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Runtime error
Runtime error
MIME type handler proof-of-concept
Browse files
notebooks/google_drive_web_vtt_vectorizer_and_storer.ipynb
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"cell_type": "code",
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"execution_count": null,
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"\u001b[32m2025-04-20 00:08:14.649\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.core.config\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m14\u001b[0m - \u001b[34m\u001b[1mCreated Settings\u001b[0m\n",
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"\u001b[32m2025-04-20 00:08:14.652\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.services.google_drive_service\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m44\u001b[0m - \u001b[34m\u001b[1mCreated GoogleDriveService\u001b[0m\n",
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"\u001b[32m2025-04-20 00:08:14.664\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.services.embeddings_model_service\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m22\u001b[0m - \u001b[34m\u001b[1mCreated EmbeddingsModelService\u001b[0m\n",
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"\u001b[32m2025-04-20 00:08:14.664\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.services.vectorization_service\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m22\u001b[0m - \u001b[34m\u001b[1mCreated VectorizationService\u001b[0m\n"
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"\u001b[32m2025-04-20 00:08:14.666\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mctp_slack_bot.db.mongo_db\u001b[0m:\u001b[36minit\u001b[0m:\u001b[36m175\u001b[0m - \u001b[1mInitializing MongoDB connection for database: ctp_slack_bot_dev\u001b[0m\n",
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"\u001b[32m2025-04-20 00:08:14.666\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.db.mongo_db\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m26\u001b[0m - \u001b[34m\u001b[1mCreated MongoDB\u001b[0m\n",
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"\u001b[32m2025-04-20 00:08:14.667\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.db.mongo_db\u001b[0m:\u001b[36mconnect\u001b[0m:\u001b[36m32\u001b[0m - \u001b[34m\u001b[1mConnecting to MongoDB using URI: mongodb+srv://ctp-slack-bot.xkipuvm.mongodb.net/?retryWrites=true&w=majority&appName=ctp-slack-bot\u001b[0m\n",
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"\u001b[32m2025-04-20 00:08:14.667\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.db.mongo_db\u001b[0m:\u001b[36mconnect\u001b[0m:\u001b[36m49\u001b[0m - \u001b[34m\u001b[1mMongoDB client initialized for database: ctp_slack_bot_dev\u001b[0m\n",
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"\u001b[32m2025-04-20 00:08:15.043\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.db.mongo_db\u001b[0m:\u001b[36mping\u001b[0m:\u001b[36m85\u001b[0m - \u001b[34m\u001b[1mMongoDB connection is active!\u001b[0m\n",
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"\u001b[32m2025-04-20 00:08:15.044\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mctp_slack_bot.db.mongo_db\u001b[0m:\u001b[36m_test_connection\u001b[0m:\u001b[36m186\u001b[0m - \u001b[1mMongoDB connection test successful!\u001b[0m\n",
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"\u001b[32m2025-04-20 00:08:15.044\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.services.vector_database_service\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m21\u001b[0m - \u001b[34m\u001b[1mCreated VectorDatabaseService\u001b[0m\n"
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"source": [
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"from datetime import datetime\n",
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"from functools import partial\n",
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"display_html = partial(display_html, raw=True)\n",
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"\n",
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"container = Container()\n",
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"google_drive_service = container.google_drive_service()\n",
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"vectorization_service = container.vectorization_service()\n",
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"vector_database_service = container.vector_database_service()"
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"cell_type": "code",
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"outputs": [],
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"source": [
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"cell_type": "code",
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"execution_count":
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"outputs": [
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{
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"data": {
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"text/html": [
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"<p>Found 11 files/folders.</p>"
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"metadata": {},
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"output_type": "display_data"
