From 21f28e1126d79bca8f7e03f404661798643868e1 Mon Sep 17 00:00:00 2001 From: Duncan Blythe Date: Mon, 18 Mar 2024 11:04:10 +0100 Subject: [PATCH] Templates for documentation --- docs/hr/content/build_use_cases.py | 59 + .../content/docs/fundamentals/architecture.md | 5 - docs/hr/content/docs/fundamentals/design.md | 7 - docs/hr/content/docs/intro.md | 2 + .../build_and_train_classifier.ipynb | 92 + .../build_and_train_classifier.md | 40 + .../build_image_embedding_model.ipynb | 80 + .../build_image_embedding_model.md | 34 + .../docs/reusable_snippets/build_llm.ipynb | 219 + .../docs/reusable_snippets/build_llm.md | 93 + .../build_multimodal_embedding_models.ipynb | 52 + .../build_multimodal_embedding_models.md | 30 + .../build_text_embedding_model.ipynb | 78 + .../build_text_embedding_model.md | 32 + .../reusable_snippets/compute_features.ipynb | 151 + .../reusable_snippets/compute_features.md | 42 + .../connect_to_superduperdb.ipynb | 73 + .../connect_to_superduperdb.md | 33 + .../connecting_listeners.ipynb | 34 + .../reusable_snippets/connecting_listeners.md | 9 + .../reusable_snippets/create_datatype.ipynb | 126 + .../docs/reusable_snippets/create_datatype.md | 56 + .../reusable_snippets/create_listener.ipynb | 33 + .../docs/reusable_snippets/create_listener.md | 12 + .../create_vector_index.ipynb | 58 + .../reusable_snippets/create_vector_index.md | 21 + .../docs/reusable_snippets/insert_data.ipynb | 129 + .../docs/reusable_snippets/insert_data.md | 85 + .../perform_a_vector_search.ipynb | 82 + .../perform_a_vector_search.md | 45 + .../setup/development_vs_production_modes.md | 0 docs/hr/content/to_docusaurus_markdown.py | 98 + .../use_cases/_multimodal_vector_search.ipynb | 102 + .../_retrieval_augmented_generation.ipynb | 106 + .../use_cases/classical_tasks/_category_.json | 10 - .../use_cases/classical_tasks/mnist_torch.md | 273 - .../classical_tasks/resnet_features.md | 184 - .../sentiment_analysis_use_case.md | 185 - .../classical_tasks/transfer_learning.md | 151 - .../use_cases/multimodal_vector_search.ipynb | 366 + .../use_cases/multimodal_vector_search.md | 191 + .../productionization/_category_.json | 10 - .../productionization/sandbox-example.md | 233 - .../use_cases/question-answering/Chatbot.md | 281 - .../question-answering/_category_.json | 10 - .../chat_with_your_database.md | 289 - .../question-answering/voice_memos.md | 226 - .../retrieval_augmented_generation.ipynb | 1 + .../retrieval_augmented_generation.md | 264 + .../use_cases/vector_search/_category_.json | 10 - .../vector_search/chunked_vector_search.md | 231 - .../vector_search/multi-modal-duckdb.md | 212 - .../multimodal_image_search_clip.md | 207 - .../vector_search/plain_vector_search.md | 198 - .../vector_search/snowflake-example.md | 205 - .../use_cases/vector_search/video_search.md | 288 - docs/hr/package-lock.json | 12204 ++-------------- docs/hr/sidebars.js | 125 +- 58 files changed, 3874 insertions(+), 14598 deletions(-) create mode 100644 docs/hr/content/build_use_cases.py delete mode 100644 docs/hr/content/docs/fundamentals/architecture.md create mode 100644 docs/hr/content/docs/reusable_snippets/build_and_train_classifier.ipynb create mode 100644 docs/hr/content/docs/reusable_snippets/build_and_train_classifier.md create mode 100644 docs/hr/content/docs/reusable_snippets/build_image_embedding_model.ipynb create mode 100644 docs/hr/content/docs/reusable_snippets/build_image_embedding_model.md create mode 100644 docs/hr/content/docs/reusable_snippets/build_llm.ipynb create mode 100644 docs/hr/content/docs/reusable_snippets/build_llm.md create mode 100644 docs/hr/content/docs/reusable_snippets/build_multimodal_embedding_models.ipynb create mode 100644 docs/hr/content/docs/reusable_snippets/build_multimodal_embedding_models.md create mode 100644 docs/hr/content/docs/reusable_snippets/build_text_embedding_model.ipynb create mode 100644 docs/hr/content/docs/reusable_snippets/build_text_embedding_model.md create mode 100644 docs/hr/content/docs/reusable_snippets/compute_features.ipynb create mode 100644 docs/hr/content/docs/reusable_snippets/compute_features.md create mode 100644 docs/hr/content/docs/reusable_snippets/connect_to_superduperdb.ipynb create mode 100644 docs/hr/content/docs/reusable_snippets/connect_to_superduperdb.md create mode 100644 docs/hr/content/docs/reusable_snippets/connecting_listeners.ipynb create mode 100644 docs/hr/content/docs/reusable_snippets/connecting_listeners.md create mode 100644 docs/hr/content/docs/reusable_snippets/create_datatype.ipynb create mode 100644 docs/hr/content/docs/reusable_snippets/create_datatype.md create mode 100644 docs/hr/content/docs/reusable_snippets/create_listener.ipynb create mode 100644 docs/hr/content/docs/reusable_snippets/create_listener.md create mode 100644 docs/hr/content/docs/reusable_snippets/create_vector_index.ipynb create mode 100644 docs/hr/content/docs/reusable_snippets/create_vector_index.md create mode 100644 docs/hr/content/docs/reusable_snippets/insert_data.ipynb create mode 100644 docs/hr/content/docs/reusable_snippets/insert_data.md create mode 100644 docs/hr/content/docs/reusable_snippets/perform_a_vector_search.ipynb create mode 100644 docs/hr/content/docs/reusable_snippets/perform_a_vector_search.md create mode 100644 docs/hr/content/docs/setup/development_vs_production_modes.md create mode 100644 docs/hr/content/to_docusaurus_markdown.py create mode 100644 docs/hr/content/use_cases/_multimodal_vector_search.ipynb create mode 100644 docs/hr/content/use_cases/_retrieval_augmented_generation.ipynb delete mode 100644 