Create visualizer_app.py

visualizer_app.py

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+import marimo
+
+__generated_with = "0.13.0"
+app = marimo.App(width="full")
+
+with app.setup:
+    # Initialization code that runs before all other cells
+    import marimo as mo
+    from typing import Dict, Optional, List, Union, Any
+    from ibm_watsonx_ai import APIClient, Credentials
+    from pathlib import Path
+    import pandas as pd
+    import mimetypes
+    import requests
+    import zipfile
+    import tempfile
+    import base64
+    import polars
+    import time
+    import json
+    import ast    
+    import os
+    import io
+    import re
+
+    def get_iam_token(api_key):
+        return requests.post(
+            'https://iam.cloud.ibm.com/identity/token',
+            headers={'Content-Type': 'application/x-www-form-urlencoded'},
+            data={'grant_type': 'urn:ibm:params:oauth:grant-type:apikey', 'apikey': api_key}
+        ).json()['access_token']
+
+    def setup_task_credentials(client):
+        # Get existing task credentials
+        existing_credentials = client.task_credentials.get_details()
+
+        # Delete existing credentials if any
+        if "resources" in existing_credentials and existing_credentials["resources"]:
+            for cred in existing_credentials["resources"]:
+                cred_id = client.task_credentials.get_id(cred)
+                client.task_credentials.delete(cred_id)
+
+        # Store new credentials
+        return client.task_credentials.store()
+
+    def get_cred_value(key, creds_var_name="baked_in_creds", default=""): ### Helper for working with preset credentials
+        """
+        Helper function to safely get a value from a credentials dictionary.
+
+        Args:
+            key: The key to look up in the credentials dictionary.
+            creds_var_name: The variable name of the credentials dictionary.
+            default: The default value to return if the key is not found.
+
+        Returns:
+            The value from the credentials dictionary if it exists and contains the key,
+            otherwise returns the default value.
+        """
+        # Check if the credentials variable exists in globals
+        if creds_var_name in globals():
+            creds_dict = globals()[creds_var_name]
+            if isinstance(creds_dict, dict) and key in creds_dict:
+                return creds_dict[key]
+        return default
+
+@app.cell
+def client_variables(client_instantiation_form):
+    if client_instantiation_form.value:
+        client_setup = client_instantiation_form.value
+    else:
+        client_setup = None
+
+    ### Extract Credential Variables:
+    if client_setup is not None:
+        wx_url = client_setup["wx_region"]
+        wx_api_key = client_setup["wx_api_key"].strip()
+        os.environ["WATSONX_APIKEY"] = wx_api_key
+
+        if client_setup["project_id"] is not None:
+            project_id = client_setup["project_id"].strip()
+        else:
+            project_id = None
+
+        if client_setup["space_id"] is not None:
+            space_id = client_setup["space_id"].strip()
+        else:
+            space_id = None
+
+    else:
+        os.environ["WATSONX_APIKEY"] = ""
+        project_id = None
+        space_id = None
+        wx_api_key = None
+        wx_url = None
+    return client_setup, project_id, space_id, wx_api_key, wx_url
+
+
+@app.cell
+def _(client_setup, wx_api_key):
+    if client_setup:
+        token = get_iam_token(wx_api_key)
+    else:
+        token = None
+    return
+
+@app.cell
+def _():
+    baked_in_creds = {
+            "purpose": "",
+            "api_key": "",
+            "project_id": "",
+            "space_id": "",
+        }
+    return baked_in_creds
+
+
+@app.cell
+def client_instantiation(
+    client_setup,
+    project_id,
+    space_id,
+    wx_api_key,
+    wx_url,
+):
+    ### Instantiate the watsonx.ai client
+    if client_setup:
+        wx_credentials = Credentials(
+            url=wx_url,
+            api_key=wx_api_key
+        )
+
+        if project_id:
+            project_client = APIClient(credentials=wx_credentials, project_id=project_id)
+        else:
+            project_client = None
+
+        if space_id:
+            deployment_client = APIClient(credentials=wx_credentials, space_id=space_id)
+        else:
+            deployment_client = None
+
+        if project_client is not None:
+            task_credentials_details = setup_task_credentials(project_client)
+        else:
+            task_credentials_details = setup_task_credentials(deployment_client)
+    else:
+        wx_credentials = None
+        project_client = None
+        deployment_client = None
+        task_credentials_details = None
+
+    client_status = mo.md("### Client Instantiation Status will turn Green When Ready")
+
+    if project_client is not None or deployment_client is not None:
+        client_callout_kind = "success"
+    else:
+        client_callout_kind = "neutral"
+    return (
+        client_callout_kind,
+        client_status,
+        deployment_client,
+        project_client,
+    )
+
+
+@app.cell
+def _():
+    mo.md(
+        r"""
+        #watsonx.ai Embedding Visualizer - Marimo Notebook
+
+        #### This marimo notebook can be used to develop a more intuitive understanding of how vector embeddings work by creating a 3D visualization of vector embeddings based on chunked PDF document pages. 
+
+        #### It can also serve as a useful tool for identifying gaps in model choice, chunking strategy or contents used in building collections by showing how far you are from what you want.
+        <br>
+
+        /// admonition
+        Created by ***Milan Mrdenovic*** [milan.mrdenovic@ibm.com] for IBM Ecosystem Client Engineering, NCEE - ***version 5.3** - 20.04.2025*
+        ///
+
+
+        >Licensed under apache 2.0, users hold full accountability for any use or modification of the code.
+        ><br>This asset is part of a set meant to support IBMers, IBM Partners, Clients in developing understanding of how to better utilize various watsonx features and generative AI as a subject matter.
+
+        <br>
+        """
+    )
+    return
+
+
+@app.cell
+def _():
+    mo.md("""###Part 1 - Client Setup, File Preparation and Chunking""")
+    return
+
+
+@app.cell
+def accordion_client_setup(client_selector, client_stack):
+    ui_accordion_part_1_1 = mo.accordion(
+        {
+            "Instantiate Client": mo.vstack([client_stack, client_selector], align="center"),
+        }
+    )
+
+    ui_accordion_part_1_1
+    return
+
+
+@app.cell
+def accordion_file_upload(select_stack):
+    ui_accordion_part_1_2 = mo.accordion(
+        {
+            "Select Model & Upload Files": select_stack
+        }
+    )
+
+    ui_accordion_part_1_2
+    return
+
+
+@app.cell
+def loaded_texts(
+    create_temp_files_from_uploads,
+    file_loader,
+    pdf_reader,
+    run_upload_button,
+    set_text_state,
+):
+    if file_loader.value is not None and run_upload_button.value:
+        filepaths = create_temp_files_from_uploads(file_loader.value)
+        loaded_texts = load_pdf_data_with_progress(pdf_reader, filepaths, file_loader.value, show_progress=True)
+
+        set_text_state(loaded_texts)
+    else:
+        filepaths = None
+        loaded_texts = None
+    return
+
+
+@app.cell
+def accordion_chunker_setup(chunker_setup):
+    ui_accordion_part_1_3 = mo.accordion(
+        {
+            "Chunker Setup": chunker_setup
+        }
+    )
+
+    ui_accordion_part_1_3
+    return
+
+
+@app.cell
+def chunk_documents_to_nodes(
+    get_text_state,
+    sentence_splitter,
+    sentence_splitter_config,
+    set_chunk_state,
+):
+    if sentence_splitter_config.value and sentence_splitter and get_text_state() is not None:
+        chunked_texts = chunk_documents(get_text_state(), sentence_splitter, show_progress=True)
+        set_chunk_state(chunked_texts)
+    else:
+        chunked_texts = None
+    return (chunked_texts,)
+
+
+@app.cell
+def _():
+    mo.md(r"""###Part 2 - Query Setup and Visualization""")
+    return
+
+
+@app.cell
+def accordion_chunk_range(chart_range_selection):
+    ui_accordion_part_2_1 = mo.accordion(
+        {
+            "Chunk Range Selection": chart_range_selection
+        }
+    )
+    ui_accordion_part_2_1
+    return
+
+
+@app.cell
+def chunk_embedding(
+    chunks_to_process,
+    embedding,
+    sentence_splitter_config,
+    set_embedding_state,
+):
+    if sentence_splitter_config.value is not None and chunks_to_process is not None:
+        with mo.status.spinner(title="Embedding Documents...", remove_on_exit=True) as _spinner:
+            output_embeddings = embedding.embed_documents(chunks_to_process)
+            _spinner.update("Almost Done")
+            time.sleep(1.5)
+            set_embedding_state(output_embeddings)
+            _spinner.update("Documents Embedded")
+    else:
+        output_embeddings = None
+    return
+
+
+@app.cell
+def preview_chunks(chunks_dict):
+    if chunks_dict is not None:
+        stats = create_stats(chunks_dict, 
+                             bordered=True, 
+                             object_names=['text','text'], 
+                             group_by_row=True, 
+                             items_per_row=5, 
+                             gap=1,
+                             label="Chunk")
+        ui_chunk_viewer = mo.accordion(
+            {