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"data": {
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"text/html": [
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"<ul><li>/Friday Building AI Applications Session</li><li>/Friday Building AI Applications Session/GMT20250411-223535_Recording.transcript.vtt</li><li>/Friday Building AI Applications Session/GMT20250404-231749_Recording.transcript.vtt</li><li>/Friday Building AI Applications Session/GMT20250328-223256_Recording.transcript.vtt</li><li>/Friday Building AI Applications Session/GMT20250321-223330_Recording.transcript.vtt</li><li>/Friday Building AI Applications Session/GMT20250314-223145_Recording.transcript.vtt</li><li>/Friday Building AI Applications Session/GMT20250307-233135_Recording.transcript.vtt</li><li>/Friday Building AI Applications Session/GMT20250228-233632_Recording.transcript.vtt</li><li>/Friday Building AI Applications Session/GMT20250221-233332_Recording.transcript.vtt</li><li>/Friday Building AI Applications Session/GMT20250214-234809_Recording.transcript.vtt</li><li>/Friday Building AI Applications Session/GMT20250207-233258_Recording.transcript.vtt</li></ul>"
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"metadata": {},
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"output_type": "display_data"
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"data": {
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"text/html": [
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"<p>11 files/folders pass the modification time (<em>2024-08-30 00:00:00+00:00</em>) cut-off.</p>"
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]
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"metadata": {},
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"output_type": "display_data"
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"data": {
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"text/html": [
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"<ul><li>/Friday Building AI Applications Session</li><li>/Friday Building AI Applications Session/GMT20250411-223535_Recording.transcript.vtt</li><li>/Friday Building AI Applications Session/GMT20250404-231749_Recording.transcript.vtt</li><li>/Friday Building AI Applications Session/GMT20250328-223256_Recording.transcript.vtt</li><li>/Friday Building AI Applications Session/GMT20250321-223330_Recording.transcript.vtt</li><li>/Friday Building AI Applications Session/GMT20250314-223145_Recording.transcript.vtt</li><li>/Friday Building AI Applications Session/GMT20250307-233135_Recording.transcript.vtt</li><li>/Friday Building AI Applications Session/GMT20250228-233632_Recording.transcript.vtt</li><li>/Friday Building AI Applications Session/GMT20250221-233332_Recording.transcript.vtt</li><li>/Friday Building AI Applications Session/GMT20250214-234809_Recording.transcript.vtt</li><li>/Friday Building AI Applications Session/GMT20250207-233258_Recording.transcript.vtt</li></ul>"
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"metadata": {},
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"output_type": "display_data"
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{
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"data": {
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"text/html": [
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"<p>11 files/folders pass the modification time (<em>2024-08-30 00:00:00+00:00</em>) cut-off and MIME type (<em>text/vtt</em>) criterion.</p>"
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]
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"metadata": {},
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"output_type": "display_data"
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"data": {
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"text/html": [
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"<ul><li>/Friday Building AI Applications Session/GMT20250411-223535_Recording.transcript.vtt</li><li>/Friday Building AI Applications Session/GMT20250404-231749_Recording.transcript.vtt</li><li>/Friday Building AI Applications Session/GMT20250328-223256_Recording.transcript.vtt</li><li>/Friday Building AI Applications Session/GMT20250321-223330_Recording.transcript.vtt</li><li>/Friday Building AI Applications Session/GMT20250314-223145_Recording.transcript.vtt</li><li>/Friday Building AI Applications Session/GMT20250307-233135_Recording.transcript.vtt</li><li>/Friday Building AI Applications Session/GMT20250228-233632_Recording.transcript.vtt</li><li>/Friday Building AI Applications Session/GMT20250221-233332_Recording.transcript.vtt</li><li>/Friday Building AI Applications Session/GMT20250214-234809_Recording.transcript.vtt</li><li>/Friday Building AI Applications Session/GMT20250207-233258_Recording.transcript.vtt</li></ul>"
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"item_metadata = google_drive_service.list_directory(\"\", True)\n",
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"display_html(f\"<p>Found {len(item_metadata)} files/folders.</p>\")\n",
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"cell_type": "code",
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"execution_count":
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"Processed 10 files."
