mirror of
https://github.com/norandom/project_bookworm.git
synced 2024-11-22 00:33:44 +00:00
5411 lines
202 KiB
Plaintext
5411 lines
202 KiB
Plaintext
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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": "18d62071e34b0d53",
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"metadata": {
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"collapsed": false,
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"id": "18d62071e34b0d53"
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},
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"source": [
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"# This is an experiment: create vectorized embeddings out of an EverNote DB (PDF, DOCX, HTML, TXT)\n",
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"\n",
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"## Features\n",
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"\n",
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"* vectorize text, html files, pdfs and docx into one vector DB, split in tables (sqlite vss)\n",
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"* use local self-hosted embeddings (CPU or GPU computed)\n",
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" * for sentences\n",
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"* query a local sqlite vss vector db, use cache from LangChain (sqlite)\n",
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"* use OpenAI API and (Ollama on-prem self-hosted) Mistral for the response processing\n",
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"* compare with LLMware Bling\n",
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"\n",
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"## Anti-Features\n",
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"\n",
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"* due to cost reasons the OpenAI embeddings don't get used. So sorry :p"
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]
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},
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{
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"cell_type": "markdown",
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"id": "94517a27e3148ff4",
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"metadata": {
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"collapsed": false,
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"id": "94517a27e3148ff4"
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},
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"source": [
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"# Configuration"
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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": 2,
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"id": "fd9747a54ea8fcef",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-04-04T10:35:54.949214Z",
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"start_time": "2024-04-04T10:35:54.945013Z"
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},
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"id": "fd9747a54ea8fcef"
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},
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"outputs": [],
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"source": [
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"import sys\n",
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"import os\n",
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"IN_COLAB = 'google.colab' in sys.modules\n",
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"\n",
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"if not IN_COLAB:\n",
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" # The Evernote DB path containing the extracted data.\n",
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" extracted_evernote_db = \"/home/marius/data/it-sec-research-extracted/IT sec research\"\n",
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"\n",
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" # Output paths containing the Evernote text notes or documents data.\n",
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" # These get generated by the data extraction process\n",
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" output_path_extracted_notes = \"/home/marius/source/bookworm/export.txt\"\n",
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" output_path_extracted_docs = \"/home/marius/source/bookworm/export.documents.txt\"\n",
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"\n",
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" # Resulting DB or vector store path.\n",
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" result_db = \"/home/marius/source/bookworm/evernote.db\"\n",
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"\n",
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"else:\n",
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" # For the Goog Colab env\n",
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" output_path_extracted_notes = \"/content/export.txt\"\n",
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" output_path_extracted_docs = \"/content/export.documents.txt\"\n",
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" result_db = \"/content/evernote.db\"\n",
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"\n",
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"# To suppress some warnings\n",
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"import os\n",
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"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"True\""
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]
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},
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{
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"cell_type": "code",
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"source": [
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"# Controls:"
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],
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"metadata": {
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"id": "8tcn27pzvpRi"
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},
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"id": "8tcn27pzvpRi",
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"print(output_path_extracted_notes)"
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],
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"metadata": {
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"id": "6SPPaVEet9EO",
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"outputId": "de267b51-fab0-4509-dfda-b8cfc7886d8b",
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"colab": {
