHUNK_TYPE VARCHAR,\n                            CREATED_TIMESTAMP TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP()\n                        )\n                        """\n                        session.sql(create_table_sql).collect()\n                        \n                        if replace_mode:\n                            st.write(":material/sync: Replace mode: Clearing existing chunks...")\n                            try:\n                                session.sql(f"TRUNCATE TABLE {full_chunk_table}").collect()\n                                st.write("   :material/check_circle: Existing chunks cleared")\n                            except Exception as e:\n                                st.write(f"   :material/warning: No existing chunks to clear")\n                        \n                        st.write(f":material/looks_3: Inserting {len(chunks)} chunk(s)...")\n                        chunks_df = pd.DataFrame(chunks)\n                        \n                        chunks_df_upper = chunks_df[['chunk_id', 'doc_id', 'file_name', 'chunk_text', \n                                                      'chunk_size', 'chunk_type']].copy()\n                        chunks_df_upper.columns = ['CHUNK_ID', 'DOC_ID', 'FILE_NAME', 'CHUNK_TEXT', \n                                                   'CHUNK_SIZE', 'CHUNK_TYPE']\n                        \n                        if replace_mode:\n                            session.write_pandas(chunks_df_upper,\n                                               table_name=st.session_state.day17_chunk_table,\n                                               database=st.session_state.day17_database,\n                                               schema=st.session_state.day17_schema,\n                                               overwrite=True)\n                        else:\n                            session.write_pandas(chunks_df_upper,\n                                               table_name=st.session_state.day17_chunk_table,\n                                               database=st.session_state.day17_database,\n                                               schema=st.session_state.day17_schema,\n                                               overwrite=False)\n                        \n                        status.update(label=":material/check_circle: Chunks saved!", state="complete", expanded=False)\n                    \n                    mode_msg = "replaced in" if replace_mode else "saved to"\n                    st.success(f":material/check_circle: Successfully {mode_msg} `{full_chunk_table}`\\n\\n:material/description: {len(chunks)} chunk(s) now in table")\n                    \n                    st.session_state.chunks_table = full_chunk_table\n                    st.session_state.chunks_database = st.session_state.day17_database\n                    st.session_state.chunks_schema = st.session_state.day17_schema\n                    st.session_state.chunk_table_saved = True\n                    \n                    st.balloons()\n                    \n                except Exception as e:\n                    st.error(f"Error saving chunks: {str(e)}")\n\nwith st.container(border=True):\n    st.subheader(":material/search: View Saved Chunks")\n    \n    full_chunk_table = f"{st.session_state.day17_database}.{st.session_state.day17_schema}.{st.session_state.day17_chunk_table}"\n    st.caption(f":material/analytics: Querying chunk table: `{full_chunk_table}`")\n    \n    query_button = st.button(":material/analytics: Query Chunk Table", type="secondary", use_container_width=True)\n    \n    if query_button:\n        try:\n            query_sql = f"""\n            SELECT \n                CHUNK_ID,\n                FILE_NAME,\n                CHUNK_SIZE,\n                CHUNK_TYPE,\n                LEFT(CHUNK_TEXT, 100) AS TEXT_PREVIEW,\n                CREATED_TIMESTAMP\n            FROM {full_chunk_table}\n            ORDER BY CHUNK_ID\n            """\n            chunks_df = session.sql(query_sql).to_pandas()\n            \n            st.session_state.queried_chunks = chunks_df\n            st.session_state.queried_chunks_table = full_chunk_table\n            st.rerun()\n                \n        except Exception as e:\n            st.error(f"Error querying chunks: {str(e)}")\n    \n    if 'queried_chunks' in st.session_state and st.session_state.get('queried_chunks_table') == full_chunk_table:\n        chunks_df = st.session_state.queried_chunks\n        \n        if len(chunks_df) > 0:\n            st.code(full_chunk_table, language="sql")\n            \n            col1, col2, col3 = st.columns(3)\n            with col1:\n                st.metric("Total Chunks", len(chunks_df))\n            with col2:\n                full_count = len(chunks_df[chunks_df['CHUNK_TYPE'] == 'full_review'])\n                st.metric("Full Reviews", full_count)\n            with col3:\n                split_count = len(chunks_df[chunks_df['CHUNK_TYPE'] == 'chunked_review'])\n                st.metric("Split Reviews", split_count)\n            \n            st.dataframe(\n                chunks_df[['CHUNK_ID', 'FILE_NAME', 'CHUNK_SIZE', 'CHUNK_TYPE', 'TEXT_PREVIEW']],\n                use_container_width=True\n            )\n            \n            with st.expander(":material/menu_book: View Full Chunk Text"):\n                chunk_id = st.selectbox(\n                    "Select Chunk ID:",\n                    options=chunks_df['CHUNK_ID'].tolist(),\n                    format_func=lambda x: f"Chunk #{x} - {chunks_df[chunks_df['CHUNK_ID']==x]['FILE_NAME'].values[0]}",\n                    key="chunk_text_selector"\n                )\n                \n                if st.button("Load Chunk Text", key="load_chunk_text_btn"):\n                    st.session_state.selected_chunk_id = chunk_id\n                    st.session_state.load_chunk_text = True\n                    st.rerun()\n                \n                if st.session_state.get('load_chunk_text') and st.session_state.get('selected_chunk_id'):\n                    text_sql = f"SELECT CHUNK_TEXT, FILE_NAME FROM {full_chunk_table} WHERE CHUNK_ID = {st.session_state.selected_chunk_id}"\n                    text_result = session.sql(text_sql).to_pandas()\n                    if len(text_result) > 0:\n                        chunk = text_result.iloc[0]\n                        st.text_area(\n                            chunk['FILE_NAME'],\n                            value=chunk['CHUNK_TEXT'],\n                            height=300,\n                            key=f"chunk_text_display_{st.session_state.selected_chunk_id}"\n                        )\n        else:\n            st.info(":material/inbox: No chunks found in table.")\n    else:\n        st.info(":material/inbox: No chunks queried yet. Click 'Query Chunk Table' to view saved chunks.")\n\nst.divider()\nst.caption("Day 17: Loading and Transforming Customer Reviews for RAG | 30 Days of AI")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHere's the chunking process in action:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-rag-app-with-streamlit-and-snowflake-cortex/day17.png\" alt=\"Day 17: Document chunking with processing options\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EGenerate Embeddings\u003C/h2\u003E\n","\u003Cp\u003EEmbeddings convert text into numerical vectors that capture semantic meaning, enabling similarity-based search. In this section, you'll generate 768-dimensional embeddings for your chunked reviews using Snowflake Cortex.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote: This section is repurposed from Day 18 of the \u003Ca href=\"https://30daysofai.streamlit.app/?day=18\"\u003E#30DaysOfAI learning challenge\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EUnderstanding Embeddings\u003C/h3\u003E\n","\u003Cp\u003EEmbeddings transform text into dense numerical vectors where similar meanings produce similar vectors. The \u003Ccode\u003Eembed_text_768\u003C/code\u003E function from Snowflake Cortex generates 768-dimensional vectors using the \u003Ccode\u003Esnowflake-arctic-embed-m\u003C/code\u003E model.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom snowflake.cortex import embed_text_768\n\nemb = embed_text_768(model='snowflake-arctic-embed-m', text=row['CHUNK_TEXT'])\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStoring Embeddings with VECTOR Type\u003C/h3\u003E\n","\u003Cp\u003ESnowflake's native \u003Ccode\u003EVECTOR(FLOAT, 768)\u003C/code\u003E data type efficiently stores embedding vectors:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Ecreate_table_sql = f"""\nCREATE OR REPLACE TABLE {full_embedding_table} (\n    CHUNK_ID NUMBER,\n    EMBEDDING VECTOR(FLOAT, 768),\n    CREATED_TIMESTAMP TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP()\n)\n"""\nsession.sql(create_table_sql).collect()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EBatch Processing with Progress Tracking\u003C/h3\u003E\n","\u003Cp\u003EProcessing embeddings in batches prevents timeouts and provides user feedback:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efor i in range(0, total_chunks, batch_size):\n    batch_end = min(i + batch_size, total_chunks)\n    st.write(f"Processing chunks {i+1} to {batch_end} of {total_chunks}...")\n    \n    for idx, row in df.iloc[i:batch_end].iterrows():\n        emb = embed_text_768(model='snowflake-arctic-embed-m', text=row['CHUNK_TEXT'])\n        embeddings.append({'chunk_id': row['CHUNK_ID'], 'embedding': emb})\n    \n    progress_bar.progress(batch_end / total_chunks)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EInserting Vector Data\u003C/h3\u003E\n","\u003Cp\u003EConvert embeddings to Snowflake's vector format for storage:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eemb_array = "[" + ",".join([str(float(x)) for x in emb_list]) + "]"\n\ninsert_sql = f"""\nINSERT INTO {full_embedding_table} (CHUNK_ID, EMBEDDING)\nSELECT {emb_data['chunk_id']}, {emb_array}::VECTOR(FLOAT, 768)\n"""\nsession.sql(insert_sql).collect()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EFull Code\u003C/h3\u003E\n","\u003Cp\u003ECopy and paste this code into \u003Ccode\u003Eday18.py\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Day 18\n# Generating Embeddings for Customer Reviews\n\nimport streamlit as st\nfrom snowflake.cortex import embed_text_768\nimport pandas as pd\nimport numpy as np\n\nst.title(":material/calculate: Embeddings Generator for Customer Reviews")\nst.write("Generate embeddings for review chunks from Day 17 to enable semantic search.")\n\n# Connect to Snowflake\ntry:\n    # Works in Streamlit in Snowflake\n    from snowflake.snowpark.context import get_active_session\n    session = get_active_session()\nexcept:\n    # Works locally and on Streamlit Community Cloud\n    from snowflake.snowpark import Session\n    session = Session.builder.configs(st.secrets["connections"]["snowflake"]).create()\n\n# Initialize session state for database configuration\nif 'day18_database' not in st.session_state:\n    # Check if we have chunks from Day 17\n    if 'chunks_database' in st.session_state:\n        st.session_state.day18_database = st.session_state.chunks_database\n        st.session_state.day18_schema = st.session_state.chunks_schema\n        st.session_state.day18_chunk_table = "REVIEW_CHUNKS"\n    else:\n        st.session_state.day18_database = "RAG_DB"\n        st.session_state.day18_schema = "RAG_SCHEMA"\n        st.session_state.day18_chunk_table = "REVIEW_CHUNKS"\n\nif 'day18_embedding_table' not in st.session_state:\n    st.session_state.day18_embedding_table = "REVIEW_EMBEDDINGS"\n\n# Explanation\nwith st.expander(":material/library_books: What are embeddings?", expanded=True):\n    st.markdown("""\n    **Embeddings** convert text into numbers (vectors) that capture meaning:\n    \n    - Similar texts → Similar vectors\n    - Different texts → Different vectors\n    - Enables "search by meaning" (semantic search)\n    \n    The model outputs **768 numbers** for any text input.\n    \n    **In RAG for Customer Reviews**: Each review (or chunk) gets its own embedding, \n    allowing semantic search to find relevant customer feedback!\n    \n    **Example**: Search for "warm gloves" will find reviews mentioning "provides good warmth", \n    "kept hands toasty", even without exact keywords!\n    """)\n\n# Source Data Configuration and Load Section\nwith st.container(border=True):\n    st.subheader(":material/analytics: Source Data Configuration")\n    \n    # Database configuration\n    col1, col2, col3 = st.columns(3)\n    with col1:\n        st.session_state.day18_database = st.text_input(\n            "Database", \n            value=st.session_state.day18_database, \n            key="day18_db_input"\n        )\n    with col2:\n        st.session_state.day18_schema = st.text_input(\n            "Schema", \n            value=st.session_state.day18_schema, \n            key="day18_schema_input"\n        )\n    with col3:\n        st.session_state.day18_chunk_table = st.text_input(\n            "Chunks Table", \n            value=st.session_state.day18_chunk_table, \n            key="day18_chunk_table_input"\n        )\n    \n    st.info(f":material/location_on: Loading from: `{st.session_state.day18_database}.{st.session_state.day18_schema}.{st.session_state.day18_chunk_table}`")\n    st.caption(":material/lightbulb: This should point to the REVIEW_CHUNKS table from Day 17")\n    \n    # Check for existing loaded data\n    if 'chunks_data' in st.session_state:\n        st.success(f":material/check_circle: **{len(st.session_state.chunks_data)} chunk(s)** already loaded")\n    \n    # Load chunks button\n    if st.button(":material/folder_open: Load Chunks", type="primary", use_container_width=True):\n        try:\n            with st.status("Loading chunks...", expanded=True) as status:\n                st.write(":material/wifi: Querying database...")\n                \n                query = f"""\n                SELECT \n                    CHUNK_ID,\n                    DOC_ID,\n                    FILE_NAME,\n                    CHUNK_TEXT,\n                    CHUNK_SIZE,\n                    CHUNK_TYPE\n                FROM {st.session_state.day18_database}.{st.session_state.day18_schema}.{st.session_state.day18_chunk_table}\n                ORDER BY CHUNK_ID\n                """\n                df = session.sql(query).to_pandas()\n                \n                st.write(f":material/check_circle: Loaded {len(df)} chunks")\n                status.update(label="Chunks loaded successfully!", state="complete", expanded=False)\n                \n                # Store in session state\n                st.session_state.chunks_data = df\n                st.rerun()\n                \n        except Exception as e:\n            st.error(f"Error loading chunks: {str(e)}")\n            st.info(":material/lightbulb: Make sure you've processed reviews in Day 17 first!")\n\n# Main content - Chunk Summary\nif 'chunks_data' in st.session_state:\n    with st.container(border=True):\n        st.subheader(":material/looks_one: Chunk Summary")\n        \n        df = st.session_state.chunks_data\n        \n        col1, col2, col3 = st.columns(3)\n        with col1:\n            st.metric("Total Chunks", len(df))\n        with col2:\n            st.metric("Unique Reviews", df['FILE_NAME'].nunique())\n        with col3:\n            st.metric("Avg Chunk Size", f"{df['CHUNK_SIZE'].mean():.0f} words")\n        \n        # Show chunk type distribution\n        if 'CHUNK_TYPE' in df.columns:\n            st.write("**Chunk Type Distribution:**")\n            chunk_type_counts = df['CHUNK_TYPE'].value_counts()\n            col1, col2 = st.columns(2)\n            with col1:\n                st.metric("Full Reviews", chunk_type_counts.get('full_review', 0))\n            with col2:\n                st.metric("Split Reviews", chunk_type_counts.get('chunked_review', 0))\n        \n        with st.expander(":material/description: Preview Chunks"):\n            st.dataframe(df.head(10), use_container_width=True)\n    \n    # Generate embeddings\n    with st.container(border=True):\n        st.subheader(":material/looks_two: Generate Embeddings")\n        \n        st.info("""\n        **What happens here:**\n        - Each review chunk is converted to a 768-dimensional vector\n        - Embeddings are stored in Snowflake for semantic search\n        - Enables finding relevant reviews based on meaning, not just keywords\n        \n        **For Customer Reviews**: This allows your RAG system to:\n        - Find reviews about "durability" even if they mention "long-lasting" or "fell apart"\n        - Search for "warm" products and find "toasty", "cold hands", "insulation"\n        - Group similar feedback together semantically\n        """)\n        \n        # Batch size selection\n        batch_size = st.selectbox("Batch Size", [10, 25, 50, 100], index=1,\n                                  help="Number of chunks to process at once")\n\n        if st.button(":material/calculate: Generate Embeddings", type="primary", use_container_width=True):\n            try:\n                with st.status("Generating embeddings...", expanded=True) as status:\n                    embeddings = []\n                    total_chunks = len(df)\n                    progress_bar = st.progress(0)\n                    \n                    for i in range(0, total_chunks, batch_size):\n                        batch_end = min(i + batch_size, total_chunks)\n                        st.write(f"Processing chunks {i+1} to {batch_end} of {total_chunks}...")\n                        \n                        for idx, row in df.iloc[i:batch_end].iterrows():\n                            # Generate embedding using the correct function signature\n                            emb = embed_text_768(model='snowflake-arctic-embed-m', text=row['CHUNK_TEXT'])\n                            embeddings.append({\n                                'chunk_id': row['CHUNK_ID'],\n                                'embedding': emb\n                            })\n                        \n                        # Update progress\n                        progress = batch_end / total_chunks\n                        progress_bar.progress(progress)\n                    \n                    status.update(label="Embeddings generated!", state="complete", expanded=False)\n                    \n                    # Store in session state\n                    st.session_state.embeddings_data = embeddings\n                    \n                    st.success(f":material/check_circle: Generated {len(embeddings)} embeddings for {len(df)} review chunks!")\n                    \n            except Exception as e:\n                st.error(f"Error generating embeddings: {str(e)}")\n    \n    # View embeddings\n    if 'embeddings_data' in st.session_state:\n        with st.container(border=True):\n            st.subheader(":material/looks_3: View Embeddings")\n            \n            embeddings = st.session_state.embeddings_data\n            \n            col1, col2 = st.columns(2)\n            with col1:\n                st.metric("Embeddings Generated", len(embeddings))\n            with col2:\n                st.metric("Dimensions per Embedding", 768)\n            \n            # Show sample embedding\n            with st.expander(":material/search: View Sample Embedding"):\n                sample_emb = embeddings[0]['embedding']\n                st.write("**First 10 values:**")\n                st.write(sample_emb[:10])\n        \n        # Save embeddings to Snowflake\n        with st.container(border=True):\n            st.subheader(":material/looks_4: Save Embeddings to Snowflake")\n            \n            embeddings = st.session_state.embeddings_data\n            \n            # Embedding table name\n            col1, col2 = st.columns([2, 1])\n            with col1:\n                st.session_state.day18_embedding_table = st.text_input(\n                    "Embeddings Table Name",\n                    value=st.session_state.day18_embedding_table,\n                    help="Table name for storing embeddings",\n                    key="day18_embedding_table_input"\n                )\n            \n            full_embedding_table = f"{st.session_state.day18_database}.