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{ |
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"cells": [ |
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{ |
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"cell_type": "markdown", |
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"id": "c25c6e94-f3de-4367-b2bf-269ba7160977", |
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"metadata": {}, |
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"source": [ |
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"## An Expert Knowledge Worker Question-Answering Agent using RAG" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "15169580-cf11-4dee-8ec7-3a4ef59b19ee", |
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"metadata": {}, |
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"source": [ |
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"Aims\n", |
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"- Reads README.md files and loads data using TextLoader\n", |
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"- Splits into chunks using CharacterTextSplitter\n", |
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"- Converts chunks into vector embeddings and creates a datastore\n", |
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"- 2D and 3D visualisations\n", |
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"- Langchain to set up a conversation retrieval chain" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "051cf881-357d-406b-8eae-1610651e40f1", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# imports\n", |
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"\n", |
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"import os\n", |
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"import glob\n", |
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"from dotenv import load_dotenv\n", |
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"import gradio as gr" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "ccfd403a-5bdb-4a8c-b3fd-d47ae79e43f7", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# imports for langchain, plotly and Chroma\n", |
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"\n", |
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"from langchain.document_loaders import DirectoryLoader, TextLoader\n", |
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"from langchain.text_splitter import CharacterTextSplitter\n", |
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"from langchain.schema import Document\n", |
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"from langchain_openai import OpenAIEmbeddings, ChatOpenAI\n", |
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"from langchain.embeddings import HuggingFaceEmbeddings\n", |
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"from langchain_chroma import Chroma\n", |
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"from langchain.memory import ConversationBufferMemory\n", |
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"from langchain.chains import ConversationalRetrievalChain\n", |
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"import numpy as np\n", |
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"from sklearn.manifold import TSNE\n", |
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"import plotly.graph_objects as go\n", |
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"import plotly.express as px\n", |
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"import matplotlib.pyplot as plt" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "2d853868-d2f6-43e1-b27c-b8e91d06b724", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"MODEL = \"gpt-4o-mini\"\n", |
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"db_name = \"vector_db\"" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "f152fc3b-0bf4-4d51-948f-95da1ebc030a", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Load environment variables in a file called .env\n", |
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"\n", |
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"load_dotenv(override=True)\n", |
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"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY')" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "24e621ac-df06-4af6-a60d-a9ed7adb884a", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Read in documents using LangChain's loaders\n", |
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"\n", |
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"folder = \"my-knowledge-base/\"\n", |
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"text_loader_kwargs={'autodetect_encoding': True}\n", |
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"\n", |
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"loader = DirectoryLoader(folder, glob=\"**/*.md\", loader_cls=TextLoader, loader_kwargs=text_loader_kwargs)\n", |
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"folder_docs = loader.load()\n", |
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"\n", |
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"for doc in folder_docs:\n", |
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" filename_md = os.path.basename(doc.metadata[\"source\"]) \n", |
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" filename, _ = os.path.splitext(filename_md) \n", |
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" doc.metadata[\"filename\"] = filename\n", |
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"\n", |
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"documents = folder_docs \n", |
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"\n", |
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"text_splitter = CharacterTextSplitter(chunk_size=400, chunk_overlap=200)\n", |
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"chunks = text_splitter.split_documents(documents)\n", |
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"\n", |
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"print(f\"Total number of chunks: {len(chunks)}\")\n", |
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"print(f\"Files found: {set(doc.metadata['filename'] for doc in documents)}\")" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "f02f08ee-5ade-4f79-a500-045a8f1a532f", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Put the chunks of data into a Vector Store that associates a Vector Embedding with each chunk\n", |
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"\n", |
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"embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\n", |
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"\n", |
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"# Delete if already exists\n", |
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"\n", |
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"if os.path.exists(db_name):\n", |
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" Chroma(persist_directory=db_name, embedding_function=embeddings).delete_collection()\n", |
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"\n", |
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"# Create vectorstore\n", |
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"\n", |
