diff --git a/EverNote_Documents_To_FAISS_Colab_GPU.ipynb b/EverNote_Documents_To_FAISS_Colab_GPU.ipynb index 0340012..a66fcbf 100644 --- a/EverNote_Documents_To_FAISS_Colab_GPU.ipynb +++ b/EverNote_Documents_To_FAISS_Colab_GPU.ipynb @@ -8,7 +8,12 @@ "id": "18d62071e34b0d53" }, "source": [ - "# This is an experiment: create vectorized embeddings out of an EverNote DB (PDF, DOCX, HTML, TXT)\n", + "# This is an experiment: create vectorized embeddings out of an EverNote DB (PDF, DOCX, HTML, TXT)\n" + ] + }, + { + "cell_type": "markdown", + "source": [ "\n", "## Features\n", "\n", @@ -17,12 +22,24 @@ " * for sentences\n", "* query a local sqlite vss vector db, use cache from LangChain (sqlite)\n", "* use OpenAI API and (Ollama on-prem self-hosted) Mistral for the response processing\n", - "* compare with LLMware Bling\n", - "\n", + "* compare with LLMware Bling" + ], + "metadata": { + "id": "aLpLL0Wy2A8M" + }, + "id": "aLpLL0Wy2A8M" + }, + { + "cell_type": "markdown", + "source": [ "## Anti-Features\n", "\n", - "* due to cost reasons the OpenAI embeddings don't get used. So sorry :p" - ] + "* due to cost reasons the OpenAI embeddings don't get used. So sorry ... not." + ], + "metadata": { + "id": "stgrzM3K2C-o" + }, + "id": "stgrzM3K2C-o" }, { "cell_type": "markdown", @@ -32,7 +49,9 @@ "id": "94517a27e3148ff4" }, "source": [ - "# Configuration" + "# Setup and configuration\n", + "\n", + "⚠ This config is automated and executes a Bash script from a GitHub repo if you execute it on Goog Colab ⚠" ] }, { @@ -55,7 +74,8 @@ "IN_COLAB = 'google.colab' in sys.modules\n", "\n", "if not IN_COLAB:\n", - " # The Evernote DB path containing the extracted data.\n", + " # The Evernote DB path containing the extracted data\n", + " # It will not be needed on Colab\n", " extracted_evernote_db = \"/home/marius/data/it-sec-research-extracted/IT sec research\"\n", "\n", " # Output paths containing the Evernote text notes or documents data.\n", @@ -67,26 +87,36 @@ " result_db = \"/home/marius/source/bookworm/evernote.db\"\n", "\n", "else:\n", - " # For the Goog Colab env\n", + " # For the Goog Colab env we use different paths\n", " output_path_extracted_notes = \"/content/export.txt\"\n", " output_path_extracted_docs = \"/content/export.documents.txt\"\n", " result_db = \"/content/evernote.db\"\n", + "\n", + " # Download the data locally (just some txt files here)\n", + " # Install pip dependencies in Colab\n", " subprocess.run('''\n", " source <(curl -s https://raw.githubusercontent.com/norandom/project_bookworm/main/scripts/prepare_colab_env.sh)\n", " ''',\n", - " shell=True, check=True,\n", - " executable='/bin/bash')\n", - "\n", - "\n", - "# To suppress some warnings\n", - "import os\n", - "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"True\"" + " shell=True, check=True, executable='/bin/bash')" ] }, + { + "cell_type": "code", + "source": [ + "# To suppress some warnings\n", + "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"False\"" + ], + "metadata": { + "id": "oHbFM-721Uwf" + }, + "id": "oHbFM-721Uwf", + "execution_count": 3, + "outputs": [] + }, { "cell_type": "markdown", "source": [ - "# Checks" + "## Checks" ], "metadata": { "id": "yuhXPdN_z2cW" @@ -103,10 +133,10 @@ "base_uri": "https://localhost:8080/" }, "id": "6SPPaVEet9EO", - "outputId": "ceb17148-270d-44d0-d23a-9690015a3cb6" + "outputId": "e7cb63ec-8192-43a4-9320-83ff5b3b2122" }, "id": "6SPPaVEet9EO", - "execution_count": 3, + "execution_count": 4, "outputs": [ { "output_type": "stream", @@ -120,7 +150,9 @@ { "cell_type": "markdown", "source": [ - "## For the progress bars in Colab" + "## For the progress bars in Colab\n", + "\n", + "⚛ If you don't add