{ "cells": [ { "cell_type": "markdown", "id": "dfe37963-1af6-44fc-a841-8e462443f5e6", "metadata": {}, "source": [ "## This notebook compares the embeddings generated by OpenAIEmbeddings.\n", "\n", "It shows that OpenAIEmbeddings embeddings can differ slightly (typically at 4 the decimal place).\n", "\n", "### Results from OpenAIEmbeddings:\n", "encodings are NOT identical on each run.\n", "\n", "### Repeating with sentence-transformers/all-MiniLM-L6-v2:\n", "encodings ARE identical on each run.\n", "\n", "Tests verify simple numerical comparisons.\n", "\n", "### Advanced Comparison\n", "A more advanced euclidean and cosine comparison is also included.\n", "\n", "## NOTES: Tests run on local Jupiter Notebook| Anaconda setup for the course." ] }, { "cell_type": "code", "execution_count": null, "id": "ba2779af-84ef-4227-9e9e-6eaf0df87e77", "metadata": {}, "outputs": [], "source": [ "# imports\n", "\n", "import os\n", "import glob\n", "from dotenv import load_dotenv\n", "import gradio as gr" ] }, { "cell_type": "code", "execution_count": null, "id": "802137aa-8a74-45e0-a487-d1974927d7ca", "metadata": {}, "outputs": [], "source": [ "# imports for langchain\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_chroma import Chroma\n", "import numpy as np\n", "from sklearn.manifold import TSNE\n", "import plotly.graph_objects as go\n", "from langchain.memory import ConversationBufferMemory\n", "from langchain.chains import ConversationalRetrievalChain" ] }, { "cell_type": "code", "execution_count": null, "id": "58c85082-e417-4708-9efe-81a5d55d1424", "metadata": {}, "outputs": [], "source": [ "# price is a factor for our company, so we're going to use a low cost model\n", "\n", "MODEL = \"gpt-4o-mini\"\n", "db_name = \"vector_db\"" ] }, { "cell_type": "code", "execution_count": null, "id": "ee78efcb-60fe-449e-a944-40bab26261af", "metadata": {}, "outputs": [], "source": [ "# Load environment variables in a file called .env\n", "\n", "load_dotenv()\n", "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')" ] }, { "cell_type": "code", "execution_count": null, "id": "730711a9-6ffe-4eee-8f48-d6cfb7314905", "metadata": {}, "outputs": [], "source": [ "# Read in documents using LangChain's loaders\n", "# Take everything in all the sub-folders of our knowledgebase\n", "\n", "folders = glob.glob(\"knowledge-base/*\")\n", "\n", "# With thanks to CG and Jon R, students on the course, for this fix needed for some users \n", "text_loader_kwargs = {'encoding': 'utf-8'}\n", "# If that doesn't work, some Windows users might need to uncomment the next line instead\n", "# text_loader_kwargs={'autodetect_encoding': True}\n", "\n", "documents = []\n", "for folder in folders:\n", " doc_type = os.path.basename(folder)\n", " loader = DirectoryLoader(folder, glob=\"**/*.md\", loader_cls=TextLoader, loader_kwargs=text_loader_kwargs)\n", " folder_docs = loader.load()\n", " for doc in folder_docs:\n", " doc.metadata[\"doc_type\"] = doc_type\n", " documents.append(doc)" ] }, { "cell_type": "code", "execution_count": null, "id": "7310c9c8-03c1-4efc-a104-5e89aec6db1a", "metadata": {}, "outputs": [], "source": [ "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n", "chunks = text_splitter.split_documents(documents)" ] }, { "cell_type": "code", "execution_count": null, "id": "cd06e02f-6d9b-44cc-a43d-e1faa8acc7bb", "metadata": {}, "outputs": [], "source": [ "len(chunks)" ] }, { "cell_type": "code", "execution_count": null, "id": "2c54b4b6-06da-463d-bee7-4dd456c2b887", "metadata": {}, "outputs": [], "source": [ "doc_types = set(chunk.metadata['doc_type'] for chunk in