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"base_uri": "https://localhost:8080/"
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}
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},
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"id": "6SPPaVEet9EO",
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"execution_count": 3,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"/content/export.txt\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"%reload_ext autoreload\n",
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"%autoreload 2"
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],
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"metadata": {
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"id": "XGYNhuvrvnUD"
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},
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"id": "XGYNhuvrvnUD",
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"execution_count": 4,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"id": "a8c8692786d83c00",
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"metadata": {
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"collapsed": false,
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"id": "a8c8692786d83c00"
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},
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"source": [
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"## Dependencies\n",
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"\n",
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"* Cryptography is used to handle some PDF functions here (signatures)"
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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": 5,
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"id": "bb34db1ea75a1edf",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-04-04T10:08:32.520341Z",
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"start_time": "2024-04-04T10:08:30.353678Z"
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},
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"id": "bb34db1ea75a1edf",
|
|||
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"outputId": "25aac151-5cae-44e7-887b-27008e986821",
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"colab": {
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"base_uri": "https://localhost:8080/"
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}
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Name: cryptography\n",
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"Version: 42.0.5\n",
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"Summary: cryptography is a package which provides cryptographic recipes and primitives to Python developers.\n",
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"Home-page: \n",
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|
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"Author: \n",
|
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"Author-email: The Python Cryptographic Authority and individual contributors <cryptography-dev@python.org>\n",
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"License: Apache-2.0 OR BSD-3-Clause\n",
|
|||
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"Location: /usr/local/lib/python3.10/dist-packages\n",
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"Requires: cffi\n",
|
|||
|
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"Required-by: pyOpenSSL\n"
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|
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]
|
|||
|
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}
|
|||
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],
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"source": [
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"%pip show cryptography"
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]
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},
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{
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|||
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"cell_type": "markdown",
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"id": "297746c807e95fbf",
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"id": "297746c807e95fbf"
|
|||
|
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},
|
|||
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"source": [
|
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|
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"* pikepdf is used to repair some PDFs"
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|||
|
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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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|||
|
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"id": "ebc8af0183532fc2",
|
|||
|
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"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2024-04-04T10:08:34.665865Z",
|
|||
|
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"start_time": "2024-04-04T10:08:32.522020Z"
|
|||
|
|
},
|
|||
|
|
"id": "ebc8af0183532fc2",
|
|||
|
|
"outputId": "2398386a-d6d5-4574-9416-c7f8f92a082c"
|
|||
|
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},
|
|||
|
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"outputs": [
|
|||
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{
|
|||
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"name": "stdout",
|
|||
|
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"output_type": "stream",
|
|||
|
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"text": [
|
|||
|
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"Name: pikepdf\r\n",
|
|||
|
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"Version: 8.13.0\r\n",
|
|||
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"Summary: Read and write PDFs with Python, powered by qpdf\r\n",
|
|||
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"Home-page: \r\n",
|
|||
|
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"Author: \r\n",
|
|||
|
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"Author-email: \"James R. Barlow\" <james@purplerock.ca>\r\n",
|
|||
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"License: MPL-2.0\r\n",
|
|||
|
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"Location: /home/marius/miniconda3/envs/llm_langchain/lib/python3.11/site-packages\r\n",
|
|||
|
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"Requires: Deprecated, lxml, packaging, Pillow\r\n",
|
|||
|
|
"Required-by: \r\n",
|
|||
|
|
"Note: you may need to restart the kernel to use updated packages.\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
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"source": [
|
|||