{st.session_state.day18_schema}.{st.session_state.day18_embedding_table}"\n            st.code(full_embedding_table, language="sql")\n            \n            # Check if embeddings table exists and show status\n            try:\n                check_query = f"""\n                SELECT COUNT(*) as count\n                FROM {full_embedding_table}\n                """\n                result = session.sql(check_query).collect()\n                current_count = result[0]['COUNT']\n                \n                if current_count > 0:\n                    st.warning(f":material/warning: **{current_count:,} embedding(s)** currently in table `{full_embedding_table}`")\n                    embedding_table_exists = True\n                else:\n                    st.info(":material/inbox: **Embedding table is empty** - No embeddings saved yet.")\n                    embedding_table_exists = False\n            except:\n                st.info(":material/inbox: **Embedding table doesn't exist yet** - Will be created when you save embeddings.")\n                embedding_table_exists = False\n            \n            # Initialize or update checkbox state based on table status\n            if 'day18_replace_mode' not in st.session_state:\n                st.session_state.day18_replace_mode = embedding_table_exists\n            else:\n                if 'day18_last_embedding_table' not in st.session_state or st.session_state.day18_last_embedding_table != full_embedding_table:\n                    st.session_state.day18_replace_mode = embedding_table_exists\n                    st.session_state.day18_last_embedding_table = full_embedding_table\n            \n            # Replace mode checkbox\n            replace_mode = st.checkbox(\n                f":material/sync: Replace Table Mode for `{st.session_state.day18_embedding_table}`",\n                help=f"When enabled, replaces all existing embeddings in {full_embedding_table}",\n                key="day18_replace_mode"\n            )\n            \n            if replace_mode:\n                st.warning("**Replace Mode Active**: Existing embeddings will be deleted before saving new ones.")\n            else:\n                st.success("**Append Mode Active**: New embeddings will be added to existing data.")\n            \n            if st.button(":material/save: Save Embeddings to Snowflake", type="primary", use_container_width=True):\n                try:\n                    with st.status("Saving embeddings...", expanded=True) as status:\n                        # Step 1: Create or truncate embeddings table\n                        st.write(":material/looks_one: Preparing table...")\n                        \n                        if replace_mode:\n                            # Replace existing data\n                            create_table_sql = f"""\n                            CREATE OR REPLACE TABLE {full_embedding_table} (\n                                CHUNK_ID NUMBER,\n                                EMBEDDING VECTOR(FLOAT, 768),\n                                CREATED_TIMESTAMP TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP()\n                            )\n                            """\n                            session.sql(create_table_sql).collect()\n                            st.write(":material/check_circle: Replaced existing table")\n                        else:\n                            # Create if doesn't exist\n                            create_table_sql = f"""\n                            CREATE TABLE IF NOT EXISTS {full_embedding_table} (\n                                CHUNK_ID NUMBER,\n                                EMBEDDING VECTOR(FLOAT, 768),\n                                CREATED_TIMESTAMP TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP()\n                            )\n                            """\n                            session.sql(create_table_sql).collect()\n                            st.write(":material/check_circle: Table ready")\n                        \n                        # Step 2: Insert embeddings\n                        st.write(f":material/looks_two: Inserting {len(embeddings)} embeddings...")\n                        \n                        for i, emb_data in enumerate(embeddings):\n                            # Get embedding list\n                            if isinstance(emb_data['embedding'], list):\n                                emb_list = emb_data['embedding']\n                            else:\n                                emb_list = list(emb_data['embedding'])\n                            \n                            # Convert to proper array format for Snowflake\n                            emb_array = "[" + ",".join([str(float(x)) for x in emb_list]) + "]"\n                            \n                            insert_sql = f"""\n                            INSERT INTO {full_embedding_table} (CHUNK_ID, EMBEDDING)\n                            SELECT {emb_data['chunk_id']}, {emb_array}::VECTOR(FLOAT, 768)\n                            """\n                            session.sql(insert_sql).collect()\n                            \n                            if (i + 1) % 10 == 0:\n                                st.write(f"Saved {i + 1} of {len(embeddings)} embeddings...")\n                        \n                        status.update(label="Embeddings saved!", state="complete", expanded=False)\n                    \n                    mode_msg = "replaced in" if replace_mode else "saved to"\n                    st.success(f":material/check_circle: Successfully {mode_msg} `{full_embedding_table}`\\n\\n:material/calculate: {len(embeddings)} embedding(s) now in table")\n                    \n                    # Store for Day 19\n                    st.session_state.embeddings_table = full_embedding_table\n                    st.session_state.embeddings_database = st.session_state.day18_database\n                    st.session_state.embeddings_schema = st.session_state.day18_schema\n                    \n                    st.balloons()\n                    \n                except Exception as e:\n                    st.error(f"Error saving embeddings: {str(e)}")\n\n# View Saved Embeddings Section\nwith st.container(border=True):\n    st.subheader(":material/search: View Saved Embeddings")\n    \n    # Check if embeddings table exists and show record count\n    full_embedding_table = f"{st.session_state.day18_database}.{st.session_state.day18_schema}.{st.session_state.day18_embedding_table}"\n    \n    try:\n        count_result = session.sql(f"""\n            SELECT COUNT(*) as CNT FROM {full_embedding_table}\n        """).collect()\n        \n        if count_result:\n            record_count = count_result[0]['CNT']\n            if record_count > 0:\n                st.warning(f":material/warning: **{record_count:,} embedding(s)** currently in table `{full_embedding_table}`")\n            else:\n                st.info(":material/inbox: **Embedding table is empty** - Generate and save embeddings above.")\n    except:\n        st.info(":material/inbox: **Embedding table doesn't exist yet** - Generate and save embeddings to create it.")\n    \n    query_button = st.button(":material/analytics: Query Embedding Table", type="secondary", use_container_width=True)\n    \n    if query_button:\n        try:\n            query = f"""\n            SELECT \n                CHUNK_ID,\n                EMBEDDING,\n                CREATED_TIMESTAMP,\n                VECTOR_L2_DISTANCE(EMBEDDING, EMBEDDING) as SELF_DISTANCE\n            FROM {full_embedding_table}\n            ORDER BY CHUNK_ID\n            """\n            result_df = session.sql(query).to_pandas()\n            \n            # Store in session state\n            st.session_state.queried_embeddings = result_df\n            st.session_state.queried_embeddings_table = full_embedding_table\n            st.rerun()\n            \n        except Exception as e:\n            st.error(f"Error querying embeddings: {str(e)}")\n    \n    # Display results if available in session state\n    if 'queried_embeddings' in st.session_state and st.session_state.get('queried_embeddings_table') == full_embedding_table:\n        emb_df = st.session_state.queried_embeddings\n        \n        if len(emb_df) > 0:\n            st.code(full_embedding_table, language="sql")\n            \n            # Summary metrics\n            col1, col2 = st.columns(2)\n            with col1:\n                st.metric("Total Embeddings", len(emb_df))\n            with col2:\n                st.metric("Dimensions", "768")\n            \n            # Display table without the EMBEDDING column for readability\n            # Check which columns exist (case-insensitive)\n            embedding_col = None\n            for col in emb_df.columns:\n                if col.upper() == 'EMBEDDING':\n                    embedding_col = col\n                    break\n            \n            if embedding_col:\n                display_df = emb_df.drop(columns=[embedding_col])\n            else:\n                display_df = emb_df\n            \n            st.dataframe(display_df, use_container_width=True)\n            \n            st.info(":material/lightbulb: Self-distance should be 0, confirming embeddings are stored correctly")\n            \n            # View individual embedding vectors (only if EMBEDDING column exists)\n            if embedding_col:\n                with st.expander(":material/search: View Individual Embedding Vectors"):\n                    st.write("Select a CHUNK_ID to view its full 768-dimensional embedding vector:")\n                    \n                    # Find CHUNK_ID column (case-insensitive)\n                    chunk_id_col = None\n                    for col in emb_df.columns:\n                        if col.upper() == 'CHUNK_ID':\n                            chunk_id_col = col\n                            break\n                    \n                    chunk_ids = emb_df[chunk_id_col].tolist()\n                    selected_chunk = st.selectbox("Select CHUNK_ID", chunk_ids, key="view_embedding_chunk")\n                    \n                    if st.button(":material/analytics: Load Embedding Vector", key="load_embedding_btn"):\n                        # Get the embedding for selected chunk\n                        selected_emb = emb_df[emb_df[chunk_id_col] == selected_chunk][embedding_col].iloc[0]\n                        \n                        # Store in session state\n                        st.session_state.loaded_embedding = selected_emb\n                        st.session_state.loaded_embedding_chunk = selected_chunk\n                        st.rerun()\n                    \n                    # Display loaded embedding\n                    if 'loaded_embedding' in st.session_state:\n                        st.write(f"**Embedding Vector for CHUNK_ID {st.session_state.loaded_embedding_chunk}:**")\n                        \n                        # Convert to list if needed\n                        emb_vector = st.session_state.loaded_embedding\n                        if isinstance(emb_vector, str):\n                            # If it's a string representation, parse it\n                            import json\n                            emb_vector = json.loads(emb_vector)\n                        elif hasattr(emb_vector, 'tolist'):\n                            emb_vector = emb_vector.tolist()\n                        elif not isinstance(emb_vector, list):\n                            emb_vector = list(emb_vector)\n                        \n                        st.caption(f"Vector length: {len(emb_vector)} dimensions")\n                        \n                        # Display the full embedding vector as code\n                        st.code(emb_vector, language="python")\n        else:\n            st.info(":material/inbox: No embeddings found in table.")\n    else:\n        st.info(":material/inbox: No embeddings queried yet. Click 'Query Embedding Table' to view saved embeddings.")\n\nst.divider()\nst.caption("Day 18: Generating Embeddings for Customer Reviews | 30 Days of AI")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHere's the embedding generator in action:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-rag-app-with-streamlit-and-snowflake-cortex/day18.png\" alt=\"Day 18: Embeddings Generator\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate Cortex Search Service\u003C/h2\u003E\n","\u003Cp\u003ECortex Search provides semantic search over your chunked data. In this section, you'll create a search service that indexes your chunks for fast retrieval.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote: This section is repurposed from Day 19 of the \u003Ca href=\"https://30daysofai.streamlit.app/?day=19\"\u003E#30DaysOfAI learning challenge\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EService Configuration\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ccode\u003ECREATE CORTEX SEARCH SERVICE\u003C/code\u003E statement defines how your data is indexed:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EON\u003C/strong\u003E: Specifies the text column to search (\u003Ccode\u003ECHUNK_TEXT\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EATTRIBUTES\u003C/strong\u003E: Additional columns returned with results (\u003Ccode\u003EFILE_NAME\u003C/code\u003E, \u003Ccode\u003ECHUNK_TYPE\u003C/code\u003E, \u003Ccode\u003ECHUNK_ID\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETARGET_LAG\u003C/strong\u003E: How often the index refreshes (1 hour keeps it reasonably fresh)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWAREHOUSE\u003C/strong\u003E: The compute resource used for indexing\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EKey Configuration Options\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EParameter\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDescription\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EON\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EThe text column to search\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EATTRIBUTES\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMetadata columns to return\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ETARGET_LAG\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHow often to refresh the index\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EWAREHOUSE\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECompute resource for indexing\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EFull Code\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE CORTEX SEARCH SERVICE RAG_DB.RAG_SCHEMA.CUSTOMER_REVIEW_SEARCH\n    ON CHUNK_TEXT\n    ATTRIBUTES FILE_NAME, CHUNK_TYPE, CHUNK_ID\n    WAREHOUSE = COMPUTE_WH\n    TARGET_LAG = '1 hour'\n    AS (\n        SELECT \n            CHUNK_ID,\n            FILE_NAME,\n            CHUNK_TYPE,\n            CHUNK_TEXT\n        FROM RAG_DB.RAG_SCHEMA.REVIEW_CHUNKS\n    );\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EVerify your service was created:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW CORTEX SEARCH SERVICES IN SCHEMA RAG_DB.RAG_SCHEMA;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHere's the Cortex Search service creation:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-rag-app-with-streamlit-and-snowflake-cortex/day19.png\" alt=\"Day 19: Cortex Search service configuration\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EQuery Cortex Search\u003C/h2\u003E\n","\u003Cp\u003EUse the Python SDK to search your documents semantically. This section builds an interactive search interface.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote: This section is repurposed from Day 20 of the \u003Ca href=\"https://30daysofai.streamlit.app/?day=20\"\u003E#30DaysOfAI learning challenge\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ECortex Search Python SDK\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ccode\u003Esnowflake.core.Root\u003C/code\u003E API provides programmatic access to Cortex Search. Navigate the object hierarchy using database, schema, and service name:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom snowflake.core import Root\n\nroot = Root(session)\nparts = service_path.split(".")\nsvc = root.databases[parts[0]].schemas[parts[1]].cortex_search_services[parts[2]]\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESearch Method\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ccode\u003Esearch()\u003C/code\u003E method accepts a natural language query and returns relevant chunks ranked by semantic similarity:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eresults = svc.search(\n    query="warm thermal gloves",\n    columns=["CHUNK_TEXT", "FILE_NAME", "CHUNK_TYPE", "CHUNK_ID"],\n    limit=5\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EDisplaying Results\u003C/h3\u003E\n","\u003Cp\u003EEach result is displayed in a bordered container with file metadata and chunk text for easy scanning.\u003C/p\u003E\n","\u003Ch3\u003EFull Code\u003C/h3\u003E\n","\u003Cp\u003ECopy and paste this code into \u003Ccode\u003Eday20.py\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport streamlit as st\nfrom snowflake.core import Root\n\ntry:\n    from snowflake.snowpark.context import get_active_session\n    session = get_active_session()\nexcept:\n    from snowflake.snowpark import Session\n    session = Session.builder.configs(st.secrets["connections"]["snowflake"]).create()\n\nst.title(":material/search: Querying Cortex Search")\n\nsearch_service = st.text_input(\n    "Search Service:",\n    value="RAG_DB.RAG_SCHEMA.CUSTOMER_REVIEW_SEARCH"\n)\n\nquery = st.text_input("Enter your search query:", value="warm thermal gloves")\nnum_results = st.slider("Number of results:", 1, 20, 5)\n\nif st.button(":material/search: Search", type="primary"):\n    if query and search_service:\n        root = Root(session)\n        parts = search_service.split(".")\n        \n        svc = root.databases[parts[0]].schemas[parts[1]].cortex_search_services[parts[2]]\n        \n        with st.spinner("Searching..."):\n            results = svc.search(\n                query=query,\n                columns=["CHUNK_TEXT", "FILE_NAME", "CHUNK_TYPE", "CHUNK_ID"],\n                limit=num_results\n            )\n        \n        st.success(f":material/check_circle: Found {len(results.results)} result(s)!")\n        \n        for i, item in enumerate(results.results, 1):\n            with st.container(border=True):\n                st.markdown(f"**Result {i}** - {item.get('FILE_NAME', 'N/A')}")\n                st.write(item.get("CHUNK_TEXT", "No text found"))\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHere's the search interface in action:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-rag-app-with-streamlit-and-snowflake-cortex/day20.png\" alt=\"Day 20: Cortex Search query interface with results\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild RAG Application\u003C/h2\u003E\n","\u003Cp\u003ECombine retrieval with LLM generation for grounded answers. The RAG pattern retrieves relevant context, then generates responses based on that context.