docs/hr/content/use_cases/classical_tasks/_category_.json delete mode 100644 docs/hr/content/use_cases/classical_tasks/mnist_torch.md delete mode 100644 docs/hr/content/use_cases/classical_tasks/resnet_features.md delete mode 100644 docs/hr/content/use_cases/classical_tasks/sentiment_analysis_use_case.md delete mode 100644 docs/hr/content/use_cases/classical_tasks/transfer_learning.md create mode 100644 docs/hr/content/use_cases/multimodal_vector_search.ipynb create mode 100644 docs/hr/content/use_cases/multimodal_vector_search.md delete mode 100644 docs/hr/content/use_cases/productionization/_category_.json delete mode 100644 docs/hr/content/use_cases/productionization/sandbox-example.md delete mode 100644 docs/hr/content/use_cases/question-answering/Chatbot.md delete mode 100644 docs/hr/content/use_cases/question-answering/_category_.json delete mode 100644 docs/hr/content/use_cases/question-answering/chat_with_your_database.md delete mode 100644 docs/hr/content/use_cases/question-answering/voice_memos.md create mode 100644 docs/hr/content/use_cases/retrieval_augmented_generation.ipynb create mode 100644 docs/hr/content/use_cases/retrieval_augmented_generation.md delete mode 100644 docs/hr/content/use_cases/vector_search/_category_.json delete mode 100644 docs/hr/content/use_cases/vector_search/chunked_vector_search.md delete mode 100644 docs/hr/content/use_cases/vector_search/multi-modal-duckdb.md delete mode 100644 docs/hr/content/use_cases/vector_search/multimodal_image_search_clip.md delete mode 100644 docs/hr/content/use_cases/vector_search/plain_vector_search.md delete mode 100644 docs/hr/content/use_cases/vector_search/snowflake-example.md delete mode 100644 docs/hr/content/use_cases/vector_search/video_search.md diff --git a/docs/hr/content/build_use_cases.py b/docs/hr/content/build_use_cases.py new file mode 100644 index 0000000000..4551b19586 --- /dev/null +++ b/docs/hr/content/build_use_cases.py @@ -0,0 +1,59 @@ +import json +import os +import re + + +def process_snippet(nb, tabs): + to_delete = [] + for i, cell in enumerate(nb['cells']): + if tabs != '*': + match = re.match('^#[ ]+]+)>', cell['source'][0]) + if match: + tab = match.groups()[0] + if tab not in tabs: + to_delete.append(i) + continue + if cell['cell_type'] == 'markdown': + for j, line in enumerate(cell['source']): + line = re.sub('^####', '##### ', line) + line = re.sub('^###', '#### ', line) + line = re.sub('^## ', '### ', line) + line = re.sub('^# ', '## ', line) + nb['cells'][i]['source'][j] = line + nb['cells'] = [cell for i, cell in enumerate(nb['cells']) if i not in to_delete] + return nb + + +def build_use_case(path): + with open(path) as f: + nb = json.load(f) + built_nb = {k: v for k, v in nb.items() if k != 'cells'} + built_nb['cells'] = [] + + for cell in nb['cells']: + if ( + cell['cell_type'] == 'raw' + and cell['source'] + and cell['source'][0].startswith('$', + cell['source'][0].strip(), + ).groups() + with open(f'docs/reusable_snippets/{snippet}.ipynb') as f: + snippet_nb = json.load(f) + snippet_nb = process_snippet(snippet_nb, tabs) + built_nb['cells'].extend(snippet_nb['cells']) + else: + built_nb['cells'].append(cell) + return built_nb + +file = '_multimodal_vector_search.ipynb' + + +for file in os.listdir('./use_cases'): + if not file.startswith('_'): + continue + built = build_use_case(f'./use_cases/{file}') + with open(f'./use_cases/{file[1:]}', 'w') as f: + json.dump(built, f) diff --git a/docs/hr/content/docs/fundamentals/architecture.md b/docs/hr/content/docs/fundamentals/architecture.md deleted file mode 100644 index 7b2c132d29..0000000000 --- a/docs/hr/content/docs/fundamentals/architecture.md +++ /dev/null @@ -1,5 +0,0 @@ -# Architecture - -Here is a detailed view of the `superduperdb` architecture: - -![architecture_detailed](/img/architecture_detailed.png) diff --git a/docs/hr/content/docs/fundamentals/design.md b/docs/hr/content/docs/fundamentals/design.md index 318d3bcc53..8822c10a31 100644 --- a/docs/hr/content/docs/fundamentals/design.md +++ b/docs/hr/content/docs/fundamentals/design.md @@ -4,13 +4,6 @@ sidebar_position: 2 # Design -## Architecture -Here is a detailed view of the `superduperdb` architecture: - -![architecture_detailed](/img/architecture_detailed.png) - -## Schematic - Here is a schematic of the `superduperdb` design. ![](/img/light.png) diff --git a/docs/hr/content/docs/intro.md b/docs/hr/content/docs/intro.md index eb6d8c062b..50049fac4e 100644 --- a/docs/hr/content/docs/intro.md +++ b/docs/hr/content/docs/intro.md @@ -4,6 +4,8 @@ sidebar_position: 1 # Welcome to SuperDuperDB! +Testing + ## What is SuperDuperDB? SuperDuperDB is an open-source framework for integrating your database with AI models, APIs, and vector search engines, providing streaming inference and scalable training/fine-tuning. diff --git a/docs/hr/content/docs/reusable_snippets/build_and_train_classifier.ipynb b/docs/hr/content/docs/reusable_snippets/build_and_train_classifier.ipynb new file mode 100644 index 0000000000..b68335f68b --- /dev/null +++ b/docs/hr/content/docs/reusable_snippets/build_and_train_classifier.ipynb @@ -0,0 +1,92 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c2da0ab6-8fc0-41fc-b8c9-0f8a127d9e8d", + "metadata": {}, + "source": [ + "\n", + "# Build and train classifier" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d3b94fca-3a0b-433f-88cf-aab5b71b8596", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "\n", + "..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bc3a91a1-0f6c-42f4-b83f-77bc563eeeff", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "\n", + "..