|
|
"%pip show pikepdf"
|
|||
|
|
]
|
|||
|
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},
|
|||
|
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{
|
|||
|
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"cell_type": "markdown",
|
|||
|
|
"id": "7c7a7f6b0db3719e",
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"id": "7c7a7f6b0db3719e"
|
|||
|
|
},
|
|||
|
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"source": [
|
|||
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|
"* pypdf with all features is needed because this DB consists of 100+ PDFs"
|
|||
|
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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": "779f81e2ab00f73c",
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2024-04-04T10:08:37.436449Z",
|
|||
|
|
"start_time": "2024-04-04T10:08:35.269255Z"
|
|||
|
|
},
|
|||
|
|
"id": "779f81e2ab00f73c",
|
|||
|
|
"outputId": "353f67d9-0d77-45ba-85d3-37d8a651580c"
|
|||
|
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},
|
|||
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"outputs": [
|
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{
|
|||
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"name": "stdout",
|
|||
|
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"output_type": "stream",
|
|||
|
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"text": [
|
|||
|
|
"Name: pypdf\r\n",
|
|||
|
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"Version: 4.0.2\r\n",
|
|||
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"Summary: A pure-python PDF library capable of splitting, merging, cropping, and transforming PDF files\r\n",
|
|||
|
|
"Home-page: \r\n",
|
|||
|
|
"Author: \r\n",
|
|||
|
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"Author-email: Mathieu Fenniak <biziqe@mathieu.fenniak.net>\r\n",
|
|||
|
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"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"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"%pip show \"pypdf\""
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 6,
|
|||
|
|
"id": "de3f715519fda6c4",
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2024-04-04T10:08:39.729429Z",
|
|||
|
|
"start_time": "2024-04-04T10:08:37.438498Z"
|
|||
|
|
},
|
|||
|
|
"id": "de3f715519fda6c4",
|
|||
|
|
"outputId": "858cd8ae-32d3-4373-9ac4-971e424079bb",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Name: torch\n",
|
|||
|
|
"Version: 2.2.1+cu121\n",
|
|||
|
|
"Summary: Tensors and Dynamic neural networks in Python with strong GPU acceleration\n",
|
|||
|
|
"Home-page: https://pytorch.org/\n",
|
|||
|
|
"Author: PyTorch Team\n",
|
|||
|
|
"Author-email: packages@pytorch.org\n",
|
|||
|
|
"License: BSD-3\n",
|
|||
|
|
"Location: /usr/local/lib/python3.10/dist-packages\n",
|
|||
|
|
"Requires: filelock, fsspec, jinja2, networkx, nvidia-cublas-cu12, nvidia-cuda-cupti-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-runtime-cu12, nvidia-cudnn-cu12, nvidia-cufft-cu12, nvidia-curand-cu12, nvidia-cusolver-cu12, nvidia-cusparse-cu12, nvidia-nccl-cu12, nvidia-nvtx-cu12, sympy, triton, typing-extensions\n",
|
|||
|
|
"Required-by: fastai, sentence-transformers, torchaudio, torchdata, torchtext, torchvision\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"%pip show torch"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"%pip show faiss_gpu"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "HARY_QMJvttI",
|
|||
|
|
"outputId": "1a3d2e43-b3d4-46f6-a526-ac0cb44bd1e6",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"id": "HARY_QMJvttI",
|
|||
|
|
"execution_count": 8,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Name: faiss-gpu\n",
|
|||
|
|
"Version: 1.7.2\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",
|
|||
|
|
"Location: /usr/local/lib/python3.10/dist-packages\n",
|
|||
|
|
"Requires: \n",
|
|||
|
|
"Required-by: \n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"id": "ce1350d2d6e3ed63",
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"id": "ce1350d2d6e3ed63"
|
|||
|
|
},
|
|||
|
|
"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",
|
|||
|
|
"\n",
|
|||
|
|
"This will be used for weighted data fusion later.\n",
|
|||
|
|
"\n",
|
|||
|
|
"* the texts are normalized:\n",
|
|||
|
|
" * unicode normalization\n",
|
|||
|
|
" * surrogate characters get replaced\n",
|
|||
|
|
" * html gets converted to text\n",
|
|||
|
|
" * pdfs get repaired\n",
|
|||
|
|
" * docx files get read\n",
|
|||
|
|
"\n",
|
|||
|
|
"* exceptions get handled (UTF-16 issues, PDF reference errors)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"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",
|
|||
|
|
"outputId": "2f7f60e9-af8d-41c2-ff67-5b6f61574d7b"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"ename": "ModuleNotFoundError",
|
|||
|
|
"evalue": "No module named 'html2text'",
|
|||
|
|
"output_type": "error",
|
|||
|
|
"traceback": [
|
|||
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|||
|
|
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
|
|||
|
|
"Cell \u001b[0;32mIn[7], line 5\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01municodedata\u001b[39;00m \u001b[38;5;66;03m# to normalize text\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mhtml2text\u001b[39;00m \u001b[38;5;66;03m# to convert html to text\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlangchain\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdocument_loaders\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m PyPDFLoader, Docx2txtLoader\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpikepdf\u001b[39;00m \u001b[38;5;66;03m# to repair PDFs\u001b[39;00m\n",
|
|||
|
|
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'html2text'"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"import glob\n",
|
|||
|
|
"import os\n",
|
|||
|
|
"\n",
|
|||
|
|
"import unicodedata # to normalize text\n",
|
|||
|
|
"import html2text # to convert html to text\n",
|
|||
|
|
"from langchain.document_loaders import PyPDFLoader, Docx2txtLoader\n",
|
|||
|
|
"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",
|
|||
|
|
"def convert_html_to_text(html_blob: str) -> str:\n",
|
|||
|
|
" \"\"\"\n",
|
|||
|
|
" Converts a html blob into a string.\n",
|
|||
|
|
" \"\"\"\n",
|
|||
|
|
" h = html2text.HTML2Text()\n",
|
|||
|
|
" h.mark_code = True\n",
|
|||
|
|
" h.escape_snob = True\n",
|
|||
|
|
" h.unicode_snob = True\n",
|
|||
|
|
" # h.use_automatic_links = True\n",
|
|||
|
|
" h.images_as_html = True\n",
|
|||
|
|
" h.single_line_break = True\n",
|
|||
|
|
" h.ignore_links = True\n",
|
|||
|
|
" return h.handle(html_blob)\n",
|
|||
|
|
"\n",
|
|||
|
|
"def normalize_text(txt_blob: str) -> str:\n",
|
|||
|
|
" \"\"\"\n",
|
|||
|
|
" Normalize a text blob using NFKD normalization.\n",
|
|||
|
|
" \"\"\"\n",
|
|||
|
|