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote: This section is repurposed from Day 21 of the \u003Ca href=\"https://30daysofai.streamlit.app/?day=21\"\u003E#30DaysOfAI learning challenge\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ERAG Architecture\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003ERetrieve\u003C/strong\u003E: Search Cortex Search for relevant chunks\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAugment\u003C/strong\u003E: Add retrieved chunks to the prompt as context\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EGenerate\u003C/strong\u003E: LLM creates an answer grounded in the context\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EBuilding the Context\u003C/h3\u003E\n","\u003Cp\u003ERetrieved chunks are joined with separators to create a context string that's passed to the LLM:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Econtext_chunks = [item.get("CHUNK_TEXT", "") for item in search_results.results]\ncontext = "\\n\\n---\\n\\n".join(context_chunks)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EPrompt Engineering\u003C/h3\u003E\n","\u003Cp\u003EThe prompt explicitly instructs the model to only use provided context, reducing hallucinations:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Erag_prompt = f"""You are a helpful assistant. Answer the user's question based ONLY on the provided context.\nIf the context doesn't contain enough information, say "I don't have enough information..."\n\nCONTEXT FROM DOCUMENTS:\n{context}\n\nUSER QUESTION: {question}\n"""\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ELLM Completion\u003C/h3\u003E\n","\u003Cp\u003EUse \u003Ccode\u003ESNOWFLAKE.CORTEX.COMPLETE()\u003C/code\u003E to call the LLM with your context-aware prompt:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eresponse_sql = f"""\nSELECT SNOWFLAKE.CORTEX.COMPLETE(\n    'claude-3-5-sonnet',\n    '{rag_prompt.replace("'", "''")}'\n) as response\n"""\nresponse = session.sql(response_sql).collect()[0][0]\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EFull Code\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport streamlit as st\nfrom snowflake.core import Root\n\ntry:\n    from snowflake.snowpark.context import get_active_session\n    session = get_active_session()\nexcept:\n    from snowflake.snowpark import Session\n    session = Session.builder.configs(st.secrets["connections"]["snowflake"]).create()\n\ndef rag_query(question: str, search_service: str, num_chunks: int = 3, model: str = "claude-3-5-sonnet"):\n    root = Root(session)\n    parts = search_service.split(".")\n    \n    svc = root.databases[parts[0]].schemas[parts[1]].cortex_search_services[parts[2]]\n    \n    search_results = svc.search(\n        query=question,\n        columns=["CHUNK_TEXT", "FILE_NAME"],\n        limit=num_chunks\n    )\n    \n    context_chunks = [item.get("CHUNK_TEXT", "") for item in search_results.results]\n    sources = [item.get("FILE_NAME", "Unknown") for item in search_results.results]\n    context = "\\n\\n---\\n\\n".join(context_chunks)\n    \n    rag_prompt = f"""You are a helpful assistant. Answer the user's question based ONLY on the provided context.\nIf the context doesn't contain enough information to answer, say "I don't have enough information to answer that based on the available documents."\n\nCONTEXT FROM DOCUMENTS:\n{context}\n\nUSER QUESTION: {question}\n\nProvide a clear, accurate answer based on the context."""\n    \n    response_sql = f"""\n    SELECT SNOWFLAKE.CORTEX.COMPLETE(\n        '{model}',\n        '{rag_prompt.replace("'", "''")}'\n    ) as response\n    """\n    \n    response = session.sql(response_sql).collect()[0][0]\n    \n    return response, context_chunks, sources\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHere's the RAG application architecture:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-rag-app-with-streamlit-and-snowflake-cortex/day21.png\" alt=\"Day 21: RAG application with retrieval and generation\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild RAG Chatbot\u003C/h2\u003E\n","\u003Cp\u003ECreate a conversational interface for your RAG system with chat history and source citations.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote: This section is repurposed from Day 21 of the \u003Ca href=\"https://30daysofai.streamlit.app/?day=21\"\u003E#30DaysOfAI learning challenge\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EChat History\u003C/h3\u003E\n","\u003Cp\u003EConversation history is stored in session state as a list of message dictionaries. Each message has a \u003Ccode\u003Erole\u003C/code\u003E (user/assistant) and \u003Ccode\u003Econtent\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eif "doc_messages" not in st.session_state:\n    st.session_state.doc_messages = []\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESidebar Configuration\u003C/h3\u003E\n","\u003Cp\u003EThe sidebar provides controls for the search service path, number of context chunks, and a clear chat button:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Ewith st.sidebar:\n    search_service = st.text_input("Cortex Search Service:", value="RAG_DB.RAG_SCHEMA.CUSTOMER_REVIEW_SEARCH")\n    num_chunks = st.slider("Context chunks:", 1, 5, 3)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESource Citations\u003C/h3\u003E\n","\u003Cp\u003EAn expander shows the source chunks used for each answer, providing transparency into retrieval:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Ewith st.expander(f":material/library_books: Sources ({len(chunks)} used)"):\n    for i, (chunk, source) in enumerate(zip(chunks, sources), 1):\n        st.caption(f"**[{i}] {source}**")\n        st.write(chunk[:200] + "...")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EFull Code\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport streamlit as st\nfrom snowflake.core import Root\n\ntry:\n    from snowflake.snowpark.context import get_active_session\n    session = get_active_session()\nexcept:\n    from snowflake.snowpark import Session\n    session = Session.builder.configs(st.secrets["connections"]["snowflake"]).create()\n\nst.title(":material/chat: Chat with Your Documents")\n\nif "doc_messages" not in st.session_state:\n    st.session_state.doc_messages = []\n\nwith st.sidebar:\n    st.header(":material/settings: Settings")\n    search_service = st.text_input(\n        "Cortex Search Service:",\n        value="RAG_DB.RAG_SCHEMA.CUSTOMER_REVIEW_SEARCH"\n    )\n    num_chunks = st.slider("Context chunks:", 1, 5, 3)\n    \n    if st.button(":material/delete: Clear Chat"):\n        st.session_state.doc_messages = []\n        st.rerun()\n\ndef rag_query(question: str, search_service: str, num_chunks: int = 3):\n    root = Root(session)\n    parts = search_service.split(".")\n    svc = root.databases[parts[0]].schemas[parts[1]].cortex_search_services[parts[2]]\n    \n    results = svc.search(query=question, columns=["CHUNK_TEXT", "FILE_NAME"], limit=num_chunks)\n    \n    context_chunks = [item.get("CHUNK_TEXT", "") for item in results.results]\n    sources = [item.get("FILE_NAME", "Unknown") for item in results.results]\n    context = "\\n\\n---\\n\\n".join(context_chunks)\n    \n    rag_prompt = f"""You are a helpful assistant. Answer based ONLY on the provided context.\n\nCONTEXT:\n{context}\n\nQUESTION: {question}\n\nAnswer:"""\n    \n    sql = f"SELECT SNOWFLAKE.CORTEX.COMPLETE('claude-3-5-sonnet', '{rag_prompt.replace(chr(39), chr(39)+chr(39))}')"\n    response = session.sql(sql).collect()[0][0]\n    \n    return response, context_chunks, sources\n\nfor msg in st.session_state.doc_messages:\n    with st.chat_message(msg["role"]):\n        st.markdown(msg["content"])\n\nif prompt := st.chat_input("Ask about your documents..."):\n    st.session_state.doc_messages.append({"role": "user", "content": prompt})\n    \n    with st.chat_message("user"):\n        st.markdown(prompt)\n    \n    with st.chat_message("assistant"):\n        with st.spinner("Searching and thinking..."):\n            response, chunks, sources = rag_query(prompt, search_service, num_chunks)\n        \n        st.markdown(response)\n        \n        with st.expander(f":material/library_books: Sources ({len(chunks)} used)"):\n            for i, (chunk, source) in enumerate(zip(chunks, sources), 1):\n                st.caption(f"**[{i}] {source}**")\n                st.write(chunk[:200] + "..." if len(chunk) > 200 else chunk)\n        \n        st.session_state.doc_messages.append({"role": "assistant", "content": response})\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHere's the RAG chatbot with sources:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-rag-app-with-streamlit-and-snowflake-cortex/day21.png\" alt=\"Day 21: RAG chatbot with source citations\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAdd Guardrails\u003C/h2\u003E\n","\u003Cp\u003EKeep your RAG chatbot focused on its domain by adding explicit constraints to the system prompt.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote: This section is repurposed from Day 22 of the \u003Ca href=\"https://30daysofai.streamlit.app/?day=22\"\u003E#30DaysOfAI learning challenge\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EWhy Guardrails?\u003C/h3\u003E\n","\u003Cp\u003EWithout guardrails, users can ask about any topic and the LLM may attempt to answer using its general knowledge. Guardrails keep the assistant focused on the intended domain (customer reviews).\u003C/p\u003E\n","\u003Ch3\u003ENumbered Guidelines\u003C/h3\u003E\n","\u003Cp\u003EExplicit numbered instructions tell the LLM exactly what topics to address and how to handle off-topic questions:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003ESTRICT GUIDELINES:\n1. ONLY use information from the provided customer review context\n2. If asked about unrelated topics, respond: "I can only answer questions about customer reviews..."\n3. If context lacks information, say: "I don't have enough information..."\n4. Stay focused on: product features, satisfaction, complaints, quality, pricing\n5. Do NOT make up information or use outside knowledge\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EOff-Topic Handling\u003C/h3\u003E\n","\u003Cp\u003EThe guardrails detect off-topic requests and politely redirect the user back to the intended domain.\u003C/p\u003E\n","\u003Ch3\u003EFull Code\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport streamlit as st\nfrom snowflake.core import Root\n\ntry:\n    from snowflake.snowpark.context import get_active_session\n    session = get_active_session()\nexcept:\n    from snowflake.snowpark import Session\n    session = Session.builder.configs(st.secrets["connections"]["snowflake"]).create()\n\ndef rag_query_with_guardrails(question: str, search_service: str, num_chunks: int = 3):\n    root = Root(session)\n    parts = search_service.split(".")\n    svc = root.databases[parts[0]].schemas[parts[1]].cortex_search_services[parts[2]]\n    \n    results = svc.search(query=question, columns=["CHUNK_TEXT", "FILE_NAME"], limit=num_chunks)\n    \n    context = "\\n\\n---\\n\\n".join([item.get("CHUNK_TEXT", "") for item in results.results])\n    \n    rag_prompt = f"""You are a customer review analysis assistant. Your role is to ONLY answer questions about customer reviews and feedback.\n\nSTRICT GUIDELINES:\n1. ONLY use information from the provided customer review context below\n2. If asked about topics unrelated to customer reviews (e.g., general knowledge, coding, math, news), respond: "I can only answer questions about customer reviews. Please ask about product feedback, customer experiences, or review insights."\n3. If the context doesn't contain relevant information, say: "I don't have enough information in the customer reviews to answer that."\n4. Stay focused on: product features, customer satisfaction, complaints, praise, quality, pricing, shipping, or customer service mentioned in reviews\n5. Do NOT make up information or use knowledge outside the provided reviews\n\nCONTEXT FROM CUSTOMER REVIEWS:\n{context}\n\nUSER QUESTION: {question}\n\nProvide a clear, helpful answer based ONLY on the customer reviews above."""\n    \n    sql = f"SELECT SNOWFLAKE.CORTEX.COMPLETE('claude-3-5-sonnet', '{rag_prompt.replace(chr(39), chr(39)+chr(39))}')"\n    response = session.sql(sql).collect()[0][0]\n    \n    return response\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHere's the guardrails keeping responses on-topic:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-rag-app-with-streamlit-and-snowflake-cortex/day22.png\" alt=\"Day 22: RAG with guardrails handling off-topic queries\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EEvaluate with TruLens\u003C/h2\u003E\n","\u003Cp\u003ETruLens provides automated evaluation of RAG quality using the RAG Triad metrics. This helps you measure and improve retrieval and generation quality.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote: This section is repurposed from Day 23 of the \u003Ca href=\"https://30daysofai.streamlit.app/?day=23\"\u003E#30DaysOfAI learning challenge\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ERAG Triad Metrics\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EMetric\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDescription\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EContext Relevance\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDid we retrieve the right documents?\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGroundedness\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EIs the answer based on context (no hallucinations)?\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAnswer Relevance\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDoes the answer address the question?\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EInstrumented RAG Class\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ccode\u003E@instrument()\u003C/code\u003E decorator marks methods for automatic tracing, capturing inputs, outputs, and timing for each step:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom trulens.core.otel.instrument import instrument\n\nclass CustomerReviewRAG:\n    @instrument()\n    def retrieve_context(self, query: str) -> str:\n        # Cortex Search retrieval\n        \n    @instrument()\n    def generate_completion(self, query: str, context: str) -> str:\n        # LLM generation\n        \n    @instrument()\n    def query(self, query: str) -> str:\n        # Full RAG pipeline\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ETruLens Session\u003C/h3\u003E\n","\u003Cp\u003EInitialize TruLens with a Snowflake connector to store evaluation results in your account:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom trulens.connectors.snowflake import SnowflakeConnector\nfrom trulens.core import TruSession\n\ntru_connector = SnowflakeConnector(snowpark_session=session)\ntru_session = TruSession(connector=tru_connector)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERunning Evaluations\u003C/h3\u003E\n","\u003Cp\u003ERegister your app and run evaluation against test questions. TruLens automatically computes the RAG Triad metrics:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Erun.compute_metrics([\n    "answer_relevance",\n    "context_relevance",\n    "groundedness",\n])\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EFull Code\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom trulens.connectors.snowflake import SnowflakeConnector\nfrom trulens.core.run import Run, RunConfig\nfrom trulens.core import TruSession\nfrom trulens.core.otel.instrument import instrument\nfrom snowflake.core import Root\n\nclass CustomerReviewRAG:\n    def __init__(self, snowpark_session):\n        self.session = snowpark_session\n        self.search_service = "RAG_DB.RAG_SCHEMA.CUSTOMER_REVIEW_SEARCH"\n        self.model = "claude-3-5-sonnet"\n    \n    @instrument()\n    def retrieve_context(self, query: str) -> str:\n        root = Root(self.session)\n        parts = self.search_service.split(".")\n        svc = root.databases[parts[0]].schemas[parts[1]].cortex_search_services[parts[2]]\n        results = svc.search(query=query, columns=["CHUNK_TEXT"], limit=3)\n        context = "\\n\\n".join([r["CHUNK_TEXT"] for r in results.results])\n        return context\n    \n    @instrument()\n    def generate_completion(self, query: str, context: str) -> str:\n        prompt = f"""Based on this context:\n\n{context}\n\nQuestion: {query}\n\nProvide a helpful answer:"""\n        \n        prompt_escaped = prompt.replace("'", "''")\n        response = self.session.sql(\n            f"SELECT SNOWFLAKE.CORTEX.COMPLETE('{self.model}', '{prompt_escaped}')"\n        ).collect()[0][0]\n        return response.strip()\n    \n    @instrument()\n    def query(self, query: str) -> str:\n        context = self.retrieve_context(query)\n        answer = self.generate_completion(query, context)\n        return answer\n\ntru_connector = SnowflakeConnector(snowpark_session=session)\ntru_session = TruSession(connector=tru_connector)\n\nrag_app = CustomerReviewRAG(session)\n\ntru_rag = tru_session.App(\n    rag_app,\n    app_name="customer_review_rag",\n    app_version="v1",\n    main_method=rag_app.query\n)\n\ntest_questions = [\n    "What do customers say about thermal gloves?",\n    "Are there any durability complaints?",\n    "Which products get the best reviews?"\n]\n\nrun_config = RunConfig(\n    run_name="eval_run_1",\n    dataset_name="test_questions",\n    description="Customer review RAG evaluation"\n)\n\nrun = tru_rag.add_run(run_config=run_config)\nrun.start()\n\nrun.compute_metrics([\n    "answer_relevance",\n    "context_relevance",\n    "groundedness",\n])\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHere's the TruLens evaluation results:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-rag-app-with-streamlit-and-snowflake-cortex/day23.png\" alt=\"Day 23: TruLens RAG evaluation metrics\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EView Results in Snowsight\u003C/h3\u003E\n","\u003Cp\u003ENavigate to \u003Cstrong\u003EAI & ML → Evaluations → customer_review_rag\u003C/strong\u003E to see:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ERAG Triad scores per question\u003C/li\u003E\u003Cli\u003EAggregate metrics across runs\u003C/li\u003E\u003Cli\u003ETrend analysis over time\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EDeploy the App\u003C/h3\u003E\n","\u003Cp\u003ESave the complete code above as \u003Ccode\u003Estreamlit_app.py\u003C/code\u003E and deploy to your preferred environment:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ELocal\u003C/strong\u003E: Run \u003Ccode\u003Estreamlit run streamlit_app.py\u003C/code\u003E in your terminal\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStreamlit Community Cloud\u003C/strong\u003E: \u003Ca href=\"https://docs.streamlit.io/deploy/streamlit-community-cloud/deploy-your-app/deploy\"\u003EDeploy your app\u003C/a\u003E from a GitHub repository\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStreamlit in Snowflake (SiS)\u003C/strong\u003E: \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/streamlit/getting-started/create-streamlit-ui\"\u003ECreate a Streamlit app\u003C/a\u003E directly in Snowsight\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've successfully built a complete RAG pipeline with Snowflake Cortex Search. You learned data preparation, chunking strategies, search service creation, building a conversational RAG chatbot with guardrails, and evaluating quality with TruLens.