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]
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"metadata": {},
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"output_type": "display_data"
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"source": [
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"web_vtts = tuple(
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"
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" for metadata\n",
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" in metadata_to_process)\n",
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"\n",
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},
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{
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"cell_type": "code",
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"execution_count":
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"Chunked GMT20250411-223535_Recording.transcript.vtt into 86 chunks."
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"metadata": {},
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"output_type": "display_data"
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"text": [
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"\u001b[32m2025-04-20 00:08:52.269\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.services.embeddings_model_service\u001b[0m:\u001b[36mget_embeddings\u001b[0m:\u001b[36m36\u001b[0m - \u001b[34m\u001b[1mCreating embeddings for 86 text string(s)…\u001b[0m\n"
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"data": {
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"text/html": [
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"Vectorized GMT20250411-223535_Recording.transcript.vtt’s 86 chunks."
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"metadata": {},
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"text": [
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"\u001b[32m2025-04-20 00:08:54.190\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.services.vector_database_service\u001b[0m:\u001b[36mstore\u001b[0m:\u001b[36m38\u001b[0m - \u001b[34m\u001b[1mGetting vectors collection for storing 86 chunks\u001b[0m\n",
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"\u001b[32m2025-04-20 00:08:54.216\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.db.mongo_db\u001b[0m:\u001b[36mping\u001b[0m:\u001b[36m85\u001b[0m - \u001b[34m\u001b[1mMongoDB connection is active!\u001b[0m\n",
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"\u001b[32m2025-04-20 00:08:54.217\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.db.mongo_db\u001b[0m:\u001b[36mget_collection\u001b[0m:\u001b[36m106\u001b[0m - \u001b[34m\u001b[1mChecking if collection 'vectors' exists…\u001b[0m\n",
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"\u001b[32m2025-04-20 00:08:54.240\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mctp_slack_bot.db.mongo_db\u001b[0m:\u001b[36mget_collection\u001b[0m:\u001b[36m110\u001b[0m - \u001b[1mCollection 'vectors' does not exist. Creating it…\u001b[0m\n",
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"\u001b[32m2025-04-20 00:08:54.301\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.db.mongo_db\u001b[0m:\u001b[36mget_collection\u001b[0m:\u001b[36m113\u001b[0m - \u001b[34m\u001b[1mSuccessfully created collection: vectors\u001b[0m\n",
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"Stored GMT20250411-223535_Recording.transcript.vtt’s 86 vectorized chunks to the database."
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588 |
-
"\u001b[32m2025-04-20 00:09:36.198\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.db.mongo_db\u001b[0m:\u001b[36mget_collection\u001b[0m:\u001b[36m115\u001b[0m - \u001b[34m\u001b[1mCollection 'vectors' already exists!\u001b[0m\n",
|
589 |
-
"\u001b[32m2025-04-20 00:09:36.314\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mctp_slack_bot.db.mongo_db\u001b[0m:\u001b[36mcreate_indexes\u001b[0m:\u001b[36m153\u001b[0m - \u001b[1mVector search index 'vectors_vector_index' created for collection vectors.\u001b[0m\n",
|
590 |
-
"\u001b[32m2025-04-20 00:09:36.316\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.services.vector_database_service\u001b[0m:\u001b[36mstore\u001b[0m:\u001b[36m62\u001b[0m - \u001b[34m\u001b[1mInserting 278 documents into vectors collection\u001b[0m\n",
|
591 |
-
"\u001b[32m2025-04-20 00:09:38.707\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mctp_slack_bot.services.vector_database_service\u001b[0m:\u001b[36mstore\u001b[0m:\u001b[36m64\u001b[0m - \u001b[1mStored 278 vector chunks in database\u001b[0m\n"
|
592 |
-
]
|
593 |
-
},
|
594 |
-
{
|
595 |
-
"data": {
|
596 |
-
"text/html": [
|
597 |
-
"Stored GMT20250221-233332_Recording.transcript.vtt’s 278 vectorized chunks to the database."