" return unicodedata.normalize(\"NFKD\", txt_blob)\n",
|
|||
|
|
"\n",
|
|||
|
|
"def repair_pdf(file_path: str) -> bool:\n",
|
|||
|
|
" \"\"\"\n",
|
|||
|
|
" Attempts to repair a PDF file using pikepdf.\n",
|
|||
|
|
" \"\"\"\n",
|
|||
|
|
" try:\n",
|
|||
|
|
" with pikepdf.open(file_path, allow_overwriting_input=True) as pdf:\n",
|
|||
|
|
" pdf.save(file_path)\n",
|
|||
|
|
" return True\n",
|
|||
|
|
" except pikepdf.PdfError as e:\n",
|
|||
|
|
" print(f\"Failed to repair PDF {file_path}: {e}\")\n",
|
|||
|
|
" return False\n",
|
|||
|
|
"\n",
|
|||
|
|
"def read_and_convert_file(file_path: str, is_html: bool, is_pdf: bool, is_docx: bool) -> str:\n",
|
|||
|
|
" \"\"\"\n",
|
|||
|
|
" Reads and converts a file from HTML, PDF, DOCX, or plain text to text.\n",
|
|||
|
|
" :param file_path:\n",
|
|||
|
|
" :param is_html:\n",
|
|||
|
|
" :param is_pdf:\n",
|
|||
|
|
" :param is_docx:\n",
|
|||
|
|
" :return:\n",
|
|||
|
|
" \"\"\"\n",
|
|||
|
|
"\n",
|
|||
|
|
" content = \"\"\n",
|
|||
|
|
" if is_html:\n",
|
|||
|
|
" try:\n",
|
|||
|
|
" with open(file_path, 'r', encoding='utf-8') as file:\n",
|
|||
|
|
" content = file.read()\n",
|
|||
|
|
" return convert_html_to_text(content)\n",
|
|||
|
|
" except Exception as e:\n",
|
|||
|
|
" print(f\"Error reading {file_path}: {e}\")\n",
|
|||
|
|
" return \"\"\n",
|
|||
|
|
"\n",
|
|||
|
|
" elif is_pdf:\n",
|
|||
|
|
" try:\n",
|
|||
|
|
" loader = PyPDFLoader(file_path)\n",
|
|||
|
|
" # ... fixes \"Multiple definitions in dictionary at byte 0xb32 for key /ExtGState\" error\n",
|
|||
|
|
" documents = loader.load()\n",
|
|||
|
|
" content = \"\\n\".join(doc.page_content for doc in documents if hasattr(doc, 'page_content'))\n",
|
|||
|
|
" except Exception as e:\n",
|
|||
|
|
" print(f\"Error loading PDF {file_path}: {e}. Attempting to repair...\")\n",
|
|||
|
|
" if repair_pdf(file_path):\n",
|
|||
|
|
" try:\n",
|
|||
|
|
" loader = PyPDFLoader(file_path)\n",
|
|||
|
|
" documents = loader.load()\n",
|
|||
|
|
" content = \"\\n\".join(doc.page_content for doc in documents if hasattr(doc, 'page_content'))\n",
|
|||
|
|
" except Exception as e:\n",
|
|||
|
|
" print(f\"Failed to process PDF {file_path} after repair: {e}\")\n",
|
|||
|
|
" return \"\"\n",
|
|||
|
|
" return normalize_text(content)\n",
|
|||
|
|
"\n",
|
|||
|
|
" elif is_docx:\n",
|
|||
|
|
" try:\n",
|
|||
|
|
" loader = Docx2txtLoader(file_path)\n",
|
|||
|
|
" content = loader.load()\n",
|
|||
|
|
" if isinstance(content, list):\n",
|
|||
|
|
" content = \"\\n\".join(content)\n",
|
|||
|
|
" except Exception as e:\n",
|
|||
|
|
" print(f\"Error reading DOCX {file_path}: {e}\")\n",
|
|||
|
|
" return \"\"\n",
|
|||
|
|
" return normalize_text(content)\n",
|
|||
|
|
"\n",
|
|||
|
|
" else: # For plain text files\n",
|
|||
|
|
" try:\n",
|
|||
|
|
" with open(file_path, 'r', encoding='utf-8') as file:\n",
|
|||
|
|
" return normalize_text(file.read())\n",
|
|||
|
|
" except Exception as e:\n",
|
|||
|
|
" print(f\"Error reading {file_path}: {e}\")\n",
|
|||
|
|
" return \"\"\n",
|
|||
|
|
"\n",
|
|||
|
|
"def sanitize_text(text):\n",
|
|||
|
|
" \"\"\"\n",
|
|||
|
|
" Removes or replaces surrogate characters from a string.\n",
|
|||
|
|
" \"\"\"\n",
|
|||
|
|
" return text.encode('utf-8', 'replace').decode('utf-8')\n",
|
|||
|
|
"\n",
|
|||
|
|
"def append_to_output(data: str, is_pdf: bool, is_docx: bool, output_path: str):\n",
|
|||
|
|
" \"\"\"\n",
|
|||
|
|
" Appends sanitized data to an output file.\n",
|
|||
|
|
" \"\"\"\n",
|
|||
|
|
" sanitized_data = sanitize_text(data)\n",
|
|||
|
|
" if is_pdf or is_docx:\n",
|
|||
|
|
" output_path = str(Path(output_path).with_suffix('')) + \".documents.txt\"\n",
|
|||
|
|
"\n",
|
|||
|
|
" with open(output_path, \"a\", encoding='utf-8') as output_file:\n",
|
|||
|
|
" output_file.write(sanitized_data)\n",
|
|||
|
|
"\n",
|
|||
|
|
"def process_file(file):\n",
|
|||
|
|
" is_html = file.endswith('.html')\n",
|
|||
|
|
" is_pdf = file.endswith('.pdf')\n",
|
|||
|
|
" is_docx = file.endswith('.docx')\n",
|
|||
|
|
"\n",
|
|||
|
|
" file_content = read_and_convert_file(file, is_html, is_pdf, is_docx)\n",
|
|||
|
|
" append_to_output(file_content, is_pdf, is_docx, output_path=output_path)\n",
|
|||
|
|
"\n",
|
|||
|
|
"def process_files_in_directory(directory: str):\n",
|
|||
|
|
" txt_html_files = glob.glob(os.path.join(directory, \"*.txt\")) + glob.glob(os.path.join(directory, \"*.html\"))\n",
|
|||
|
|
" pdf_docx_files = glob.glob(os.path.join(directory, \"img\", \"*.pdf\")) + glob.glob(os.path.join(directory, \"img\", \"*.docx\"))\n",
|
|||
|
|
" all_files = txt_html_files + pdf_docx_files\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Initialize the progress bar\n",
|
|||
|
|
" pbar = tqdm(total=len(all_files), desc=\"Processing files\")\n",
|
|||
|
|
"\n",
|
|||
|
|
" with ThreadPoolExecutor(max_workers=3) as executor:\n",
|
|||
|
|
" # Submit all files to the executor and store future objects\n",
|
|||
|
|
" futures = [executor.submit(process_file, file) for file in all_files]\n",
|
|||
|
|
"\n",
|
|||
|
|
" # As tasks complete, update the progress bar\n",
|
|||
|
|
" for future in as_completed(futures):\n",
|
|||
|
|
" pbar.update(1) # Update the progress bar by one for each task completed\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Ensure the progress bar is closed upon completion\n",
|
|||
|
|
" pbar.close()\n",
|
|||
|
|
"\n",
|
|||
|
|
"process_files_in_directory(extracted_evernote_db)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"id": "e1bcc07f980c865f",
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"id": "e1bcc07f980c865f"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"## Chunking of the texts\n",
|
|||
|
|
"\n",
|
|||
|
|
"The texts need to get chunked (pre-processing) before the embedding process."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 9,
|
|||
|
|
"id": "de8d9f18d8342c57",
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2024-04-04T10:09:23.408646Z",
|
|||
|
|
"start_time": "2024-04-04T10:08:56.104045Z"
|
|||
|
|
},
|
|||
|
|
"id": "de8d9f18d8342c57",
|
|||
|
|
"outputId": "2368c73f-dd6c-4737-f997-a5b1a285a15d",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Now you have 723845 chunks in /content/export.txt\n",
|
|||
|
|
"Now you have 151259 chunks in /content/export.documents.txt\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
|
|||
|
|
"\n",
|
|||
|
|
"def chunk_text_data(txt_file=output_path_extracted_notes):\n",
|
|||
|
|
"\n",
|
|||
|
|
" with open(txt_file) as f:\n",
|
|||
|
|
" text_notes = f.read()\n",
|
|||
|
|
"\n",
|
|||
|
|
" text_splitter = RecursiveCharacterTextSplitter(\n",
|
|||
|
|
" chunk_size=100,\n",
|
|||
|
|
" chunk_overlap=20,\n",
|
|||
|
|
" length_function=len\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" chunks = text_splitter.create_documents([text_notes])\n",
|
|||
|
|
" print(f'Now you have {len(chunks)} chunks in {txt_file}')\n",