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EPreparing and chunking documents for RAG (Days 16-17)\u003C/li\u003E\u003Cli\u003EGenerating embeddings for semantic search (Day 18)\u003C/li\u003E\u003Cli\u003ECreating and configuring Cortex Search services (Day 19)\u003C/li\u003E\u003Cli\u003EQuerying Cortex Search for semantic retrieval (Day 20)\u003C/li\u003E\u003Cli\u003EBuilding RAG applications with grounded responses (Day 21)\u003C/li\u003E\u003Cli\u003EAdding guardrails to keep responses on-topic (Day 22)\u003C/li\u003E\u003Cli\u003EEvaluating RAG quality with TruLens and AI Observability (Day 23)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n","\u003Cp\u003EDocumentation:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview\"\u003ECortex Search Overview\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/ai-observability\"\u003ESnowflake AI Observability\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.trulens.org/\"\u003ETruLens Documentation\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EAdditional Reading:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/overview-tutorials\"\u003EBuilding RAG Applications\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ESource Material\u003C/h3\u003E\n","\u003Cp\u003EThis quickstart was adapted from \u003Cstrong\u003EDays 16-23\u003C/strong\u003E of the 30 Days of AI challenge:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDay 16: Document extraction and upload\u003C/li\u003E\u003Cli\u003EDay 17: Data preparation and chunking\u003C/li\u003E\u003Cli\u003EDay 18: Generating embeddings\u003C/li\u003E\u003Cli\u003EDay 19: Creating Cortex Search service\u003C/li\u003E\u003Cli\u003EDay 20: Querying Cortex Search\u003C/li\u003E\u003Cli\u003EDay 21: RAG chatbot\u003C/li\u003E\u003Cli\u003EDay 22: Guardrails for RAG\u003C/li\u003E\u003Cli\u003EDay 23: RAG evaluation with TruLens\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ELearn more:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://30daysofai.streamlit.app/\"\u003E30 Days of AI Challenge\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/streamlit/30daysofai\"\u003EGitHub Repository\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"Build a complete RAG pipeline with Snowflake Cortex Search, from data preparation and chunking to creating a conversational chatbot with TruLens evaluation.","title":"Build a RAG App with Streamlit and Snowflake Cortex","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nRetrieval-Augmented Generation (RAG) enhances LLM responses by grounding them in your own data. Instead of relying solely on a model's training knowledge, RAG retrieves relevant documents and uses them as context thereby reducing hallucinations and enabling accurate, domain-specific answers.\n\nIn this quickstart, you'll build a complete RAG pipeline using Snowflake Cortex Search. Starting from raw documents, you'll learn data preparation, text chunking, creating a search service, building a conversational RAG chatbot, and evaluating quality with TruLens. \n\nThis tutorial distills key lessons from the [#30DaysOfAI](https://30daysofai.streamlit.app/) learning challenge into a hands-on guide.\n\n### What You'll Learn\n- How to prepare and chunk documents for RAG (Days 16-17)\n- How to generate embeddings for semantic search (Day 18)\n- How to create and configure a Cortex Search service (Day 19)\n- How to query Cortex Search for semantic retrieval (Day 20)\n- How to build a RAG chatbot with grounded responses (Day 21)\n- How to add guardrails to keep responses on-topic (Day 22)\n- How to evaluate RAG quality with TruLens and AI Observability (Day 23)\n\n### What You'll Build\nA complete RAG system including a Cortex Search service for customer reviews and a conversational chatbot that answers questions grounded in retrieved documents, with TruLens evaluation for quality metrics.\n\n### Prerequisites\n- Access to a [Snowflake account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides)\n- Basic knowledge of Python and Streamlit\n- A Snowflake warehouse with Cortex Search and LLM access\n\n\u003C!-- ------------------------ --\u003E\n## Getting Started\n\nClone or download the code from the [30daysofai](https://github.com/streamlit/30daysofai) GitHub repository:\n\n```bash\ngit clone https://github.com/streamlit/30DaysOfAI.git\ncd 30DaysOfAI/app\n```\n\nThe app code for this quickstart:\n- [Day 16: Document Extraction](https://github.com/streamlit/30DaysOfAI/blob/main/app/day16.py)\n- [Day 17: Data Preparation & Chunking](https://github.com/streamlit/30DaysOfAI/blob/main/app/day17.py)\n- [Day 18: Generate Embeddings](https://github.com/streamlit/30DaysOfAI/blob/main/app/day18.py)\n- [Day 19: Create Search Service](https://github.com/streamlit/30DaysOfAI/blob/main/app/day19.py)\n- [Day 20: Query Cortex Search](https://github.com/streamlit/30DaysOfAI/blob/main/app/day20.py)\n- [Day 21: RAG Chatbot](https://github.com/streamlit/30DaysOfAI/blob/main/app/day21.py)\n- [Day 22: Chat with Guardrails](https://github.com/streamlit/30DaysOfAI/blob/main/app/day22.py)\n- [Day 23: TruLens Evaluation](https://github.com/streamlit/30DaysOfAI/blob/main/app/day23.py)\n\n### Requirements\n\nSave the appropriate requirements content to `requirements.txt` in your project directory, or add them to your Streamlit in Snowflake app's package configuration.\n\n**Days 16-22** share the same core dependencies:\n\n```txt\nstreamlit\nsnowflake-snowpark-python\u003E=1.18.0,\u003C2.0\nsnowflake\u003E=1.0.0\npypdf\npandas\n```\n\n**Day 23 (TruLens Evaluation)** requires additional packages:\n\n```txt\nstreamlit\nsnowflake-snowpark-python\u003E=1.18.0,\u003C2.0\nsnowflake\u003E=1.0.0\npypdf\npandas\ntrulens-core\ntrulens-providers-cortex\ntrulens-connectors-snowflake\u003E=2.5.0\nsetuptools\u003C81\n```\n\n\u003C!-- ------------------------ --\u003E\n## Extract and Upload Documents\n\nThe foundation of any RAG system is well-prepared data. In this section, you'll upload documents, extract their text content, and save to Snowflake.\n\n\u003E Note: This section is repurposed from Day 16 of the [#30DaysOfAI learning challenge](https://30daysofai.streamlit.app/?day=16).\n\n### Snowflake Connection\n\nThe app establishes a Snowflake connection that works both in Streamlit in Snowflake (SiS) and locally. In SiS, it uses the active session; locally, it reads credentials from `st.secrets`:\n\n```python\ntry:\n    from snowflake.snowpark.context import get_active_session\n    session = get_active_session()\nexcept:\n    from snowflake.snowpark import Session\n    session = Session.builder.configs(st.secrets[\"connections\"][\"snowflake\"]).create()\n```\n\n### Database Configuration\n\nUsers configure the target database, schema, and table name through text inputs. Session state persists these values across reruns:\n\n```python\nif 'database' not in st.session_state:\n    st.session_state.database = \"RAG_DB\"\nif 'schema' not in st.session_state:\n    st.session_state.schema = \"RAG_SCHEMA\"\nif 'table_name' not in st.session_state:\n    st.session_state.table_name = \"EXTRACTED_DOCUMENTS\"\n```\n\n### File Upload and Text Extraction\n\nThe file uploader accepts TXT, Markdown, and PDF files. For PDFs, `pypdf.PdfReader` extracts text from each page. A progress bar shows extraction status for batch uploads:\n\n```python\nuploaded_files = st.file_uploader(\n    \"Choose file(s)\",\n    type=[\"txt\", \"md\", \"pdf\"],\n    accept_multiple_files=True\n)\n```\n\n### Replace vs Append Mode\n\nA checkbox lets users choose between replacing existing data or appending to it. The default is based on whether the table already contains records:\n\n```python\nreplace_mode = st.checkbox(\n    f\":material/sync: Replace Table Mode for `{st.session_state.table_name}`\",\n    value=table_exists\n)\n```\n\n### Save to Snowflake\n\nThe app creates the database, schema, and table if they don't exist, then inserts each document with metadata including word count and character count.\n\n### Full Code\n\n```python\nimport streamlit as st\nfrom pypdf import PdfReader\nimport io\nimport pandas as pd\nfrom datetime import datetime\n\ntry:\n    from snowflake.snowpark.context import get_active_session\n    session = get_active_session()\nexcept:\n    from snowflake.snowpark import Session\n    session = Session.builder.configs(st.secrets[\"connections\"][\"snowflake\"]).create()\n\nst.title(\":material/description: Batch Document Text Extractor\")\nst.write(\"Upload multiple documents at once to extract text and save to Snowflake for RAG applications.\")\n\nif 'database' not in st.session_state:\n    st.session_state.database = \"RAG_DB\"\nif 'schema' not in st.session_state:\n    st.session_state.schema = \"RAG_SCHEMA\"\nif 'table_name' not in st.session_state:\n    st.session_state.table_name = \"EXTRACTED_DOCUMENTS\"\n\nwith st.container(border=True):\n    st.subheader(\":material/analytics: Database Setup\")\n\n    col1, col2, col3 = st.columns(3)\n    with col1:\n        st.session_state.database = st.text_input(\"Database\", value=st.session_state.database, key=\"db_input\")\n    with col2:\n        st.session_state.schema = st.text_input(\"Schema\", value=st.session_state.schema, key=\"schema_input\")\n    with col3:\n        st.session_state.table_name = st.text_input(\"Table Name\", value=st.session_state.table_name, key=\"table_input\")\n    \n    st.info(f\":material/location_on: Target location: `{st.session_state.database}.{st.session_state.schema}.{st.session_state.table_name}`\")\n    st.caption(\":material/lightbulb: Database will be created automatically when you save documents\")\n    \n    st.divider()\n    \n    st.subheader(\":material/download: Download Review Data\")\n    st.write(\"To get started quickly, download our sample dataset of 100 customer reviews from Avalanche winter sports equipment.\")\n    \n    col1, col2 = st.columns([2, 1])\n    with col1:\n        st.info(\":material/info: **Sample Dataset**: 100 customer review files (TXT format) with product feedback, sentiment scores, and order information.\")\n    with col2:\n        st.link_button(\n            \":material/download: Download review.zip\",\n            \"https://github.com/streamlit/30DaysOfAI/raw/refs/heads/main/assets/review.zip\",\n            use_container_width=True\n        )\n    \n    with st.expander(\":material/help: How to use the sample data\"):\n        st.markdown(\"\"\"\n        **Steps:**\n        1. Click the **Download review.zip** button above\n        2. Unzip the downloaded file on your computer\n        3. Use the **Upload Documents** section below to select all 100 review files\n        4. Click **Extract Text** to process and save to Snowflake\n        \n        **What's included:**\n        - 100 customer review files (`review-001.txt` to `review-100.txt`)\n        - Each review contains: product name, date, review summary, sentiment score, and order ID\n        - Perfect for testing batch processing and building RAG applications\n        \n        **Tip:** You can upload all 100 files at once for optimal batch processing!\n        \"\"\")\n    \n    st.divider()\n    \n    st.subheader(\":material/upload: Upload Documents\")\n    uploaded_files = st.file_uploader(\n        \"Choose file(s)\",\n        type=[\"txt\", \"md\", \"pdf\"],\n        accept_multiple_files=True,\n        help=\"Supported formats: TXT, MD, PDF. Upload multiple files at once!\"\n    )\n\n    table_exists = False\n    try:\n        check_result = session.sql(f\"\"\"\n            SELECT COUNT(*) as CNT FROM {st.session_state.database}.{st.session_state.schema}.{st.session_state.table_name}\n        \"\"\").collect()\n        table_exists = True\n    except:\n        table_exists = False\n    \n    replace_mode = st.checkbox(\n        f\":material/sync: Replace Table Mode for `{st.session_state.table_name}`\",\n        value=table_exists,\n        help=f\"When enabled, clears all existing data in {st.session_state.database}.{st.session_state.schema}.{st.session_state.table_name} before saving new documents\"\n    )\n    \n    if replace_mode:\n        st.warning(f\":material/warning: **Replace Mode Enabled** - All existing documents in `{st.session_state.table_name}` will be deleted before saving new ones.\")\n    else:\n        st.info(f\":material/add: **Append Mode** - New documents will be added to `{st.session_state.table_name}`.\")\n\ndatabase = st.session_state.database\nschema = st.session_state.schema\ntable_name = st.session_state.table_name\n\nif uploaded_files:\n    with st.container(border=True):\n        st.subheader(\":material/upload: Uploaded Documents\")\n        st.success(f\":material/folder: {len(uploaded_files)} file(s) uploaded\")\n        \n        with st.expander(\":material/assignment: View Selected Files\", expanded=False):\n            file_list_df = pd.DataFrame([\n                {\n                    \"File Name\": f.name,\n                    \"Size\": f\"{f.size:,} bytes\",\n                    \"Type\": \"TXT\" if f.name.lower().endswith('.txt') \n                           else \"Markdown\" if f.name.lower().endswith('.md')\n                           else \"PDF\" if f.name.lower().endswith('.pdf')\n                           else \"Unknown\"\n                }\n                for f in uploaded_files\n            ])\n            st.dataframe(file_list_df, use_container_width=True)\n        \n        process_button = st.button(\n            f\":material/sync: Extract Text from {len(uploaded_files)} File(s)\",\n            type=\"primary\",\n            use_container_width=True\n        )\n    \n    if process_button:\n        success_count = 0\n        error_count = 0\n        extracted_data = []\n        \n        progress_bar = st.progress(0, text=\"Starting extraction...\")\n        status_container = st.empty()\n        \n        for idx, uploaded_file in enumerate(uploaded_files):\n            progress_pct = (idx + 1) / len(uploaded_files)\n            progress_bar.progress(progress_pct, text=f\"Processing {idx+1}/{len(uploaded_files)}: {uploaded_file.name}\")\n            \n            try:\n                if uploaded_file.name.lower().endswith('.txt'):\n                    file_type = \"TXT\"\n                elif uploaded_file.name.lower().endswith('.md'):\n                    file_type = \"Markdown\"\n                elif uploaded_file.name.lower().endswith('.pdf'):\n                    file_type = \"PDF\"\n                else:\n                    file_type = \"Unknown\"\n                \n                uploaded_file.seek(0)\n                extracted_text = \"\"\n                \n                if uploaded_file.name.lower().endswith(('.txt', '.md')):\n                    extracted_text = uploaded_file.read().decode(\"utf-8\")\n                elif uploaded_file.name.lower().endswith('.pdf'):\n                    pdf_reader = PdfReader(io.BytesIO(uploaded_file.read()))\n                    for page in pdf_reader.pages:\n                        page_text = page.extract_text()\n                        if page_text:\n                            extracted_text += page_text + \"\\n\\n\"\n                \n                if extracted_text and extracted_text.strip():\n                    word_count = len(extracted_text.split())\n                    char_count = len(extracted_text)\n                    \n                    extracted_data.append({\n                        'file_name': uploaded_file.name,\n                        'file_type': file_type,\n                        'file_size': uploaded_file.size,\n                        'extracted_text': extracted_text,\n                        'word_count': word_count,\n                        'char_count': char_count\n                    })\n                    success_count += 1\n                else:\n                    error_count += 1\n                    status_container.warning(f\":material/warning: No text extracted from: {uploaded_file.name}\")\n                    \n            except Exception as e:\n                error_count += 1\n                status_container.error(f\":material/cancel: Error processing {uploaded_file.name}: {str(e)}\")\n        \n        progress_bar.empty()\n        status_container.empty()\n        \n        with st.container(border=True):\n            st.subheader(\":material/analytics: Documents Written to a Database Table\")\n            \n            col1, col2, col3 = st.columns(3)\n            with col1:\n                st.metric(\":material/check_circle: Successful\", success_count)\n            with col2:\n                st.metric(\":material/cancel: Failed\", error_count)\n            with col3:\n                st.metric(\":material/analytics: Total Words\", f\"{sum(d['word_count'] for d in extracted_data):,}\")\n            \n            if extracted_data:\n                st.session_state.extracted_data = extracted_data\n                st.success(f\":material/check_circle: Successfully extracted text from {success_count} file(s)!\")\n                \n                with st.expander(\":material/visibility: Preview First 3 Files\"):\n                    for data in extracted_data[:3]:\n                        with st.container(border=True):\n                            st.markdown(f\"**{data['file_name']}**\")\n                            st.caption(f\"{data['word_count']:,} words\")\n                            preview_text = data['extracted_text'][:200]\n                            if len(data['extracted_text']) \u003E 200:\n                                preview_text += \"...\"\n                            st.text(preview_text)\n                    \n                    if len(extracted_data) \u003E 3:\n                        st.caption(f\"... and {len(extracted_data) - 3} more\")\n                \n                with st.status(\"Saving to Snowflake...\", expanded=True) as status:\n                    try:\n                        st.write(\":material/looks_one: Setting up database structure...\")\n                        session.sql(f\"CREATE DATABASE IF NOT EXISTS {database}\").collect()\n                        session.sql(f\"CREATE SCHEMA IF NOT EXISTS {database}.{schema}\").collect()\n                        \n                        st.write(\":material/looks_two: Creating table if needed...\")\n                        create_table_sql = f\"\"\"\n                        CREATE TABLE IF NOT EXISTS {database}.{schema}.{table_name} (\n                            DOC_ID NUMBER AUTOINCREMENT,\n                            FILE_NAME VARCHAR,\n                            FILE_TYPE VARCHAR,\n                            FILE_SIZE NUMBER,\n                            EXTRACTED_TEXT VARCHAR,\n                            UPLOAD_TIMESTAMP TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP(),\n                            WORD_COUNT NUMBER,\n                            CHAR_COUNT NUMBER\n                        )\n                        \"\"\"\n                        session.sql(create_table_sql).collect()\n                        \n                        if replace_mode:\n                            st.write(\":material/sync: Replace mode: Clearing existing data...\")\n                            try:\n                                session.sql(f\"TRUNCATE TABLE {database}.{schema}.{table_name}\").collect()\n                                st.write(\"   :material/check_circle: Existing data cleared\")\n                            except Exception as e:\n                                st.write(f\"   :material/warning: No existing data to clear\")\n                        \n                        st.write(f\":material/looks_3: Inserting {len(extracted_data)} document(s)...\")\n                        \n                        for idx, data in enumerate(extracted_data, 1):\n                            st.caption(f\"Saving {idx}/{len(extracted_data)}: {data['file_name']}\")\n                            safe_text = data['extracted_text'].replace(\"'\", \"''\")\n                            insert_sql = f\"\"\"\n                            INSERT INTO {database}.{schema}.{table_name}\n                            (FILE_NAME, FILE_TYPE, FILE_SIZE, EXTRACTED_TEXT, WORD_COUNT, CHAR_COUNT)\n                            VALUES ('{data['file_name']}', '{data['file_type']}', {data['file_size']}, \n                                    '{safe_text}', {data['word_count']}, {data['char_count']})\n                            \"\"\"\n                            session.sql(insert_sql).collect()\n                        \n                        status.update(label=\":material/check_circle: All documents saved!