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a65f96f9-d6ca-4008-9540-cee4706ec0c0", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "\n", + "..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b8f9919c-ee0a-4123-a592-7d84d1bec3d0", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "\n", + "..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f53c67e9-db8a-4d3b-8c39-ec62075191ab", + "metadata": {}, + "outputs": [], + "source": [ + "db.add(model)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/hr/content/docs/reusable_snippets/build_and_train_classifier.md b/docs/hr/content/docs/reusable_snippets/build_and_train_classifier.md new file mode 100644 index 0000000000..7761ecfed9 --- /dev/null +++ b/docs/hr/content/docs/reusable_snippets/build_and_train_classifier.md @@ -0,0 +1,40 @@ +--- +sidebar_label: Build and train classifier +--- +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + + +# Build and train classifier + + + + + ```python + + ... + ``` + + + ```python + + ... + ``` + + + ```python + + ... + ``` + + + ```python + + ... + ``` + + +```python +db.add(model) +``` + diff --git a/docs/hr/content/docs/reusable_snippets/build_image_embedding_model.ipynb b/docs/hr/content/docs/reusable_snippets/build_image_embedding_model.ipynb new file mode 100644 index 0000000000..17b8e4ae4d --- /dev/null +++ b/docs/hr/content/docs/reusable_snippets/build_image_embedding_model.ipynb @@ -0,0 +1,80 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "db56aac9-cac5-49c7-99a5-012cd2ab86c5", + "metadata": {}, + "source": [ + "\n", + "# Build image embedding model\n", + "\n", + "..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec250b81-e9d2-46bd-929c-c71358b5fac6", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "63b72f37-041e-4b4f-bc43-6cf2259ccd48", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "591f5a19-51f7-4a5c-be82-cf629298e1d5", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c9226610-33d9-445d-9352-3073f229d8f7", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "..." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/hr/content/docs/reusable_snippets/build_image_embedding_model.md b/docs/hr/content/docs/reusable_snippets/build_image_embedding_model.md new file mode 100644 index 0000000000..6f2189728e --- /dev/null +++ b/docs/hr/content/docs/reusable_snippets/build_image_embedding_model.md @@ -0,0 +1,34 @@ +--- +sidebar_label: Build image embedding model +--- +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + + +# Build image embedding model + +... + + + + + ```python + ... + ``` + + + ```python + ... + ``` + + + ```python + ... + ``` + + + ```python + ... + ``` + + diff --git a/docs/hr/content/docs/reusable_snippets/build_llm.ipynb b/docs/hr/content/docs/reusable_snippets/build_llm.ipynb new file mode 100644 index 0000000000..9763b47ec5 --- /dev/null +++ b/docs/hr/content/docs/reusable_snippets/build_llm.ipynb @@ -0,0 +1,219 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1179a67b-4e40-496b-9851-98f32d42faa0", + "metadata": {}, + "source": [ + "\n", + "# Build LLM" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f98e5ff4", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "\n", + "..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9bf39c47", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "\n", + "..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "95e48deb", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "\n", + "..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe4ac344", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "\n", + "..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29f98dca", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "\n", + "..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3778ab16", + "metadata": {}, + "outputs": [], + "source": [ + "llm.predict_one(X='Tell me about SuperDuperDB')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9c9689c", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "from superduperdb.components.model import QueryModel\n", + "from superduperdb import Variable, Document\n", + "\n", + "query_model = QueryModel(\n", + " select=collection.find().like(Document({'my_key': Variable('item')}))\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "63f44f55", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "from superduperdb.components.model import QueryModel\n", + "from superduperdb import Variable, Document\n", + "\n", + "query_model = QueryModel(\n", + " select=table.like(Document({'my_key': Variable('item')}))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8c4420a7", + "metadata": {}, + "source": [ + "In order to combine this vector-search component with " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c28fdaf6", + "metadata": {}, + "outputs": [], + "source": [ + "from superduperdb.components.graph import Graph\n", + "from superduperdb.components.model import Input\n", + "from superduperdb import superduper\n", + "\n", + "\n", + "@superduper\n", + "class PromptBuilder:\n", + " def __init__(self, initial_prompt, post_prompt, key):\n", + " self.inital_prompt = initial_prompt\n", + " self.post_prompt = post_prompt\n", + " self.key = key\n", + "\n", + " def __call__(self, X, context):\n", + " return (\n", + " self.initial_prompt + '\\n\\n'\n", + " + [r[self.key] for r in context]\n", + " + self.post_prompt + '\\n\\n'\n", + " + X\n", + " )\n", + "\n", + "\n", + "prompt_builder = PromptBuilder(\n", + " initial_prompt='Answer the following question based on the following facts:',\n", + " post_prompt='Here\\'s the question:',\n", + " key='my_key',\n", + ")\n", + "\n", + "input = Input('X')\n", + "query_results = query_model(item=input)\n", + "prompt = prompt_builder(X=input, context=query_results)\n", + "output = llm(X=prompt)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "963c5856", + "metadata": {}, + "outputs": [], + "source": [ + "type(output) == AbstractGraph\n", + "True" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d04788fb", + "metadata": {}, + "outputs": [], + "source": [ + "stack = output.to_listeners(identifier='', select=collection.find(), key='txt')\n", + "\n", + "db.add(stack)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3219a838", + "metadata": {}, + "outputs": [], + "source": [ + "graph_model = output.to_graph_model()\n", + "\n", + "graph_model.predict_one(...)