From 9ba4350a41ace4dc6e17e1aae359513d496cb762 Mon Sep 17 00:00:00 2001 From: Zoya Hammad Date: Wed, 19 Mar 2025 12:49:08 +0500 Subject: [PATCH] Added contribution to community-contributions --- ...ay3_vector_embeddings_from_text_file.ipynb | 235 ++++++++++++++++++ 1 file changed, 235 insertions(+) create mode 100644 week5/community-contributions/day3_vector_embeddings_from_text_file.ipynb diff --git a/week5/community-contributions/day3_vector_embeddings_from_text_file.ipynb b/week5/community-contributions/day3_vector_embeddings_from_text_file.ipynb new file mode 100644 index 0000000..8519256 --- /dev/null +++ b/week5/community-contributions/day3_vector_embeddings_from_text_file.ipynb @@ -0,0 +1,235 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "fad6ee3f-45b8-4ac3-aa39-4a44dac91994", + "metadata": {}, + "source": [ + "## Creating Text Embeddings From a Text File\n", + "- Loading data using TextLoader\n", + "- Splitting into chunks using CharacterTextSplitter\n", + "- Converting chunks into vector embeddings and creating a vectorstore\n", + "- Retreiving, reducing dimensions to 2D and displaying text embeddings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "33b79f0d-7bd5-4e82-9295-2cc5cfa9495b", + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "\n", + "import os\n", + "from dotenv import load_dotenv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "391d12b3-ea25-4c66-93ba-71ef7c590be3", + "metadata": {}, + "outputs": [], + "source": [ + "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", + "import numpy as np\n", + "from sklearn.manifold import TSNE\n", + "import plotly.graph_objects as go" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "365d4346-bcf7-48b3-be13-b492f1877fab", + "metadata": {}, + "outputs": [], + "source": [ + "MODEL = \"gpt-4o-mini\"\n", + "db_name = \"my_vector_db\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "93887c1e-fb5e-4f9a-95f6-91a284e49695", + "metadata": {}, + "outputs": [], + "source": [ + "load_dotenv(override=True)\n", + "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86289eb8-25d8-405f-b1bb-3d9d9fed8671", + "metadata": {}, + "outputs": [], + "source": [ + "loader = TextLoader(\"data.txt\", encoding=\"utf-8\")\n", + "data = loader.load()\n", + "\n", + "documents = []\n", + "for text in data:\n", + " documents.append(text)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32320fff-2321-40ea-9b7d-294dc2dfba3a", + "metadata": {}, + "outputs": [], + "source": [ + "text_splitter = CharacterTextSplitter(chunk_size=20, chunk_overlap=5)\n", + "chunks = text_splitter.split_documents(documents)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fce762a5-4c78-4102-ab55-f95ee0c97286", + "metadata": {}, + "outputs": [], + "source": [ + "len(chunks)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ddb5bc12-af30-476d-bbbb-f91a3ae8af2f", + "metadata": {}, + "outputs": [], + "source": [ + "embeddings = OpenAIEmbeddings()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "75ba81ec-9178-4ce4-83e2-82f937c85902", + "metadata": {}, + "outputs": [], + "source": [ + "if os.path.exists(db_name):\n", + " Chroma(persist_directory=db_name, embedding_function=embeddings).delete_collection()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c3ca2632-a8b3-4e7e-8370-d91579d31c23", + "metadata": {}, + "outputs": [], + "source": [ + "vectorstore = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=db_name)\n", + "print(f\"Vectorstore created with {vectorstore._collection.count()} documents\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0de67066-73f5-446f-9033-a00d45b0cdc1", + "metadata": {}, + "outputs": [], + "source": [ + "# Get one vector and find how many dimensions it has\n", + "\n", + "collection = vectorstore._collection\n", + "sample_embedding = collection.get(limit=1, include=[\"embeddings\"])[\"embeddings\"][0] # represents a single vector\n", + "dimensions = len(sample_embedding)\n", + "print(f\"The vectors have {dimensions:,} dimensions\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e50d972c-d740-4f0a-8bc2-e55ebe462a41", + "metadata": {}, + "outputs": [], + "source": [ + "sample_embedding" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aa96105d-b882-48d9-b088-6aab5db7b1e9", + "metadata": {}, + "outputs": [], + "source": [ + "result = collection.get(include=['embeddings','documents'])\n", + "vectors = np.array(result['embeddings']) \n", + "documents = result['documents']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "213b4cf2-db0a-4610-8d8f-97607996ed17", + "metadata": {}, + "outputs": [], + "source": [ + "# Reduce dimensionality to 2D using t-SNE\n", + "tsne = TSNE(n_components=2,perplexity=5, random_state=42)\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, opacity=0.8),\n", + " text=[f\"Text: {d[:200]}...\" for d in 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", + "fig.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d13aa60-da3e-4c61-af69-1ba9087e0181", + "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 +}