|
598 |
-
]
|
599 |
-
},
|
600 |
-
"metadata": {},
|
601 |
-
"output_type": "display_data"
|
602 |
-
},
|
603 |
-
{
|
604 |
-
"data": {
|
605 |
-
"text/html": [
|
606 |
-
"Chunked GMT20250214-234809_Recording.transcript.vtt into 97 chunks."
|
607 |
-
]
|
608 |
-
},
|
609 |
-
"metadata": {},
|
610 |
-
"output_type": "display_data"
|
611 |
-
},
|
612 |
-
{
|
613 |
-
"name": "stderr",
|
614 |
-
"output_type": "stream",
|
615 |
-
"text": [
|
616 |
-
"\u001b[32m2025-04-20 00:09:38.710\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.services.embeddings_model_service\u001b[0m:\u001b[36mget_embeddings\u001b[0m:\u001b[36m36\u001b[0m - \u001b[34m\u001b[1mCreating embeddings for 97 text string(s)…\u001b[0m\n"
|
617 |
-
]
|
618 |
-
},
|
619 |
-
{
|
620 |
-
"data": {
|
621 |
-
"text/html": [
|
622 |
-
"Vectorized GMT20250214-234809_Recording.transcript.vtt’s 97 chunks."
|
623 |
-
]
|
624 |
-
},
|
625 |
-
"metadata": {},
|
626 |
-
"output_type": "display_data"
|
627 |
-
},
|
628 |
-
{
|
629 |
-
"name": "stderr",
|
630 |
-
"output_type": "stream",
|
631 |
-
"text": [
|
632 |
-
"\u001b[32m2025-04-20 00:09:40.479\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.services.vector_database_service\u001b[0m:\u001b[36mstore\u001b[0m:\u001b[36m38\u001b[0m - \u001b[34m\u001b[1mGetting vectors collection for storing 97 chunks\u001b[0m\n",
|
633 |
-
"\u001b[32m2025-04-20 00:09:40.499\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.db.mongo_db\u001b[0m:\u001b[36mping\u001b[0m:\u001b[36m85\u001b[0m - \u001b[34m\u001b[1mMongoDB connection is active!\u001b[0m\n",
|
634 |
-
"\u001b[32m2025-04-20 00:09:40.499\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.db.mongo_db\u001b[0m:\u001b[36mget_collection\u001b[0m:\u001b[36m106\u001b[0m - \u001b[34m\u001b[1mChecking if collection 'vectors' exists…\u001b[0m\n",
|
635 |
-
"\u001b[32m2025-04-20 00:09:40.529\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.db.mongo_db\u001b[0m:\u001b[36mget_collection\u001b[0m:\u001b[36m115\u001b[0m - \u001b[34m\u001b[1mCollection 'vectors' already exists!\u001b[0m\n",
|
636 |
-
"\u001b[32m2025-04-20 00:09:40.529\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.services.vector_database_service\u001b[0m:\u001b[36mstore\u001b[0m:\u001b[36m42\u001b[0m - \u001b[34m\u001b[1mCreating vector search index for vectors collection\u001b[0m\n",
|
637 |
-
"\u001b[32m2025-04-20 00:09:40.548\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.db.mongo_db\u001b[0m:\u001b[36mping\u001b[0m:\u001b[36m85\u001b[0m - \u001b[34m\u001b[1mMongoDB connection is active!\u001b[0m\n",
|
638 |
-
"\u001b[32m2025-04-20 00:09:40.548\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.db.mongo_db\u001b[0m:\u001b[36mget_collection\u001b[0m:\u001b[36m106\u001b[0m - \u001b[34m\u001b[1mChecking if collection 'vectors' exists…\u001b[0m\n",
|
639 |
-