+                "View Chunks": stats,
+            }
+        )
+    else:
+        ui_chunk_viewer = None
+
+    ui_chunk_viewer
+    return
+
+
+@app.cell
+def accordion_query_view(chart_visualization, query_stack):
+    ui_accordion_part_2_2 = mo.accordion(
+        {
+            "Query": mo.vstack([query_stack, mo.hstack([chart_visualization])], align="center", gap=3)
+        }
+    )
+    ui_accordion_part_2_2
+    return
+
+
+@app.cell
+def chunker_setup(sentence_splitter_config):
+    chunker_setup = mo.hstack([sentence_splitter_config], justify="space-around", align="center", widths=[0.55])
+    return (chunker_setup,)
+
+
+@app.cell
+def file_and_model_select(
+    file_loader,
+    get_embedding_model_list,
+    run_upload_button,
+):
+    select_stack = mo.hstack([get_embedding_model_list(), mo.vstack([file_loader, run_upload_button], align="center")], justify="space-around", align="center", widths=[0.3,0.3])
+    return (select_stack,)
+
+
+@app.cell
+def client_instantiation_form():
+    # Endpoints
+    wx_platform_url = "https://api.dataplatform.cloud.ibm.com"
+    regions = {
+        "US": "https://us-south.ml.cloud.ibm.com",
+        "EU": "https://eu-de.ml.cloud.ibm.com",
+        "GB": "https://eu-gb.ml.cloud.ibm.com",
+        "JP": "https://jp-tok.ml.cloud.ibm.com",
+        "AU": "https://au-syd.ml.cloud.ibm.com",
+        "CA": "https://ca-tor.ml.cloud.ibm.com"
+    }
+
+    # Create a form with multiple elements
+    client_instantiation_form = (
+        mo.md('''
+        ###**watsonx.ai credentials:**
+
+        {wx_region}
+
+        {wx_api_key}
+
+        {project_id}
+
+        {space_id}
+    ''')
+        .batch(
+            wx_region = mo.ui.dropdown(regions, label="Select your watsonx.ai region:", value="US", searchable=True),
+            wx_api_key = mo.ui.text(placeholder="Add your IBM Cloud api-key...", label="IBM Cloud Api-key:", 
+                                    kind="password", value=get_cred_value('api_key', creds_var_name='baked_in_creds')),
+            project_id = mo.ui.text(placeholder="Add your watsonx.ai project_id...", label="Project_ID:", 
+                                    kind="text", value=get_cred_value('project_id', creds_var_name='baked_in_creds')),
+            space_id = mo.ui.text(placeholder="Add your watsonx.ai space_id...", label="Space_ID:", 
+                                  kind="text", value=get_cred_value('space_id', creds_var_name='baked_in_creds'))
+        ,)
+        .form(show_clear_button=True, bordered=False)
+    )
+    return (client_instantiation_form,)
+
+
+@app.cell
+def instantiation_status(
+    client_callout_kind,
+    client_instantiation_form,
+    client_status,
+):
+    client_callout = mo.callout(client_status, kind=client_callout_kind)
+    client_stack = mo.hstack([client_instantiation_form, client_callout], align="center", justify="space-around", gap=10)
+    return (client_stack,)
+
+
+@app.cell
+def client_selector(deployment_client, project_client):
+    if deployment_client is not None:
+        client_options = {"Deployment Client":deployment_client}
+        
+    elif project_client is not None:
+        client_options = {"Project Client":project_client}
+
+    elif project_client is not None and deployment_client is not None:
+        client_options = {"Project Client":project_client,"Deployment Client":deployment_client}
+    
+    else:
+        client_options = {"No Client": "Instantiate a Client"}
+
+    default_client = next(iter(client_options))
+    client_selector = mo.ui.dropdown(client_options, value=default_client, label="**Select your active client:**")
+    
+    return (client_selector,)
+
+
+@app.cell
+def active_client(client_selector):
+    client = client_selector.value
+    return (client,)
+
+
+@app.cell
+def emb_model_selection(client, set_embedding_model_list):
+    if client:
+        model_specs = client.foundation_models.get_embeddings_model_specs()
+        # model_specs = client.foundation_models.get_model_specs()
+        resources = model_specs["resources"]
+        # Define embedding models reference data
+        embedding_models = {
+            "ibm/granite-embedding-107m-multilingual": {"max_tokens": 512, "embedding_dimensions": 384},
+            "ibm/granite-embedding-278m-multilingual": {"max_tokens": 512, "embedding_dimensions": 768},
+            "ibm/slate-125m-english-rtrvr-v2": {"max_tokens": 512, "embedding_dimensions": 768},
+            "ibm/slate-125m-english-rtrvr": {"max_tokens": 512, "embedding_dimensions": 768},
+            "ibm/slate-30m-english-rtrvr-v2": {"max_tokens": 512, "embedding_dimensions": 384},
+            "ibm/slate-30m-english-rtrvr": {"max_tokens": 512, "embedding_dimensions": 384},
+            "sentence-transformers/all-minilm-l6-v2": {"max_tokens": 128, "embedding_dimensions": 384},
+            "sentence-transformers/all-minilm-l12-v2": {"max_tokens": 128, "embedding_dimensions": 384},
+            "intfloat/multilingual-e5-large": {"max_tokens": 512, "embedding_dimensions": 1024}
+        }
+
+        # Get model IDs from resources
+        model_id_list = []
+        for resource in resources:
+            model_id_list.append(resource["model_id"])
+
+        # Create enhanced model data for the table
+        embedding_model_data = []
+        for model_id in model_id_list:
+            model_entry = {"model_id": model_id}
+
+            # Add properties if model exists in our reference, otherwise use 0
+            if model_id in embedding_models:
+                model_entry["max_tokens"] = embedding_models[model_id]["max_tokens"]
+                model_entry["embedding_dimensions"] = embedding_models[model_id]["embedding_dimensions"]
+            else:
+                model_entry["max_tokens"] = 0
+                model_entry["embedding_dimensions"] = 0
+
+            embedding_model_data.append(model_entry)
+
+        embedding_model_selection = mo.ui.table(
+            embedding_model_data,
+            selection="single",  # Only allow selecting one row
+            label="Select an embedding model to use.",
+            page_size=30,
+            initial_selection=[1]
+        )
+        set_embedding_model_list(embedding_model_selection)
+    else:
+        default_model_data = [{
+            "model_id": "ibm/granite-embedding-107m-multilingual",
+            "max_tokens": 512,
+            "embedding_dimensions": 384
+        }]
+
+        set_embedding_model_list(create_emb_model_selection_table(default_model_data, initial_selection=0, selection_type="single", label="Select a model to use."))
+    return
+
+
+@app.function
+def create_emb_model_selection_table(model_data, initial_selection=0, selection_type="single", label="Select a model to use."):
+    embedding_model_selection = mo.ui.table(
+        model_data,
+        selection=selection_type,  # Only allow selecting one row
+        label=label,
+        page_size=30,
+        initial_selection=[initial_selection]
+    )
+    return embedding_model_selection
+
+
+@app.cell
+def embedding_model():
+    get_embedding_model_list, set_embedding_model_list = mo.state(None)
+    return get_embedding_model_list, set_embedding_model_list
+
+
+@app.cell
+def emb_model_parameters(emb_model_max_tk):
+    from ibm_watsonx_ai.foundation_models import Embeddings
+    from ibm_watsonx_ai.metanames import EmbedTextParamsMetaNames as EmbedParams
+
+    embed_params = {
+         EmbedParams.TRUNCATE_INPUT_TOKENS: emb_model_max_tk,
+         EmbedParams.RETURN_OPTIONS: {
+         'input_text': True
+         }
+     }
+    return Embeddings, embed_params
+
+
+@app.cell
+def emb_model_state(get_embedding_model_list):
+    embedding_model = get_embedding_model_list()
+    return (embedding_model,)
+
+
+@app.cell
+def emb_model_setup(embedding_model):
+    emb_model = embedding_model.value[0]['model_id']
+    emb_model_max_tk = embedding_model.value[0]['max_tokens']
+    emb_model_emb_dim = embedding_model.value[0]['embedding_dimensions']
+    return emb_model, emb_model_emb_dim, emb_model_max_tk
+
+
+@app.cell
+def emb_model_instantiation(Embeddings, client, emb_model, embed_params):
+    if client is not None:
+        embedding = Embeddings(
+             model_id=emb_model,
+             api_client=client,
+             params=embed_params,
+             batch_size=1000,
+             concurrency_limit=10
+             )
+    else:
+        embedding = None
+    return (embedding,)
+
+
+@app.cell
+def _():
+    get_embedding_state, set_embedding_state = mo.state(None)
+    return get_embedding_state, set_embedding_state
+
+
+@app.cell
+def _():
+    get_query_state, set_query_state = mo.state(None)
+    return get_query_state, set_query_state
+
+
+@app.cell
+def file_loader_input():
+    file_loader = mo.ui.file(
+            kind="area",
+            filetypes=[".pdf"],
+            label="   Load .pdf files  ",
+            multiple=True
+        )
+    return (file_loader,)
+
+
+@app.cell
+def file_loader_run(file_loader):
+    if file_loader.value is not None:
+        run_upload_button = mo.ui.run_button(label="Load Files")
+    else:
+        run_upload_button = mo.ui.run_button(disabled=True, label="Load Files")
+    return (run_upload_button,)
+
+
+@app.cell
+def helper_function_tempfiles():
+    def create_temp_files_from_uploads(upload_results) -> List[str]:
+        """
+        Creates temporary files from a tuple of FileUploadResults objects and returns their paths.
