Merge pull request #265 from zoya-hammad/community-contributions
Added my contributions to community-contributions
This commit is contained in:
Ed Donner
GitHub
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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": "fad6ee3f-45b8-4ac3-aa39-4a44dac91994",
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"metadata": {},
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"source": [
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"## Creating Text Embeddings From a Text File\n",
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"- Loading data using TextLoader\n",
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"- Splitting into chunks using CharacterTextSplitter\n",
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"- Converting chunks into vector embeddings and creating a vectorstore\n",
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"- Retreiving, reducing dimensions to 2D and displaying text embeddings"
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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": "33b79f0d-7bd5-4e82-9295-2cc5cfa9495b",
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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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"from dotenv import load_dotenv"
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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": "391d12b3-ea25-4c66-93ba-71ef7c590be3",
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"metadata": {},
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"outputs": [],
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"source": [
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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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"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"
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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": "365d4346-bcf7-48b3-be13-b492f1877fab",
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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 = \"my_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": "93887c1e-fb5e-4f9a-95f6-91a284e49695",
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"metadata": {},
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"outputs": [],
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"source": [
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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": "86289eb8-25d8-405f-b1bb-3d9d9fed8671",
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"metadata": {},
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"outputs": [],
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"source": [
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"loader = TextLoader(\"data.txt\", encoding=\"utf-8\")\n",
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"data = loader.load()\n",
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"\n",
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"documents = []\n",
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"for text in data:\n",
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" documents.append(text)"
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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": "32320fff-2321-40ea-9b7d-294dc2dfba3a",
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"metadata": {},
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"outputs": [],
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"source": [
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"text_splitter = CharacterTextSplitter(chunk_size=20, chunk_overlap=5)\n",
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"chunks = text_splitter.split_documents(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": "fce762a5-4c78-4102-ab55-f95ee0c97286",
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"metadata": {},
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"outputs": [],
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"source": [
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"len(chunks)"
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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": "ddb5bc12-af30-476d-bbbb-f91a3ae8af2f",
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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings = OpenAIEmbeddings()"
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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": "75ba81ec-9178-4ce4-83e2-82f937c85902",
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"metadata": {},
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"outputs": [],
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"source": [
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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()"
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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": "c3ca2632-a8b3-4e7e-8370-d91579d31c23",
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"metadata": {},
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"outputs": [],
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"source": [
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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": "0de67066-73f5-446f-9033-a00d45b0cdc1",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Get one vector and find how many dimensions it has\n",
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"\n",
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"collection = vectorstore._collection\n",
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"sample_embedding = collection.get(limit=1, include=[\"embeddings\"])[\"embeddings\"][0] # represents a single vector\n",
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"dimensions = len(sample_embedding)\n",
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"print(f\"The vectors have {dimensions:,} dimensions\")"
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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": "e50d972c-d740-4f0a-8bc2-e55ebe462a41",
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"metadata": {},
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"outputs": [],
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"source": [
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"sample_embedding"
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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": "aa96105d-b882-48d9-b088-6aab5db7b1e9",
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"metadata": {},
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"outputs": [],
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"source": [
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"result = collection.get(include=['embeddings','documents'])\n",
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"vectors = np.array(result['embeddings']) \n",
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"documents = result['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": "213b4cf2-db0a-4610-8d8f-97607996ed17",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Reduce dimensionality to 2D using t-SNE\n",
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"tsne = TSNE(n_components=2,perplexity=5, random_state=42)\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, opacity=0.8),\n",
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" text=[f\"Text: {d[:200]}...\" for d in 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()\n"
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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": "7d13aa60-da3e-4c61-af69-1ba9087e0181",
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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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