\", state=\"complete\", expanded=False)\n                        \n                        mode_msg = \"replaced in\" if replace_mode else \"saved to\"\n                        st.success(f\":material/check_circle: Successfully {mode_msg} `{database}.{schema}.{table_name}`\\n\\n:material/description: {len(extracted_data)} document(s) now in table\")\n                        \n                        st.session_state.rag_source_table = f\"{database}.{schema}.{table_name}\"\n                        st.session_state.rag_source_database = database\n                        st.session_state.rag_source_schema = schema\n                        \n                        st.balloons()\n                    \n                    except Exception as e:\n                        st.error(f\"Error saving to Snowflake: {str(e)}\")\n            else:\n                st.warning(\"No text was successfully extracted from any file.\")\n\nst.divider()\n\nwith st.container(border=True):\n    st.subheader(\":material/search: View Saved Documents\")\n    \n    try:\n        count_result = session.sql(f\"\"\"\n            SELECT COUNT(*) as CNT FROM {database}.{schema}.{table_name}\n        \"\"\").collect()\n        \n        if count_result:\n            record_count = count_result[0]['CNT']\n            if record_count \u003E 0:\n                st.warning(f\":material/warning: **{record_count} record(s)** currently in table `{database}.{schema}.{table_name}`\")\n            else:\n                st.info(\":material/inbox: **Table is empty** - No documents uploaded yet.\")\n    except:\n        st.info(\":material/inbox: **Table doesn't exist yet** - Upload and save documents to create it.\")\n    \n    query_button = st.button(\"Query Table\", type=\"secondary\", use_container_width=True)\n    \n    if query_button:\n        try:\n            full_table_name = f\"{database}.{schema}.{table_name}\"\n            \n            query_sql = f\"\"\"\n            SELECT DOC_ID, FILE_NAME, FILE_TYPE, FILE_SIZE, UPLOAD_TIMESTAMP, WORD_COUNT, CHAR_COUNT\n            FROM {full_table_name}\n            ORDER BY UPLOAD_TIMESTAMP DESC\n            \"\"\"\n            df = session.sql(query_sql).to_pandas()\n        \n            st.session_state.queried_docs = df\n            st.session_state.full_table_name = full_table_name\n            st.rerun()\n                \n        except Exception as e:\n            st.error(f\"Error: {str(e)}\")\n            st.info(\":material/lightbulb: Table may not exist yet. Upload and save documents first!\")\n    \n    if 'queried_docs' in st.session_state and 'full_table_name' in st.session_state:\n        current_full_table_name = f\"{st.session_state.database}.{st.session_state.schema}.{st.session_state.table_name}\"\n        \n        if st.session_state.full_table_name == current_full_table_name:\n            df = st.session_state.queried_docs\n            \n            if len(df) \u003E 0:\n                st.code(f\"{current_full_table_name}\", language=\"sql\")\n                \n                col1, col2, col3 = st.columns(3)\n                with col1:\n                    st.metric(\"Documents\", len(df))\n                with col2:\n                    st.metric(\"Words\", f\"{df['WORD_COUNT'].sum():,}\")\n                with col3:\n                    st.metric(\"Characters\", f\"{df['CHAR_COUNT'].sum():,}\")\n                \n                st.divider()\n                \n                st.dataframe(\n                    df[['DOC_ID', 'FILE_NAME', 'FILE_TYPE', 'WORD_COUNT', 'UPLOAD_TIMESTAMP']],\n                    use_container_width=True\n                )\n                \n                with st.expander(\":material/menu_book: View Full Document Text\"):\n                    doc_id = st.selectbox(\n                        \"Select Document ID:\",\n                        options=df['DOC_ID'].tolist(),\n                        format_func=lambda x: f\"Doc #{x} - {df[df['DOC_ID']==x]['FILE_NAME'].values[0]}\"\n                    )\n                    \n                    if st.button(\"Load Text\"):\n                        text_sql = f\"SELECT EXTRACTED_TEXT, FILE_NAME FROM {current_full_table_name} WHERE DOC_ID = {doc_id}\"\n                        text_result = session.sql(text_sql).to_pandas()\n                        if len(text_result) \u003E 0:\n                            doc = text_result.iloc[0]\n                            st.session_state.loaded_doc_text = doc['EXTRACTED_TEXT']\n                            st.session_state.loaded_doc_name = doc['FILE_NAME']\n                    \n                    if 'loaded_doc_text' in st.session_state:\n                        st.text_area(\n                            st.session_state.loaded_doc_name,\n                            value=st.session_state.loaded_doc_text,\n                            height=400\n                        )\n            else:\n                st.info(\":material/inbox: Table is empty. Upload files above!\")\n        else:\n            st.info(f\":material/sync: Showing results for a different table. Click 'Query Table' to refresh.\")\n    else:\n        st.info(\":material/inbox: No documents queried yet. Click 'Query Table' to view saved documents.\")\n\nst.divider()\nst.caption(\"Day 16: Batch Document Text Extractor for RAG | 30 Days of AI\")\n```\n\nHere's the document extraction app in action:\n\n![Day 16: Batch document text extractor](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-rag-app-with-streamlit-and-snowflake-cortex/day16.png)\n\n\u003C!-- ------------------------ --\u003E\n## Prepare and Chunk Documents\n\nText chunking is crucial for effective retrieval. In this section, you'll load the extracted documents from Day 16 and process them into searchable chunks.\n\n\u003E Note: This section is repurposed from Day 17 of the [#30DaysOfAI learning challenge](https://30daysofai.streamlit.app/?day=17).\n\n### Session State Initialization\n\nThe app checks if database configuration was passed from Day 16 via session state. If available, it inherits those values; otherwise, it uses defaults:\n\n```python\nif 'day17_database' not in st.session_state:\n    if 'rag_source_database' in st.session_state:\n        st.session_state.day17_database = st.session_state.rag_source_database\n        st.session_state.day17_schema = st.session_state.rag_source_schema\n        st.session_state.day17_table_name = \"EXTRACTED_DOCUMENTS\"\n    else:\n        st.session_state.day17_database = \"RAG_DB\"\n        st.session_state.day17_schema = \"RAG_SCHEMA\"\n        st.session_state.day17_table_name = \"EXTRACTED_DOCUMENTS\"\n```\n\n### Loading Documents\n\nDocuments are loaded from Snowflake with a status indicator showing progress. The `st.rerun()` call ensures the UI updates after loading:\n\n```python\nif st.button(\":material/folder_open: Load Reviews\", type=\"primary\", use_container_width=True):\n    with st.status(\"Loading reviews from Snowflake...\", expanded=True) as status:\n        query = f\"\"\"SELECT DOC_ID, FILE_NAME, EXTRACTED_TEXT, WORD_COUNT\n                    FROM {st.session_state.day17_database}.{st.session_state.day17_schema}.{st.session_state.day17_table_name}\"\"\"\n        df = session.sql(query).to_pandas()\n        st.session_state.loaded_data = df\n        st.rerun()\n```\n\n### Processing Strategy\n\nUsers choose between keeping each review as a single chunk (recommended for short reviews) or splitting longer reviews with configurable chunk size and overlap:\n\n```python\nprocessing_option = st.radio(\n    \"Select processing strategy:\",\n    [\"Keep each review as a single chunk (Recommended)\", \n     \"Chunk reviews longer than threshold\"]\n)\n```\n\n### Chunking Logic\n\nFor chunking, the app uses a sliding window approach with overlap to maintain context between chunks:\n\n```python\nfor i in range(0, len(words), chunk_size - overlap):\n    chunk_words = words[i:i + chunk_size]\n    chunk_text = ' '.join(chunk_words)\n```\n\n### Bulk Insert with write_pandas\n\nThe `session.write_pandas()` method efficiently inserts all chunks in a single operation, with options for replace or append mode.\n\n### Full Code\n\n```python\nimport streamlit as st\nimport pandas as pd\nimport re\n\ntry:\n    from snowflake.snowpark.context import get_active_session\n    session = get_active_session()\nexcept:\n    from snowflake.snowpark import Session\n    session = Session.builder.configs(st.secrets[\"connections\"][\"snowflake\"]).create()\n\nst.title(\":material/sync: Prepare and Chunk Data for RAG\")\nst.write(\"Load customer reviews from Day 16, process them, and prepare searchable chunks for RAG.\")\n\nif 'day17_database' not in st.session_state:\n    if 'rag_source_database' in st.session_state:\n        st.session_state.day17_database = st.session_state.rag_source_database\n        st.session_state.day17_schema = st.session_state.rag_source_schema\n        st.session_state.day17_table_name = \"EXTRACTED_DOCUMENTS\"\n    else:\n        st.session_state.day17_database = \"RAG_DB\"\n        st.session_state.day17_schema = \"RAG_SCHEMA\"\n        st.session_state.day17_table_name = \"EXTRACTED_DOCUMENTS\"\n\nif 'day17_chunk_table' not in st.session_state:\n    st.session_state.day17_chunk_table = \"REVIEW_CHUNKS\"\n\nwith st.container(border=True):\n    st.subheader(\":material/analytics: Source Data Configuration\")\n    \n    col1, col2, col3 = st.columns(3)\n    with col1:\n        st.session_state.day17_database = st.text_input(\n            \"Database\", \n            value=st.session_state.day17_database, \n            key=\"day17_db_input\"\n        )\n    with col2:\n        st.session_state.day17_schema = st.text_input(\n            \"Schema\", \n            value=st.session_state.day17_schema, \n            key=\"day17_schema_input\"\n        )\n    with col3:\n        st.session_state.day17_table_name = st.text_input(\n            \"Source Table\", \n            value=st.session_state.day17_table_name, \n            key=\"day17_table_input\"\n        )\n    \n    st.info(f\":material/location_on: Loading from: `{st.session_state.day17_database}.{st.session_state.day17_schema}.{st.session_state.day17_table_name}`\")\n    st.caption(\":material/lightbulb: This should point to the EXTRACTED_DOCUMENTS table from Day 16\")\n    \n    if 'loaded_data' in st.session_state:\n        st.success(f\":material/check_circle: **{len(st.session_state.loaded_data)} document(s)** already loaded\")\n\n    if st.button(\":material/folder_open: Load Reviews\", type=\"primary\", use_container_width=True):\n        try:\n            with st.status(\"Loading reviews from Snowflake...\", expanded=True) as status:\n                st.write(\":material/wifi: Querying database...\")\n                \n                query = f\"\"\"\n                SELECT \n                    DOC_ID,\n                    FILE_NAME,\n                    FILE_TYPE,\n                    EXTRACTED_TEXT,\n                    UPLOAD_TIMESTAMP,\n                    WORD_COUNT,\n                    CHAR_COUNT\n                FROM {st.session_state.day17_database}.{st.session_state.day17_schema}.{st.session_state.day17_table_name}\n                ORDER BY FILE_NAME\n                \"\"\"\n                df = session.sql(query).to_pandas()\n                \n                st.write(f\":material/check_circle: Loaded {len(df)} review(s)\")\n                status.update(label=\"Reviews loaded successfully!\", state=\"complete\", expanded=False)\n                \n                st.session_state.loaded_data = df\n                st.session_state.source_table = f\"{st.session_state.day17_database}.{st.session_state.day17_schema}.{st.session_state.day17_table_name}\"\n                st.rerun()\n                \n        except Exception as e:\n            st.error(f\"Error loading reviews: {str(e)}\")\n            st.info(\":material/lightbulb: Make sure you've uploaded review files in Day 16 first!\")\n\nif 'loaded_data' in st.session_state:\n    with st.container(border=True):\n        st.subheader(\":material/looks_one: Review Summary\")\n        \n        df = st.session_state.loaded_data\n                \n        col1, col2, col3 = st.columns(3)\n        with col1:\n            st.metric(\"Total Reviews\", len(df))\n        with col2:\n            st.metric(\"Total Words\", f\"{df['WORD_COUNT'].sum():,}\")\n        with col3:\n            st.metric(\"Avg Words/Review\", f\"{df['WORD_COUNT'].mean():.0f}\")\n            \n        st.dataframe(df[['DOC_ID', 'FILE_NAME', 'FILE_TYPE', 'UPLOAD_TIMESTAMP', 'WORD_COUNT']], \n                    use_container_width=True)\n                \n    with st.container(border=True):\n        st.subheader(\":material/looks_two: Choose Processing Strategy\")\n        \n        st.info(\"\"\"\n        **Customer Review Processing Options:**\n        \n        Since customer reviews are typically short (~150 words each), you have two options:\n        - **Option 1**: Use each review as-is (Recommended for reviews)\n        - **Option 2**: Chunk longer reviews (For reviews \u003E200 words)\n        \"\"\")\n        \n        processing_option = st.radio(\n            \"Select processing strategy:\",\n            [\"Keep each review as a single chunk (Recommended)\", \n             \"Chunk reviews longer than threshold\"],\n            index=0\n        )\n        \n        if \"Chunk reviews\" in processing_option:\n            col1, col2 = st.columns(2)\n            with col1:\n                chunk_size = st.slider(\n                    \"Chunk Size (words):\",\n                    min_value=50,\n                    max_value=500,\n                    value=200,\n                    step=50,\n                    help=\"Maximum number of words per chunk\"\n                )\n            with col2:\n                overlap = st.slider(\n                    \"Overlap (words):\",\n                    min_value=0,\n                    max_value=100,\n                    value=50,\n                    step=10,\n                    help=\"Number of overlapping words between chunks\"\n                )\n            st.caption(f\"Reviews with \u003E{chunk_size} words will be split into chunks of {chunk_size} words with {overlap} word overlap\")\n        else:\n            chunk_size = 200\n            overlap = 50\n        \n        if st.button(\":material/flash_on: Process Reviews\", type=\"primary\", use_container_width=True):\n            chunks = []\n            \n            with st.status(\"Processing reviews...\", expanded=True) as status:\n                if \"Keep each review\" in processing_option:\n                    st.write(\":material/edit_note: Creating one chunk per review...\")\n                    \n                    for idx, row in df.iterrows():\n                        chunks.append({\n                            'doc_id': row['DOC_ID'],\n                            'file_name': row['FILE_NAME'],\n                            'chunk_id': idx + 1,\n                            'chunk_text': row['EXTRACTED_TEXT'],\n                            'chunk_size': row['WORD_COUNT'],\n                            'chunk_type': 'full_review'\n                        })\n                    \n                    st.write(f\":material/check_circle: Created {len(chunks)} chunks (1 per review)\")\n                    \n                else:\n                    st.write(f\":material/edit_note: Chunking reviews longer than {chunk_size} words...\")\n                    chunk_id = 1\n                    \n                    for idx, row in df.iterrows():\n                        text = row['EXTRACTED_TEXT']\n                        words = text.split()\n                        \n                        if len(words) \u003C= chunk_size:\n                            chunks.append({\n                                'doc_id': row['DOC_ID'],\n                                'file_name': row['FILE_NAME'],\n                                'chunk_id': chunk_id,\n                                'chunk_text': text,\n                                'chunk_size': len(words),\n                                'chunk_type': 'full_review'\n                            })\n                            chunk_id += 1\n                        else:\n                            for i in range(0, len(words), chunk_size - overlap):\n                                chunk_words = words[i:i + chunk_size]\n                                chunk_text = ' '.join(chunk_words)\n                                \n                                chunks.append({\n                                    'doc_id': row['DOC_ID'],\n                                    'file_name': row['FILE_NAME'],\n                                    'chunk_id': chunk_id,\n                                    'chunk_text': chunk_text,\n                                    'chunk_size': len(chunk_words),\n                                    'chunk_type': 'chunked_review'\n                                })\n                                chunk_id += 1\n                    \n                    st.write(f\":material/check_circle: Created {len(chunks)} chunks from {len(df)} reviews\")\n                \n                status.update(label=\"Processing complete!\", state=\"complete\", expanded=False)\n                    \n            st.session_state.review_chunks = chunks\n            st.session_state.processing_option = processing_option\n            \n            st.success(f\":material/check_circle: Processed {len(df)} reviews into {len(chunks)} searchable chunks!\")\n    \n    if 'review_chunks' in st.session_state:\n        with st.container(border=True):\n            st.subheader(\":material/looks_3: Processed Review Chunks\")\n            \n            chunks = st.session_state.review_chunks\n            \n            col1, col2, col3 = st.columns(3)\n            with col1:\n                st.metric(\"Total Chunks\", len(chunks))\n            with col2:\n                full_reviews = len([c for c in chunks if c['chunk_type'] == 'full_review'])\n                st.metric(\"Full Reviews\", full_reviews)\n            with col3:\n                split_reviews = len([c for c in chunks if c['chunk_type'] == 'chunked_review'])\n                st.metric(\"Split Reviews\", split_reviews)\n            \n            with st.expander(\":material/description: View Chunks\"):\n                chunks_df = pd.DataFrame(chunks)\n                st.dataframe(chunks_df[['chunk_id', 'file_name', 'chunk_size', 'chunk_type', 'chunk_text']], \n                            use_container_width=True)\n        \n        with st.container(border=True):\n            st.subheader(\":material/looks_4: Save Chunks to Snowflake\")\n            \n            chunks = st.session_state.review_chunks\n            \n            col1, col2 = st.columns([2, 1])\n            with col1:\n                st.session_state.day17_chunk_table = st.text_input(\n                    \"Chunk Table Name\",\n                    value=st.session_state.day17_chunk_table,\n                    help=\"Table name for storing review chunks\",\n                    key=\"day17_chunk_table_input\"\n                )\n            \n            full_chunk_table = f\"{st.session_state.day17_database}.{st.session_state.day17_schema}.{st.session_state.day17_chunk_table}\"\n            st.code(full_chunk_table, language=\"sql\")\n            \n            chunk_table_exists = False\n            try:\n                count_result = session.sql(f\"\"\"\n                    SELECT COUNT(*) as CNT FROM {full_chunk_table}\n                \"\"\").collect()\n                \n                if count_result:\n                    record_count = count_result[0]['CNT']\n                    if record_count \u003E 0:\n                        st.warning(f\":material/warning: **{record_count} chunk(s)** currently in table `{full_chunk_table}`\")\n                        chunk_table_exists = True\n                    else:\n                        st.info(\":material/inbox: **Chunk table is empty** - No chunks saved yet.