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/hr/content/docs/reusable_snippets/build_llm.md b/docs/hr/content/docs/reusable_snippets/build_llm.md new file mode 100644 index 0000000000..3cf0ac920d --- /dev/null +++ b/docs/hr/content/docs/reusable_snippets/build_llm.md @@ -0,0 +1,93 @@ +--- +sidebar_label: Build LLM +--- +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + + +# Build LLM + + + + + ```python + + ... + ``` + + + ```python + + ... + ``` + + + ```python + + ... + ``` + + + ```python + + ... + ``` + + + ```python + + ... + ``` + + +```python +llm.predict_one(X='Tell me about SuperDuperDB') +``` + + + + + ```python + from superduperdb.components.model import QueryModel + from superduperdb import Variable, Document + + query_model = QueryModel( + select=collection.find().like(Document({'my_key': Variable('item')})) + ) + ``` + + +```python +from superduperdb.components.graph import Graph, Input +from superduperdb import superduper + + +@superduper +class PromptBuilder: + def __init__(self, initial_prompt, post_prompt, key): + self.inital_prompt = initial_prompt + self.post_prompt = post_prompt + self.key = key + + def __call__(self, X, context): + return ( + self.initial_prompt + '\n\n' + + [r[self.key] for r in context] + + self.post_prompt + '\n\n' + + X + ) + + +prompt_builder = PromptBuilder( + initial_prompt='Answer the following question based on the following facts:', + post_prompt='Here\'s the question:', + key='my_key', +) + +with Graph() as G: + input = Input('X') + query_results = query_model(item=input) + prompt = prompt_builder(X=input, context=query_results) + output = llm(X=prompt) +``` + diff --git a/docs/hr/content/docs/reusable_snippets/build_multimodal_embedding_models.ipynb b/docs/hr/content/docs/reusable_snippets/build_multimodal_embedding_models.ipynb new file mode 100644 index 0000000000..87d50b34be --- /dev/null +++ b/docs/hr/content/docs/reusable_snippets/build_multimodal_embedding_models.ipynb @@ -0,0 +1,52 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Build multimodal embedding models" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "\n", + "..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "\n", + "..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "\n", + "..." + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/hr/content/docs/reusable_snippets/build_multimodal_embedding_models.md b/docs/hr/content/docs/reusable_snippets/build_multimodal_embedding_models.md new file mode 100644 index 0000000000..186ad12907 --- /dev/null +++ b/docs/hr/content/docs/reusable_snippets/build_multimodal_embedding_models.md @@ -0,0 +1,30 @@ +--- +sidebar_label: Build multimodal embedding models +--- +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + + +# Build multimodal embedding models + + + + + ```python + + ... + ``` + + + ```python + + ... + ``` + + + ```python + + ... + ``` + + diff --git a/docs/hr/content/docs/reusable_snippets/build_text_embedding_model.ipynb b/docs/hr/content/docs/reusable_snippets/build_text_embedding_model.ipynb new file mode 100644 index 0000000000..8a9caddffd --- /dev/null +++ b/docs/hr/content/docs/reusable_snippets/build_text_embedding_model.ipynb @@ -0,0 +1,78 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c9a2cd87-723f-4cee-87c7-9b8181c9e54b", + "metadata": {}, + "source": [ + "\n", + "# Build text embedding model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a9b1f538-65ca-499e-b6d0-2dd733f81723", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e83facd8-8823-492f-a2c6-659f38d8e6ec", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b4a9a60-41df-461d-b165-1d136ee25694", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b1219380-13ce-4301-90e6-6ede2eee1497", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "..." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/hr/content/docs/reusable_snippets/build_text_embedding_model.md b/docs/hr/content/docs/reusable_snippets/build_text_embedding_model.md new file mode 100644 index 0000000000..a81987f0ea --- /dev/null +++ b/docs/hr/content/docs/reusable_snippets/build_text_embedding_model.md @@ -0,0 +1,32 @@ +--- +sidebar_label: Build text embedding model +--- +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + + +# Build text embedding model + + + + + ```python + ... + ``` + + + ```python + ... + ``` + + + ```python + ... + ``` + + + ```python + ... + ``` + + diff --git a/docs/hr/content/docs/reusable_snippets/compute_features.ipynb b/docs/hr/content/docs/reusable_snippets/compute_features.ipynb new file mode 100644 index 0000000000..942dd64ad6 --- /dev/null +++ b/docs/hr/content/docs/reusable_snippets/compute_features.ipynb @@ -0,0 +1,151 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9e703b58-a46d-4b1f-98fd-f50d46b168fe", + "metadata": {}, + "source": [ + "\n", + "# Compute features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b4a022e-0d40-47ac-bf76-dd42b9a27ffb", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "\n", + "db = ...