|
|||
|
|
" return chunks\n",
|
|||
|
|
"\n",
|
|||
|
|
"# chunk individual text file containing the data\n",
|
|||
|
|
"text_chunks = chunk_text_data(txt_file=output_path_extracted_notes)\n",
|
|||
|
|
"doc_chunks = chunk_text_data(txt_file=output_path_extracted_docs)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"id": "aea7ceb111fed5f3",
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"id": "aea7ceb111fed5f3"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"### Embedding costs - why no OpenAI?\n",
|
|||
|
|
"\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:"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"id": "afb2c8feb9ca0bb4",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "afb2c8feb9ca0bb4"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def print_embedding_cost(texts):\n",
|
|||
|
|
" import tiktoken\n",
|
|||
|
|
" enc = tiktoken.encoding_for_model('gpt-4')\n",
|
|||
|
|
" total_tokens = sum([len(enc.encode(page.page_content)) for page in texts])\n",
|
|||
|
|
" print(f'Total Tokens: {total_tokens}')\n",
|
|||
|
|
" print(f'Embedding Cost in USD: { (0.03 / 1_000) * total_tokens}')\n",
|
|||
|
|
"\n",
|
|||
|
|
"print_embedding_cost(text_chunks)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"id": "8012516604037e2f",
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"id": "8012516604037e2f"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"## Use Hugging Face Embeddings Sentence Transformers\n",
|
|||
|
|
"\n",
|
|||
|
|
"Here we:\n",
|
|||
|
|
"\n",
|
|||
|
|
"* use a self-hosted on-premises model for the embedding and vectorization\n",
|
|||
|
|
"* configure it for the use with the CPU or GPU\n",
|
|||
|
|
"\n",
|
|||
|
|
"This model is from the Beijing Academy of Artificial Intelligence\n",
|
|||
|
|
"* 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."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"id": "3081256c9cf22780",
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2024-04-04T10:09:29.687485Z",
|
|||
|
|
"start_time": "2024-04-04T10:09:23.410187Z"
|
|||
|
|
},
|
|||
|
|
"id": "3081256c9cf22780",
|
|||
|
|
"outputId": "33f99ec5-f3ec-4cf7-e489-4bb5c2b91f32",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"__CUDNN VERSION: 8902\n",
|
|||
|
|
"__Number CUDA Devices: 1\n",
|
|||
|
|
"__CUDA Device Name: Tesla V100-SXM2-16GB\n",
|
|||
|
|
"__CUDA Device Total Memory [GB]: 16.935682048\n",
|
|||
|
|
"GPU enabled\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"import torch\n",
|
|||
|
|
"use_cuda = torch.cuda.is_available()\n",
|
|||
|
|
"\n",
|
|||
|
|
"USE_GPU=False\n",
|
|||
|
|
"\n",
|
|||
|
|
"if use_cuda:\n",
|
|||
|
|
" print('__CUDNN VERSION:', torch.backends.cudnn.version())\n",
|
|||
|
|
" print('__Number CUDA Devices:', torch.cuda.device_count())\n",
|
|||
|
|
" print('__CUDA Device Name:',torch.cuda.get_device_name(0))\n",
|
|||
|
|
" print('__CUDA Device Total Memory [GB]:',torch.cuda.get_device_properties(0).total_memory/1e9)\n",
|
|||
|
|
" USE_GPU=True\n",
|
|||
|
|
" print(\"GPU enabled\")\n",
|
|||
|
|
"\n",
|
|||
|
|
"if not use_cuda:\n",
|
|||
|
|
" print('No CUDA available')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 11,
|
|||
|
|
"id": "c1ca979bbc1610bb",
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2024-04-04T10:09:29.889360Z",
|
|||
|
|
"start_time": "2024-04-04T10:09:29.688832Z"
|
|||
|
|
},
|
|||
|
|
"id": "c1ca979bbc1610bb"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"from langchain.embeddings import HuggingFaceEmbeddings\n",
|
|||
|
|
"\n",
|
|||
|
|
"# pre-trained model path\n",
|
|||
|
|
"modelPath = \"BAAI/bge-large-en-v1.5\"\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Create a dictionary with model configuration options, specifying to use the CPU or GPU for computations\n",
|
|||
|
|
"if not USE_GPU:\n",
|
|||
|
|
" model_kwargs = {'device':'cpu'}\n",
|
|||
|
|
"else:\n",
|
|||
|
|
" model_kwargs = {}\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Create a dictionary with encoding options, specifically setting 'normalize_embeddings' to True\n",
|
|||
|
|
"encode_kwargs = {'normalize_embeddings': True}"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"id": "3c2b9cd67f161714",
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2024-04-04T10:09:55.733575Z",
|
|||
|
|
"start_time": "2024-04-04T10:09:34.059018Z"
|
|||
|
|
},
|
|||
|
|
"id": "3c2b9cd67f161714",
|
|||
|
|
"outputId": "a6f1ac07-a1ae-4261-9108-7bb35f2a7048",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 496,
|
|||
|
|
"referenced_widgets": [
|
|||
|
|
"8483f598cf82452e8cc1e72632e2776b",
|
|||
|
|
"beef757d2c4f483eb81e81cdf4979c53",
|
|||
|
|
"f2d44ed3ad784d85b89fe85eacb4902e",
|
|||
|
|
"6323e24242ec448bbd4d0bcc8474c2be",
|
|||
|
|
"2a2b2e89b8304c4298c844b8211de167",
|
|||
|
|
"8f79ebe13fe54840bce483acaaca04ff",
|
|||
|
|
"9c5f5ee9bbf340bcbb06221fda88634c",
|
|||
|
|
"8a089f081a50414d9655f4a16d85cf12",
|
|||
|
|
"697a8e3d4ed5431aacd0c2cf379efa0e",
|
|||
|
|
"929a2b83e0ef405e8340aca838f67fae",
|
|||
|
|
"a207830eae514dba968ea69ba57b8c83",
|
|||
|
|
"632f1e5872804ecb95630168ddfedc67",
|
|||
|
|
"9ca5b11369cf4f74aa54b1ad08a90d13",
|
|||
|
|
"c38f5766a3ef4e3cbea0c7fea9e34b73",
|
|||
|
|
"b4cf9727180945fea027e40338a3036c",
|
|||
|
|
"32573efabe434e1a8b22ee40efffd06a",
|
|||
|
|
"b49f8ef7eb7043de869049a5be4282c2",
|
|||
|
|
"ee8cf1ab475e4251b9ff585b38d6ac49",
|
|||
|
|
"18a1ae367b0d4c539807a2d0136d5c74",
|
|||
|
|
"9ce5cd9f2dd946c79eebb2fee078128e",
|
|||
|
|
"36d267e780e34370adfd3c3108155ae9",
|
|||
|
|
"50348c7badbb4b34b0e3dea1c200843d",
|
|||
|
|
"d49be4c05cfa40afb113d381d8e607c1",
|
|||
|
|
"26b039146a21460c8398e17c2402d424",
|
|||
|
|
"066de3c247694e61a40ee7ce164b93b0",
|
|||
|
|
"fbb11ab5105e4b9dbac42887adf18ab3",
|
|||
|
|
"9382fca054b44477ae20df3273749ee5",
|
|||
|
|
"57bca8e245c1430494876a4b11253a56",
|
|||
|
|
"bf4db72d99b84e5086fb9a4d63e5f600",
|
|||
|
|
"56e6c08acc2f44f097294471ff59068d",
|
|||
|
|
"14543db7b0d04b8bab4259f3e503a709",
|
|||
|
|
"fee292c094444cb89bbe0faad87f000c",
|
|||
|
|
"35947ca3bc824bba945274ebb3df15f3",
|
|||
|
|
"99e0d045915b4a38b54ee54d51bb0a4b",
|
|||
|
|
"21511e55ed8a4f80b02ead5371fb9fc6",
|
|||
|
|
"58aaf75e5b1a4941807fdcd6545c2814",
|
|||
|
|
"a7c480a65ee94121abe6cf2f6f6721a0",
|
|||
|
|
"6fa4c19fcf304768bd8f9dc730e3136a",
|
|||
|
|
"c0b1966a0124461596798fe4fe93e242",
|
|||
|
|
"9c5e2ad4c55c43fcb4d4be2bacf5f13a",
|
|||
|
|
"39fb1201bdb847c6b9f5c1f14afff125",
|
|||
|
|
"c9381dbd77d9418fb67c8863b68c8659",
|
|||
|
|
"caca4e93a0634784bfd46a9ac9eb402d",
|
|||
|
|
"c94f4cecd5584206ba5367f98086c019",
|
|||
|
|
"ce2de3d85d1744ddb534e772b9175e72",
|
|||
|
|
"530d12787679463f91c95bbbabbdc27f",
|
|||
|
|
"a5ca8277d58846c4b7af700ec2306d91",
|
|||
|
|
"7e4d67cd575c4f478dfb5b51415d63f7",
|
|||
|
|
"207707226b69440096d3a609314c3028",
|
|||
|
|
"1f41ff403f6a4a73bd09077b3e71bee3",
|
|||
|
|
"52235364698646a39662ab8c7f15d496",