\")\n                        chunk_table_exists = False\n            except:\n                st.info(\":material/inbox: **Chunk table doesn't exist yet** - Will be created when you save chunks.\")\n                chunk_table_exists = False\n            \n            if 'day17_replace_mode' not in st.session_state:\n                st.session_state.day17_replace_mode = chunk_table_exists\n            else:\n                if 'day17_last_chunk_table' not in st.session_state or st.session_state.day17_last_chunk_table != full_chunk_table:\n                    st.session_state.day17_replace_mode = chunk_table_exists\n                    st.session_state.day17_last_chunk_table = full_chunk_table\n            \n            replace_mode = st.checkbox(\n                f\":material/sync: Replace Table Mode for `{st.session_state.day17_chunk_table}`\",\n                help=f\"When enabled, clears all existing data in {full_chunk_table} before saving new chunks\",\n                key=\"day17_replace_mode\"\n            )\n            \n            if replace_mode:\n                st.warning(\"**Replace Mode Active**: Existing chunks will be deleted before saving new ones.\")\n            else:\n                st.success(\"**Append Mode Active**: New chunks will be added to existing data.\")\n            \n            if st.button(\":material/save: Save Chunks to Snowflake\", type=\"primary\", use_container_width=True):\n                try:\n                    with st.status(\"Saving chunks to Snowflake...\", expanded=True) as status:\n                        st.write(\":material/looks_one: Checking table...\")\n                        create_table_sql = f\"\"\"\n                        CREATE TABLE IF NOT EXISTS {full_chunk_table} (\n                            CHUNK_ID NUMBER,\n                            DOC_ID NUMBER,\n                            FILE_NAME VARCHAR,\n                            CHUNK_TEXT VARCHAR,\n                            CHUNK_SIZE NUMBER,\n                            CHUNK_TYPE VARCHAR,\n                            CREATED_TIMESTAMP TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP()\n                        )\n                        \"\"\"\n                        session.sql(create_table_sql).collect()\n                        \n                        if replace_mode:\n                            st.write(\":material/sync: Replace mode: Clearing existing chunks...\")\n                            try:\n                                session.sql(f\"TRUNCATE TABLE {full_chunk_table}\").collect()\n                                st.write(\"   :material/check_circle: Existing chunks cleared\")\n                            except Exception as e:\n                                st.write(f\"   :material/warning: No existing chunks to clear\")\n                        \n                        st.write(f\":material/looks_3: Inserting {len(chunks)} chunk(s)...\")\n                        chunks_df = pd.DataFrame(chunks)\n                        \n                        chunks_df_upper = chunks_df[['chunk_id', 'doc_id', 'file_name', 'chunk_text', \n                                                      'chunk_size', 'chunk_type']].copy()\n                        chunks_df_upper.columns = ['CHUNK_ID', 'DOC_ID', 'FILE_NAME', 'CHUNK_TEXT', \n                                                   'CHUNK_SIZE', 'CHUNK_TYPE']\n                        \n                        if replace_mode:\n                            session.write_pandas(chunks_df_upper,\n                                               table_name=st.session_state.day17_chunk_table,\n                                               database=st.session_state.day17_database,\n                                               schema=st.session_state.day17_schema,\n                                               overwrite=True)\n                        else:\n                            session.write_pandas(chunks_df_upper,\n                                               table_name=st.session_state.day17_chunk_table,\n                                               database=st.session_state.day17_database,\n                                               schema=st.session_state.day17_schema,\n                                               overwrite=False)\n                        \n                        status.update(label=\":material/check_circle: Chunks saved!\", state=\"complete\", expanded=False)\n                    \n                    mode_msg = \"replaced in\" if replace_mode else \"saved to\"\n                    st.success(f\":material/check_circle: Successfully {mode_msg} `{full_chunk_table}`\\n\\n:material/description: {len(chunks)} chunk(s) now in table\")\n                    \n                    st.session_state.chunks_table = full_chunk_table\n                    st.session_state.chunks_database = st.session_state.day17_database\n                    st.session_state.chunks_schema = st.session_state.day17_schema\n                    st.session_state.chunk_table_saved = True\n                    \n                    st.balloons()\n                    \n                except Exception as e:\n                    st.error(f\"Error saving chunks: {str(e)}\")\n\nwith st.container(border=True):\n    st.subheader(\":material/search: View Saved Chunks\")\n    \n    full_chunk_table = f\"{st.session_state.day17_database}.{st.session_state.day17_schema}.{st.session_state.day17_chunk_table}\"\n    st.caption(f\":material/analytics: Querying chunk table: `{full_chunk_table}`\")\n    \n    query_button = st.button(\":material/analytics: Query Chunk Table\", type=\"secondary\", use_container_width=True)\n    \n    if query_button:\n        try:\n            query_sql = f\"\"\"\n            SELECT \n                CHUNK_ID,\n                FILE_NAME,\n                CHUNK_SIZE,\n                CHUNK_TYPE,\n                LEFT(CHUNK_TEXT, 100) AS TEXT_PREVIEW,\n                CREATED_TIMESTAMP\n            FROM {full_chunk_table}\n            ORDER BY CHUNK_ID\n            \"\"\"\n            chunks_df = session.sql(query_sql).to_pandas()\n            \n            st.session_state.queried_chunks = chunks_df\n            st.session_state.queried_chunks_table = full_chunk_table\n            st.rerun()\n                \n        except Exception as e:\n            st.error(f\"Error querying chunks: {str(e)}\")\n    \n    if 'queried_chunks' in st.session_state and st.session_state.get('queried_chunks_table') == full_chunk_table:\n        chunks_df = st.session_state.queried_chunks\n        \n        if len(chunks_df) \u003E 0:\n            st.code(full_chunk_table, language=\"sql\")\n            \n            col1, col2, col3 = st.columns(3)\n            with col1:\n                st.metric(\"Total Chunks\", len(chunks_df))\n            with col2:\n                full_count = len(chunks_df[chunks_df['CHUNK_TYPE'] == 'full_review'])\n                st.metric(\"Full Reviews\", full_count)\n            with col3:\n                split_count = len(chunks_df[chunks_df['CHUNK_TYPE'] == 'chunked_review'])\n                st.metric(\"Split Reviews\", split_count)\n            \n            st.dataframe(\n                chunks_df[['CHUNK_ID', 'FILE_NAME', 'CHUNK_SIZE', 'CHUNK_TYPE', 'TEXT_PREVIEW']],\n                use_container_width=True\n            )\n            \n            with st.expander(\":material/menu_book: View Full Chunk Text\"):\n                chunk_id = st.selectbox(\n                    \"Select Chunk ID:\",\n                    options=chunks_df['CHUNK_ID'].tolist(),\n                    format_func=lambda x: f\"Chunk #{x} - {chunks_df[chunks_df['CHUNK_ID']==x]['FILE_NAME'].values[0]}\",\n                    key=\"chunk_text_selector\"\n                )\n                \n                if st.button(\"Load Chunk Text\", key=\"load_chunk_text_btn\"):\n                    st.session_state.selected_chunk_id = chunk_id\n                    st.session_state.load_chunk_text = True\n                    st.rerun()\n                \n                if st.session_state.get('load_chunk_text') and st.session_state.get('selected_chunk_id'):\n                    text_sql = f\"SELECT CHUNK_TEXT, FILE_NAME FROM {full_chunk_table} WHERE CHUNK_ID = {st.session_state.selected_chunk_id}\"\n                    text_result = session.sql(text_sql).to_pandas()\n                    if len(text_result) \u003E 0:\n                        chunk = text_result.iloc[0]\n                        st.text_area(\n                            chunk['FILE_NAME'],\n                            value=chunk['CHUNK_TEXT'],\n                            height=300,\n                            key=f\"chunk_text_display_{st.session_state.selected_chunk_id}\"\n                        )\n        else:\n            st.info(\":material/inbox: No chunks found in table.\")\n    else:\n        st.info(\":material/inbox: No chunks queried yet. Click 'Query Chunk Table' to view saved chunks.\")\n\nst.divider()\nst.caption(\"Day 17: Loading and Transforming Customer Reviews for RAG | 30 Days of AI\")\n```\n\nHere's the chunking process in action:\n\n![Day 17: Document chunking with processing options](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-rag-app-with-streamlit-and-snowflake-cortex/day17.png)\n\n\u003C!-- ------------------------ --\u003E\n## Generate Embeddings\n\nEmbeddings convert text into numerical vectors that capture semantic meaning, enabling similarity-based search. In this section, you'll generate 768-dimensional embeddings for your chunked reviews using Snowflake Cortex.\n\n\u003E Note: This section is repurposed from Day 18 of the [#30DaysOfAI learning challenge](https://30daysofai.streamlit.app/?day=18).\n\n### Understanding Embeddings\n\nEmbeddings transform text into dense numerical vectors where similar meanings produce similar vectors. The `embed_text_768` function from Snowflake Cortex generates 768-dimensional vectors using the `snowflake-arctic-embed-m` model.\n\n```python\nfrom snowflake.cortex import embed_text_768\n\nemb = embed_text_768(model='snowflake-arctic-embed-m', text=row['CHUNK_TEXT'])\n```\n\n### Storing Embeddings with VECTOR Type\n\nSnowflake's native `VECTOR(FLOAT, 768)` data type efficiently stores embedding vectors:\n\n```python\ncreate_table_sql = f\"\"\"\nCREATE OR REPLACE TABLE {full_embedding_table} (\n    CHUNK_ID NUMBER,\n    EMBEDDING VECTOR(FLOAT, 768),\n    CREATED_TIMESTAMP TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP()\n)\n\"\"\"\nsession.sql(create_table_sql).collect()\n```\n\n### Batch Processing with Progress Tracking\n\nProcessing embeddings in batches prevents timeouts and provides user feedback:\n\n```python\nfor i in range(0, total_chunks, batch_size):\n    batch_end = min(i + batch_size, total_chunks)\n    st.write(f\"Processing chunks {i+1} to {batch_end} of {total_chunks}...\")\n    \n    for idx, row in df.iloc[i:batch_end].iterrows():\n        emb = embed_text_768(model='snowflake-arctic-embed-m', text=row['CHUNK_TEXT'])\n        embeddings.append({'chunk_id': row['CHUNK_ID'], 'embedding': emb})\n    \n    progress_bar.progress(batch_end / total_chunks)\n```\n\n### Inserting Vector Data\n\nConvert embeddings to Snowflake's vector format for storage:\n\n```python\nemb_array = \"[\" + \",\".join([str(float(x)) for x in emb_list]) + \"]\"\n\ninsert_sql = f\"\"\"\nINSERT INTO {full_embedding_table} (CHUNK_ID, EMBEDDING)\nSELECT {emb_data['chunk_id']}, {emb_array}::VECTOR(FLOAT, 768)\n\"\"\"\nsession.sql(insert_sql).collect()\n```\n\n### Full Code\n\nCopy and paste this code into `day18.py`:\n\n```python\n# Day 18\n# Generating Embeddings for Customer Reviews\n\nimport streamlit as st\nfrom snowflake.cortex import embed_text_768\nimport pandas as pd\nimport numpy as np\n\nst.title(\":material/calculate: Embeddings Generator for Customer Reviews\")\nst.write(\"Generate embeddings for review chunks from Day 17 to enable semantic search.\")\n\n# Connect to Snowflake\ntry:\n    # Works in Streamlit in Snowflake\n    from snowflake.snowpark.context import get_active_session\n    session = get_active_session()\nexcept:\n    # Works locally and on Streamlit Community Cloud\n    from snowflake.snowpark import Session\n    session = Session.builder.configs(st.secrets[\"connections\"][\"snowflake\"]).create()\n\n# Initialize session state for database configuration\nif 'day18_database' not in st.session_state:\n    # Check if we have chunks from Day 17\n    if 'chunks_database' in st.session_state:\n        st.session_state.day18_database = st.session_state.chunks_database\n        st.session_state.day18_schema = st.session_state.chunks_schema\n        st.session_state.day18_chunk_table = \"REVIEW_CHUNKS\"\n    else:\n        st.session_state.day18_database = \"RAG_DB\"\n        st.session_state.day18_schema = \"RAG_SCHEMA\"\n        st.session_state.day18_chunk_table = \"REVIEW_CHUNKS\"\n\nif 'day18_embedding_table' not in st.session_state:\n    st.session_state.day18_embedding_table = \"REVIEW_EMBEDDINGS\"\n\n# Explanation\nwith st.expander(\":material/library_books: What are embeddings?\", expanded=True):\n    st.markdown(\"\"\"\n    **Embeddings** convert text into numbers (vectors) that capture meaning:\n    \n    - Similar texts → Similar vectors\n    - Different texts → Different vectors\n    - Enables \"search by meaning\" (semantic search)\n    \n    The model outputs **768 numbers** for any text input.\n    \n    **In RAG for Customer Reviews**: Each review (or chunk) gets its own embedding, \n    allowing semantic search to find relevant customer feedback!\n    \n    **Example**: Search for \"warm gloves\" will find reviews mentioning \"provides good warmth\", \n    \"kept hands toasty\", even without exact keywords!\n    \"\"\")\n\n# Source Data Configuration and Load Section\nwith st.container(border=True):\n    st.subheader(\":material/analytics: Source Data Configuration\")\n    \n    # Database configuration\n    col1, col2, col3 = st.columns(3)\n    with col1:\n        st.session_state.day18_database = st.text_input(\n            \"Database\", \n            value=st.session_state.day18_database, \n            key=\"day18_db_input\"\n        )\n    with col2:\n        st.session_state.day18_schema = st.text_input(\n            \"Schema\", \n            value=st.session_state.day18_schema, \n            key=\"day18_schema_input\"\n        )\n    with col3:\n        st.session_state.day18_chunk_table = st.text_input(\n            \"Chunks Table\", \n            value=st.session_state.day18_chunk_table, \n            key=\"day18_chunk_table_input\"\n        )\n    \n    st.info(f\":material/location_on: Loading from: `{st.session_state.day18_database}.{st.session_state.day18_schema}.{st.session_state.day18_chunk_table}`\")\n    st.caption(\":material/lightbulb: This should point to the REVIEW_CHUNKS table from Day 17\")\n    \n    # Check for existing loaded data\n    if 'chunks_data' in st.session_state:\n        st.success(f\":material/check_circle: **{len(st.session_state.chunks_data)} chunk(s)** already loaded\")\n    \n    # Load chunks button\n    if st.button(\":material/folder_open: Load Chunks\", type=\"primary\", use_container_width=True):\n        try:\n            with st.status(\"Loading chunks...\", expanded=True) as status:\n                st.write(\":material/wifi: Querying database...\")\n                \n                query = f\"\"\"\n                SELECT \n                    CHUNK_ID,\n                    DOC_ID,\n                    FILE_NAME,\n                    CHUNK_TEXT,\n                    CHUNK_SIZE,\n                    CHUNK_TYPE\n                FROM {st.session_state.day18_database}.{st.session_state.day18_schema}.{st.session_state.day18_chunk_table}\n                ORDER BY CHUNK_ID\n                \"\"\"\n                df = session.sql(query).to_pandas()\n                \n                st.write(f\":material/check_circle: Loaded {len(df)} chunks\")\n                status.update(label=\"Chunks loaded successfully!\", state=\"complete\", expanded=False)\n                \n                # Store in session state\n                st.session_state.chunks_data = df\n                st.rerun()\n                \n        except Exception as e:\n            st.error(f\"Error loading chunks: {str(e)}\")\n            st.info(\":material/lightbulb: Make sure you've processed reviews in Day 17 first!\")\n\n# Main content - Chunk Summary\nif 'chunks_data' in st.session_state:\n    with st.container(border=True):\n        st.subheader(\":material/looks_one: Chunk Summary\")\n        \n        df = st.session_state.chunks_data\n        \n        col1, col2, col3 = st.columns(3)\n        with col1:\n            st.metric(\"Total Chunks\", len(df))\n        with col2:\n            st.metric(\"Unique Reviews\", df['FILE_NAME'].nunique())\n        with col3:\n            st.metric(\"Avg Chunk Size\", f\"{df['CHUNK_SIZE'].mean():.0f} words\")\n        \n        # Show chunk type distribution\n        if 'CHUNK_TYPE' in df.columns:\n            st.write(\"**Chunk Type Distribution:**\")\n            chunk_type_counts = df['CHUNK_TYPE'].value_counts()\n            col1, col2 = st.columns(2)\n            with col1:\n                st.metric(\"Full Reviews\", chunk_type_counts.get('full_review', 0))\n            with col2:\n                st.metric(\"Split Reviews\", chunk_type_counts.get('chunked_review', 0))\n        \n        with st.expander(\":material/description: Preview Chunks\"):\n            st.dataframe(df.head(10), use_container_width=True)\n    \n    # Generate embeddings\n    with st.container(border=True):\n        st.subheader(\":material/looks_two: Generate Embeddings\")\n        \n        st.info(\"\"\"\n        **What happens here:**\n        - Each review chunk is converted to a 768-dimensional vector\n        - Embeddings are stored in Snowflake for semantic search\n        - Enables finding relevant reviews based on meaning, not just keywords\n        \n        **For Customer Reviews**: This allows your RAG system to:\n        - Find reviews about \"durability\" even if they mention \"long-lasting\" or \"fell apart\"\n        - Search for \"warm\" products and find \"toasty\", \"cold hands\", \"insulation\"\n        - Group similar feedback together semantically\n        \"\"\")\n        \n        # Batch size selection\n        batch_size = st.selectbox(\"Batch Size\", [10, 25, 50, 100], index=1,\n                                  help=\"Number of chunks to process at once\")\n\n        if st.button(\":material/calculate: Generate Embeddings\", type=\"primary\", use_container_width=True):\n            try:\n                with st.status(\"Generating embeddings...\", expanded=True) as status:\n                    embeddings = []\n                    total_chunks = len(df)\n                    progress_bar = st.progress(0)\n                    \n                    for i in range(0, total_chunks, batch_size):\n                        batch_end = min(i + batch_size, total_chunks)\n                        st.write(f\"Processing chunks {i+1} to {batch_end} of {total_chunks}...\")\n                        \n                        for idx, row in df.iloc[i:batch_end].iterrows():\n                            # Generate embedding using the correct function signature\n                            emb = embed_text_768(model='snowflake-arctic-embed-m', text=row['CHUNK_TEXT'])\n                            embeddings.append({\n                                'chunk_id': row['CHUNK_ID'],\n                                'embedding': emb\n                            })\n                        \n                        # Update progress\n                        progress = batch_end / total_chunks\n                        progress_bar.progress(progress)\n                    \n                    status.update(label=\"Embeddings generated!