+        Args:
+            upload_results: Object containing a value attribute that is a tuple of FileUploadResults
+        Returns:
+            List of temporary file paths
+        """
+        temp_file_paths = []
+
+        # Get the number of items in the tuple
+        num_items = len(upload_results)
+
+        # Process each item by index
+        for i in range(num_items):
+            result = upload_results[i]  # Get item by index
+
+            # Create a temporary file with the original filename
+            temp_dir = tempfile.gettempdir()
+            file_name = result.name
+            temp_path = os.path.join(temp_dir, file_name)
+            # Write the contents to the temp file
+            with open(temp_path, 'wb') as temp_file:
+                temp_file.write(result.contents)
+            # Add the path to our list
+            temp_file_paths.append(temp_path)
+
+        return temp_file_paths
+
+    def cleanup_temp_files(temp_file_paths: List[str]) -> None:
+        """Delete temporary files after use."""
+        for path in temp_file_paths:
+            if os.path.exists(path):
+                os.unlink(path)
+    return (create_temp_files_from_uploads,)
+
+
+@app.function
+def load_pdf_data_with_progress(pdf_reader, filepaths, file_loader_value, show_progress=True):
+    """
+    Loads PDF data for each file path and organizes results by original filename.
+    Args:
+        pdf_reader: The PyMuPDFReader instance
+        filepaths: List of temporary file paths
+        file_loader_value: The original upload results value containing file information
+        show_progress: Whether to show a progress bar during loading (default: False)
+    Returns:
+        Dictionary mapping original filenames to their loaded text content
+    """
+    results = {}
+
+    # Process files with or without progress bar
+    if show_progress:
+        import marimo as mo
+        # Use progress bar with the length of filepaths as total
+        with mo.status.progress_bar(
+            total=len(filepaths),
+            title="Loading PDFs",
+            subtitle="Processing documents...",
+            completion_title="PDF Loading Complete",
+            completion_subtitle=f"{len(filepaths)} documents processed",
+            remove_on_exit=True
+        ) as bar:
+            # Process each file path
+            for i, file_path in enumerate(filepaths):
+
+                original_file_name = file_loader_value[i].name
+                bar.update(subtitle=f"Processing {original_file_name}...")
+                loaded_text = pdf_reader.load_data(file_path=file_path, metadata=True)
+
+                # Store the result with the original filename as the key
+                results[original_file_name] = loaded_text
+                # Update progress bar
+                bar.update(increment=1)
+    else:
+        # Original logic without progress bar
+        for i, file_path in enumerate(filepaths):
+            original_file_name = file_loader_value[i].name
+            loaded_text = pdf_reader.load_data(file_path=file_path, metadata=True)
+            results[original_file_name] = loaded_text
+
+    return results
+
+
+@app.cell
+def file_readers():
+    from llama_index.readers.file import PyMuPDFReader
+    from llama_index.readers.file import FlatReader
+    from llama_index.core.node_parser import SentenceSplitter
+
+    ### File Readers
+    pdf_reader = PyMuPDFReader()
+    # flat_file_reader = FlatReader()
+    return SentenceSplitter, pdf_reader
+
+
+@app.cell
+def sentence_splitter_setup():
+    ### Chunker Setup
+    sentence_splitter_config = (
+        mo.md('''
+        ###**Chunking Setup:**
+
+        > Unless you want to do some advanced sentence splitting, it's best to stick to adjusting only the chunk size and overlap. Changing the other settings might result in unexpected results.
+
+        Separator value is set to **" "** by default, while the paragraph separator is **"\\n\\n\\n"**.
+
+        {chunk_size} {chunk_overlap}
+
+        {separator} {paragraph_separator}
+
+        {secondary_chunking_regex}
+
+        {include_metadata}
+
+    ''')
+        .batch(
+            chunk_size = mo.ui.slider(start=100, stop=5000, step=1, label="**Chunk SizeL**", value=350, show_value=True),
+            chunk_overlap = mo.ui.slider(start=1, stop=1000, step=1, label="**Chunk Overlap:**", value=50, show_value=True),
+            separator = mo.ui.text(placeholder="Define a separator", label="**Separator:**", kind="text", value=" "),
+            paragraph_separator = mo.ui.text(placeholder="Define a paragraph separator", 
+                                             label="**Paragraph Separator:**", kind="text", 
+                                             value="\n\n\n"),
+            secondary_chunking_regex = mo.ui.text(placeholder="Define a secondary chunking regex", 
+                                                  label="**Chunking Regex:**", kind="text", 
+                                                  value="[^,.;?!]+[,.;?!]?"),
+            include_metadata= mo.ui.checkbox(value=True, label="**Include Metadata**")
+        )
+        .form(show_clear_button=True, bordered=False)
+    )
+    return (sentence_splitter_config,)
+
+
+@app.cell
+def sentence_splitter_instantiation(
+    SentenceSplitter,
+    sentence_splitter_config,
+):
+    ### Chunker/Sentence Splitter 
+    def simple_whitespace_tokenizer(text):
+        return text.split()
+    
+    if sentence_splitter_config.value is not None:
+        sentence_splitter_config_values = sentence_splitter_config.value
+        validated_chunk_overlap = min(sentence_splitter_config_values.get("chunk_overlap"), 
+                     int(sentence_splitter_config_values.get("chunk_size") * 0.3))
+
+        sentence_splitter = SentenceSplitter(
+            chunk_size=sentence_splitter_config_values.get("chunk_size"),
+            chunk_overlap=validated_chunk_overlap,
+            separator=sentence_splitter_config_values.get("separator"),
+            paragraph_separator=sentence_splitter_config_values.get("paragraph_separator"),
+            secondary_chunking_regex=sentence_splitter_config_values.get("secondary_chunking_regex"),
+            include_metadata=sentence_splitter_config_values.get("include_metadata"),
+            tokenizer=simple_whitespace_tokenizer
+        )
+
+    else:
+        sentence_splitter = SentenceSplitter(
+            chunk_size=2048,
+            chunk_overlap=204,
+            separator=" ",
+            paragraph_separator="\n\n\n",
+            secondary_chunking_regex="[^,.;?!]+[,.;?!]?",
+            include_metadata=True,
+            tokenizer=simple_whitespace_tokenizer
+        )
+    return (sentence_splitter,)
+
+
+@app.cell
+def text_state():
+    get_text_state, set_text_state = mo.state(None)
+    return get_text_state, set_text_state
+
+
+@app.cell
+def chunk_state():
+    get_chunk_state, set_chunk_state = mo.state(None)
+    return get_chunk_state, set_chunk_state
+
+
+@app.function
+def chunk_documents(loaded_texts, sentence_splitter, show_progress=True):
+    """
+    Process each document in the loaded_texts dictionary using the sentence_splitter,
+    with an optional marimo progress bar tracking progress at document level.