"\u001b[32m2025-04-20 00:09:40.568\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.db.mongo_db\u001b[0m:\u001b[36mget_collection\u001b[0m:\u001b[36m115\u001b[0m - \u001b[34m\u001b[1mCollection 'vectors' already exists!\u001b[0m\n",
|
640 |
-
"\u001b[32m2025-04-20 00:09:40.678\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mctp_slack_bot.db.mongo_db\u001b[0m:\u001b[36mcreate_indexes\u001b[0m:\u001b[36m153\u001b[0m - \u001b[1mVector search index 'vectors_vector_index' created for collection vectors.\u001b[0m\n",
|
641 |
-
"\u001b[32m2025-04-20 00:09:40.679\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.services.vector_database_service\u001b[0m:\u001b[36mstore\u001b[0m:\u001b[36m62\u001b[0m - \u001b[34m\u001b[1mInserting 97 documents into vectors collection\u001b[0m\n",
|
642 |
-
"\u001b[32m2025-04-20 00:09:41.562\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mctp_slack_bot.services.vector_database_service\u001b[0m:\u001b[36mstore\u001b[0m:\u001b[36m64\u001b[0m - \u001b[1mStored 97 vector chunks in database\u001b[0m\n"
|
643 |
-
]
|
644 |
-
},
|
645 |
-
{
|
646 |
-
"data": {
|
647 |
-
"text/html": [
|
648 |
-
"Stored GMT20250214-234809_Recording.transcript.vtt’s 97 vectorized chunks to the database."
|
649 |
-
]
|
650 |
-
},
|
651 |
-
"metadata": {},
|
652 |
-
"output_type": "display_data"
|
653 |
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},
|
654 |
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{
|
655 |
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"data": {
|
656 |
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"text/html": [
|
657 |
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"Chunked GMT20250207-233258_Recording.transcript.vtt into 209 chunks."
|
658 |
-
]
|
659 |
-
},
|
660 |
-
"metadata": {},
|
661 |
-
"output_type": "display_data"
|
662 |
-
},
|
663 |
-
{
|
664 |
-
"name": "stderr",
|
665 |
-
"output_type": "stream",
|
666 |
-
"text": [
|
667 |
-
"\u001b[32m2025-04-20 00:09:41.565\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.services.embeddings_model_service\u001b[0m:\u001b[36mget_embeddings\u001b[0m:\u001b[36m36\u001b[0m - \u001b[34m\u001b[1mCreating embeddings for 209 text string(s)…\u001b[0m\n"
|
668 |
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]
|
669 |
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},
|
670 |
-
{
|
671 |
-
"data": {
|
672 |
-
"text/html": [
|
673 |
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"Vectorized GMT20250207-233258_Recording.transcript.vtt’s 209 chunks."
|
674 |
-
]
|
675 |
-
},
|
676 |
-
"metadata": {},
|
677 |
-
"output_type": "display_data"
|
678 |
-
},
|
679 |
-
{
|
680 |
-
"name": "stderr",
|
681 |
-
"output_type": "stream",
|
682 |
-
"text": [
|
683 |
-
"\u001b[32m2025-04-20 00:09:44.152\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.services.vector_database_service\u001b[0m:\u001b[36mstore\u001b[0m:\u001b[36m38\u001b[0m - \u001b[34m\u001b[1mGetting vectors collection for storing 209 chunks\u001b[0m\n",