|
|||
|
|
"4cf0cd0027484070bd1987b19cd5e1e0",
|
|||
|
|
"f070bc651b8e44bbb9ee27a8c5df4bc7",
|
|||
|
|
"a14087c6a60e4ae89d4bb4ade00891be",
|
|||
|
|
"eedf7d8e981f4e849a82546bf96937c7",
|
|||
|
|
"691e9f7c36c244fb9c6c4ac823d200ea",
|
|||
|
|
"43e6a4b9fa91493fb829720492d40d75",
|
|||
|
|
"fdb3b06da2ee4c0b857b8e5e0fd68592",
|
|||
|
|
"4e247055f9a0459c8ea06859af21da4e",
|
|||
|
|
"4409342104f04c38b1a753ce74ebb749",
|
|||
|
|
"7e2d3506b38b47ca896e65f0964f9289",
|
|||
|
|
"2c2885076c684599aa01111f69a2797a",
|
|||
|
|
"23e1537216bf4d02962ad389faf5e0d4",
|
|||
|
|
"8f6a77d9dd664ddb9e05a2c1c6e7f48b",
|
|||
|
|
"f74d0ace48c9436b9d1f601b17d82d05",
|
|||
|
|
"93a2ada054574e509bba17f1a6fc88e8",
|
|||
|
|
"470aeb10ce004cbbb9ab0b4454d1c7d2",
|
|||
|
|
"1db0f861eec140ba907855d471afc8b4",
|
|||
|
|
"38c02eaa287742bb9b9981ac9418b095",
|
|||
|
|
"aafe7804e780441196d7bf8774e14dfb",
|
|||
|
|
"96452021cacb4c819ae0496daf2686a9",
|
|||
|
|
"e6cec6bb9c2341679e5b83c31881c389",
|
|||
|
|
"770a2acc6b5342a3b3ec7cb2a87366cd",
|
|||
|
|
"77eef81327aa4245b4e725393423296d",
|
|||
|
|
"53d165b361764f45a3b7b6049cda67c7",
|
|||
|
|
"0d7e4fa9f537434eb7656a85c99926fb",
|
|||
|
|
"4950a63544b041d284938b798bd6b2a8",
|
|||
|
|
"3de4705948064cab86533d9c85810082",
|
|||
|
|
"6f6d31664256410987be90f2a05b7db2",
|
|||
|
|
"1c83038d2bb346b49e04b5bb62af58d6",
|
|||
|
|
"3a981dbfc24a41289b8f5bcc06abe820",
|
|||
|
|
"936cf565614f4ac98a18efafb98e76c3",
|
|||
|
|
"9296b09ff8ca442c995ccb3b76370638",
|
|||
|
|
"cdab0625d4aa4e1fabfa034381d116a1",
|
|||
|
|
"34b74f2be8bb4198885fbaf0b3aae845",
|
|||
|
|
"2a565db566a049d1b2d5daa7ef030733",
|
|||
|
|
"fcb7c210124641969eb43ea8cea973ad",
|
|||
|
|
"d96b7c65c1ae4a3582c4e7fb5562471d",
|
|||
|
|
"d9b684e20f2b444cb1120422b3027759",
|
|||
|
|
"c7d30e7b587243caa3aaaffbe87c0a88",
|
|||
|
|
"57995c76fc1a497193b9ca9ec6478cab",
|
|||
|
|
"1d4bdd4783c344f59efc4c6328723cb9",
|
|||
|
|
"f33c382245334d4aa58fe06d42fa902e",
|
|||
|
|
"c05deca308774c6dbde37815c5bd916e",
|
|||
|
|
"3101294e893541fd8f93e79fd303bcb3",
|
|||
|
|
"fd9804cc2d1e4454982cf105de9d1efb",
|
|||
|
|
"21d082c9eceb4321ad58a3cb37bc840a",
|
|||
|
|
"a75ff77436274553b0b7965337ff5c04",
|
|||
|
|
"4b103ceee9c44da9bf8938ddecb27c57",
|
|||
|
|
"3d853e79133442c2a0d2eaac4525bef7",
|
|||
|
|
"558975101c044828a619345b8661a600",
|
|||
|
|
"2b2e2b073035442792276bd80dd3eb68",
|
|||
|
|
"840cbc2c5d1a485a96903a8a4aa89479",
|
|||
|
|
"b6fe5088145b45e7a1a0803f9ffd1905",
|
|||
|
|
"f244fc7cc56c44e49f2efacfcc7eca54",
|
|||
|
|
"76ba48b5c01749908aae0044d4d0eb73",
|
|||
|
|
"9eb2a7d4dc63477ba64e9c45e09faa8c",
|
|||
|
|
"d3ea136c2bf24aa098e46d7417b79d88",
|
|||
|
|
"b503ace6123b49a5bfaf55d319db3aaf",
|
|||
|
|
"27c1c34295f7427fb0dde07698f550b4",
|
|||
|
|
"1c74a9271f52488d94f940fbc74e6b7d",
|
|||
|
|
"5bbffaaa66e540aab0056be199e874e2",
|
|||
|
|
"0f6026caa9f3449694a5b559a7e9ddf6",
|
|||
|
|
"ba0dcb34176b4f389cbca76f14eaacc6",
|
|||
|
|
"efba751c47f84807bf8400e4ab4dbe53",
|
|||
|
|
"481ee1d08f494739afee50cd6669237f",
|
|||
|
|
"482773282c9e424b85c2b606929d72e9",
|
|||
|
|
"1722973c296f4ebfb04760daddeba44a",
|
|||
|
|
"648dd0b1a8be4cd1be934beea9e70925",
|
|||
|
|
"8feb439b07ee4c20a2e035b597681a44",
|
|||
|
|
"9e1280773fae4460b8e72068ebea3e1f"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stderr",
|
|||
|
|
"text": [
|
|||
|
|
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
|
|||
|
|
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
|
|||
|
|
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
|
|||
|
|
"You will be able to reuse this secret in all of your notebooks.\n",
|
|||
|
|
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
|
|||
|
|
" warnings.warn(\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"modules.json: 0%| | 0.00/349 [00:00<?, ?B/s]"
|
|||
|
|
],
|
|||
|
|
"application/vnd.jupyter.widget-view+json": {
|
|||
|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0,
|
|||
|
|
"model_id": "8483f598cf82452e8cc1e72632e2776b"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"config_sentence_transformers.json: 0%| | 0.00/124 [00:00<?, ?B/s]"
|
|||
|
|
],
|
|||
|
|
"application/vnd.jupyter.widget-view+json": {
|
|||
|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0,
|
|||
|
|
"model_id": "632f1e5872804ecb95630168ddfedc67"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
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|
}
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],
|
|||
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|
"source": [
|
|||
|
|
"# Initialize an instance of HuggingFaceEmbeddings with the specified parameters\n",
|
|||
|
|
"# this model requires sentence_transformers\n",
|
|||
|
|
"\n",
|
|||
|
|
"embeddings = HuggingFaceEmbeddings(\n",
|
|||
|
|
" model_name=modelPath, # Provide the pre-trained model's path\n",
|
|||
|
|
" model_kwargs=model_kwargs, # Pass the model configuration options\n",
|
|||
|
|
" encode_kwargs=encode_kwargs # Pass the encoding options\n",
|
|||
|
|
" )"
|
|||
|
|
]
|
|||
|
|
},
|
|||
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{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 13,
|
|||
|
|
"id": "3b9ff8cad49442cf",
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2024-04-04T10:10:01.717769Z",
|
|||
|
|
"start_time": "2024-04-04T10:09:58.740831Z"
|
|||
|
|
},
|
|||
|
|
"id": "3b9ff8cad49442cf",
|
|||
|
|
"outputId": "098077b5-f1c6-4d50-a063-229d0dfd6e8a",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"1024 dimensions are going to be used\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"vector = embeddings.embed_query(text_chunks[0].page_content)\n",
|
|||
|
|
"# print(vector)\n",
|
|||
|
|
"n_dimensions = len(vector)\n",
|
|||
|
|
"print(n_dimensions, \" dimensions are going to be used\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"id": "b347fb5ee68daf60",
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"id": "b347fb5ee68daf60"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"## Batch process the embedding\n",
|
|||
|
|
"\n",
|
|||
|
|
"Many data-science tasks require to split a larger processing operation into batch jobs.\n",
|
|||
|
|
"Like in the good old Mainframe days.\n",
|
|||
|
|
"\n",
|
|||
|
|
"The vector DB: https://github.com/asg017/sqlite-vss\n",
|
|||
|
|
"Basis: https://faiss.ai/ - a library for efficient similarity search and clustering of dense vectors.\n",
|
|||
|
|
"\n",
|
|||
|
|
"We add vectors of 1024 dimensions per chunk (sentence, line break delimited) to the DB.\n",
|
|||
|
|
"The processing is done in batches of 50 chunks, using 3 threads."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 14,
|
|||
|
|
"id": "b03bfcb6c666db1",
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2024-04-04T10:10:08.134514Z",
|
|||
|
|
"start_time": "2024-04-04T10:10:07.895943Z"
|
|||
|
|
},
|
|||
|
|
"id": "b03bfcb6c666db1"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"from concurrent.futures import ThreadPoolExecutor, as_completed\n",