+
+    Args:
+        loaded_texts (dict): Dictionary containing lists of Document objects
+        sentence_splitter: The sentence splitter object with get_nodes_from_documents method
+        show_progress (bool): Whether to show a progress bar during processing
+
+    Returns:
+        dict: Dictionary with the same structure but containing chunked texts
+    """
+    chunked_texts_dict = {}
+
+    # Get the total number of documents across all keys
+    total_docs = sum(len(docs) for docs in loaded_texts.values())
+    processed_docs = 0
+
+    # Process with or without progress bar
+    if show_progress:
+        import marimo as mo
+        # Use progress bar with the total number of documents as total
+        with mo.status.progress_bar(
+            total=total_docs,
+            title="Processing Documents",
+            subtitle="Chunking documents...",
+            completion_title="Processing Complete",
+            completion_subtitle=f"{total_docs} documents processed",
+            remove_on_exit=True
+        ) as bar:
+            # Process each key-value pair in the loaded_texts dictionary
+            for key, documents in loaded_texts.items():
+                # Update progress bar subtitle to show current key
+                doc_count = len(documents)
+                bar.update(subtitle=f"Chunking {key}... ({doc_count} documents)")
+
+                # Apply the sentence splitter to each list of documents
+                chunked_texts = sentence_splitter.get_nodes_from_documents(
+                    documents, 
+                    show_progress=False  # Disable internal progress to avoid nested bars
+                )
+
+                # Store the result with the same key
+                chunked_texts_dict[key] = chunked_texts
+                time.sleep(0.15)
+
+                # Update progress bar with the number of documents in this batch
+                bar.update(increment=doc_count)
+                processed_docs += doc_count
+    else:
+        # Process without progress bar
+        for key, documents in loaded_texts.items():
+            chunked_texts = sentence_splitter.get_nodes_from_documents(
+                documents, 
+                show_progress=True  # Use the internal progress bar if no marimo bar
+            )
+            chunked_texts_dict[key] = chunked_texts
+
+    return chunked_texts_dict
+
+
+@app.cell
+def chunked_nodes(chunked_texts, get_chunk_state, sentence_splitter):
+    if chunked_texts is not None and sentence_splitter:
+        chunked_documents = get_chunk_state()
+    else:
+        chunked_documents = None
+    return (chunked_documents,)
+
+
+@app.cell
+def prep_cumulative_df(chunked_documents, llamaindex_convert_docs_multi):
+    if chunked_documents is not None:
+        dict_from_nodes = llamaindex_convert_docs_multi(chunked_documents)
+        nodes_from_dict = llamaindex_convert_docs_multi(dict_from_nodes)
+    else:
+        dict_from_nodes = None
+        nodes_from_dict = None
+    return (dict_from_nodes,)
+
+
+@app.cell
+def chunks_to_process(
+    dict_from_nodes,
+    document_range_stack,
+    get_data_in_range_triplequote,
+):
+    if dict_from_nodes is not None and document_range_stack.value is not None:
+
+        chunk_dict_df = create_cumulative_dataframe(dict_from_nodes)
+
+        if document_range_stack.value is not None:
+            chunk_start_idx = document_range_stack.value[0]
+            chunk_end_idx = document_range_stack.value[1]
+        else:
+            chunk_start_idx = 0
+            chunk_end_idx = len(chunk_dict_df)
+
+        chunk_range_index = [chunk_start_idx, chunk_end_idx]
+        chunks_dict = get_data_in_range_triplequote(chunk_dict_df,
+                                                    index_range=chunk_range_index, 
+                                                    columns_to_include=["text"])
+
+        chunks_to_process = chunks_dict['text'] if 'text' in chunks_dict else []
+    else:
+        chunk_objects = None
+        chunks_dict = None
+        chunks_to_process = None
+    return chunks_dict, chunks_to_process
+
+
+@app.cell
+def helper_function_doc_formatting():
+    def llamaindex_convert_docs_multi(items):
+        """
+        Automatically convert between document objects and dictionaries.
+
+        This function handles:
+        - Converting dictionaries to document objects
+        - Converting document objects to dictionaries
+        - Processing lists or individual items
+        - Supporting dictionary structures where values are lists of documents
+
+        Args:
+            items: A document object, dictionary, or list of either.
+                  Can also be a dictionary mapping filenames to lists of documents.
+
+        Returns:
+            Converted item(s) maintaining the original structure
+        """
+        # Handle empty or None input
+        if not items:
+            return []
+
+        # Handle dictionary mapping filenames to document lists (from load_pdf_data)
+        if isinstance(items, dict) and all(isinstance(v, list) for v in items.values()):
+            result = {}
+            for filename, doc_list in items.items():
+                result[filename] = llamaindex_convert_docs(doc_list)
+            return result
+
+        # Handle single items (not in a list)
+        if not isinstance(items, list):
+            # Single dictionary to document
+            if isinstance(items, dict):
+                # Determine document class
+                doc_class = None
+                if 'doc_type' in items:
+                    import importlib
+                    module_path, class_name = items['doc_type'].rsplit('.', 1)
+                    module = importlib.import_module(module_path)
+                    doc_class = getattr(module, class_name)
+                if not doc_class:
+                    from llama_index.core.schema import Document
+                    doc_class = Document
+                return doc_class.from_dict(items)
+            # Single document to dictionary
+            elif hasattr(items, 'to_dict'):
+                return items.to_dict()
+            # Return as is if can't convert
+            return items
+
+        # Handle list input
+        result = []
+
+        # Handle empty list
+        if len(items) == 0:
+            return result
+
+        # Determine the type of conversion based on the first non-None item
+        first_item = next((item for item in items if item is not None), None)
+
+        # If we found no non-None items, return empty list
+        if first_item is None:
+            return result
+
+        # Convert dictionaries to documents
+        if isinstance(first_item, dict):
+            # Get the right document class from the items themselves
+            doc_class = None
+            # Try to get doc class from metadata if available
+            if 'doc_type' in first_item:
+                import importlib
+                module_path, class_name = first_item['doc_type'].rsplit('.', 1)
+                module = importlib.import_module(module_path)
+                doc_class = getattr(module, class_name)
+            if not doc_class:
+                # Fallback to default Document class from llama_index
+                from llama_index.core.schema import Document
+                doc_class = Document
+
+            # Convert each dictionary to document
+            for item in items:
+                if isinstance(item, dict):
+                    result.append(doc_class.from_dict(item))
+                elif item is None:
+                    result.append(None)
+                elif isinstance(item, list):
+                    result.append(llamaindex_convert_docs(item))
+                else:
+                    result.append(item)
+
+        # Convert documents to dictionaries
+        else:
+            for item in items:
+                if hasattr(item, 'to_dict'):
+                    result.append(item.to_dict())
+                elif item is None:
+                    result.append(None)
+                elif isinstance(item, list):
+                    result.append(llamaindex_convert_docs(item))
+                else:
+                    result.append(item)
+
+        return result
+
+    def llamaindex_convert_docs(items):
+        """
+        Automatically convert between document objects and dictionaries.
+
+        Args:
+            items: A list of document objects or dictionaries
+
+        Returns:
+            List of converted items (dictionaries or document objects)
+        """
+        result = []
+
+        # Handle empty or None input
+        if not items:
+            return result
+
+        # Determine the type of conversion based on the first item
+        if isinstance(items[0], dict):
+            # Get the right document class from the items themselves
+            # Look for a 'doc_type' or '__class__' field in the dictionary
+            doc_class = None
+
+            # Try to get doc class from metadata if available
+            if 'doc_type' in items[0]:
+                import importlib
+                module_path, class_name = items[0]['doc_type'].rsplit('.', 1)
+                module = importlib.import_module(module_path)
+                doc_class = getattr(module, class_name)
+
+            if not doc_class:
+                # Fallback to default Document class from llama_index
+                from llama_index.core.schema import Document
+                doc_class = Document
+
+            # Convert dictionaries to documents
+            for item in items:
+                if isinstance(item, dict):
+                    result.append(doc_class.from_dict(item))
+        else:
+            # Convert documents to dictionaries
+            for item in items:
+                if hasattr(item, 'to_dict'):
+                    result.append(item.to_dict())
+
+        return result
+    return (llamaindex_convert_docs_multi,)
+
+
+@app.cell
+def helper_function_create_df():
+    def create_document_dataframes(dict_from_docs):
+        """
+        Creates a pandas DataFrame for each file in the dictionary.