|
684 |
-
"\u001b[32m2025-04-20 00:09:44.178\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.db.mongo_db\u001b[0m:\u001b[36mping\u001b[0m:\u001b[36m85\u001b[0m - \u001b[34m\u001b[1mMongoDB connection is active!\u001b[0m\n",
|
685 |
-
"\u001b[32m2025-04-20 00:09:44.178\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.db.mongo_db\u001b[0m:\u001b[36mget_collection\u001b[0m:\u001b[36m106\u001b[0m - \u001b[34m\u001b[1mChecking if collection 'vectors' exists…\u001b[0m\n",
|
686 |
-
"\u001b[32m2025-04-20 00:09:44.197\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.db.mongo_db\u001b[0m:\u001b[36mget_collection\u001b[0m:\u001b[36m115\u001b[0m - \u001b[34m\u001b[1mCollection 'vectors' already exists!\u001b[0m\n",
|
687 |
-
"\u001b[32m2025-04-20 00:09:44.198\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.services.vector_database_service\u001b[0m:\u001b[36mstore\u001b[0m:\u001b[36m42\u001b[0m - \u001b[34m\u001b[1mCreating vector search index for vectors collection\u001b[0m\n",
|
688 |
-
"\u001b[32m2025-04-20 00:09:44.221\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.db.mongo_db\u001b[0m:\u001b[36mping\u001b[0m:\u001b[36m85\u001b[0m - \u001b[34m\u001b[1mMongoDB connection is active!\u001b[0m\n",
|
689 |
-
"\u001b[32m2025-04-20 00:09:44.222\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.db.mongo_db\u001b[0m:\u001b[36mget_collection\u001b[0m:\u001b[36m106\u001b[0m - \u001b[34m\u001b[1mChecking if collection 'vectors' exists…\u001b[0m\n",
|
690 |
-
"\u001b[32m2025-04-20 00:09:44.247\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.db.mongo_db\u001b[0m:\u001b[36mget_collection\u001b[0m:\u001b[36m115\u001b[0m - \u001b[34m\u001b[1mCollection 'vectors' already exists!\u001b[0m\n",
|
691 |
-
"\u001b[32m2025-04-20 00:09:44.390\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mctp_slack_bot.db.mongo_db\u001b[0m:\u001b[36mcreate_indexes\u001b[0m:\u001b[36m153\u001b[0m - \u001b[1mVector search index 'vectors_vector_index' created for collection vectors.\u001b[0m\n",
|
692 |
-
"\u001b[32m2025-04-20 00:09:44.391\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mctp_slack_bot.services.vector_database_service\u001b[0m:\u001b[36mstore\u001b[0m:\u001b[36m62\u001b[0m - \u001b[34m\u001b[1mInserting 209 documents into vectors collection\u001b[0m\n",
|
693 |
-
"\u001b[32m2025-04-20 00:09:46.410\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mctp_slack_bot.services.vector_database_service\u001b[0m:\u001b[36mstore\u001b[0m:\u001b[36m64\u001b[0m - \u001b[1mStored 209 vector chunks in database\u001b[0m\n"
|
694 |
-
]
|
695 |
-
},
|
696 |
-
{
|
697 |
-
"data": {
|
698 |
-
"text/html": [
|
699 |
-
"Stored GMT20250207-233258_Recording.transcript.vtt’s 209 vectorized chunks to the database."