\n", + "select = ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "584ff759-d8d4-4c20-93c2-02d05be84e28", + "metadata": {}, + "outputs": [], + "source": [ + "# " + ] + }, + { + "cell_type": "markdown", + "id": "68a6715b-a40c-4068-8eb0-78de67ed4960", + "metadata": {}, + "source": [ + "# Auto generated\n", + "This snippet builds on top of:\n", + "\n", + "- [Connect to SuperDuperDB](connect_to_superduperdb)\n", + "- [Build a query](build_a_query)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "55a8ba02-f130-449b-a0aa-b4e88905959a", + "metadata": {}, + "outputs": [], + "source": [ + "Computing features combines Listener with different model varieties" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae2e1588-fec8-45a6-b678-fef05fc7b57f", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "from superduperdb.ext.sentence_transformers import Pipeline\n", + "\n", + "db.add(\n", + " Listener(\n", + " model=model,\n", + " select=select\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae9d4b45-3676-4084-a3d8-9dbe9fa6bf47", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "from torchvision import resnet50\n", + "\n", + "db.add(\n", + " Listener(\n", + " model=TorchModel(resnet50),\n", + " select=select\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "22a32bd1-c2c0-4789-aba9-0fc5d1fbfe09", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "from CLIP import ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e49af16d-782d-47ce-bdc5-15cd9b6e61be", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "import numpy\n", + "\n", + "..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa5a5f79-4b80-4534-a322-03260dc9caf7", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "\n", + "..." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/hr/content/docs/reusable_snippets/compute_features.md b/docs/hr/content/docs/reusable_snippets/compute_features.md new file mode 100644 index 0000000000..bd65cb7450 --- /dev/null +++ b/docs/hr/content/docs/reusable_snippets/compute_features.md @@ -0,0 +1,42 @@ +--- +sidebar_label: Compute features +--- +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + + +# Compute features + + + + + ```python + from superduperdb.ext.sentence_transformers import Pipeline + + ... + ``` + + + ```python + from torchvision import resnet50 + ``` + + + ```python + from CLIP import ... + ``` + + + ```python + import numpy + + ... + ``` + + + ```python + + ... + ``` + + diff --git a/docs/hr/content/docs/reusable_snippets/connect_to_superduperdb.ipynb b/docs/hr/content/docs/reusable_snippets/connect_to_superduperdb.ipynb new file mode 100644 index 0000000000..8a0e501cf3 --- /dev/null +++ b/docs/hr/content/docs/reusable_snippets/connect_to_superduperdb.ipynb @@ -0,0 +1,73 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "32f8484d-2e35-472a-9b24-1a30ec1d144b", + "metadata": {}, + "source": [ + "\n", + "# Connect to SuperDuperDB" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "61976f44-8139-41c0-a73e-569c6d16c4b1", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "from superduperdb import superduper\n", + "\n", + "db = superduper('mongodb://localhost:27017/documents')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e981a457", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "from superduperdb import superduper\n", + "\n", + "db = superduper('sqlite://my_db.db')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d2297295", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "from superduperdb import superduper\n", + "\n", + "db = superduper('postgres://localhost:1234')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/hr/content/docs/reusable_snippets/connect_to_superduperdb.md b/docs/hr/content/docs/reusable_snippets/connect_to_superduperdb.md new file mode 100644 index 0000000000..d896eafa48 --- /dev/null +++ b/docs/hr/content/docs/reusable_snippets/connect_to_superduperdb.md @@ -0,0 +1,33 @@ +--- +sidebar_label: Connect to SuperDuperDB +--- +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + + +# Connect to SuperDuperDB + + + + + ```python + from superduperdb import superduper + + db = superduper('mongodb://localhost:27017/documents') + ``` + + + ```python + from superduperdb import superduper + + db = superduper('sqlite://my_db.db') + ``` + + + ```python + from superduperdb import superduper + + db = superduper('postgres://localhost:1234') + ``` + + diff --git a/docs/hr/content/docs/reusable_snippets/connecting_listeners.ipynb b/docs/hr/content/docs/reusable_snippets/connecting_listeners.ipynb new file mode 100644 index 0000000000..2a80efcdaa --- /dev/null +++ b/docs/hr/content/docs/reusable_snippets/connecting_listeners.ipynb @@ -0,0 +1,34 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b71c8174-d6ed-4199-8319-97b6dc0f5b25", + "metadata": {}, + "source": [ + "\n", + "# Connecting listeners" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/hr/content/docs/reusable_snippets/connecting_listeners.md b/docs/hr/content/docs/reusable_snippets/connecting_listeners.md new file mode 100644 index 0000000000..3391df72e9 --- /dev/null +++ b/docs/hr/content/docs/reusable_snippets/connecting_listeners.md @@ -0,0 +1,9 @@ +--- +sidebar_label: Connecting listeners +--- +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + + +# Connecting listeners + diff --git a/docs/hr/content/docs/reusable_snippets/create_datatype.ipynb b/docs/hr/content/docs/reusable_snippets/create_datatype.ipynb new file mode 100644 index 