this magic commands the `tqdm` progress bars will not update properly ⚛" ], "metadata": { "id": "B02AY_Gez61T" @@ -137,7 +169,7 @@ "id": "XGYNhuvrvnUD" }, "id": "XGYNhuvrvnUD", - "execution_count": 4, + "execution_count": 5, "outputs": [] }, { @@ -148,14 +180,14 @@ "id": "a8c8692786d83c00" }, "source": [ - "## Dependencies\n", + "## Select key dependencies\n", "\n", - "* Cryptography is used to handle some PDF functions here (signatures)" + "* `cryptography` is used to handle some PDF functions here (signatures)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "bb34db1ea75a1edf", "metadata": { "ExecuteTime": { @@ -166,7 +198,7 @@ "base_uri": "https://localhost:8080/" }, "id": "bb34db1ea75a1edf", - "outputId": "25aac151-5cae-44e7-887b-27008e986821" + "outputId": "26af1f05-f9c1-4849-88e1-dc5c18cd3884" }, "outputs": [ { @@ -198,37 +230,39 @@ "id": "297746c807e95fbf" }, "source": [ - "* pikepdf is used to repair some PDFs" + "* `pikepdf` is used to repair some PDFs" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "ebc8af0183532fc2", "metadata": { "ExecuteTime": { "end_time": "2024-04-04T10:08:34.665865Z", "start_time": "2024-04-04T10:08:32.522020Z" }, + "colab": { + "base_uri": "https://localhost:8080/" + }, "id": "ebc8af0183532fc2", - "outputId": "2398386a-d6d5-4574-9416-c7f8f92a082c" + "outputId": "7966230e-ec57-4b64-acf2-09d013bb2608" }, "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ - "Name: pikepdf\r\n", - "Version: 8.13.0\r\n", - "Summary: Read and write PDFs with Python, powered by qpdf\r\n", - "Home-page: \r\n", - "Author: \r\n", - "Author-email: \"James R. Barlow\" \r\n", - "License: MPL-2.0\r\n", - "Location: /home/marius/miniconda3/envs/llm_langchain/lib/python3.11/site-packages\r\n", - "Requires: Deprecated, lxml, packaging, Pillow\r\n", - "Required-by: \r\n", - "Note: you may need to restart the kernel to use updated packages.\n" + "Name: pikepdf\n", + "Version: 8.13.0\n", + "Summary: Read and write PDFs with Python, powered by qpdf\n", + "Home-page: \n", + "Author: \n", + "Author-email: \"James R. Barlow\" \n", + "License: MPL-2.0\n", + "Location: /usr/local/lib/python3.10/dist-packages\n", + "Requires: Deprecated, lxml, packaging, Pillow\n", + "Required-by: \n" ] } ], @@ -244,37 +278,39 @@ "id": "7c7a7f6b0db3719e" }, "source": [ - "* pypdf with all features is needed because this DB consists of 100+ PDFs" + "* `pypdf` with all features is needed because this DB consists of 100+ PDFs" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "779f81e2ab00f73c", "metadata": { "ExecuteTime": { "end_time": "2024-04-04T10:08:37.436449Z", "start_time": "2024-04-04T10:08:35.269255Z" }, + "colab": { + "base_uri": "https://localhost:8080/" + }, "id": "779f81e2ab00f73c", - "outputId": "353f67d9-0d77-45ba-85d3-37d8a651580c" + "outputId": "e1fcc840-7e3c-4d4a-9457-745be8db60c4" }, "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ - "Name: pypdf\r\n", - "Version: 4.0.2\r\n", - "Summary: A pure-python PDF library capable of splitting, merging, cropping, and transforming PDF files\r\n", - "Home-page: \r\n", - "Author: \r\n", - "Author-email: Mathieu Fenniak \r\n", - "License: \r\n", - "Location: /home/marius/miniconda3/envs/llm_langchain/lib/python3.11/site-packages\r\n", - "Requires: \r\n", - "Required-by: \r\n", - "Note: you may need to restart the kernel to use updated packages.