+
+        Args:
+            dict_from_docs: Dictionary mapping filenames to lists of documents
+
+        Returns:
+            List of pandas DataFrames, each representing all documents from a single file
+        """
+        dataframes = []
+
+        for filename, docs in dict_from_docs.items():
+            # Create a list to hold all document records for this file
+            file_records = []
+
+            for i, doc in enumerate(docs):
+                # Convert the document to a format compatible with DataFrame
+                if hasattr(doc, 'to_dict'):
+                    doc_data = doc.to_dict()
+                elif isinstance(doc, dict):
+                    doc_data = doc
+                else:
+                    doc_data = {'content': str(doc)}
+
+                # Add document index information
+                doc_data['doc_index'] = i
+
+                # Add to the list of records for this file
+                file_records.append(doc_data)
+
+            # Create a single DataFrame for all documents in this file
+            if file_records:
+                df = pd.DataFrame(file_records)
+                df['filename'] = filename  # Add filename as a column
+                dataframes.append(df)
+
+        return dataframes
+
+    def create_dataframe_previews(dataframe_list, page_size=5):
+        """
+        Creates a list of mo.ui.dataframe components, one for each DataFrame in the input list.
+
+        Args:
+            dataframe_list: List of pandas DataFrames (output from create_document_dataframes)
+            page_size: Number of rows to show per page for each component
+
+        Returns:
+            List of mo.ui.dataframe components
+        """
+        # Create a list of mo.ui.dataframe components
+        preview_components = []
+
+        for df in dataframe_list:
+            # Create a mo.ui.dataframe component for this DataFrame
+            preview = mo.ui.dataframe(df, page_size=page_size)
+            preview_components.append(preview)
+
+        return preview_components
+    return
+
+
+@app.cell
+def helper_function_chart_preparation():
+    import altair as alt
+    import numpy as np
+    import plotly.express as px
+    from sklearn.manifold import TSNE
+
+    def prepare_embedding_data(embeddings, texts, model_id=None, embedding_dimensions=None):
+        """
+        Prepare embedding data for visualization
+
+        Args:
+            embeddings: List of embeddings arrays
+            texts: List of text strings
+            model_id: Embedding model ID (optional)
+            embedding_dimensions: Embedding dimensions (optional)
+
+        Returns:
+            DataFrame with processed data and metadata
+        """
+        # Flatten embeddings (in case they're nested)
+        flattened_embeddings = []
+        for emb in embeddings:
+            if isinstance(emb, list) and len(emb) > 0 and isinstance(emb[0], list):
+                flattened_embeddings.append(emb[0])  # Take first element if nested
+            else:
+                flattened_embeddings.append(emb)
+
+        # Convert to numpy array
+        embedding_array = np.array(flattened_embeddings)
+
+        # Apply dimensionality reduction (t-SNE)
+        tsne = TSNE(n_components=2, random_state=42, perplexity=min(30, len(embedding_array)-1))
+        reduced_embeddings = tsne.fit_transform(embedding_array)
+
+        # Create truncated texts for display
+        truncated_texts = [text[:50] + "..." if len(text) > 50 else text for text in texts]
+
+        # Create dataframe for visualization
+        df = pd.DataFrame({
+            "x": reduced_embeddings[:, 0],
+            "y": reduced_embeddings[:, 1],
+            "text": truncated_texts,
+            "full_text": texts,
+            "index": range(len(texts))
+        })
+
+        # Add metadata
+        metadata = {
+            "model_id": model_id,
+            "embedding_dimensions": embedding_dimensions
+        }
+
+        return df, metadata
+
+    def create_embedding_chart(df, metadata=None):
+        """
+        Create an Altair chart for embedding visualization
+
+        Args:
+            df: DataFrame with x, y coordinates and text
+            metadata: Dictionary with model_id and embedding_dimensions
+
+        Returns:
+            Altair chart
+        """
+        model_id = metadata.get("model_id") if metadata else None
+        embedding_dimensions = metadata.get("embedding_dimensions") if metadata else None
+
+        selection = alt.selection_multi(fields=['index'])
+
+        base = alt.Chart(df).encode(
+            x=alt.X("x:Q", title="Dimension 1"),
+            y=alt.Y("y:Q", title="Dimension 2"),
+            tooltip=["text", "index"]
+        )
+
+        points = base.mark_circle(size=100).encode(
+            color=alt.Color("index:N", legend=None),
+            opacity=alt.condition(selection, alt.value(1), alt.value(0.2))
+        ).add_selection(selection)  # Add this line to apply the selection
+
+        text = base.mark_text(align="left", dx=7).encode(
+            text="index:N"
+        )
+
+        return (points + text).properties(
+            width=700, 
+            height=500,
+            title=f"Embedding Visualization{f' - Model: {model_id}' if model_id else ''}{f' ({embedding_dimensions} dimensions)' if embedding_dimensions else ''}"
+        ).interactive()
+
+    def show_selected_text(indices, texts):
+        """
+        Create markdown display for selected texts
+
+        Args:
+            indices: List of selected indices
+            texts: List of all texts
+
+        Returns:
+            Markdown string
+        """
+        if not indices:
+            return "No text selected"
+
+        selected_texts = [texts[i] for i in indices if i < len(texts)]
+        return "\n\n".join([f"**Document {i}**:\n{text}" for i, text in zip(indices, selected_texts)])
+
+    def prepare_embedding_data_3d(embeddings, texts, model_id=None, embedding_dimensions=None):
+            """
+            Prepare embedding data for 3D visualization
+
+            Args:
+                embeddings: List of embeddings arrays
+                texts: List of text strings
+                model_id: Embedding model ID (optional)
+                embedding_dimensions: Embedding dimensions (optional)
+
+            Returns:
+                DataFrame with processed data and metadata
+            """
+            # Flatten embeddings (in case they're nested)
+            flattened_embeddings = []
+            for emb in embeddings:
+                if isinstance(emb, list) and len(emb) > 0 and isinstance(emb[0], list):
+                    flattened_embeddings.append(emb[0])
+                else:
+                    flattened_embeddings.append(emb)
+
+            # Convert to numpy array
+            embedding_array = np.array(flattened_embeddings)
+
+            # Handle the case of a single embedding differently
+            if len(embedding_array) == 1:
+                # For a single point, we don't need t-SNE, just use a fixed position
+                reduced_embeddings = np.array([[0.0, 0.0, 0.0]])
+            else:
+                # Apply dimensionality reduction to 3D
+                # Fix: Ensure perplexity is at least 1.0
+                perplexity_value = max(1.0, min(30, len(embedding_array)-1))
+                tsne = TSNE(n_components=3, random_state=42, perplexity=perplexity_value)
+                reduced_embeddings = tsne.fit_transform(embedding_array)
+
+            # Format texts for display
+            formatted_texts = []
+            for text in texts:
+                # Truncate if needed
+                if len(text) > 500:
+                    text = text[:500] + "..."