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"vectorstore = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=db_name)\n", |
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"print(f\"Vectorstore created with {vectorstore._collection.count()} documents\")" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "7f665f4d-ccb1-43fb-b901-040117925732", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Let's investigate the vectors\n", |
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"\n", |
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"collection = vectorstore._collection\n", |
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"count = collection.count()\n", |
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"\n", |
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"sample_embedding = collection.get(limit=1, include=[\"embeddings\"])[\"embeddings\"][0]\n", |
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"dimensions = len(sample_embedding)\n", |
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"print(f\"There are {count:,} vectors with {dimensions:,} dimensions in the vector store\")" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "6208a971-e8b7-48bc-be7a-6dcb82967fd2", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# pre work\n", |
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"\n", |
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"result = collection.get(include=['embeddings','documents','metadatas'])\n", |
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"vectors = np.array(result['embeddings']) \n", |
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"documents = result['documents']\n", |
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"metadatas = result['metadatas']\n", |
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"filenames = [metadata['filename'] for metadata in metadatas]" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "eb27bc8a-453b-4b19-84b4-dc495bb0e544", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import random\n", |
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"def random_color():\n", |
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" return f\"rgb({random.randint(0,255)},{random.randint(0,255)},{random.randint(0,255)})\"" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "78db67e5-ef10-4581-b8ac-3e0281ceba45", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def show_embeddings_2d(result):\n", |
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" vectors = np.array(result['embeddings']) \n", |
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" documents = result['documents']\n", |
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" metadatas = result['metadatas']\n", |
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" filenames = [metadata['filename'] for metadata in metadatas]\n", |
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" filenames_unique = sorted(set(filenames))\n", |
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"\n", |
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" # color assignment\n", |
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" color_map = {name: random_color() for name in filenames_unique}\n", |
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" colors = [color_map[name] for name in filenames]\n", |
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"\n", |
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" tsne = TSNE(n_components=2, random_state=42,perplexity=4)\n", |
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" reduced_vectors = tsne.fit_transform(vectors)\n", |
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"\n", |
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" # Create the 2D scatter plot\n", |
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" fig = go.Figure(data=[go.Scatter(\n", |
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" x=reduced_vectors[:, 0],\n", |
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" y=reduced_vectors[:, 1],\n", |
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" mode='markers',\n", |
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" marker=dict(size=5,color=colors, opacity=0.8),\n", |
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" text=[f\"Type: {t}<br>Text: {d[:100]}...\" for t, d in zip(filenames, documents)],\n", |
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" hoverinfo='text'\n", |
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" )])\n", |
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"\n", |
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" fig.update_layout(\n", |
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" title='2D Chroma Vector Store Visualization',\n", |
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" scene=dict(xaxis_title='x',yaxis_title='y'),\n", |
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" width=800,\n", |
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" height=600,\n", |
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" margin=dict(r=20, b=10, l=10, t=40)\n", |
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" )\n", |
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"\n", |
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" fig.show()" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "2c250166-cb5b-4a75-8981-fae2d6dfe509", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"show_embeddings_2d(result)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "3b290e38-0800-4453-b664-7a7622ff5ed2", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def show_embeddings_3d(result):\n", |
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" vectors = np.array(result['embeddings']) \n", |
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" documents = result['documents']\n", |
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" metadatas = result['metadatas']\n", |
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" filenames = [metadata['filename'] for metadata in metadatas]\n", |
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" filenames_unique = sorted(set(filenames))\n", |
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"\n", |
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" # color assignment\n", |
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" color_map = {name: random_color() for name in filenames_unique}\n", |
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" colors = [color_map[name] for name in filenames]\n", |
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"\n", |
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" tsne = TSNE(n_components=3, random_state=42)\n", |
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" reduced_vectors = tsne.fit_transform(vectors)\n", |
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"\n", |
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" fig = go.Figure(data=[go.Scatter3d(\n", |
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" x=reduced_vectors[:, 0],\n", |
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" y=reduced_vectors[:, 1],\n", |
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" z=reduced_vectors[:, 2],\n", |
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" mode='markers',\n", |
