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https://github.com/norandom/project_bookworm.git
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First status of bookworm
This commit is contained in:
parent
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commit
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"cell_type": "markdown",
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"id": "18d62071e34b0d53",
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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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@ -23,7 +26,10 @@
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"cell_type": "markdown",
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"id": "a8c8692786d83c00",
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"source": [
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"## Dependencies\n",
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@ -40,7 +46,10 @@
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"end_time": "2024-03-21T15:10:31.827945Z",
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"start_time": "2024-03-21T15:10:29.646399Z"
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@ -69,7 +78,10 @@
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"cell_type": "markdown",
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"id": "297746c807e95fbf",
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"metadata": {
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},
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"source": [
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"* pikepdf is used to repair some PDFs"
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"end_time": "2024-03-21T15:12:47.900384Z",
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"start_time": "2024-03-21T15:12:45.782477Z"
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},
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"collapsed": false
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"outputs": [
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{
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@ -113,7 +128,10 @@
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"cell_type": "markdown",
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"id": "7c7a7f6b0db3719e",
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"metadata": {
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}
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},
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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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@ -128,7 +146,10 @@
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"end_time": "2024-03-21T15:17:00.760871Z",
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"start_time": "2024-03-21T15:16:58.635484Z"
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},
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"collapsed": false
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},
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"outputs": [
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{
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@ -157,7 +178,10 @@
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"cell_type": "markdown",
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"id": "ce1350d2d6e3ed63",
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"metadata": {
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}
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},
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"source": [
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"## Text extraction\n",
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"end_time": "2024-03-17T15:34:05.847778Z",
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"start_time": "2024-03-17T15:25:49.787814Z"
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},
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"collapsed": false
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},
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"outputs": [
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{
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@ -1038,7 +1065,10 @@
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"cell_type": "markdown",
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"id": "e1bcc07f980c865f",
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"source": [
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"## Chunking of the texts\n",
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"end_time": "2024-03-17T16:13:14.479469Z",
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"start_time": "2024-03-17T16:13:14.476765Z"
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},
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"collapsed": false
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"collapsed": false,
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}
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},
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"outputs": [],
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"source": [
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@ -1071,7 +1104,10 @@
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"end_time": "2024-03-21T15:17:53.867414Z",
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"start_time": "2024-03-21T15:17:32.731232Z"
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},
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"collapsed": false
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"collapsed": false,
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"outputs": [
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{
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@ -1107,7 +1143,10 @@
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"cell_type": "markdown",
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"id": "aea7ceb111fed5f3",
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"metadata": {
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}
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},
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"source": [
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"### Embedding costs - why no OpenAI?\n",
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@ -1126,7 +1165,10 @@
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"end_time": "2024-03-21T15:18:51.003585Z",
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"start_time": "2024-03-21T15:18:31.411234Z"
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},
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"outputs": [
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{
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@ -1153,7 +1195,10 @@
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"cell_type": "markdown",
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"id": "8012516604037e2f",
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"metadata": {
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}
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},
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"source": [
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"## Use Hugging Face Embeddings Sentence Transformers\n",
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"end_time": "2024-03-21T15:19:15.167038Z",
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"start_time": "2024-03-21T15:19:15.031139Z"
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},
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"collapsed": false
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}
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},
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"outputs": [],
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"source": [
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@ -1201,7 +1249,10 @@
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"end_time": "2024-03-21T15:42:28.163005Z",
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"start_time": "2024-03-21T15:42:26.222594Z"
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},
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"collapsed": false
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"collapsed": false,
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"jupyter": {
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}
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},
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"outputs": [],
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"source": [
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@ -1224,7 +1275,10 @@
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"end_time": "2024-03-21T16:18:45.930652Z",
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"start_time": "2024-03-21T16:18:42.989032Z"
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},
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"collapsed": false
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"collapsed": false,
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}
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},
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"outputs": [
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{
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@ -1246,7 +1300,10 @@
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"cell_type": "markdown",
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"id": "b347fb5ee68daf60",
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"metadata": {
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"collapsed": false
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"collapsed": false,
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}
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},
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"source": [
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"## Batch process the embedding\n",
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"end_time": "2024-03-21T16:04:44.572979Z",
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"start_time": "2024-03-21T16:04:43.521107Z"
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},
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"collapsed": false
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"jupyter": {
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},
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"outputs": [],
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"source": [
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"end_time": "2024-03-21T16:10:22.121211Z",
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"start_time": "2024-03-21T16:08:20.585372Z"
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},
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"collapsed": false
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"collapsed": false,
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"jupyter": {
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}
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},
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"outputs": [
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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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"outputs": [],
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"source": [
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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"version": 3
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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"pygments_lexer": "ipython3",
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"version": "3.11.8"
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}
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},
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"nbformat": 4,
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