+
+                # Insert line breaks for wrapping
+                wrapped_text = ""
+                for i in range(0, len(text), 50):
+                    wrapped_text += text[i:i+50] + "<br>"
+
+                formatted_texts.append("<b>"+wrapped_text+"</b>")
+
+            # Create dataframe for visualization
+            df = pd.DataFrame({
+                "x": reduced_embeddings[:, 0],
+                "y": reduced_embeddings[:, 1],
+                "z": reduced_embeddings[:, 2],
+                "text": formatted_texts,
+                "full_text": texts,
+                "index": range(len(texts)),
+                "embedding": flattened_embeddings  # Store the original embeddings for later use
+            })
+
+            # Add metadata
+            metadata = {
+                "model_id": model_id,
+                "embedding_dimensions": embedding_dimensions
+            }
+
+            return df, metadata
+
+    def create_3d_embedding_chart(df, metadata=None, chart_width=1200, chart_height=800, marker_size_var: int=3):
+        """
+        Create a 3D Plotly chart for embedding visualization with proximity-based coloring
+        """
+        model_id = metadata.get("model_id") if metadata else None
+        embedding_dimensions = metadata.get("embedding_dimensions") if metadata else None
+
+        # Calculate the proximity between points
+        from scipy.spatial.distance import pdist, squareform
+        # Get the coordinates as a numpy array
+        coords = df[['x', 'y', 'z']].values
+
+        # Calculate pairwise distances
+        dist_matrix = squareform(pdist(coords))
+
+        # For each point, find its average distance to all other points
+        avg_distances = np.mean(dist_matrix, axis=1)
+
+        # Add this to the dataframe - smaller values = closer to other points
+        df['proximity'] = avg_distances
+
+        # Create 3D scatter plot with proximity-based coloring
+        fig = px.scatter_3d(
+            df, 
+            x='x', 
+            y='y', 
+            z='z',
+            # x='petal_length',  # Changed from 'x' to 'petal_length'
+            # y='petal_width',   # Changed from 'y' to 'petal_width'
+            # z='petal_height',
+            color='proximity',  # Color based on proximity
+            color_continuous_scale='Viridis_r',  # Reversed so closer points are warmer colors
+            hover_data=['text', 'index', 'proximity'],
+            labels={'x': 'Dimension 1', 'y': 'Dimension 2', 'z': 'Dimension 3', 'proximity': 'Avg Distance'},
+            # labels={'x': 'Dimension 1', 'y': 'Dimension 2', 'z': 'Dimension 3', 'proximity': 'Avg Distance'},
+            title=f"<b>3D Embedding Visualization</b>{f' - Model: <b>{model_id}</b>' if model_id else ''}{f'  <i>({embedding_dimensions} dimensions)</i>' if embedding_dimensions else ''}",
+            text='index',
+            # size_max=marker_size_var
+        )
+
+        # Update marker size and layout
+        # fig.update_traces(marker=dict(size=3), selector=dict(mode='markers'))
+        fig.update_traces(
+            marker=dict(
+                size=marker_size_var,              # Very small marker size
+                opacity=0.7,         # Slightly transparent 
+                symbol="diamond",      # Use circle markers (other options: "square", "diamond", "cross", "x")
+                line=dict(
+                    width=0.5,       # Very thin border
+                    color="white"    # White outline makes small dots more visible
+                )
+            ),
+            textfont=dict(
+                color="rgba(255, 255, 255, 0.3)",
+                size=8
+            ),
+            # hovertemplate="<b>index=%{text}</b><br>%{customdata[0]}<br><br>Avg Distance=%{customdata[2]:.4f}<extra></extra>", ### Hover Changes
+            hovertemplate="text:<br><b>%{customdata[0]}</b><br>index: <b>%{text}</b><br><br>Avg Distance: <b>%{customdata[2]:.4f}</b><extra></extra>",
+            hoverinfo="text+name",
+            hoverlabel=dict(
+                bgcolor="white",     # White background for hover labels
+                font_size=12         # Font size for hover text
+            ),
+            selector=dict(type='scatter3d')
+        )
+
+        # Keep your existing layout settings
+        fig.update_layout(
+            scene=dict(
+                xaxis=dict(
+                    title='Dimension 1',
+                    nticks=40,
+                    backgroundcolor="rgb(10, 10, 20, 0.1)",
+                    gridcolor="white",
+                    showbackground=True,
+                    gridwidth=0.35,
+                    zerolinecolor="white",
+                ),
+                yaxis=dict(
+                    title='Dimension 2',
+                    nticks=40,
+                    backgroundcolor="rgb(10, 10, 20, 0.1)",
+                    gridcolor="white",
+                    showbackground=True,
+                    gridwidth=0.35,
+                    zerolinecolor="white",
+                ),
+                zaxis=dict(
+                    title='Dimension 3',
+                    nticks=40,
+                    backgroundcolor="rgb(10, 10, 20, 0.1)",
+                    gridcolor="white",
+                    showbackground=True,
+                    gridwidth=0.35,
+                    zerolinecolor="white",
+                ),
+                # Control camera view angle
+                camera=dict(
+                    up=dict(x=0, y=0, z=1),
+                    center=dict(x=0, y=0, z=0),
+                    eye=dict(x=1.25, y=1.25, z=1.25),
+                ),
+                aspectratio=dict(x=1, y=1, z=1),
+                aspectmode='data'
+            ),
+            width=int(chart_width),
+            height=int(chart_height),
+            margin=dict(r=20, l=10, b=10, t=50),
+            paper_bgcolor="rgb(0, 0, 0)",
+            plot_bgcolor="rgb(0, 0, 0)",
+            coloraxis_colorbar=dict(
+                title="Average Distance",
+                thicknessmode="pixels", thickness=20,
+                lenmode="pixels", len=400,
+                yanchor="top", y=1,
+                ticks="outside",
+                dtick=0.1
+            )
+        )
+
+        return fig
+    return create_3d_embedding_chart, prepare_embedding_data_3d
+
+
+@app.cell
+def helper_function_text_preparation():
+    def convert_table_to_json_docs(df, selected_columns=None):
+        """
+        Convert a pandas DataFrame or dictionary to a list of JSON documents.
+        Dynamically includes columns based on user selection.
+        Column names are standardized to lowercase with underscores instead of spaces
+        and special characters removed.
+
+        Args:
+            df: The DataFrame or dictionary to process
+            selected_columns: List of column names to include in the output documents
+
+        Returns:
+            list: A list of dictionaries, each representing a row as a JSON document
+        """
+        import pandas as pd
+        import re
+
+        def standardize_key(key):
+            """Convert a column name to lowercase with underscores instead of spaces and no special characters"""
+            if not isinstance(key, str):
+                return str(key).lower()
+            # Replace spaces with underscores and convert to lowercase
+            key = key.lower().replace(' ', '_')
+            # Remove special characters (keeping alphanumeric and underscores)
+            return re.sub(r'[^\w]', '', key)
+
+        # Handle case when input is a dictionary
+        if isinstance(df, dict):
+            # Filter the dictionary to include only selected columns
+            if selected_columns:
+                return [{standardize_key(k): df.get(k, None) for k in selected_columns}]
+            else:
+                # If no columns selected, return all key-value pairs with standardized keys
+                return [{standardize_key(k): v for k, v in df.items()}]
+
+        # Handle case when df is None
+        if df is None:
+            return []
+
+        # Ensure df is a DataFrame
+        if not isinstance(df, pd.DataFrame):
+            try:
+                df = pd.DataFrame(df)
+            except:
+                return []  # Return empty list if conversion fails
+
+        # Now check if DataFrame is empty
+        if df.empty:
+            return []
+
+        # If no columns are specifically selected, use all available columns
+        if not selected_columns or not isinstance(selected_columns, list) or len(selected_columns) == 0:
+            selected_columns = list(df.columns)
+
+        # Determine which columns exist in the DataFrame
+        available_columns = []
+        columns_lower = {col.lower(): col for col in df.columns if isinstance(col, str)}
+
+        for col in selected_columns:
+            if col in df.columns:
+                available_columns.append(col)
+            elif isinstance(col, str) and col.lower() in columns_lower:
+                available_columns.append(columns_lower[col.lower()])
+
+        # If no valid columns found, return empty list
+        if not available_columns:
+            return []
+
+        # Process rows
+        json_docs = []
+        for _, row in df.iterrows():
+            doc = {}
+            for col in available_columns:
+                value = row[col]
+                # Standardize the column name when adding to document
+                std_col = standardize_key(col)
+                doc[std_col] = None if pd.isna(value) else value
+            json_docs.append(doc)
+
+        return json_docs
+
+    def get_column_values(df, columns_to_include):
+        """
+        Extract values from specified columns of a dataframe as lists.
+
+        Args:
+            df: A pandas DataFrame
+            columns_to_include: A list of column names to extract
+
+        Returns:
+            Dictionary with column names as keys and their values as lists
+        """
+        result = {}
+
+        # Validate that columns exist in the dataframe
+        valid_columns = [col for col in columns_to_include if col in df.columns]
+        invalid_columns = set(columns_to_include) - set(valid_columns)
+
+        if invalid_columns:
+            print(f"Warning: These columns don't exist in the dataframe: {list(invalid_columns)}")
+
+        # Extract values for each valid column
+        for col in valid_columns:
+            result[col] = df[col].tolist()
+
+        return result
+
+    def get_data_in_range(doc_dict_df, index_range, columns_to_include):
+        """
+        Extract values from specified columns of a dataframe within a given index range.
+
+        Args:
+            doc_dict_df: The pandas DataFrame to extract data from
+            index_range: An integer specifying the number of rows to include (from 0 to index_range-1)
+            columns_to_include: A list of column names to extract
+
+        Returns:
+            Dictionary with column names as keys and their values (within the index range) as lists
+        """
+        # Validate the index range
+        max_index = len(doc_dict_df)
+        if index_range <= 0:
+            print(f"Warning: Invalid index range {index_range}. Must be positive.")
+            return {}
+
+        # Adjust index_range if it exceeds the dataframe length
+        if index_range > max_index:
+            print(f"Warning: Index range {index_range} exceeds dataframe length {max_index}. Using maximum length.")
+            index_range = max_index
+
+        # Slice the dataframe to get rows from 0 to index_range-1
+        df_subset = doc_dict_df.iloc[:index_range]
+
+        # Use the provided get_column_values function to extract column data
+        return get_column_values(df_subset, columns_to_include)
+
+    def get_data_in_range_triplequote(doc_dict_df, index_range, columns_to_include):
+        """
+        Extract values from specified columns of a dataframe within a given index range.
+        Wraps string values with triple quotes and escapes URLs.