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" marker=dict(size=5, color=colors, opacity=0.8),\n", |
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" text=[f\"Type: {t}<br>Text: {d[:100]}...\" for t, d in zip(filenames, documents)],\n", |
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" hoverinfo='text'\n", |
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" )])\n", |
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"\n", |
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" fig.update_layout(\n", |
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" title='3D Chroma Vector Store Visualization',\n", |
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" scene=dict(xaxis_title='x', yaxis_title='y', zaxis_title='z'),\n", |
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" width=900,\n", |
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" height=700,\n", |
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" margin=dict(r=20, b=10, l=10, t=40)\n", |
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" )\n", |
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"\n", |
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" fig.show()" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "45d1d034-2503-4176-b1e4-f248e31c4770", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"show_embeddings_3d(result)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "e79946a1-f93a-4b3a-8d19-deef40dec223", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# create a new Chat with OpenAI\n", |
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"llm = ChatOpenAI(temperature=0.7, model_name=MODEL)\n", |
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"\n", |
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"# set up the conversation memory for the chat\n", |
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"memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n", |
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"\n", |
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"# the retriever is an abstraction over the VectorStore that will be used during RAG\n", |
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"retriever = vectorstore.as_retriever(search_kwargs={\"k\": 50})\n", |
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"\n", |
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"# putting it together: set up the conversation chain with the GPT 3.5 LLM, the vector store and memory\n", |
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"conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "59f90c85-c113-4482-8574-8a728ef25459", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def chat(question, history):\n", |
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" result = conversation_chain.invoke({\"question\": question})\n", |
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" return result[\"answer\"]" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "0520a8ff-01a4-4fa6-9dc8-57da87272edc", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"view = gr.ChatInterface(chat, type=\"messages\").launch(inbrowser=True)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "b4949b17-cd9c-4bff-bd5b-0f80df72e7dc", |
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"metadata": {}, |
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"outputs": [], |
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"source": [] |
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} |
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], |
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"metadata": { |
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"kernelspec": { |
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"display_name": "Python 3 (ipykernel)", |
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"language": "python", |
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"name": "python3" |
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}, |
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"language_info": { |
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"codemirror_mode": { |
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"name": "ipython", |
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"version": 3 |
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}, |
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"file_extension": ".py", |
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"mimetype": "text/x-python", |
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"name": "python", |
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"nbconvert_exporter": "python", |
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"pygments_lexer": "ipython3", |
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"version": "3.11.11" |
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} |
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}, |
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"nbformat": 4, |
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"nbformat_minor": 5 |
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} |
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{ |
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"cells": [ |
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{ |
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"cell_type": "markdown", |
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"id": "d13be0fd-db15-4ab1-860a-b00257051339", |
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"metadata": {}, |
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"source": [ |
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"## Gradio UI for Markdown-Based Q&A with Visualization" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "bc63fbdb-66a9-4c10-8dbd-11476b5e2d21", |
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"metadata": {}, |
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"source": [ |
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"This interface enables users to:\n", |
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"- Upload Markdown files for processing\n", |
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"- Visualize similarity between document chunks in 2D and 3D using embeddings\n", |
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"- Ask questions and receive RAG enabled responses\n", |
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"- Mantain conversation context for better question answering\n", |
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"- Clear chat history when required for fresh sessions\n", |
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"- Store and retrieve embeddings using ChromaDB\n", |
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"\n", |
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"Integrates LangChain, ChromaDB, and OpenAI to process, store, and retrieve information efficiently." |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "91da28d8-8e29-44b7-a62a-a3a109753727", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# imports\n", |
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"\n", |
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"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" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
Loading…
Reference in new issue