|
|||
|
|
"import os\n",
|
|||
|
|
"\n",
|
|||
|
|
"from tqdm.notebook import tqdm # Import tqdm for notebook\n",
|
|||
|
|
"from typing import List\n",
|
|||
|
|
"from langchain.schema.document import Document\n",
|
|||
|
|
"\n",
|
|||
|
|
"from langchain_community.vectorstores import FAISS"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 16,
|
|||
|
|
"id": "e6ffc345c26298ad",
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2024-04-04T10:32:48.905517Z",
|
|||
|
|
"start_time": "2024-04-04T10:30:48.115043Z"
|
|||
|
|
},
|
|||
|
|
"id": "e6ffc345c26298ad",
|
|||
|
|
"outputId": "bf472df0-40bf-4255-cc78-2e269d38c39a",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 790,
|
|||
|
|
"referenced_widgets": [
|
|||
|
|
"2d7fc8cf21224a0290120a398d07ebf7",
|
|||
|
|
"a76ed11fca8c4d24bfba93e535e4d9dc",
|
|||
|
|
"f09b113f18eb4dd0b09d08bfdc77d425",
|
|||
|
|
"7f52f0bf4d6c43e1be130c3d4a39a1fd",
|
|||
|
|
"9f775e37eac24e25a30cf8525a6c3695",
|
|||
|
|
"3440f2e53a8648458be111e704fdc67d",
|
|||
|
|
"a511312d268248bf9b911345ceaca827",
|
|||
|
|
"fa5b136b89714f8f893cd751b06d6e6b",
|
|||
|
|
"38605ab14a234e5aa64293bfe7c1d622",
|
|||
|
|
"4fd0894102314e0fb970a6459972d677",
|
|||
|
|
"476a1f07c1b24f8a845a410857ffd80e"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stderr",
|
|||
|
|
"text": [
|
|||
|
|
"[autoreload of faiss.swigfaiss failed: Traceback (most recent call last):\n",
|
|||
|
|
" File \"/usr/local/lib/python3.10/dist-packages/IPython/extensions/autoreload.py\", line 245, in check\n",
|
|||
|
|
" superreload(m, reload, self.old_objects)\n",
|
|||
|
|
" File \"/usr/local/lib/python3.10/dist-packages/IPython/extensions/autoreload.py\", line 394, in superreload\n",
|
|||
|
|
" module = reload(module)\n",
|
|||
|
|
" File \"/usr/lib/python3.10/imp.py\", line 315, in reload\n",
|
|||
|
|
" return importlib.reload(module)\n",
|
|||
|
|
" File \"/usr/lib/python3.10/importlib/__init__.py\", line 169, in reload\n",
|
|||
|
|
" _bootstrap._exec(spec, module)\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 619, in _exec\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap_external>\", line 883, in exec_module\n",
|
|||
|
|
" File \"<frozen importlib._bootstrap>\", line 241, in _call_with_frames_removed\n",
|
|||
|
|
" File \"/usr/local/lib/python3.10/dist-packages/faiss/swigfaiss.py\", line 711, in <module>\n",
|
|||
|
|
" class ParameterRangeVector(object):\n",
|
|||
|
|
" File \"/usr/local/lib/python3.10/dist-packages/faiss/swigfaiss.py\", line 738, in ParameterRangeVector\n",
|
|||
|
|
" __swig_destroy__ = _swigfaiss.delete_ParameterRangeVector\n",
|
|||
|
|
"AttributeError: module 'faiss._swigfaiss' has no attribute 'delete_ParameterRangeVector'\n",
|
|||
|
|
"]\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Exception occurred in add_texts_in_batches: name 'swig_ptr' is not defined\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"Processing batches: 0%| | 0/14477 [00:00<?, ?it/s]"
|
|||
|
|
],
|
|||
|
|
"application/vnd.jupyter.widget-view+json": {
|
|||
|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0,
|
|||
|
|
"model_id": "2d7fc8cf21224a0290120a398d07ebf7"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "error",
|
|||
|
|
"ename": "KeyboardInterrupt",
|
|||
|
|
"evalue": "",
|
|||
|
|
"traceback": [
|
|||
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|||
|
|
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
|
|||
|
|
"\u001b[0;32m<ipython-input-16-0343d54ea26d>\u001b[0m in \u001b[0;36mvectorize_data_in_batches\u001b[0;34m(chunks, embeddings)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mfuture\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mas_completed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfutures\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 38\u001b[0m \u001b[0;31m# Each time a future completes, update the progress and collect the result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|||
|
|
"\u001b[0;32m/usr/lib/python3.10/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mas_completed\u001b[0;34m(fs, timeout)\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwait_timeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
|||
|
|
"\u001b[0;32m/usr/lib/python3.10/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 606\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 607\u001b[0;31m \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 608\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|||
|
|
"\u001b[0;32m/usr/lib/python3.10/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 319\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 320\u001b[0;31m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 321\u001b[0m \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|||
|
|
"\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
|
|||
|
|
"\nDuring handling of the above exception, another exception occurred:\n",
|
|||
|
|
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
|
|||
|
|
"\u001b[0;32m<ipython-input-16-0343d54ea26d>\u001b[0m in \u001b[0;36m<cell line: 55>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0mfaiss_db\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_local\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"faiss_index\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m \u001b[0mvectorize_data_in_batches\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtext_chunks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0membeddings\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0membeddings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
|
|||
|
|
"\u001b[0;32m<ipython-input-16-0343d54ea26d>\u001b[0m in \u001b[0;36mvectorize_data_in_batches\u001b[0;34m(chunks, embeddings)\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0mfaiss_db\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;31m# List to collect the returned db objects\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mThreadPoolExecutor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_workers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_workers\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexecutor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 31\u001b[0m \u001b[0;31m# Submit all the batches for processing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[0mfutures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mexecutor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubmit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madd_texts_in_batches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0membeddings\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0membeddings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbatch\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mbatch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbatches\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|||