+
+        Args:
+            doc_dict_df: The pandas DataFrame to extract data from
+            index_range: A list of two integers specifying the start and end indices of rows to include
+                        (e.g., [0, 10] includes rows from index 0 to 9 inclusive)
+            columns_to_include: A list of column names to extract
+        """
+        # Validate the index range
+        start_idx, end_idx = index_range
+        max_index = len(doc_dict_df)
+
+        # Validate start index
+        if start_idx < 0:
+            print(f"Warning: Invalid start index {start_idx}. Using 0 instead.")
+            start_idx = 0
+
+        # Validate end index
+        if end_idx <= start_idx:
+            print(f"Warning: End index {end_idx} must be greater than start index {start_idx}. Using {start_idx + 1} instead.")
+            end_idx = start_idx + 1
+
+        # Adjust end index if it exceeds the dataframe length
+        if end_idx > max_index:
+            print(f"Warning: End index {end_idx} exceeds dataframe length {max_index}. Using maximum length.")
+            end_idx = max_index
+
+        # Slice the dataframe to get rows from start_idx to end_idx-1
+        # Using .loc with slice to preserve original indices
+        df_subset = doc_dict_df.iloc[start_idx:end_idx]
+
+        # Use the provided get_column_values function to extract column data
+        result = get_column_values(df_subset, columns_to_include)
+
+        # Process each string result to wrap in triple quotes
+        for col in result:
+            if isinstance(result[col], list):
+                # Create a new list with items wrapped in triple quotes
+                processed_items = []
+                for item in result[col]:
+                    if isinstance(item, str):
+                        # Replace http:// and https:// with escaped versions
+                        item = item.replace("http://", "http\\://").replace("https://", "https\\://")
+                        # processed_items.append('"""' + item + '"""')
+                        processed_items.append(item)
+                    else:
+                        processed_items.append(item)
+                result[col] = processed_items
+        return result
+    return (get_data_in_range_triplequote,)
+
+
+@app.cell
+def prepare_doc_select(sentence_splitter_config):
+    def prepare_document_selection(node_dict):
+        """
+        Creates document selection UI component.
+        Args:
+            node_dict: Dictionary mapping filenames to lists of documents
+        Returns:
+            mo.ui component for document selection
+        """
+        # Calculate total number of documents across all files
+        total_docs = sum(len(docs) for docs in node_dict.values())
+
+        # Create a combined DataFrame of all documents for table selection
+        all_docs_records = []
+        doc_index_global = 0
+        for filename, docs in node_dict.items():
+            for i, doc in enumerate(docs):
+                # Convert the document to a format compatible with DataFrame
+                if hasattr(doc, 'to_dict'):
+                    doc_data = doc.to_dict()
+                elif isinstance(doc, dict):
+                    doc_data = doc
+                else:
+                    doc_data = {'content': str(doc)}
+
+                # Add metadata
+                doc_data['filename'] = filename
+                doc_data['doc_index'] = i
+                doc_data['global_index'] = doc_index_global
+                all_docs_records.append(doc_data)
+                doc_index_global += 1
+
+        # Create UI component
+        stop_value = max(total_docs, 2)
+        llama_docs = mo.ui.range_slider(
+            start=1, 
+            stop=stop_value, 
+            step=1, 
+            full_width=True, 
+            show_value=True, 
+            label="**Select a Range of Chunks to Visualize:**"
+        ).form(submit_button_disabled=check_state(sentence_splitter_config.value))
+
+        return llama_docs
+    return (prepare_document_selection,)
+
+
+@app.cell
+def document_range_selection(
+    dict_from_nodes,
+    prepare_document_selection,
+    set_range_slider_state,
+):
+    if dict_from_nodes is not None:
+        llama_docs = prepare_document_selection(dict_from_nodes)
+        set_range_slider_state(llama_docs)
+    else:
+        bare_dict = {}
+        llama_docs = prepare_document_selection(bare_dict)
+    return
+
+
+@app.function
+def create_cumulative_dataframe(dict_from_docs):
+    """
+    Creates a cumulative DataFrame from a nested dictionary of documents.
+
+    Args:
+        dict_from_docs: Dictionary mapping filenames to lists of documents
+
+    Returns:
+        DataFrame with all documents flattened with global indices
+    """
+    # Create a list to hold all document records
+    all_records = []
+    global_idx = 1  # Start from 1 to match range slider expectations
+
+    for filename, docs in dict_from_docs.items():
+        for i, doc in enumerate(docs):
+            # Convert the document to a dict format
+            if hasattr(doc, 'to_dict'):
+                doc_data = doc.to_dict()
+            elif isinstance(doc, dict):
+                doc_data = doc.copy()
+            else:
+                doc_data = {'content': str(doc)}
+
+            # Add additional metadata
+            doc_data['filename'] = filename
+            doc_data['doc_index'] = i
+            doc_data['global_index'] = global_idx
+
+            # If there's 'content' but no 'text', create a 'text' field
+            if 'content' in doc_data and 'text' not in doc_data:
+                doc_data['text'] = doc_data['content']
+
+            all_records.append(doc_data)
+            global_idx += 1
+
+    # Create DataFrame from all records
+    return pd.DataFrame(all_records)
+
+
+@app.function
+def create_stats(texts_dict, bordered=False, object_names=None, group_by_row=False, items_per_row=6, gap=2, label="Chunk"):
+    """
+    Create a list of stat objects for each item in the specified dictionary.
+
+    Parameters:
+    - texts_dict (dict): Dictionary containing the text data
+    - bordered (bool): Whether the stats should be bordered
+    - object_names (list or tuple): Two object names to use for label and value
+                                   [label_object, value_object]
+    - group_by_row (bool): Whether to group stats in rows (horizontal stacks)
+    - items_per_row (int): Number of stat objects per row when group_by_row is True
+
+    Returns:
+    - object: A vertical stack of stat objects or rows of stat objects
+    """
+    if not object_names or len(object_names) < 2:
+        raise ValueError("You must provide two object names as a list or tuple")
+
+    label_object = object_names[0]
+    value_object = object_names[1]
+
+    # Validate that both objects exist in the dictionary
+    if label_object not in texts_dict:
+        raise ValueError(f"Label object '{label_object}' not found in texts_dict")
+    if value_object not in texts_dict:
+        raise ValueError(f"Value object '{value_object}' not found in texts_dict")
+
+    # Determine how many items to process (based on the label object length)
+    num_items = len(texts_dict[label_object])
+
+    # Create individual stat objects
+    individual_stats = []
+    for i in range(num_items):
+        stat = mo.stat(
+            label=texts_dict[label_object][i],
+            value=f"{label} Number: {len(texts_dict[value_object][i])}",
+            bordered=bordered
+        )
+        individual_stats.append(stat)
+
+    # If grouping is not enabled, just return a vertical stack of all stats
+    if not group_by_row:
+        return mo.vstack(individual_stats, wrap=False)
+
+    # Group stats into rows based on items_per_row
+    rows = []
+    for i in range(0, num_items, items_per_row):
+        # Get a slice of stats for this row (up to items_per_row items)
+        row_stats = individual_stats[i:i+items_per_row]
+        # Create a horizontal stack for this row
+        widths = [0.35] * len(row_stats)
+        row = mo.hstack(row_stats, gap=gap, align="start", justify="center", widths=widths)
+        rows.append(row)
+
+    # Return a vertical stack of all rows
+    return mo.vstack(rows)
+
+
+@app.cell
+def prepare_chart_embeddings(
+    chunks_to_process,
+    emb_model,
+    emb_model_emb_dim,
+    get_embedding_state,
+    prepare_embedding_data_3d,
+):
+    # chart_dataframe, chart_metadata = None, None
+    if chunks_to_process is not None and get_embedding_state() is not None:
+        chart_dataframe, chart_metadata = prepare_embedding_data_3d(
+            get_embedding_state(), 
+            chunks_to_process,
+            model_id=emb_model,
+            embedding_dimensions=emb_model_emb_dim
+        )
+    else:
+        chart_dataframe, chart_metadata = None, None
+    return chart_dataframe, chart_metadata
+
+
+@app.cell
+def chart_dims():
+    chart_dimensions = (
+        mo.md('''
+        > **Adjust Chart Window**
+
+        {chart_height}
+
+        {chat_width}
+
+        ''').batch(
+                chart_height = mo.ui.slider(start=500, step=30, stop=1000, label="**Height:**", value=800, show_value=True),
+                chat_width = mo.ui.slider(start=900, step=50, stop=1400, label="**Width:**", value=1200, show_value=True)
+            )
+    )
+    return (chart_dimensions,)
+
+
+@app.cell
+def chart_dim_values(chart_dimensions):
+    chart_height = chart_dimensions.value['chart_height']
+    chart_width = chart_dimensions.value['chat_width']
+    return chart_height, chart_width
+
+
+@app.cell
+def create_baseline_chart(
+    chart_dataframe,
+    chart_height,
+    chart_metadata,
+    chart_width,
+    create_3d_embedding_chart,
+):
+    if chart_dataframe is not None and chart_metadata is not None:
+        emb_plot = create_3d_embedding_chart(chart_dataframe, chart_metadata, chart_width, chart_height, marker_size_var=9)
+        chart = mo.ui.plotly(emb_plot)
+    else:
+        emb_plot = None
+        chart = None
+    return (emb_plot,)
+
+
+@app.cell
+def test_query(get_chunk_state):
+    placeholder = """How can i use watsonx.data to perform vector search?"""
