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{
"cells": [
{
"cell_type": "markdown",
"id": "d13be0fd-db15-4ab1-860a-b00257051339",
"metadata": {},
"source": [
"## Gradio UI for Markdown-Based Q&A with Visualization"
]
},
{
"cell_type": "markdown",
"id": "bc63fbdb-66a9-4c10-8dbd-11476b5e2d21",
"metadata": {},
"source": [
"This interface enables users to:\n",
"- Upload Markdown files for processing\n",
"- Visualize similarity between document chunks in 2D and 3D using embeddings\n",
"- Ask questions and receive RAG enabled responses\n",
"- Mantain conversation context for better question answering\n",
"- Clear chat history when required for fresh sessions\n",
"- Store and retrieve embeddings using ChromaDB\n",
"\n",
"Integrates LangChain, ChromaDB, and OpenAI to process, store, and retrieve information efficiently."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "91da28d8-8e29-44b7-a62a-a3a109753727",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"from dotenv import load_dotenv\n",
"import gradio as gr"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e47f670a-e2cb-4700-95d0-e59e440677a1",
"metadata": {},
"outputs": [],
"source": [
"# imports for langchain, plotly and Chroma\n",
"\n",
"from langchain.document_loaders import DirectoryLoader, TextLoader\n",
"from langchain.text_splitter import CharacterTextSplitter\n",
"from langchain.schema import Document\n",
"from langchain_openai import OpenAIEmbeddings, ChatOpenAI\n",
"from langchain.embeddings import HuggingFaceEmbeddings\n",
"from langchain_chroma import Chroma\n",
"from langchain.memory import ConversationBufferMemory\n",
"from langchain.chains import ConversationalRetrievalChain\n",
"import numpy as np\n",
"from sklearn.manifold import TSNE\n",
"import plotly.graph_objects as go\n",
"import plotly.express as px\n",
"import matplotlib.pyplot as plt\n",
"from random import randint\n",
"import shutil"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "362d4976-2553-4ed8-8fbb-49806145cad1",
"metadata": {},
"outputs": [],
"source": [
"!pip install --upgrade gradio"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "968b6e96-557e-439f-b2f1-942c05168641",
"metadata": {},
"outputs": [],
"source": [
"MODEL = \"gpt-4o-mini\"\n",
"db_name = \"vector_db\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "537f66de-6abf-4b34-8e05-6b9a9df8ae82",
"metadata": {},
"outputs": [],
"source": [
"# Load environment variables in a file called .env\n",
"\n",
"load_dotenv(override=True)\n",
"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "246c1c1b-fcfa-4f4c-b99c-024598751361",
"metadata": {},
"outputs": [],
"source": [
"folder = \"my-knowledge-base/\"\n",
"db_name = \"vectorstore_db\"\n",
"\n",
"def process_files(files):\n",
" os.makedirs(folder, exist_ok=True)\n",
"\n",
" processed_files = []\n",
" for file in files:\n",
" file_path = os.path.join(folder, os.path.basename(file)) # Get filename\n",
" shutil.copy(file, file_path)\n",
" processed_files.append(os.path.basename(file))\n",
"\n",
" # Load documents using LangChain's DirectoryLoader\n",
" text_loader_kwargs = {'autodetect_encoding': True}\n",
" loader = DirectoryLoader(folder, glob=\"**/*.md\", loader_cls=TextLoader, loader_kwargs=text_loader_kwargs)\n",
" folder_docs = loader.load()\n",
"\n",
" # Assign filenames as metadata\n",
" for doc in folder_docs:\n",
" filename_md = os.path.basename(doc.metadata[\"source\"])\n",
" filename, _ = os.path.splitext(filename_md)\n",
" doc.metadata[\"filename\"] = filename\n",
"\n",
" documents = folder_docs \n",
"\n",
" # Split documents into chunks\n",
" text_splitter = CharacterTextSplitter(chunk_size=400, chunk_overlap=200)\n",
" chunks = text_splitter.split_documents(documents)\n",
"\n",
" # Initialize embeddings\n",
" embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
"\n",
" # Delete previous vectorstore\n",
" if os.path.exists(db_name):\n",
" Chroma(persist_directory=db_name, embedding_function=embeddings).delete_collection()\n",
"\n",
" # Store in ChromaDB\n",
" vectorstore = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=db_name)\n",
"\n",
" # Retrieve results\n",
" collection = vectorstore._collection\n",
" result = collection.get(include=['embeddings', 'documents', 'metadatas'])\n",
"\n",
" llm = ChatOpenAI(temperature=0.7, model_name=MODEL)\n",
" memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n",
" retriever = vectorstore.as_retriever(search_kwargs={\"k\": 35})\n",
" global conversation_chain\n",
" conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)\n",
"\n",
" processed_text = \"**Processed Files:**\\n\\n\" + \"\\n\".join(f\"- {file}\" for file in processed_files)\n",
" return result, processed_text"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "48678d3a-0ab2-4aa4-aa9e-4160c6a9cb24",
"metadata": {},
"outputs": [],
"source": [
"def random_color():\n",