\", state=\"complete\", expanded=False)\n                    \n                    # Store in session state\n                    st.session_state.embeddings_data = embeddings\n                    \n                    st.success(f\":material/check_circle: Generated {len(embeddings)} embeddings for {len(df)} review chunks!\")\n                    \n            except Exception as e:\n                st.error(f\"Error generating embeddings: {str(e)}\")\n    \n    # View embeddings\n    if 'embeddings_data' in st.session_state:\n        with st.container(border=True):\n            st.subheader(\":material/looks_3: View Embeddings\")\n            \n            embeddings = st.session_state.embeddings_data\n            \n            col1, col2 = st.columns(2)\n            with col1:\n                st.metric(\"Embeddings Generated\", len(embeddings))\n            with col2:\n                st.metric(\"Dimensions per Embedding\", 768)\n            \n            # Show sample embedding\n            with st.expander(\":material/search: View Sample Embedding\"):\n                sample_emb = embeddings[0]['embedding']\n                st.write(\"**First 10 values:**\")\n                st.write(sample_emb[:10])\n        \n        # Save embeddings to Snowflake\n        with st.container(border=True):\n            st.subheader(\":material/looks_4: Save Embeddings to Snowflake\")\n            \n            embeddings = st.session_state.embeddings_data\n            \n            # Embedding table name\n            col1, col2 = st.columns([2, 1])\n            with col1:\n                st.session_state.day18_embedding_table = st.text_input(\n                    \"Embeddings Table Name\",\n                    value=st.session_state.day18_embedding_table,\n                    help=\"Table name for storing embeddings\",\n                    key=\"day18_embedding_table_input\"\n                )\n            \n            full_embedding_table = f\"{st.session_state.day18_database}.{st.session_state.day18_schema}.{st.session_state.day18_embedding_table}\"\n            st.code(full_embedding_table, language=\"sql\")\n            \n            # Check if embeddings table exists and show status\n            try:\n                check_query = f\"\"\"\n                SELECT COUNT(*) as count\n                FROM {full_embedding_table}\n                \"\"\"\n                result = session.sql(check_query).collect()\n                current_count = result[0]['COUNT']\n                \n                if current_count \u003E 0:\n                    st.warning(f\":material/warning: **{current_count:,} embedding(s)** currently in table `{full_embedding_table}`\")\n                    embedding_table_exists = True\n                else:\n                    st.info(\":material/inbox: **Embedding table is empty** - No embeddings saved yet.\")\n                    embedding_table_exists = False\n            except:\n                st.info(\":material/inbox: **Embedding table doesn't exist yet** - Will be created when you save embeddings.\")\n                embedding_table_exists = False\n            \n            # Initialize or update checkbox state based on table status\n            if 'day18_replace_mode' not in st.session_state:\n                st.session_state.day18_replace_mode = embedding_table_exists\n            else:\n                if 'day18_last_embedding_table' not in st.session_state or st.session_state.day18_last_embedding_table != full_embedding_table:\n                    st.session_state.day18_replace_mode = embedding_table_exists\n                    st.session_state.day18_last_embedding_table = full_embedding_table\n            \n            # Replace mode checkbox\n            replace_mode = st.checkbox(\n                f\":material/sync: Replace Table Mode for `{st.session_state.day18_embedding_table}`\",\n                help=f\"When enabled, replaces all existing embeddings in {full_embedding_table}\",\n                key=\"day18_replace_mode\"\n            )\n            \n            if replace_mode:\n                st.warning(\"**Replace Mode Active**: Existing embeddings will be deleted before saving new ones.\")\n            else:\n                st.success(\"**Append Mode Active**: New embeddings will be added to existing data.\")\n            \n            if st.button(\":material/save: Save Embeddings to Snowflake\", type=\"primary\", use_container_width=True):\n                try:\n                    with st.status(\"Saving embeddings...\", expanded=True) as status:\n                        # Step 1: Create or truncate embeddings table\n                        st.write(\":material/looks_one: Preparing table...\")\n                        \n                        if replace_mode:\n                            # Replace existing data\n                            create_table_sql = f\"\"\"\n                            CREATE OR REPLACE TABLE {full_embedding_table} (\n                                CHUNK_ID NUMBER,\n                                EMBEDDING VECTOR(FLOAT, 768),\n                                CREATED_TIMESTAMP TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP()\n                            )\n                            \"\"\"\n                            session.sql(create_table_sql).collect()\n                            st.write(\":material/check_circle: Replaced existing table\")\n                        else:\n                            # Create if doesn't exist\n                            create_table_sql = f\"\"\"\n                            CREATE TABLE IF NOT EXISTS {full_embedding_table} (\n                                CHUNK_ID NUMBER,\n                                EMBEDDING VECTOR(FLOAT, 768),\n                                CREATED_TIMESTAMP TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP()\n                            )\n                            \"\"\"\n                            session.sql(create_table_sql).collect()\n                            st.write(\":material/check_circle: Table ready\")\n                        \n                        # Step 2: Insert embeddings\n                        st.write(f\":material/looks_two: Inserting {len(embeddings)} embeddings...\")\n                        \n                        for i, emb_data in enumerate(embeddings):\n                            # Get embedding list\n                            if isinstance(emb_data['embedding'], list):\n                                emb_list = emb_data['embedding']\n                            else:\n                                emb_list = list(emb_data['embedding'])\n                            \n                            # Convert to proper array format for Snowflake\n                            emb_array = \"[\" + \",\".join([str(float(x)) for x in emb_list]) + \"]\"\n                            \n                            insert_sql = f\"\"\"\n                            INSERT INTO {full_embedding_table} (CHUNK_ID, EMBEDDING)\n                            SELECT {emb_data['chunk_id']}, {emb_array}::VECTOR(FLOAT, 768)\n                            \"\"\"\n                            session.sql(insert_sql).collect()\n                            \n                            if (i + 1) % 10 == 0:\n                                st.write(f\"Saved {i + 1} of {len(embeddings)} embeddings...\")\n                        \n                        status.update(label=\"Embeddings saved!\", state=\"complete\", expanded=False)\n                    \n                    mode_msg = \"replaced in\" if replace_mode else \"saved to\"\n                    st.success(f\":material/check_circle: Successfully {mode_msg} `{full_embedding_table}`\\n\\n:material/calculate: {len(embeddings)} embedding(s) now in table\")\n                    \n                    # Store for Day 19\n                    st.session_state.embeddings_table = full_embedding_table\n                    st.session_state.embeddings_database = st.session_state.day18_database\n                    st.session_state.embeddings_schema = st.session_state.day18_schema\n                    \n                    st.balloons()\n                    \n                except Exception as e:\n                    st.error(f\"Error saving embeddings: {str(e)}\")\n\n# View Saved Embeddings Section\nwith st.container(border=True):\n    st.subheader(\":material/search: View Saved Embeddings\")\n    \n    # Check if embeddings table exists and show record count\n    full_embedding_table = f\"{st.session_state.day18_database}.{st.session_state.day18_schema}.{st.session_state.day18_embedding_table}\"\n    \n    try:\n        count_result = session.sql(f\"\"\"\n            SELECT COUNT(*) as CNT FROM {full_embedding_table}\n        \"\"\").collect()\n        \n        if count_result:\n            record_count = count_result[0]['CNT']\n            if record_count \u003E 0:\n                st.warning(f\":material/warning: **{record_count:,} embedding(s)** currently in table `{full_embedding_table}`\")\n            else:\n                st.info(\":material/inbox: **Embedding table is empty** - Generate and save embeddings above.\")\n    except:\n        st.info(\":material/inbox: **Embedding table doesn't exist yet** - Generate and save embeddings to create it.\")\n    \n    query_button = st.button(\":material/analytics: Query Embedding Table\", type=\"secondary\", use_container_width=True)\n    \n    if query_button:\n        try:\n            query = f\"\"\"\n            SELECT \n                CHUNK_ID,\n                EMBEDDING,\n                CREATED_TIMESTAMP,\n                VECTOR_L2_DISTANCE(EMBEDDING, EMBEDDING) as SELF_DISTANCE\n            FROM {full_embedding_table}\n            ORDER BY CHUNK_ID\n            \"\"\"\n            result_df = session.sql(query).to_pandas()\n            \n            # Store in session state\n            st.session_state.queried_embeddings = result_df\n            st.session_state.queried_embeddings_table = full_embedding_table\n            st.rerun()\n            \n        except Exception as e:\n            st.error(f\"Error querying embeddings: {str(e)}\")\n    \n    # Display results if available in session state\n    if 'queried_embeddings' in st.session_state and st.session_state.get('queried_embeddings_table') == full_embedding_table:\n        emb_df = st.session_state.queried_embeddings\n        \n        if len(emb_df) \u003E 0:\n            st.code(full_embedding_table, language=\"sql\")\n            \n            # Summary metrics\n            col1, col2 = st.columns(2)\n            with col1:\n                st.metric(\"Total Embeddings\", len(emb_df))\n            with col2:\n                st.metric(\"Dimensions\", \"768\")\n            \n            # Display table without the EMBEDDING column for readability\n            # Check which columns exist (case-insensitive)\n            embedding_col = None\n            for col in emb_df.columns:\n                if col.upper() == 'EMBEDDING':\n                    embedding_col = col\n                    break\n            \n            if embedding_col:\n                display_df = emb_df.drop(columns=[embedding_col])\n            else:\n                display_df = emb_df\n            \n            st.dataframe(display_df, use_container_width=True)\n            \n            st.info(\":material/lightbulb: Self-distance should be 0, confirming embeddings are stored correctly\")\n            \n            # View individual embedding vectors (only if EMBEDDING column exists)\n            if embedding_col:\n                with st.expander(\":material/search: View Individual Embedding Vectors\"):\n                    st.write(\"Select a CHUNK_ID to view its full 768-dimensional embedding vector:\")\n                    \n                    # Find CHUNK_ID column (case-insensitive)\n                    chunk_id_col = None\n                    for col in emb_df.columns:\n                        if col.upper() == 'CHUNK_ID':\n                            chunk_id_col = col\n                            break\n                    \n                    chunk_ids = emb_df[chunk_id_col].tolist()\n                    selected_chunk = st.selectbox(\"Select CHUNK_ID\", chunk_ids, key=\"view_embedding_chunk\")\n                    \n                    if st.button(\":material/analytics: Load Embedding Vector\", key=\"load_embedding_btn\"):\n                        # Get the embedding for selected chunk\n                        selected_emb = emb_df[emb_df[chunk_id_col] == selected_chunk][embedding_col].iloc[0]\n                        \n                        # Store in session state\n                        st.session_state.loaded_embedding = selected_emb\n                        st.session_state.loaded_embedding_chunk = selected_chunk\n                        st.rerun()\n                    \n                    # Display loaded embedding\n                    if 'loaded_embedding' in st.session_state:\n                        st.write(f\"**Embedding Vector for CHUNK_ID {st.session_state.loaded_embedding_chunk}:**\")\n                        \n                        # Convert to list if needed\n                        emb_vector = st.session_state.loaded_embedding\n                        if isinstance(emb_vector, str):\n                            # If it's a string representation, parse it\n                            import json\n                            emb_vector = json.loads(emb_vector)\n                        elif hasattr(emb_vector, 'tolist'):\n                            emb_vector = emb_vector.tolist()\n                        elif not isinstance(emb_vector, list):\n                            emb_vector = list(emb_vector)\n                        \n                        st.caption(f\"Vector length: {len(emb_vector)} dimensions\")\n                        \n                        # Display the full embedding vector as code\n                        st.code(emb_vector, language=\"python\")\n        else:\n            st.info(\":material/inbox: No embeddings found in table.\")\n    else:\n        st.info(\":material/inbox: No embeddings queried yet. Click 'Query Embedding Table' to view saved embeddings.\")\n\nst.divider()\nst.caption(\"Day 18: Generating Embeddings for Customer Reviews | 30 Days of AI\")\n```\n\nHere's the embedding generator in action:\n\n![Day 18: Embeddings Generator](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-rag-app-with-streamlit-and-snowflake-cortex/day18.png)\n\n\u003C!-- ------------------------ --\u003E\n## Create Cortex Search Service\n\nCortex Search provides semantic search over your chunked data. In this section, you'll create a search service that indexes your chunks for fast retrieval.\n\n\u003E Note: This section is repurposed from Day 19 of the [#30DaysOfAI learning challenge](https://30daysofai.streamlit.app/?day=19).\n\n### Service Configuration\n\nThe `CREATE CORTEX SEARCH SERVICE` statement defines how your data is indexed:\n\n- **ON**: Specifies the text column to search (`CHUNK_TEXT`)\n- **ATTRIBUTES**: Additional columns returned with results (`FILE_NAME`, `CHUNK_TYPE`, `CHUNK_ID`)\n- **TARGET_LAG**: How often the index refreshes (1 hour keeps it reasonably fresh)\n- **WAREHOUSE**: The compute resource used for indexing\n\n### Key Configuration Options\n\n| Parameter | Description |\n|-----------|-------------|\n| `ON` | The text column to search |\n| `ATTRIBUTES` | Metadata columns to return |\n| `TARGET_LAG` | How often to refresh the index |\n| `WAREHOUSE` | Compute resource for indexing |\n\n### Full Code\n\n```sql\nCREATE OR REPLACE CORTEX SEARCH SERVICE RAG_DB.RAG_SCHEMA.CUSTOMER_REVIEW_SEARCH\n    ON CHUNK_TEXT\n    ATTRIBUTES FILE_NAME, CHUNK_TYPE, CHUNK_ID\n    WAREHOUSE = COMPUTE_WH\n    TARGET_LAG = '1 hour'\n    AS (\n        SELECT \n            CHUNK_ID,\n            FILE_NAME,\n            CHUNK_TYPE,\n            CHUNK_TEXT\n        FROM RAG_DB.RAG_SCHEMA.REVIEW_CHUNKS\n    );\n```\n\nVerify your service was created:\n\n```sql\nSHOW CORTEX SEARCH SERVICES IN SCHEMA RAG_DB.RAG_SCHEMA;\n```\n\nHere's the Cortex Search service creation:\n\n![Day 19: Cortex Search service configuration](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-rag-app-with-streamlit-and-snowflake-cortex/day19.png)\n\n\u003C!-- ------------------------ --\u003E\n## Query Cortex Search\n\nUse the Python SDK to search your documents semantically. This section builds an interactive search interface.\n\n\u003E Note: This section is repurposed from Day 20 of the [#30DaysOfAI learning challenge](https://30daysofai.streamlit.app/?day=20).\n\n### Cortex Search Python SDK\n\nThe `snowflake.core.Root` API provides programmatic access to Cortex Search. Navigate the object hierarchy using database, schema, and service name:\n\n```python\nfrom snowflake.core import Root\n\nroot = Root(session)\nparts = service_path.split(\".\")\nsvc = root.databases[parts[0]].schemas[parts[1]].cortex_search_services[parts[2]]\n```\n\n### Search Method\n\nThe `search()` method accepts a natural language query and returns relevant chunks ranked by semantic similarity:\n\n```python\nresults = svc.search(\n    query=\"warm thermal gloves\",\n    columns=[\"CHUNK_TEXT\", \"FILE_NAME\", \"CHUNK_TYPE\", \"CHUNK_ID\"],\n    limit=5\n)\n```\n\n### Displaying Results\n\nEach result is displayed in a bordered container with file metadata and chunk text for easy scanning.\n\n### Full Code\n\nCopy and paste this code into `day20.py`:\n\n```python\nimport streamlit as st\nfrom snowflake.core import Root\n\ntry:\n    from snowflake.snowpark.context import get_active_session\n    session = get_active_session()\nexcept:\n    from snowflake.snowpark import Session\n    session = Session.builder.configs(st.secrets[\"connections\"][\"snowflake\"]).create()\n\nst.title(\":material/search: Querying Cortex Search\")\n\nsearch_service = st.text_input(\n    \"Search Service:\",\n    value=\"RAG_DB.RAG_SCHEMA.CUSTOMER_REVIEW_SEARCH\"\n)\n\nquery = st.text_input(\"Enter your search query:\", value=\"warm thermal gloves\")\nnum_results = st.slider(\"Number of results:\", 1, 20, 5)\n\nif st.button(\":material/search: Search\", type=\"primary\"):\n    if query and search_service:\n        root = Root(session)\n        parts = search_service.split(\".\")\n        \n        svc = root.databases[parts[0]].schemas[parts[1]].cortex_search_services[parts[2]]\n        \n        with st.spinner(\"Searching...\"):\n            results = svc.search(\n                query=query,\n                columns=[\"CHUNK_TEXT\", \"FILE_NAME\", \"CHUNK_TYPE\", \"CHUNK_ID\"],\n                limit=num_results\n            )\n        \n        st.success(f\":material/check_circle: Found {len(results.results)} result(s)!\")\n        \n        for i, item in enumerate(results.results, 1):\n            with st.container(border=True):\n                st.markdown(f\"**Result {i}** - {item.get('FILE_NAME', 'N/A')}\")\n                st.write(item.get(\"CHUNK_TEXT\", \"No text found\"))\n```\n\nHere's the search interface in action:\n\n![Day 20: Cortex Search query interface with results](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-rag-app-with-streamlit-and-snowflake-cortex/day20.png)\n\n\u003C!-- ------------------------ --\u003E\n## Build RAG Application\n\nCombine retrieval with LLM generation for grounded answers. The RAG pattern retrieves relevant context, then generates responses based on that context.\n\n\u003E Note: This section is repurposed from Day 21 of the [#30DaysOfAI learning challenge](https://30daysofai.streamlit.app/?day=21).