\n" + "Name: pypdf\n", + "Version: 4.0.2\n", + "Summary: A pure-python PDF library capable of splitting, merging, cropping, and transforming PDF files\n", + "Home-page: \n", + "Author: \n", + "Author-email: Mathieu Fenniak \n", + "License: \n", + "Location: /usr/local/lib/python3.10/dist-packages\n", + "Requires: \n", + "Required-by: \n" ] } ], @@ -282,9 +318,19 @@ "%pip show \"pypdf\"" ] }, + { + "cell_type": "markdown", + "source": [ + "* `torch` is used for tensors, and GPU processing" + ], + "metadata": { + "id": "A5l3rFo03NKq" + }, + "id": "A5l3rFo03NKq" + }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "id": "de3f715519fda6c4", "metadata": { "ExecuteTime": { @@ -295,7 +341,7 @@ "base_uri": "https://localhost:8080/" }, "id": "de3f715519fda6c4", - "outputId": "858cd8ae-32d3-4373-9ac4-971e424079bb" + "outputId": "9f328109-f568-411b-c78a-0c1be41090bb" }, "outputs": [ { @@ -319,34 +365,44 @@ "%pip show torch" ] }, + { + "cell_type": "markdown", + "source": [ + "* `faiss` is used in the CPU version as a general vector store library. The data is being serialzed with `LangChain`. FAISS CPU version uses AVX2. The GPU port has some implementation issues with disk persistance and merging." + ], + "metadata": { + "id": "ZxyhRz6-3p-c" + }, + "id": "ZxyhRz6-3p-c" + }, { "cell_type": "code", "source": [ - "%pip show faiss_gpu" + "%pip show faiss_cpu" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "HARY_QMJvttI", - "outputId": "1a3d2e43-b3d4-46f6-a526-ac0cb44bd1e6" + "outputId": "bb7f5758-135e-435d-bf26-9aa3a1417e81" }, "id": "HARY_QMJvttI", - "execution_count": 8, + "execution_count": 10, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Name: faiss-gpu\n", - "Version: 1.7.2\n", + "Name: faiss-cpu\n", + "Version: 1.8.0\n", "Summary: A library for efficient similarity search and clustering of dense vectors.\n", - "Home-page: https://github.com/kyamagu/faiss-wheels\n", - "Author: Kota Yamaguchi\n", - "Author-email: KotaYamaguchi1984@gmail.com\n", - "License: MIT\n", + "Home-page: \n", + "Author: \n", + "Author-email: Kota Yamaguchi \n", + "License: MIT License\n", "Location: /usr/local/lib/python3.10/dist-packages\n", - "Requires: \n", + "Requires: numpy\n", "Required-by: \n" ] } @@ -362,10 +418,11 @@ "source": [ "## Text extraction\n", "\n", - "* Here the html and text data is extracted into one txt file\n", - "* The PDF and DOCX data is extracted into another txt file\n", + "⬛ This doesn't need to get executed if you already have the `*.txt` files.\n", "\n", - "This will be used for weighted data fusion later.\n", + "\n", + "* Here the html and text data is extracted into txt\n", + "* The PDF and DOCX data is extracted into another txt file. This will be used for weighted data fusion later.\n", "\n", "* the texts are normalized:\n", " * unicode normalization\n", @@ -379,44 +436,6 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "b557444b8b1d4839", - "metadata": { - "ExecuteTime": { - "end_time": "2024-04-04T09:25:39.388933Z", - "start_time": "2024-04-04T09:25:39.320902Z" - }, - "colab": { - "base_uri": "https://localhost:8080/", - "height": 394 - }, - "id": "b557444b8b1d4839", - "outputId": "9487736e-c65b-4b1e-bc32-f743bf73a035" - }, - "outputs": [ - { - "output_type": "error", - "ename": "ModuleNotFoundError", - "evalue": "No module named 'html2text'", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0municodedata\u001b[0m \u001b[0;31m# to normalize text\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mhtml2text\u001b[0m \u001b[0;31m# to convert html to text\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mlangchain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdocument_loaders\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPyPDFLoader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDocx2txtLoader\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpikepdf\u001b[0m \u001b[0;31m# to repair PDFs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'html2text'", - "", - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n" - ], - "errorDetails": { - "actions": [ - { - "action": "open_url", - "actionText": "Open Examples", - "url": "/notebooks/snippets/importing_libraries.ipynb" - } - ] - } - } - ], "source": [ "import glob\n", "import os\n", @@ -427,8 +446,28 @@ "import pikepdf # to repair PDFs\n", "from pathlib import Path\n", "from tqdm.notebook import tqdm\n", - "from concurrent.futures import ThreadPoolExecutor, as_completed\n", - "\n", + "from concurrent.futures import ThreadPoolExecutor, as_completed" + ], + "metadata": { + "id": "yrzYeheF40jt" + }, + "id": "yrzYeheF40jt", + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b557444b8b1d4839", + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-04T09:25:39.388933Z", + "start_time": "2024-04-04T09:25:39.320902Z" + }, + "id": "b557444b8b1d4839" + }, + "outputs": [], + "source": [ "def convert_html_to_text(html_blob: str) -> str:\n", " \"\"\"\n", " Converts a html blob into a string.\n", @@ -573,14 +612,14 @@ "id": "e1bcc07f980c865f" }, "source": [ - "## Chunking of the texts\n", + "# Chunking of the texts\n", "\n", - "The texts need to get chunked (pre-processing) before the embedding process." + "The texts need to get chunked (pre-processing) before the embedding process. We are processing text for the sake of similarity detection. Therefore we can use overlaps. For log-processing and detection engineering, overlaps would be counter-productive." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 12, "id": "de8d9f18d8342c57", "metadata": { "ExecuteTime": { @@ -591,7 +630,7 @@ "base_uri": "https://localhost:8080/" }, "id": "de8d9f18d8342c57", - "outputId": "6d45ef7e-67a8-4539-bd5f-253c1e1f9c52" + "outputId": "31fd5ade-1af8-4592-8d18-aa2b8d635c44" }, "outputs": [ { @@ -638,17 +677,30 @@ "\n", "The OpenAI API has a cost for the embeddings.\n", "At this point there seems to be no way to pre-estimate the costs reliably.\n", - "The following calculation is probably flawed:" + "The following calculation is probably flawed. But if it's correct, I wish the OpenAPI team the best of luck with finding a new pricing model." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "afb2c8feb9ca0bb4", "metadata": { - "id": "afb2c8feb9ca0bb4" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "afb2c8feb9ca0bb4", + "outputId": "4bf4a714-b59e-4835-f3ac-3110b329b7b4" }, - "outputs": [], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Total Tokens: 15769414\n", + "Embedding Cost in USD: 473.08241999999996\n" + ] + } + ], "source": [ "def print_embedding_cost(texts):\n", " import tiktoken\n", @@ -668,7 +720,7 @@ "id": "8012516604037e2f" }, "source": [ - "## Use Hugging Face Embeddings Sentence Transformers\n", + "# Use Hugging Face Embeddings Sentence Transformers\n", "\n", "Here we:\n", "\n", @@ -679,12 +731,24 @@ "* https://huggingface.co/BAAI/bge-large-en-v1.5\n", "* It uses: https://huggingface.co/docs/transformers/model_doc/auto\n", "\n", - "It will produce embeddings of 1024 dimensions, roughly 500 less than OpenAI Embeddings." + "It will produce embeddings of 1024 dimensions, roughly 500 less than OpenAI Embeddings I wanted to use initially." ] }, + { + "cell_type": "markdown", + "source": [ + "## GPU detection (CUDA)\n", + "\n", + "Here we detect whether a GPU is present, and if that is the case, we initialize the model to use it later. If not, we can use the CPU as a fallback. But for this use-case / implementation the Nvidia V100 GPU is about 60x faster (estimation)." + ], + "metadata": { + "id": "LJIwSxNf5sm7" + }, + "id": "LJIwSxNf5sm7" + }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 14, "id": "3081256c9cf22780", "metadata": { "ExecuteTime": { @@ -695,7 +759,7 @@ "base_uri": "https://localhost:8080/" }, "id": "3081256c9cf22780", - "outputId": "0a02f0bc-42ce-4f50-e670-fd8ca48111a9" + "outputId": "9b120f43-f2cd-47c2-b763-e089aca15ee2" }, "outputs": [ { @@ -729,9 +793,21 @@ " USE_GPU=False\n" ] }, + { + "cell_type": "markdown", + "source": [ + "## BAAI Model\n", + "\n", + "The Beijing Academy of Artificial Intelligence (BAAI) is a leading organization, which provides state of the art models on HuggingFace. Here the model is being used to create the Embeddings. An Embedding here isn't a plain Word2Vec style projection of text to a vector space. It has a semantic integration. I still have to research the details." + ], + "metadata": { + "id": "GY_nYdSO6JTc" + }, + "id": "GY_nYdSO6JTc" + }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 15, "id": "c1ca979bbc1610bb", "metadata": { "ExecuteTime": { @@ -757,9 +833,19 @@ "encode_kwargs = {'normalize_embeddings': True}" ] }, + { + "cell_type": "markdown", + "source": [ + "### Initialization of the Embedding model" + ], + "metadata": { + "id": "JSTLqLQj6ref" + }, + "id": "JSTLqLQj6ref" + }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 16, "id": "3c2b9cd67f161714", "metadata": { "ExecuteTime": { @@ -767,10 +853,134 @@ "start_time": "2024-04-04T10:09:34.059018Z" }, "colab": { - "base_uri": "https://localhost:8080/" + "base_uri": "https://localhost:8080/", + "height": 496, + "referenced_widgets": [ + "a1b39dadf1fd474296d47c00498b1d97", + "ce3cebc4664b4b4094f965e3d98b1ec3", + "cbd1ebc865344b2cbe09aaad9341f447", + "1fe3e7dd24a143b38fcc1f16048fce75", + 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swallowed by the ThreadPoolExecutor.\n", - "\n", " The exception handling serves the same purpose.\n", + " Exceptions can cost performance, but only on the CPU level here.\n", " \"\"\"\n", "\n", " try:\n", - " db = FAISS.from_documents(batch, embeddings)\n", + " db = FAISS.from_documents(batch, embeddings, distance_strategy=\"COSINE\")\n", " return db\n", "\n", " except Exception as e:\n", " print(f\"Exception occurred in add_texts_in_batches: {e}\")\n", "\n", + "\n", "def divide_chunks(chunks, n):\n", " \"\"\"\n", - " Divide and conquer\n", + " Divide and conquer :)\n", " \"\"\"\n", " for i in range(0, len(chunks), n):\n", " yield chunks[i:i + n]\n", "\n", "\n", "def vectorize_data_in_batches(chunks, embeddings):\n", + " \"\"\"\n", + " This function orchestrates the embedding vectorization in batches.\n", + " \"\"\"\n", "\n", " num_workers = 3\n", " batch_size = 500 # Adjust based on your needs and memory constraints\n", @@ -970,7 +1365,7 @@ "\n", " progress_bar.close() # Ensure the progress bar is closed at the end\n", "\n", - " faiss_db.save_local(\"faiss_index\")\n", + " faiss_db.save_local(\"faiss_index_cosine\")\n", " print(\"All texts have been added to the database.\")\n", "\n", "\n", @@ -983,18 +1378,20 @@ "from langchain_community.vectorstores import FAISS\n", "\n", "texts = [\"FAISS is an important library\", \"LangChain supports FAISS\"]\n", - "faiss = FAISS.from_texts(texts, embeddings)\n", - "print(type(faiss))" + "faiss = FAISS.from_texts(texts, embeddings, distance_strategy=\"COSINE\")\n", + "print(type(faiss))\n", + "\n", + "faiss.save_local(\"test\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "v6bhYHU5_9oo", - "outputId": "01bbcac4-01dc-4efe-e9e9-31c8d2b6ca56" + "outputId": "0fec4528-d10b-4a83-f5b5-c75476d2b4d1" }, "id": "v6bhYHU5_9oo", - "execution_count": 15, + "execution_count": 21, "outputs": [ { "output_type": "stream", @@ -1025,12 +1422,14 @@ }, "colab": { "provenance": [], + "toc_visible": true, + 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