0000000000..df4cb5fdba --- /dev/null +++ b/docs/hr/content/docs/reusable_snippets/create_datatype.ipynb @@ -0,0 +1,126 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b31257e4-06fa-4cc7-9626-bb4d03fdc029", + "metadata": {}, + "source": [ + "\n", + "# Create datatype" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9ab5581c-0500-4a22-a9fd-bad8debd0314", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "from superduperdb import vector\n", + "\n", + "db.add(vector)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0fd1f632-fc4d-4e21-9610-51bad591bbf1", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "from superduperdb.ext.torch import tensor\n", + "\n", + "db.add()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9326e078-c54f-4966-afc7-a22cad48ff31", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5bba2555-da0b-46f2-9a1a-00c5fa107ecc", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "from superduperdb.ext.pillow import pil_image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "876cfb3b-8d2f-4950-86fb-4bf65accfb98", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e844c762-3391-401d-9047-ed8617a9c946", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12d70f4b-fecd-4269-98c6-5c0770ac634a", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1681d70f-9b64-426f-b619-cbd961f1913e", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "..." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/hr/content/docs/reusable_snippets/create_datatype.md b/docs/hr/content/docs/reusable_snippets/create_datatype.md new file mode 100644 index 0000000000..4bb3894edb --- /dev/null +++ b/docs/hr/content/docs/reusable_snippets/create_datatype.md @@ -0,0 +1,56 @@ +--- +sidebar_label: Create datatype +--- +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + + +# Create datatype + + + + + ```python + from superduperdb import vector + + db.add(vector) + ``` + + + ```python + from superduperdb.ext.torch import tensor + + db.add() + ``` + + + ```python + ... + ``` + + + ```python + from superduperdb.ext.pillow import pil_image + ``` + + + ```python + ... + ``` + + + ```python + ... + ``` + + + ```python + ... + ``` + + + ```python + ... + ``` + + diff --git a/docs/hr/content/docs/reusable_snippets/create_listener.ipynb b/docs/hr/content/docs/reusable_snippets/create_listener.ipynb new file mode 100644 index 0000000000..a9e8c9464d --- /dev/null +++ b/docs/hr/content/docs/reusable_snippets/create_listener.ipynb @@ -0,0 +1,33 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create listener" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "db.add(\n", + " Listener(\n", + " key='my_key',\n", + " model=my_model,\n", + " select=select,\n", + " )\n", + ")" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/hr/content/docs/reusable_snippets/create_listener.md b/docs/hr/content/docs/reusable_snippets/create_listener.md new file mode 100644 index 0000000000..59c80e9357 --- /dev/null +++ b/docs/hr/content/docs/reusable_snippets/create_listener.md @@ -0,0 +1,12 @@ +# Create listener + + +```python +db.add( + Listener( + key='my_key', + model=my_model, + select=select, + ) +) +``` diff --git a/docs/hr/content/docs/reusable_snippets/create_vector_index.ipynb b/docs/hr/content/docs/reusable_snippets/create_vector_index.ipynb new file mode 100644 index 0000000000..8b1430b182 --- /dev/null +++ b/docs/hr/content/docs/reusable_snippets/create_vector_index.ipynb @@ -0,0 +1,58 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "41b8b40d-3750-4d7b-aa60-62e07b734b04", + "metadata": {}, + "source": [ + "# Create vector-index\n", + "\n", + "- `model` is a `_Predictor` which converts input data to `vector`, `array` or `tensor`.\n", + "- `select` is a `Select` query telling which data to search over.\n", + "- `key` is a `str`, tuple of `str` or `dict` telling the models how to consume the content of `Document` instances." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ffba61c1-2ec8-4411-870a-228439ce4ba5", + "metadata": {}, + "outputs": [], + "source": [ + "from superduperdb import VectorIndex, Listener\n", + "\n", + "db.add(\n", + " VectorIndex(\n", + " 'my-vector-index',\n", + " listener=Listener(\n", + " key='', # the `Document` key `model` should ingest to create embedding\n", + " select=select, # a `Select` query telling which data to search over\n", + " model=model, # a `_Predictor` how to convert data to embeddings\n", + " )\n", + " )\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/hr/content/docs/reusable_snippets/create_vector_index.md b/docs/hr/content/docs/reusable_snippets/create_vector_index.md new file mode 100644 index 0000000000..a20e18987b --- /dev/null +++ b/docs/hr/content/docs/reusable_snippets/create_vector_index.md @@ -0,0 +1,21 @@ +# Create vector-index + +- `model` is a `_Predictor` which converts input data to `vector`, `array` or `tensor`. +- `select` is a `Select` query telling which data to search over. +- `key` is a `str`, tuple of `str` or `dict` telling the models how to consume the content of `Document` instances. + + +```python +from superduperdb import VectorIndex, Listener + +db.add( + VectorIndex( + 'my-vector-index', + listener=Listener( + key='', # the `Document` key `model` should ingest to create embedding + select=select, # a `Select` query telling which data to search over + model=model, # a `_Predictor` how to convert data to embeddings + ) + ) +) +``` diff --git a/docs/hr/content/docs/reusable_snippets/insert_data.ipynb b/docs/hr/content/docs/reusable_snippets/insert_data.ipynb new file mode 100644 index 0000000000..9709eb242b --- /dev/null +++ b/docs/hr/content/docs/reusable_snippets/insert_data.ipynb @@ -0,0 +1,129 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Insert data\n", + "\n", + "In order to create data, we need create a `Schema` for encoding our special `Datatype` column(s) in the databackend." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's some sample data to work with:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "!curl -O https://jupyter-sessions.s3.us-east-2.amazonaws.com/text.json\n", + "\n", + "import json\n", + "with open('text.json') as f:\n", + " data = json.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "!curl -O https://jupyter-sessions.s3.us-east-2.amazonaws.com/images.zip\n", + "!unzip images.zip\n", + "\n", + "import os\n", + "data = [{'image': f'file://image/{file}'} for file in os.listdir('./images')]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "!curl -O https://jupyter-sessions.s3.us-east-2.amazonaws.com/audio.zip\n", + "!unzip audio.zip\n", + "\n", + "import os\n", + "data = [{'audio': f'file://audio/{file}'} for file in os.listdir('./audio')]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next code-block is only necessary if you're working with a custom `DataType`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from superduperdb import Schema, Document\n", + "\n", + "schema = Schema(\n", + " 'my_schema',\n", + " fields={\n", + " 'my_key': dt\n", + " }\n", + ")\n", + "\n", + "data = [\n", + " Document({'my_key': item}) for item in data\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "from superduperdb.backends.mongodb import Collection\n", + "\n", + "collection = Collection('documents')\n", + "\n", + "db.execute(collection.insert_many(data))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "from superduperdb.backends.ibis import Table\n", + "\n", + "table = Table(\n", + " 'my_table',\n", + " schema=schema,\n", + ")\n", + "\n", + "db.add(table)\n", + "db.execute(table.insert(data))" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/hr/content/docs/reusable_snippets/insert_data.md b/docs/hr/content/docs/reusable_snippets/insert_data.md new file mode 100644 index 0000000000..ba2036b83f --- /dev/null +++ b/docs/hr/content/docs/reusable_snippets/insert_data.md @@ -0,0 +1,85 @@ +--- +sidebar_label: Insert data +--- +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + + +# Insert data + +In order to create data, we need create a `Schema` for encoding our special `Datatype` column(s) in the databackend. + +Here's some sample data to work with: + + + + + ```python + !curl -O https://jupyter-sessions.s3.us-east-2.amazonaws.com/text.json + + import json + with open('text.json') as f: + data = json.load(f) + ``` + + + ```python + !curl -O https://jupyter-sessions.s3.us-east-2.amazonaws.com/images.zip + !unzip images.zip + + import os + data = [{'image': f'file://image/{file}'} for file in os.listdir('./images')] + ``` + + + ```python + !curl -O https://jupyter-sessions.s3.us-east-2.amazonaws.com/audio.zip + !unzip audio.zip + + import os + data = [{'audio': f'file://audio/{file}'} for file in os.listdir('./audio')] + ``` + + +The next code-block is only necessary if you're working with a custom `DataType`: + +```python +from superduperdb import Schema, Document + +schema = Schema( + 'my_schema', + fields={ + 'my_key': dt + } +) + +data = [ + Document({'my_key': item}) for item in data +] +``` + + + + + ```python + from superduperdb.backends.mongodb import Collection + + collection = Collection('documents') + + db.execute(collection.insert_many(data)) + ``` + + + ```python + from superduperdb.backends.ibis import Table + + table = Table( + 'my_table', + schema=schema, + ) + + db.add(table) + db.execute(table.insert(data)) + ``` + + diff --git a/docs/hr/content/docs/reusable_snippets/perform_a_vector_search.ipynb b/docs/hr/content/docs/reusable_snippets/perform_a_vector_search.ipynb new file mode 100644 index 0000000000..ce99107568 --- /dev/null +++ b/docs/hr/content/docs/reusable_snippets/perform_a_vector_search.ipynb @@ -0,0 +1,82 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Perform a vector search" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- `item` is the item which is to be encoded\n", + "- `dt` is the `DataType` instance to apply" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from superduperdb import Document\n", + "\n", + "item = Document({'my_key': dt(item)})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once we have this search target, we can execute a search as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "from superduperdb.backends.mongodb import Collection\n", + "\n", + "collection = Collection('documents')\n", + "\n", + "select = collection.find().like(item)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "\n", + "# Table was created earlier, before preparing vector-search\n", + "table = db.load('table', 'documents')\n", + "\n", + "select = table.like(item)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results = db.execute(select)" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/hr/content/docs/reusable_snippets/perform_a_vector_search.md b/docs/hr/content/docs/reusable_snippets/perform_a_vector_search.md new file mode 100644 index 0000000000..f1c974c138 --- /dev/null +++ b/docs/hr/content/docs/reusable_snippets/perform_a_vector_search.md @@ -0,0 +1,45 @@ +--- +sidebar_label: Perform a vector search +--- +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + + +# Perform a vector search + +- `item` is the item which is to be encoded +- `dt` is the `DataType` instance to apply + +```python +from superduperdb