|
|
"\u001b[0;32m/usr/lib/python3.10/concurrent/futures/_base.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, exc_type, exc_val, exc_tb)\u001b[0m\n\u001b[1;32m 647\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 648\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__exit__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_tb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 649\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshutdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 650\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 651\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
|||
|
|
"\u001b[0;32m/usr/lib/python3.10/concurrent/futures/thread.py\u001b[0m in \u001b[0;36mshutdown\u001b[0;34m(self, wait, cancel_futures)\u001b[0m\n\u001b[1;32m 233\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 234\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_threads\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 235\u001b[0;31m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 236\u001b[0m \u001b[0mshutdown\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_base\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExecutor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshutdown\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|||
|
|
"\u001b[0;32m/usr/lib/python3.10/threading.py\u001b[0m in \u001b[0;36mjoin\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 1094\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1095\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1096\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wait_for_tstate_lock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1097\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[0;31m# the behavior of a negative timeout isn't documented, but\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|||
|
|
"\u001b[0;32m/usr/lib/python3.10/threading.py\u001b[0m in \u001b[0;36m_wait_for_tstate_lock\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m 1114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1115\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1116\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1117\u001b[0m \u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1118\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|||
|
|
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"def add_texts_in_batches(batch: List[Document], sqlite_table: str = \"evernote\", embeddings=embeddings) -> None:\n",
|
|||
|
|
" \"\"\"\n",
|
|||
|
|
" Using type hints is a good idea here, because error messages get swallowed by the ThreadPoolExecutor.\n",
|
|||
|
|
"\n",
|
|||
|
|
" The exception handling serves the same purpose.\n",
|
|||
|
|
" \"\"\"\n",
|
|||
|
|
"\n",
|
|||
|
|
" try:\n",
|
|||
|
|
" db = FAISS.from_documents(batch, embeddings)\n",
|
|||
|
|
" return db\n",
|
|||
|
|
"\n",
|
|||
|
|
" except Exception as e:\n",
|
|||
|
|
" print(f\"Exception occurred in add_texts_in_batches: {e}\")\n",
|
|||
|
|
"\n",
|
|||
|
|
"def divide_chunks(chunks, n):\n",
|
|||
|
|
" \"\"\"\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",
|
|||
|
|
" num_workers = 3\n",
|
|||
|
|
" batch_size = 50 # Adjust based on your needs and memory constraints\n",
|
|||
|
|
"\n",
|
|||
|
|
" batches = list(divide_chunks(chunks, batch_size))\n",
|
|||
|
|
" faiss_db = None # List to collect the returned db objects\n",
|
|||
|
|
"\n",
|
|||
|
|
" with ThreadPoolExecutor(max_workers=num_workers) as executor:\n",
|
|||
|
|
" # Submit all the batches for processing\n",
|
|||
|
|
" futures = {executor.submit(add_texts_in_batches, batch, embeddings=embeddings): batch for batch in batches}\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Setup the tqdm progress bar\n",
|
|||
|
|
" progress_bar = tqdm(total=len(futures), desc=\"Processing batches\")\n",
|
|||
|
|
"\n",
|
|||
|
|
" for future in as_completed(futures):\n",
|
|||
|
|
" # Each time a future completes, update the progress and collect the result\n",
|
|||
|
|
" progress_bar.update(1)\n",
|
|||
|
|
" try:\n",
|
|||
|
|
" db_result = future.result() # This is where you get the returned value from add_texts_in_batches\n",
|
|||
|
|
" if faiss_db is not None:\n",
|
|||
|
|
" faiss_db = db_result.merge_from(faiss_db)\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" faiss_db = db_result\n",
|
|||
|
|
"\n",
|
|||
|
|
" except Exception as e:\n",
|
|||
|
|
" print(f\"An error occurred: {e}\")\n",
|
|||
|
|
"\n",
|
|||
|
|
" progress_bar.close() # Ensure the progress bar is closed at the end\n",
|
|||
|
|
"\n",
|
|||
|
|
" print(\"All texts have been added to the database.\")\n",
|
|||
|
|
" faiss_db.save_local(\"faiss_index\")\n",
|
|||
|
|
"\n",
|
|||
|
|
"vectorize_data_in_batches(chunks=text_chunks, embeddings=embeddings)"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "Python 3",
|
|||
|
|
"name": "python3"
|
|||
|
|
},
|
|||
|
|
"language_info": {
|
|||
|
|
"codemirror_mode": {
|
|||
|
|
"name": "ipython",
|
|||
|
|
"version": 2
|
|||
|
|
},
|
|||
|
|
"file_extension": ".py",
|
|||
|
|
"mimetype": "text/x-python",
|
|||
|
|
"name": "python",
|
|||
|
|
"nbconvert_exporter": "python",
|
|||
|
|
"pygments_lexer": "ipython2",
|
|||
|
|
"version": "2.7.6"
|
|||
|
|
},
|
|||
|
|
"colab": {
|
|||
|
|
"provenance": [],
|
|||
|
|
"gpuType": "V100"
|
|||
|
|
},
|
|||
|
|
"accelerator": "GPU",
|
|||
|
|
"widgets": {
|
|||
|
|
"application/vnd.jupyter.widget-state+json": {
|
|||
|
|
"8483f598cf82452e8cc1e72632e2776b": {
|
|||
|
|
"model_module": "@jupyter-widgets/controls",
|
|||
|
|
"model_name": "HBoxModel",
|
|||
|
|
"model_module_version": "1.5.0",
|
|||
|
|
"state": {
|
|||
|
|
"_dom_classes": [],
|
|||
|
|
"_model_module": "@jupyter-widgets/controls",
|
|||
|
|
"_model_module_version": "1.5.0",
|
|||
|
|
"_model_name": "HBoxModel",
|
|||
|
|
"_view_count": null,
|
|||
|
|
"_view_module": "@jupyter-widgets/controls",
|
|||
|
|
"_view_module_version": "1.5.0",
|
|||
|
|
"_view_name": "HBoxView",
|
|||
|
|
"box_style": "",
|
|||
|
|
"children": [
|
|||
|
|
"IPY_MODEL_beef757d2c4f483eb81e81cdf4979c53",
|
|||
|
|
"IPY_MODEL_f2d44ed3ad784d85b89fe85eacb4902e",
|
|||
|
|
"IPY_MODEL_6323e24242ec448bbd4d0bcc8474c2be"
|
|||
|
|
],
|
|||
|
|
"layout": "IPY_MODEL_2a2b2e89b8304c4298c844b8211de167"
|
|||
|
|
}
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"model_module_version": "1.5.0",
|
|||
|
|
"state": {
|
|||
|
|
"_model_module": "@jupyter-widgets/controls",
|
|||
|
|
"_model_module_version": "1.5.0",
|
|||
|
|
"_model_name": "DescriptionStyleModel",
|
|||
|
|
"_view_count": null,
|
|||
|
|
"_view_module": "@jupyter-widgets/base",
|
|||
|
|
"_view_module_version": "1.2.0",
|
|||
|
|
"_view_name": "StyleView",
|
|||
|
|
"description_width": ""
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 5
|
|||
|
|
}
|