+
+    query = mo.ui.text_area(label="**Write text to check:**", full_width=True, rows=8, value=placeholder).form(show_clear_button=True, submit_button_disabled=check_state(get_chunk_state()))
+    return (query,)
+
+
+@app.cell
+def query_stack(chart_dimensions, query):
+    # query_stack = mo.hstack([query], justify="space-around", align="center", widths=[0.65])
+    query_stack = mo.hstack([query, chart_dimensions], justify="space-around", align="center", gap=15)
+    return (query_stack,)
+
+
+@app.function
+def check_state(variable):
+    return variable is None
+
+
+@app.cell
+def helper_function_add_query_to_chart():
+    def add_query_to_embedding_chart(existing_chart, query_coords, query_text, marker_size=12):
+        """
+        Add a query point to an existing 3D embedding chart as a large red dot.
+
+        Args:
+            existing_chart: The existing plotly figure or chart data
+            query_coords: Dictionary with 'x', 'y', 'z' coordinates for the query point
+            query_text: Text of the query to display on hover
+            marker_size: Size of the query marker (default: 18, typically 2x other markers)
+
+        Returns:
+            A modified plotly figure with the query point added as a red dot
+        """
+        import plotly.graph_objects as go
+
+        # Create a deep copy of the existing chart to avoid modifying the original
+        import copy
+        chart_copy = copy.deepcopy(existing_chart)
+
+        # Handle case where chart_copy is a dictionary or list (from mo.ui.plotly)
+        if isinstance(chart_copy, (dict, list)):
+            # Create a new plotly figure from the data
+            import plotly.graph_objects as go
+
+            if isinstance(chart_copy, list):
+                # If it's a list, assume it's a list of traces
+                fig = go.Figure(data=chart_copy)
+            else:
+                # If it's a dict with 'data' and 'layout'
+                fig = go.Figure(data=chart_copy.get('data', []), layout=chart_copy.get('layout', {}))
+
+            chart_copy = fig
+
+        # Create the query trace
+        query_trace = go.Scatter3d(
+            x=[query_coords['x']],
+            y=[query_coords['y']],
+            z=[query_coords['z']],
+            mode='markers',
+            name='Query',
+            marker=dict(
+                size=marker_size,  # Typically 2x the size of other markers
+                color='red',       # Bright red color
+                symbol='circle',   # Circle shape
+                opacity=0.70,       # Fully opaque
+                line=dict(
+                    width=1,       # Thin white border
+                    color='white'
+                )
+            ),
+            # text=['Query: ' + query_text],
+            text=['<b>Query:</b><br>' + '<br>'.join([query_text[i:i+50] for i in range(0, len(query_text), 50)])], ### Text Wrapping
+            hoverinfo="text+name"
+        )
+
+        # Add the query trace to the chart copy
+        chart_copy.add_trace(query_trace)
+
+        return chart_copy
+
+
+    def get_query_coordinates(reference_embeddings=None, query_embedding=None):
+        """
+        Calculate appropriate coordinates for a query point based on reference embeddings.
+
+        This function handles several scenarios:
+        1. If both reference embeddings and query embedding are provided, it places the
+           query near similar documents.
+        2. If only reference embeddings are provided, it places the query at a visible 
+           location near the center of the chart.
+        3. If neither are provided, it returns default origin coordinates.
+
+        Args:
+            reference_embeddings: DataFrame with x, y, z coordinates from the main chart
+            query_embedding: The embedding vector of the query
+
+        Returns:
+            Dictionary with x, y, z coordinates for the query point
+        """
+        import numpy as np
+
+        # Default coordinates (origin with slight offset)
+        default_coords = {'x': 0.0, 'y': 0.0, 'z': 0.0}
+
+        # If we don't have reference embeddings, return default
+        if reference_embeddings is None or len(reference_embeddings) == 0:
+            return default_coords
+
+        # If we have reference embeddings but no query embedding, 
+        # position at a visible location near the center
+        if query_embedding is None:
+            center_coords = {
+                'x': reference_embeddings['x'].mean(),
+                'y': reference_embeddings['y'].mean(),
+                'z': reference_embeddings['z'].mean()
+            }
+            return center_coords
+
+        # If we have both reference embeddings and query embedding,
+        # try to position near similar documents
+        try:
+            from sklearn.metrics.pairwise import cosine_similarity
+
+            # Check if original embeddings are in the dataframe
+            if 'embedding' in reference_embeddings.columns:
+                # Get all document embeddings as a 2D array
+                if isinstance(reference_embeddings['embedding'].iloc[0], list):
+                    doc_embeddings = np.array(reference_embeddings['embedding'].tolist())
+                else:
+                    doc_embeddings = np.array([emb for emb in reference_embeddings['embedding'].values])
+
+                # Reshape query embedding for comparison
+                query_emb_array = np.array(query_embedding)
+                if query_emb_array.ndim == 1:
+                    query_emb_array = query_emb_array.reshape(1, -1)
+
+                # Calculate cosine similarities
+                similarities = cosine_similarity(query_emb_array, doc_embeddings)[0]
+
+                # Find the closest document
+                closest_idx = np.argmax(similarities)
+
+                # Use the position of the closest document, with slight offset for visibility
+                query_coords = {
+                    'x': reference_embeddings['x'].iloc[closest_idx] + 0.2,
+                    'y': reference_embeddings['y'].iloc[closest_idx] + 0.2,
+                    'z': reference_embeddings['z'].iloc[closest_idx] + 0.2
+                }
+                return query_coords
+        except Exception as e:
+            print(f"Error positioning query near similar documents: {e}")
+
+        # Fallback to center position if similarity calculation fails
+        center_coords = {
+            'x': reference_embeddings['x'].mean(),
+            'y': reference_embeddings['y'].mean(),
+            'z': reference_embeddings['z'].mean()
+        }
+        return center_coords
+    return add_query_to_embedding_chart, get_query_coordinates
+
+
+@app.cell
+def combined_chart_visualization(
+    add_query_to_embedding_chart,
+    chart_dataframe,
+    emb_plot,
+    embedding,
+    get_query_coordinates,
+    get_query_state,
+    query,
+    set_chart_state,
+    set_query_state,
+):
+    # Usage with highlight_closest=True
+    if chart_dataframe is not None and query.value:
+        # Get the query embedding
+        query_emb = embedding.embed_documents([query.value])
+        set_query_state(query_emb)
+
+        # Get appropriate coordinates for the query
+        query_coords = get_query_coordinates(
+            reference_embeddings=chart_dataframe,
+            query_embedding=get_query_state()
+        )
+
+        # Add the query to the chart with closest points highlighted
+        result = add_query_to_embedding_chart(
+            existing_chart=emb_plot,
+            query_coords=query_coords,
+            query_text=query.value,
+        )
+
+        chart_with_query = result
+
+        # Create the visualization
+        combined_viz = mo.ui.plotly(chart_with_query)
+        set_chart_state(combined_viz)
+    else:
+        combined_viz = None
+    return
+
+
+@app.cell
+def _():
+    get_range_slider_state, set_range_slider_state = mo.state(None)
+    return get_range_slider_state, set_range_slider_state
+
+
+@app.cell
+def _(get_range_slider_state):
+    if get_range_slider_state() is not None:
+        document_range_stack = get_range_slider_state()
+    else:
+        document_range_stack = None
+    return (document_range_stack,)
+
+
+@app.cell
+def _():
+    get_chart_state, set_chart_state = mo.state(None)
+    return get_chart_state, set_chart_state
+
+
+@app.cell
+def _(get_chart_state, query):
+    if query.value is not None:
+        chart_visualization = get_chart_state()
+    else:
+        chart_visualization = None
+    return (chart_visualization,)
+
+
+@app.cell
+def c(document_range_stack):
+    chart_range_selection = mo.hstack([document_range_stack], justify="space-around", align="center", widths=[0.65])
+    return (chart_range_selection,)
+
+
+if __name__ == "__main__":
+    app.run()