chunks)\n", "print(f\"Document types found: {', '.join(doc_types)}\")" ] }, { "cell_type": "code", "execution_count": null, "id": "a8b5ef27-70c2-4111-bce7-854bc1ebd02a", "metadata": {}, "outputs": [], "source": [ "# Use a where filter to specify the metadata condition\n", "# Get the 3 company vectors (corresponds to our 3 yellow dots)\n", "\n", "def get_company_vectors(collection):\n", " company_vectors = collection.get(\n", " where={\"doc_type\": \"company\"}, # Filter for documents where source = \"XXXX\"\n", " limit=10,\n", " include=[\"embeddings\", \"metadatas\", \"documents\"]\n", " )\n", " print(f\"Found {len(company_vectors)} company vectors\")\n", " return company_vectors\n" ] }, { "cell_type": "code", "execution_count": null, "id": "d688b873-b52b-4d80-9df2-f70b389f5dc7", "metadata": {}, "outputs": [], "source": [ "\n", "def print_vectors_summary(vectors):\n", " for i in range(len(vectors[\"documents\"])):\n", " print(f\"\\n--- Chunk {i+1} ---\")\n", " \n", " # Print document content (first 100 chars)\n", " print(f\"Content: {vectors['documents'][i][:100]}...\")\n", " \n", " # Print metadata\n", " print(f\"Metadata: {vectors['metadatas'][i]}\")\n", " \n", " # Print embedding info (not the full vector as it would be too long)\n", " embedding = vectors[\"embeddings\"][i]\n", " print(f\"Embedding: Vector of length {len(embedding)}, first 5 values: {embedding[:5]}\")\n", "\n", "\n", "def get_dimensions_for_vectors(vectors):\n", " dimensions = []\n", "\n", " for i in range(len(vectors[\"documents\"])):\n", " embedding = vectors[\"embeddings\"][i]\n", " dimensions.append(embedding)\n", "\n", " return dimensions\n" ] }, { "cell_type": "code", "execution_count": null, "id": "0b195184-4920-404a-9bfa-0231f1dbe276", "metadata": {}, "outputs": [], "source": [ "# Quick check if any single value is different\n", "def quick_diff_check(emb1, emb2):\n", " result = \"Embeddings are identical\"\n", " print(\"\\n\\nComparing two embeddings:\\n\\n\")\n", " print(emb1)\n", " print(emb2)\n", " for i, (v1, v2) in enumerate(zip(emb1, emb2)):\n", " if v1 != v2:\n", " result = f\"Different at dimension {i}: {v1} vs {v2}\"\n", " break\n", " print(result)\n", " return result\n", "\n", "#quick_diff_check(dimensions[0], dimensions[1])" ] }, { "cell_type": "code", "execution_count": null, "id": "06ba838d-d179-4e2d-b208-dd9cc1fd0097", "metadata": {}, "outputs": [], "source": [ "\n", "embeddings = OpenAIEmbeddings()\n", "\n", "def create_vectorstores(embeddings):\n", "\n", " if os.path.exists(\"vectorstore1\"):\n", " Chroma(persist_directory=\"vectorstore1\", embedding_function=embeddings).delete_collection()\n", " if os.path.exists(\"vectorstore2\"):\n", " Chroma(persist_directory=\"vectorstore2\", embedding_function=embeddings).delete_collection()\n", " \n", " \n", " # Create vectorstore 1\n", " vectorstore1 = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=\"vectorstore1\")\n", " print(f\"Vectorstore 1 created with {vectorstore1._collection.count()} documents\")\n", " \n", " # Create vectorstore 2\n", " vectorstore2 = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=\"vectorstore2\")\n", " print(f\"Vectorstore 2 created with {vectorstore2._collection.count()} documents\")\n", "\n", " return vectorstore1, vectorstore2\n", "\n", "vectorstore1, vectorstore2 = create_vectorstores(embeddings)\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "e24242eb-613a-4edb-a081-6b8937f106a7", "metadata": {}, "outputs": [], "source": [ "## Uncomment this and rerun cells below, \n", "## to see that HuggingFaceEmbeddings is idential\n", "\n", "#from langchain.embeddings import