|
700 |
-
]
|
701 |
-
},
|
702 |
-
"metadata": {},
|
703 |
-
"output_type": "display_data"
|
704 |
-
}
|
705 |
-
],
|
706 |
"source": [
|
707 |
"for web_vtt in web_vtts:\n",
|
708 |
" chunks = web_vtt.get_chunks()\n",
|
709 |
" display_html(f\"Chunked {web_vtt.get_metadata().get(\"filename\")} into {len(chunks)} chunks.\")\n",
|
710 |
" vectorized_chunks = vectorization_service.vectorize(chunks)\n",
|
711 |
" display_html(f\"Vectorized {web_vtt.get_metadata().get(\"filename\")}’s {len(vectorized_chunks)} chunks.\")\n",
|
712 |
-
" await
|
713 |
" display_html(f\"Stored {web_vtt.get_metadata().get(\"filename\")}’s {len(vectorized_chunks)} vectorized chunks to the database.\")"
|
714 |
]
|
715 |
}
|
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|
11 |
"cell_type": "code",
|
12 |
"execution_count": null,
|
13 |
"metadata": {},
|
14 |
+
"outputs": [],
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|
15 |
"source": [
|
16 |
"from datetime import datetime\n",
|
17 |
"from functools import partial\n",
|
|
|
27 |
"display_html = partial(display_html, raw=True)\n",
|
28 |
"\n",
|
29 |
"container = Container()\n",
|
30 |
+
"mongo_db = await container.mongo_db()\n",
|
31 |
"google_drive_service = container.google_drive_service()\n",
|
32 |
"vectorization_service = container.vectorization_service()\n",
|
33 |
+
"vector_database_service = await container.vector_database_service()"
|
34 |
]
|
35 |
},
|
36 |
{
|
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|
44 |
},
|
45 |
{
|
46 |
"cell_type": "code",
|
47 |
+
"execution_count": null,
|
48 |
"metadata": {},
|
49 |
"outputs": [],
|
50 |
"source": [
|
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|
62 |
},
|
63 |
{
|
64 |
"cell_type": "code",
|
65 |
+
"execution_count": null,
|
66 |
"metadata": {},
|
67 |
+
"outputs": [],
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|
68 |
"source": [
|
69 |
"item_metadata = google_drive_service.list_directory(\"\", True)\n",
|
70 |
"display_html(f\"<p>Found {len(item_metadata)} files/folders.</p>\")\n",
|
|
|
81 |
},
|
82 |
{
|
83 |
"cell_type": "code",
|
84 |
+
"execution_count": null,
|
85 |
+
"metadata": {},
|
86 |
+
"outputs": [],
|
87 |
+
"source": [
|
88 |
+
"web_vtt_parser = container.mime_type_handler_factory(MIME_TYPE)\n",
|
89 |
+
"display_html(f\"<p>{escape(str(type(web_vtt_parser)))}</p>\")"
|
90 |
+
]
|
91 |
+
},
|
92 |
+
{
|
93 |
+
"cell_type": "code",
|
94 |
+
"execution_count": null,
|
95 |
"metadata": {},
|
96 |
+
"outputs": [],
|
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|
97 |
"source": [
|
98 |
+
"web_vtts = tuple(web_vtt_parser.from_bytes(f\"googledrive:{metadata.folder_path}/{metadata.name}\",\n",
|
99 |
+
" {\n",
|
100 |
+
" \"filename\": metadata.name,\n",
|
101 |
+
" \"mimeType\": metadata.mime_type,\n",
|
102 |
+
" \"modificationTime\": metadata.modified_time\n",
|
103 |
+
" },\n",
|
104 |
+
" google_drive_service.read_file_by_id(metadata.id))\n",
|
105 |
" for metadata\n",
|
106 |
" in metadata_to_process)\n",
|
107 |
"\n",
|
|
|
110 |
},
|
111 |
{
|
112 |
"cell_type": "code",
|
113 |
+
"execution_count": null,
|
114 |
"metadata": {},
|
115 |
+
"outputs": [],
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116 |
"source": [
|
117 |
"for web_vtt in web_vtts:\n",
|
118 |
" chunks = web_vtt.get_chunks()\n",
|
119 |
" display_html(f\"Chunked {web_vtt.get_metadata().get(\"filename\")} into {len(chunks)} chunks.\")\n",
