diff --git a/week5/community-contributions/verify-encodings.ipynb b/week5/community-contributions/verify-encodings.ipynb new file mode 100644 index 0000000..63477df --- /dev/null +++ b/week5/community-contributions/verify-encodings.ipynb @@ -0,0 +1,405 @@ +{ + "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 +}