HuggingFaceEmbeddings\n", "#embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\n", "#vectorstore1, vectorstore2 = create_vectorstores(embeddings)\n" ] }, { "cell_type": "code", "execution_count": null, "id": "000b9e70-2958-40db-bbed-56a00e4249ce", "metadata": {}, "outputs": [], "source": [ "# Get the 3 company doc_type vectors\n", "collection1 = vectorstore1._collection\n", "collection2 = vectorstore2._collection\n", "\n", "company_vectors1=get_company_vectors(collection1)\n", "company_vectors2=get_company_vectors(collection2)\n" ] }, { "cell_type": "code", "execution_count": null, "id": "63cd63e4-9d3e-405a-8ef9-dac16fe2570e", "metadata": {}, "outputs": [], "source": [ "# Lets print out summary info just to see we have the same chunks.\n", "\n", "def print_summary_info (vectors):\n", " print(\"VECTORS SUMMARY\\n\")\n", " print_vectors_summary(vectors)\n", "\n", "\n", "print(\"\\n\\n\\n========= VECTORS 1 =========\\n\\n\")\n", "print_summary_info(company_vectors1)\n", "\n", "print(\"\\n\\n\\n========= VECTORS 2 =========\\n\\n\")\n", "print_summary_info(company_vectors2)\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "bc085a35-f0ec-4ddb-955c-244cb2d3eb2a", "metadata": {}, "outputs": [], "source": [ "dimensions1 = get_dimensions_for_vectors(company_vectors1)\n", "dimensions2 = get_dimensions_for_vectors(company_vectors2)\n", "\n", "result1 = quick_diff_check(dimensions1[0], dimensions2[0]) \n", "result2 = quick_diff_check(dimensions1[1], dimensions2[1]) \n", "result3 = quick_diff_check(dimensions1[2], dimensions2[2]) \n", "\n", "print(\"\\n\\nSUMMARY RESULTS:\")\n", "print(\"================\\n\\n\")\n", "print(result1) \n", "print(result2)\n", "print(result3)\n" ] }, { "cell_type": "code", "execution_count": null, "id": "164cf94d-9d63-4bae-91f9-4b02da1537ae", "metadata": {}, "outputs": [], "source": [ "## ADVANCED COMPARISONS:\n", "# More advanced comparisons (from Claude 3.7 Sonnet):\n", "\n", "\n", "## !IMPORTANT *** Uncomment final line to execute ***\n", "\n", "\n", "import numpy as np\n", "from scipy.spatial.distance import cosine\n", "\n", "# Method 1: Euclidean distance (L2 norm)\n", "def compare_embeddings_euclidean(emb1, emb2):\n", " emb1_array = np.array(emb1)\n", " emb2_array = np.array(emb2)\n", " distance = np.linalg.norm(emb1_array - emb2_array)\n", " return {\n", " \"different\": distance > 0,\n", " \"distance\": distance,\n", " \"similarity\": 1/(1+distance) # Converts distance to similarity score\n", " }\n", "\n", "# Method 2: Cosine similarity (common for embeddings)\n", "def compare_embeddings_cosine(emb1, emb2):\n", " emb1_array = np.array(emb1)\n", " emb2_array = np.array(emb2)\n", " similarity = 1 - cosine(emb1_array, emb2_array) # Cosine returns distance, so subtract from 1\n", " return {\n", " \"different\": similarity < 0.9999, # Almost identical if > 0.9999\n", " \"similarity\": similarity\n", " }\n", "\n", "# Method 3: Simple exact equality check\n", "def are_embeddings_identical(emb1, emb2):\n", " return np.array_equal(np.array(emb1), np.array(emb2))\n", "\n", "\n", "def run_advanced_comparisons():\n", " for i in range(0, 3):\n", " print(f\"\\n\\nComparing vector dimensions for dimension[{i}]....\\n\")\n", " print(\"Exactly identical? ---> \", are_embeddings_identical(dimensions1[i], dimensions2[i]))\n", " print(\"Cosine comparison: ---> \", compare_embeddings_cosine(dimensions1[i], dimensions2[i]))\n", " print(\"Euclidean comparison: ---> \", compare_embeddings_euclidean(dimensions1[i], dimensions2[i]))\n", "\n", "\n", "#run_advanced_comparisons()" ] } ], "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 }