" return f\"rgb({randint(0,255)},{randint(0,255)},{randint(0,255)})\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6caed889-9bb4-42ad-b1c2-da051aefc802",
"metadata": {},
"outputs": [],
"source": [
"def show_embeddings_2d(result):\n",
" vectors = np.array(result['embeddings']) \n",
" documents = result['documents']\n",
" metadatas = result['metadatas']\n",
" filenames = [metadata['filename'] for metadata in metadatas]\n",
" filenames_unique = sorted(set(filenames))\n",
"\n",
" # color assignment\n",
" color_map = {name: random_color() for name in filenames_unique}\n",
" colors = [color_map[name] for name in filenames]\n",
"\n",
" tsne = TSNE(n_components=2, random_state=42,perplexity=4)\n",
" reduced_vectors = tsne.fit_transform(vectors)\n",
"\n",
" # Create the 2D scatter plot\n",
" fig = go.Figure(data=[go.Scatter(\n",
" x=reduced_vectors[:, 0],\n",
" y=reduced_vectors[:, 1],\n",
" mode='markers',\n",
" marker=dict(size=5,color=colors, opacity=0.8),\n",
" text=[f\"Type: {t}<br>Text: {d[:100]}...\" for t, d in zip(filenames, documents)],\n",
" hoverinfo='text'\n",
" )])\n",
"\n",
" fig.update_layout(\n",
" title='2D Chroma Vector Store Visualization',\n",
" scene=dict(xaxis_title='x',yaxis_title='y'),\n",
" width=800,\n",
" height=600,\n",
" margin=dict(r=20, b=10, l=10, t=40)\n",
" )\n",
"\n",
" return fig"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "de993495-c8cd-4313-a6bb-7d27494ecc13",
"metadata": {},
"outputs": [],
"source": [
"def show_embeddings_3d(result):\n",
" vectors = np.array(result['embeddings']) \n",
" documents = result['documents']\n",
" metadatas = result['metadatas']\n",
" filenames = [metadata['filename'] for metadata in metadatas]\n",
" filenames_unique = sorted(set(filenames))\n",
"\n",
" # color assignment\n",
" color_map = {name: random_color() for name in filenames_unique}\n",
" colors = [color_map[name] for name in filenames]\n",
"\n",
" tsne = TSNE(n_components=3, random_state=42)\n",
" reduced_vectors = tsne.fit_transform(vectors)\n",
"\n",
" fig = go.Figure(data=[go.Scatter3d(\n",
" x=reduced_vectors[:, 0],\n",
" y=reduced_vectors[:, 1],\n",
" z=reduced_vectors[:, 2],\n",
" mode='markers',\n",
" marker=dict(size=5, color=colors, opacity=0.8),\n",
" text=[f\"Type: {t}<br>Text: {d[:100]}...\" for t, d in zip(filenames, documents)],\n",
" hoverinfo='text'\n",
" )])\n",
"\n",
" fig.update_layout(\n",
" title='3D Chroma Vector Store Visualization',\n",
" scene=dict(xaxis_title='x', yaxis_title='y', zaxis_title='z'),\n",
" width=900,\n",
" height=700,\n",
" margin=dict(r=20, b=10, l=10, t=40)\n",
" )\n",
"\n",
" return fig"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7b7bf62b-c559-4e97-8135-48cd8d97a40e",
"metadata": {},
"outputs": [],
"source": [
"def chat(question, history):\n",
" result = conversation_chain.invoke({\"question\": question})\n",
" return result[\"answer\"]\n",
"\n",
"def visualise_data(result):\n",
" fig_2d = show_embeddings_2d(result)\n",
" fig_3d = show_embeddings_3d(result)\n",
" return fig_2d,fig_3d"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "99217109-fbee-4269-81c7-001e6f768a72",
"metadata": {},
"outputs": [],
"source": [
"css = \"\"\"\n",
".btn {background-color: #1d53d1;}\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e1429ea1-1d9f-4be6-b270-01997864c642",
"metadata": {},
"outputs": [],
"source": [
"with gr.Blocks(css=css) as ui:\n",
" gr.Markdown(\"# Markdown-Based Q&A with Visualization\")\n",
" with gr.Row():\n",
" file_input = gr.Files(file_types=[\".md\"], label=\"Upload Markdown Files\")\n",
" with gr.Column(scale=1):\n",
" processed_output = gr.Markdown(\"Progress\")\n",
" with gr.Row():\n",
" process_btn = gr.Button(\"Process Files\",elem_classes=[\"btn\"])\n",
" with gr.Row():\n",
" question = gr.Textbox(label=\"Chat \", lines=10)\n",
" answer = gr.Markdown(label= \"Response\")\n",
" with gr.Row():\n",
" question_btn = gr.Button(\"Ask a Question\",elem_classes=[\"btn\"])\n",
" clear_btn = gr.Button(\"Clear Output\",elem_classes=[\"btn\"])\n",
" with gr.Row():\n",
" plot_2d = gr.Plot(label=\"2D Visualization\")\n",
" plot_3d = gr.Plot(label=\"3D Visualization\")\n",
" with gr.Row():\n",
" visualise_btn = gr.Button(\"Visualise Data\",elem_classes=[\"btn\"])\n",
"\n",
" result = gr.State([])\n",
" # Action: When button is clicked, process files and update visualization\n",
" clear_btn.click(fn=lambda:(\"\", \"\"), inputs=[],outputs=[question, answer])\n",
" process_btn.click(process_files, inputs=[file_input], outputs=[result,processed_output])\n",
" question_btn.click(chat, inputs=[question], outputs= [answer])\n",
" visualise_btn.click(visualise_data, inputs=[result], outputs=[plot_2d,plot_3d])\n",
"\n",
"# Launch Gradio app\n",
"ui.launch(inbrowser=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d3686048-ac29-4df1-b816-e58996913ef1",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
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"nbformat": 4,
"nbformat_minor": 5
}