|
120 |
" vectorized_chunks = vectorization_service.vectorize(chunks)\n",
|
121 |
" display_html(f\"Vectorized {web_vtt.get_metadata().get(\"filename\")}’s {len(vectorized_chunks)} chunks.\")\n",
|
122 |
+
" await vector_database_service.store(vectorized_chunks)\n",
|
123 |
" display_html(f\"Stored {web_vtt.get_metadata().get(\"filename\")}’s {len(vectorized_chunks)} vectorized chunks to the database.\")"
|
124 |
]
|
125 |
}
|
src/ctp_slack_bot/mime_type_handlers/text/vtt.py
CHANGED
@@ -1,6 +1,7 @@
|
|
1 |
from datetime import datetime
|
2 |
from io import BytesIO
|
3 |
from re import compile as compile_re
|
|
|
4 |
from typing import Any, Mapping, Optional, Self
|
5 |
from webvtt import WebVTT
|
6 |
|
@@ -28,7 +29,7 @@ class WebVTTMimeTypeHandler(BaseMimeTypeHandler):
|
|
28 |
for result
|
29 |
in map(ISO_DATE_TIME_PATTERN.findall, web_vtt.header_comments)
|
30 |
if result)
|
31 |
-
except ValueError:
|
32 |
return None
|
33 |
|
34 |
def from_bytes(self: Self, id: str, metadata: Mapping[str, Any], buffer: bytes) -> WebVTTContent:
|
@@ -36,4 +37,4 @@ class WebVTTMimeTypeHandler(BaseMimeTypeHandler):
|
|
36 |
frames = tuple(WebVTTFrame.from_webvtt_caption(caption, index)
|
37 |
for index, caption
|
38 |
in enumerate(web_vtt.captions, 1))
|
39 |
-
return WebVTTContent(id=id, metadata=metadata, start_time=WebVTTMimeTypeHandler.__get_start_time(web_vtt), frames=frames)
|
|
|
1 |
from datetime import datetime
|
2 |
from io import BytesIO
|
3 |
from re import compile as compile_re
|
4 |
+
from types import MappingProxyType
|
5 |
from typing import Any, Mapping, Optional, Self
|
6 |
from webvtt import WebVTT
|
7 |
|
|
|
29 |
for result
|
30 |
in map(ISO_DATE_TIME_PATTERN.findall, web_vtt.header_comments)
|
31 |
if result)
|
32 |
+
except (StopIteration, ValueError):
|
33 |
return None
|
34 |
|
35 |
def from_bytes(self: Self, id: str, metadata: Mapping[str, Any], buffer: bytes) -> WebVTTContent:
|
|
|
37 |
frames = tuple(WebVTTFrame.from_webvtt_caption(caption, index)
|
38 |
for index, caption
|
39 |
in enumerate(web_vtt.captions, 1))
|
40 |
+
return WebVTTContent(id=id, metadata=MappingProxyType(metadata), start_time=WebVTTMimeTypeHandler.__get_start_time(web_vtt), frames=frames)
|
src/ctp_slack_bot/models/webvtt.py
CHANGED
@@ -69,21 +69,3 @@ class WebVTTContent(Content):
|
|
69 |
|
70 |
def get_metadata(self: Self) -> Mapping[str, Any]:
|
71 |
return MappingProxyType(self.metadata)
|
72 |
-
|
73 |
-
@classmethod
|
74 |
-
def __get_start_time(cls, web_vtt: WebVTT) -> Optional[datetime]:
|
75 |
-
try:
|
76 |
-
return next(datetime.fromisoformat(result[0])
|
77 |
-
for result
|
78 |
-
in map(ISO_DATE_TIME_PATTERN.findall, web_vtt.header_comments)
|
79 |
-
if result)
|
80 |
-
except (StopIteration, ValueError):
|
81 |
-
return None
|
82 |
-
|
83 |
-
@classmethod
|
84 |
-
def from_bytes(cls, id: str, metadata: Mapping[str, Any], buffer: bytes) -> Self:
|
85 |
-
web_vtt = WebVTT.from_buffer(BytesIO(buffer))
|
86 |
-
frames = tuple(WebVTTFrame.from_webvtt_caption(caption, index)
|
87 |
-
for index, caption
|
88 |
-
in enumerate(web_vtt.captions, 1))
|
89 |
-
return WebVTTContent(id=id, metadata=MappingProxyType(metadata), start_time=cls.__get_start_time(web_vtt), frames=frames)
|
|
|
69 |
|
70 |
def get_metadata(self: Self) -> Mapping[str, Any]:
|
71 |
return MappingProxyType(self.metadata)
|
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