\n\n### RAG Architecture\n\n1. **Retrieve**: Search Cortex Search for relevant chunks\n2. **Augment**: Add retrieved chunks to the prompt as context\n3. **Generate**: LLM creates an answer grounded in the context\n\n### Building the Context\n\nRetrieved chunks are joined with separators to create a context string that's passed to the LLM:\n\n```python\ncontext_chunks = [item.get(\"CHUNK_TEXT\", \"\") for item in search_results.results]\ncontext = \"\\n\\n---\\n\\n\".join(context_chunks)\n```\n\n### Prompt Engineering\n\nThe prompt explicitly instructs the model to only use provided context, reducing hallucinations:\n\n```python\nrag_prompt = f\"\"\"You are a helpful assistant. Answer the user's question based ONLY on the provided context.\nIf the context doesn't contain enough information, say \"I don't have enough information...\"\n\nCONTEXT FROM DOCUMENTS:\n{context}\n\nUSER QUESTION: {question}\n\"\"\"\n```\n\n### LLM Completion\n\nUse `SNOWFLAKE.CORTEX.COMPLETE()` to call the LLM with your context-aware prompt:\n\n```python\nresponse_sql = f\"\"\"\nSELECT SNOWFLAKE.CORTEX.COMPLETE(\n    'claude-3-5-sonnet',\n    '{rag_prompt.replace(\"'\", \"''\")}'\n) as response\n\"\"\"\nresponse = session.sql(response_sql).collect()[0][0]\n```\n\n### Full Code\n\n```python\nimport streamlit as st\nfrom snowflake.core import Root\n\ntry:\n    from snowflake.snowpark.context import get_active_session\n    session = get_active_session()\nexcept:\n    from snowflake.snowpark import Session\n    session = Session.builder.configs(st.secrets[\"connections\"][\"snowflake\"]).create()\n\ndef rag_query(question: str, search_service: str, num_chunks: int = 3, model: str = \"claude-3-5-sonnet\"):\n    root = Root(session)\n    parts = search_service.split(\".\")\n    \n    svc = root.databases[parts[0]].schemas[parts[1]].cortex_search_services[parts[2]]\n    \n    search_results = svc.search(\n        query=question,\n        columns=[\"CHUNK_TEXT\", \"FILE_NAME\"],\n        limit=num_chunks\n    )\n    \n    context_chunks = [item.get(\"CHUNK_TEXT\", \"\") for item in search_results.results]\n    sources = [item.get(\"FILE_NAME\", \"Unknown\") for item in search_results.results]\n    context = \"\\n\\n---\\n\\n\".join(context_chunks)\n    \n    rag_prompt = f\"\"\"You are a helpful assistant. Answer the user's question based ONLY on the provided context.\nIf the context doesn't contain enough information to answer, say \"I don't have enough information to answer that based on the available documents.\"\n\nCONTEXT FROM DOCUMENTS:\n{context}\n\nUSER QUESTION: {question}\n\nProvide a clear, accurate answer based on the context.\"\"\"\n    \n    response_sql = f\"\"\"\n    SELECT SNOWFLAKE.CORTEX.COMPLETE(\n        '{model}',\n        '{rag_prompt.replace(\"'\", \"''\")}'\n    ) as response\n    \"\"\"\n    \n    response = session.sql(response_sql).collect()[0][0]\n    \n    return response, context_chunks, sources\n```\n\nHere's the RAG application architecture:\n\n![Day 21: RAG application with retrieval and generation](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-rag-app-with-streamlit-and-snowflake-cortex/day21.png)\n\n\u003C!-- ------------------------ --\u003E\n## Build RAG Chatbot\n\nCreate a conversational interface for your RAG system with chat history and source citations.\n\n\u003E Note: This section is repurposed from Day 21 of the [#30DaysOfAI learning challenge](https://30daysofai.streamlit.app/?day=21).\n\n### Chat History\n\nConversation history is stored in session state as a list of message dictionaries. Each message has a `role` (user/assistant) and `content`:\n\n```python\nif \"doc_messages\" not in st.session_state:\n    st.session_state.doc_messages = []\n```\n\n### Sidebar Configuration\n\nThe sidebar provides controls for the search service path, number of context chunks, and a clear chat button:\n\n```python\nwith st.sidebar:\n    search_service = st.text_input(\"Cortex Search Service:\", value=\"RAG_DB.RAG_SCHEMA.CUSTOMER_REVIEW_SEARCH\")\n    num_chunks = st.slider(\"Context chunks:\", 1, 5, 3)\n```\n\n### Source Citations\n\nAn expander shows the source chunks used for each answer, providing transparency into retrieval:\n\n```python\nwith st.expander(f\":material/library_books: Sources ({len(chunks)} used)\"):\n    for i, (chunk, source) in enumerate(zip(chunks, sources), 1):\n        st.caption(f\"**[{i}] {source}**\")\n        st.write(chunk[:200] + \"...\")\n```\n\n### Full Code\n\n```python\nimport streamlit as st\nfrom snowflake.core import Root\n\ntry:\n    from snowflake.snowpark.context import get_active_session\n    session = get_active_session()\nexcept:\n    from snowflake.snowpark import Session\n    session = Session.builder.configs(st.secrets[\"connections\"][\"snowflake\"]).create()\n\nst.title(\":material/chat: Chat with Your Documents\")\n\nif \"doc_messages\" not in st.session_state:\n    st.session_state.doc_messages = []\n\nwith st.sidebar:\n    st.header(\":material/settings: Settings\")\n    search_service = st.text_input(\n        \"Cortex Search Service:\",\n        value=\"RAG_DB.RAG_SCHEMA.CUSTOMER_REVIEW_SEARCH\"\n    )\n    num_chunks = st.slider(\"Context chunks:\", 1, 5, 3)\n    \n    if st.button(\":material/delete: Clear Chat\"):\n        st.session_state.doc_messages = []\n        st.rerun()\n\ndef rag_query(question: str, search_service: str, num_chunks: int = 3):\n    root = Root(session)\n    parts = search_service.split(\".\")\n    svc = root.databases[parts[0]].schemas[parts[1]].cortex_search_services[parts[2]]\n    \n    results = svc.search(query=question, columns=[\"CHUNK_TEXT\", \"FILE_NAME\"], limit=num_chunks)\n    \n    context_chunks = [item.get(\"CHUNK_TEXT\", \"\") for item in results.results]\n    sources = [item.get(\"FILE_NAME\", \"Unknown\") for item in results.results]\n    context = \"\\n\\n---\\n\\n\".join(context_chunks)\n    \n    rag_prompt = f\"\"\"You are a helpful assistant. Answer based ONLY on the provided context.\n\nCONTEXT:\n{context}\n\nQUESTION: {question}\n\nAnswer:\"\"\"\n    \n    sql = f\"SELECT SNOWFLAKE.CORTEX.COMPLETE('claude-3-5-sonnet', '{rag_prompt.replace(chr(39), chr(39)+chr(39))}')\"\n    response = session.sql(sql).collect()[0][0]\n    \n    return response, context_chunks, sources\n\nfor msg in st.session_state.doc_messages:\n    with st.chat_message(msg[\"role\"]):\n        st.markdown(msg[\"content\"])\n\nif prompt := st.chat_input(\"Ask about your documents...\"):\n    st.session_state.doc_messages.append({\"role\": \"user\", \"content\": prompt})\n    \n    with st.chat_message(\"user\"):\n        st.markdown(prompt)\n    \n    with st.chat_message(\"assistant\"):\n        with st.spinner(\"Searching and thinking...\"):\n            response, chunks, sources = rag_query(prompt, search_service, num_chunks)\n        \n        st.markdown(response)\n        \n        with st.expander(f\":material/library_books: Sources ({len(chunks)} used)\"):\n            for i, (chunk, source) in enumerate(zip(chunks, sources), 1):\n                st.caption(f\"**[{i}] {source}**\")\n                st.write(chunk[:200] + \"...\" if len(chunk) \u003E 200 else chunk)\n        \n        st.session_state.doc_messages.append({\"role\": \"assistant\", \"content\": response})\n```\n\nHere's the RAG chatbot with sources:\n\n![Day 21: RAG chatbot with source citations](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-rag-app-with-streamlit-and-snowflake-cortex/day21.png)\n\n\u003C!-- ------------------------ --\u003E\n## Add Guardrails\n\nKeep your RAG chatbot focused on its domain by adding explicit constraints to the system prompt.\n\n\u003E Note: This section is repurposed from Day 22 of the [#30DaysOfAI learning challenge](https://30daysofai.streamlit.app/?day=22).\n\n### Why Guardrails?\n\nWithout guardrails, users can ask about any topic and the LLM may attempt to answer using its general knowledge. Guardrails keep the assistant focused on the intended domain (customer reviews).\n\n### Numbered Guidelines\n\nExplicit numbered instructions tell the LLM exactly what topics to address and how to handle off-topic questions:\n\n```python\nSTRICT GUIDELINES:\n1. ONLY use information from the provided customer review context\n2. If asked about unrelated topics, respond: \"I can only answer questions about customer reviews...\"\n3. If context lacks information, say: \"I don't have enough information...\"\n4. Stay focused on: product features, satisfaction, complaints, quality, pricing\n5. Do NOT make up information or use outside knowledge\n```\n\n### Off-Topic Handling\n\nThe guardrails detect off-topic requests and politely redirect the user back to the intended domain.\n\n### Full Code\n\n```python\nimport streamlit as st\nfrom snowflake.core import Root\n\ntry:\n    from snowflake.snowpark.context import get_active_session\n    session = get_active_session()\nexcept:\n    from snowflake.snowpark import Session\n    session = Session.builder.configs(st.secrets[\"connections\"][\"snowflake\"]).create()\n\ndef rag_query_with_guardrails(question: str, search_service: str, num_chunks: int = 3):\n    root = Root(session)\n    parts = search_service.split(\".\")\n    svc = root.databases[parts[0]].schemas[parts[1]].cortex_search_services[parts[2]]\n    \n    results = svc.search(query=question, columns=[\"CHUNK_TEXT\", \"FILE_NAME\"], limit=num_chunks)\n    \n    context = \"\\n\\n---\\n\\n\".join([item.get(\"CHUNK_TEXT\", \"\") for item in results.results])\n    \n    rag_prompt = f\"\"\"You are a customer review analysis assistant. Your role is to ONLY answer questions about customer reviews and feedback.\n\nSTRICT GUIDELINES:\n1. ONLY use information from the provided customer review context below\n2. If asked about topics unrelated to customer reviews (e.g., general knowledge, coding, math, news), respond: \"I can only answer questions about customer reviews. Please ask about product feedback, customer experiences, or review insights.\"\n3. If the context doesn't contain relevant information, say: \"I don't have enough information in the customer reviews to answer that.\"\n4. Stay focused on: product features, customer satisfaction, complaints, praise, quality, pricing, shipping, or customer service mentioned in reviews\n5. Do NOT make up information or use knowledge outside the provided reviews\n\nCONTEXT FROM CUSTOMER REVIEWS:\n{context}\n\nUSER QUESTION: {question}\n\nProvide a clear, helpful answer based ONLY on the customer reviews above.\"\"\"\n    \n    sql = f\"SELECT SNOWFLAKE.CORTEX.COMPLETE('claude-3-5-sonnet', '{rag_prompt.replace(chr(39), chr(39)+chr(39))}')\"\n    response = session.sql(sql).collect()[0][0]\n    \n    return response\n```\n\nHere's the guardrails keeping responses on-topic:\n\n![Day 22: RAG with guardrails handling off-topic queries](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-rag-app-with-streamlit-and-snowflake-cortex/day22.png)\n\n\u003C!-- ------------------------ --\u003E\n## Evaluate with TruLens\n\nTruLens provides automated evaluation of RAG quality using the RAG Triad metrics. This helps you measure and improve retrieval and generation quality.\n\n\u003E Note: This section is repurposed from Day 23 of the [#30DaysOfAI learning challenge](https://30daysofai.streamlit.app/?day=23).\n\n### RAG Triad Metrics\n\n| Metric | Description |\n|--------|-------------|\n| Context Relevance | Did we retrieve the right documents? |\n| Groundedness | Is the answer based on context (no hallucinations)? |\n| Answer Relevance | Does the answer address the question? |\n\n### Instrumented RAG Class\n\nThe `@instrument()` decorator marks methods for automatic tracing, capturing inputs, outputs, and timing for each step:\n\n```python\nfrom trulens.core.otel.instrument import instrument\n\nclass CustomerReviewRAG:\n    @instrument()\n    def retrieve_context(self, query: str) -\u003E str:\n        # Cortex Search retrieval\n        \n    @instrument()\n    def generate_completion(self, query: str, context: str) -\u003E str:\n        # LLM generation\n        \n    @instrument()\n    def query(self, query: str) -\u003E str:\n        # Full RAG pipeline\n```\n\n### TruLens Session\n\nInitialize TruLens with a Snowflake connector to store evaluation results in your account:\n\n```python\nfrom trulens.connectors.snowflake import SnowflakeConnector\nfrom trulens.core import TruSession\n\ntru_connector = SnowflakeConnector(snowpark_session=session)\ntru_session = TruSession(connector=tru_connector)\n```\n\n### Running Evaluations\n\nRegister your app and run evaluation against test questions. TruLens automatically computes the RAG Triad metrics:\n\n```python\nrun.compute_metrics([\n    \"answer_relevance\",\n    \"context_relevance\",\n    \"groundedness\",\n])\n```\n\n### Full Code\n\n```python\nfrom trulens.connectors.snowflake import SnowflakeConnector\nfrom trulens.core.run import Run, RunConfig\nfrom trulens.core import TruSession\nfrom trulens.core.otel.instrument import instrument\nfrom snowflake.core import Root\n\nclass CustomerReviewRAG:\n    def __init__(self, snowpark_session):\n        self.session = snowpark_session\n        self.search_service = \"RAG_DB.RAG_SCHEMA.CUSTOMER_REVIEW_SEARCH\"\n        self.model = \"claude-3-5-sonnet\"\n    \n    @instrument()\n    def retrieve_context(self, query: str) -\u003E str:\n        root = Root(self.session)\n        parts = self.search_service.split(\".\")\n        svc = root.databases[parts[0]].schemas[parts[1]].cortex_search_services[parts[2]]\n        results = svc.search(query=query, columns=[\"CHUNK_TEXT\"], limit=3)\n        context = \"\\n\\n\".join([r[\"CHUNK_TEXT\"] for r in results.results])\n        return context\n    \n    @instrument()\n    def generate_completion(self, query: str, context: str) -\u003E str:\n        prompt = f\"\"\"Based on this context:\n\n{context}\n\nQuestion: {query}\n\nProvide a helpful answer:\"\"\"\n        \n        prompt_escaped = prompt.replace(\"'\", \"''\")\n        response = self.session.sql(\n            f\"SELECT SNOWFLAKE.CORTEX.COMPLETE('{self.model}', '{prompt_escaped}')\"\n        ).collect()[0][0]\n        return response.strip()\n    \n    @instrument()\n    def query(self, query: str) -\u003E str:\n        context = self.retrieve_context(query)\n        answer = self.generate_completion(query, context)\n        return answer\n\ntru_connector = SnowflakeConnector(snowpark_session=session)\ntru_session = TruSession(connector=tru_connector)\n\nrag_app = CustomerReviewRAG(session)\n\ntru_rag = tru_session.App(\n    rag_app,\n    app_name=\"customer_review_rag\",\n    app_version=\"v1\",\n    main_method=rag_app.query\n)\n\ntest_questions = [\n    \"What do customers say about thermal gloves?\",\n    \"Are there any durability complaints?\",\n    \"Which products get the best reviews?\"\n]\n\nrun_config = RunConfig(\n    run_name=\"eval_run_1\",\n    dataset_name=\"test_questions\",\n    description=\"Customer review RAG evaluation\"\n)\n\nrun = tru_rag.add_run(run_config=run_config)\nrun.start()\n\nrun.compute_metrics([\n    \"answer_relevance\",\n    \"context_relevance\",\n    \"groundedness\",\n])\n```\n\nHere's the TruLens evaluation results:\n\n![Day 23: TruLens RAG evaluation metrics](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-rag-app-with-streamlit-and-snowflake-cortex/day23.png)\n\n### View Results in Snowsight\n\nNavigate to **AI & ML → Evaluations → customer_review_rag** to see:\n- RAG Triad scores per question\n- Aggregate metrics across runs\n- Trend analysis over time\n\n### Deploy the App\n\nSave the complete code above as `streamlit_app.py` and deploy to your preferred environment:\n\n- **Local**: Run `streamlit run streamlit_app.py` in your terminal\n- **Streamlit Community Cloud**: [Deploy your app](https://docs.streamlit.io/deploy/streamlit-community-cloud/deploy-your-app/deploy) from a GitHub repository\n- **Streamlit in Snowflake (SiS)**: [Create a Streamlit app](https://docs.snowflake.com/en/developer-guide/streamlit/getting-started/create-streamlit-ui) directly in Snowsight\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\n\nCongratulations! You've successfully built a complete RAG pipeline with Snowflake Cortex Search. You learned data preparation, chunking strategies, search service creation, building a conversational RAG chatbot with guardrails, and evaluating quality with TruLens.\n\n### What You Learned\n- Preparing and chunking documents for RAG (Days 16-17)\n- Generating embeddings for semantic search (Day 18)\n- Creating and configuring Cortex Search services (Day 19)\n- Querying Cortex Search for semantic retrieval (Day 20)\n- Building RAG applications with grounded responses (Day 21)\n- Adding guardrails to keep responses on-topic (Day 22)\n- Evaluating RAG quality with TruLens and AI Observability (Day 23)\n\n### Related Resources\n\nDocumentation:\n- [Cortex Search Overview](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview)\n- [Snowflake AI Observability](https://docs.snowflake.com/en/user-guide/snowflake-cortex/ai-observability)\n- [TruLens Documentation](https://www.trulens.org/)\n\nAdditional Reading:\n- [Building RAG Applications](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/overview-tutorials)\n\n### Source Material\n\nThis quickstart was adapted from **Days 16-23** of the 30 Days of AI challenge:\n- Day 16: Document extraction and upload\n- Day 17: Data preparation and chunking\n- Day 18: Generating embeddings\n- Day 19: Creating Cortex Search service\n- Day 20: Querying Cortex Search\n- Day 21: RAG chatbot\n- Day 22: Guardrails for RAG\n- Day 23: RAG evaluation with TruLens\n\nLearn more:\n- [30 Days of AI Challenge](https://30daysofai.streamlit.app/)\n- [GitHub Repository](https://github.com/streamlit/30daysofai)\n",":type":"text/x-markdown","multiValue":false},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image",":type":"text/plain","multiValue":false}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":items":{},":itemsOrder":[],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-aed060b5f1","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-977fe63547",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-44d08092ef","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-02-18",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-b3fe6221b9","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake instances\u003C/span\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"}},":itemsOrder":["quickstart_last_modi","text"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-75f65ddd3f",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{},":itemsOrder":[]},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["contentfragment","flexible_column_cont"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-e67549aa66",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_table_of_":{"layout":"SIMPLE","id":"container-fc3abd2dff","isDeveloperGuidesPage":false,":type":"snowflake-site/components/quickstart/quickstart-table-of-content/quickstart-table-of-content-container",":items":{"quickstart_table_of_":{"id":"quickstart-table-of-content-1b1dc5cdba",":type":"snowflake-site/components/quickstart/quickstart-table-of-content","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/build-rag-app-with-streamlit-and-snowflake-cortex","headings":["\u003Ch2\u003EOverview\u003C/h2\u003E","\u003Ch2\u003EGetting Started\u003C/h2\u003E","\u003Ch2\u003EExtract and Upload Documents\u003C/h2\u003E","\u003Ch2\u003EPrepare and Chunk Documents\u003C/h2\u003E","\u003Ch2\u003EGenerate Embeddings\u003C/h2\u003E","\u003Ch2\u003ECreate Cortex Search Service\u003C/h2\u003E","\u003Ch2\u003EQuery Cortex Search\u003C/h2\u003E","\u003Ch2\u003EBuild RAG Application\u003C/h2\u003E","\u003Ch2\u003EBuild RAG Chatbot\u003C/h2\u003E","\u003Ch2\u003EAdd Guardrails\u003C/h2\u003E","\u003Ch2\u003EEvaluate with TruLens\u003C/h2\u003E","\u003Ch2\u003EConclusion And 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