import Document + +item = Document({'my_key': dt(item)}) +``` + +Once we have this search target, we can execute a search as follows: + + + + + ```python + from superduperdb.backends.mongodb import Collection + + collection = Collection('documents') + + select = collection.find().like(item) + ``` + + + ```python + + # Table was created earlier, before preparing vector-search + table = db.load('table', 'documents') + + select = table.like(item) + ``` + + +```python +results = db.execute(select) +``` + diff --git a/docs/hr/content/docs/setup/development_vs_production_modes.md b/docs/hr/content/docs/setup/development_vs_production_modes.md new file mode 100644 index 0000000000..e69de29bb2 diff --git a/docs/hr/content/to_docusaurus_markdown.py b/docs/hr/content/to_docusaurus_markdown.py new file mode 100644 index 0000000000..441b31bb30 --- /dev/null +++ b/docs/hr/content/to_docusaurus_markdown.py @@ -0,0 +1,98 @@ +import json +import os +import re +import sys + + +CODE_BLOCK = ['```python\n', '\n ```'] +PREAMBLE = """import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +""" +TABS_WRAPPER = """ + +{body} + +""" + +TABS_ITEM = """ + {content} + """ + + +def render_chunks_as_md(chunks): + output = '' + for chunk in chunks: + if 'content' in chunk: + output += ''.join(chunk['content']) + elif 'tabs' in chunk: + tabs = [] + for tab in chunk['tabs']: + tab = TABS_ITEM.format( + content=' '.join(tab['content']), + title=tab['title'] + ) + tabs.append(tab) + output += TABS_WRAPPER.format(body='\n'.join(tabs)) + print(output) + return PREAMBLE + output + + +def render_notebook_as_chunks(nb): + chunks = [] + for cell in nb['cells']: + if cell['cell_type'] == 'markdown': + chunks.append({ + 'content': cell['source'] + ['\n', '\n'] + }) + + elif cell['cell_type'] == 'code': + match = re.match('^#[ ]+]+)>', cell['source'][0]) + if match: + title = match.groups()[0] + if 'tabs' not in chunks[-1]: + chunks.append({'tabs': []}) + chunks[-1]['tabs'].append({ + 'content': [CODE_BLOCK[0], *cell['source'][1:], CODE_BLOCK[-1]], + 'title': title, + }) + else: + chunks.append({ + 'content': [CODE_BLOCK[0], *cell['source'], CODE_BLOCK[-1].replace(' ', '') + '\n\n'] + } + ) + else: + raise Exception(f'Unknown source type {cell["cell_type"]}') + + return chunks + + + +if __name__ == '__main__': + directory = sys.argv[1] + + for file in os.listdir(directory): + if file.startswith('_'): + continue + if not file.endswith('.ipynb'): + continue + with open(f'{directory}/{file}') as f: + content = json.load(f) + head = content['cells'][0]['source'][0].strip() + if head == '': + title = content['cells'][0]['source'][1].strip().replace('# ', '') + print(f'processing {file} with tabs...') + chunks = render_notebook_as_chunks(content) + md = render_chunks_as_md(chunks) + + md = f'---\nsidebar_label: {title}\n---\n' + md + + file = file.split('.')[0] + target = '_'.join(title.lower().strip().split()) + assert file == target, f'{file} != {target}' + with open(f'{directory}/{file}.md', 'w') as f: + f.write(md) + else: + print(f'processing {directory}/{file} with Jupyter convert...') + os.system(f'jupyter-nbconvert --clear-output {directory}/{file}') + os.system(f'jupyter-nbconvert --to=markdown {directory}/{file}') \ No newline at end of file diff --git a/docs/hr/content/use_cases/_multimodal_vector_search.ipynb b/docs/hr/content/use_cases/_multimodal_vector_search.ipynb new file mode 100644 index 0000000000..18ff3bbf80 --- /dev/null +++ b/docs/hr/content/use_cases/_multimodal_vector_search.ipynb @@ -0,0 +1,102 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "38c1a328-fd86-4c5f-bd54-b8664f433608", + "metadata": {}, + "source": [ + "\n", + "# Multimodal vector search" + ] + }, + { + "cell_type": "markdown", + "id": "ec24191c-ed06-4264-9cd1-c8c0d7c23f0b", + "metadata": {}, + "source": [ + "The first step in any SuperDuperDB application is to connect to your data-backend with SuperDuperDB:" + ] + }, + { + "cell_type": "raw", + "id": "db0ce690-8ff7-4988-a377-6b0d8a1fa9f8", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "id": "e40544ac-0d97-46df-8bb5-4151baa72406", + "metadata": {}, + "source": [ + "Once you have done that you are ready to define your datatype(s) which you would like to \"search\"." + ] + }, + { + "cell_type": "raw", + "id": "5dc961ea-1861-4877-8597-4219ae8e2700", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "raw", + "id": "a5475216-6260-4674-890f-b3c990db79d9", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "" + ] + }, + { + "cell_type": "raw", + "id": "a1725b87-e687-46cc-a169-7cbe0baabe3f", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "" + ] + }, + { + "cell_type": "raw", + "id": "d661f375-f2f4-42b3-b4f1-50a2c0334a3d", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/hr/content/use_cases/_retrieval_augmented_generation.ipynb b/docs/hr/content/use_cases/_retrieval_augmented_generation.ipynb new file mode 100644 index 0000000000..77a4063909 --- /dev/null +++ b/docs/hr/content/use_cases/_retrieval_augmented_generation.ipynb @@ -0,0 +1,106 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "38c1a328-fd86-4c5f-bd54-b8664f433608", + "metadata": {}, + "source": [ + "\n", + "# Retrieval augmented generation" + ] + }, + { + "cell_type": "markdown", + "id": "ec24191c-ed06-4264-9cd1-c8c0d7c23f0b", + "metadata": {}, + "source": [ + "The first step in any SuperDuperDB application is to connect to your data-backend with SuperDuperDB:" + ] + }, + { + "cell_type": "raw", + "id": "db0ce690-8ff7-4988-a377-6b0d8a1fa9f8", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "id": "e40544ac-0d97-46df-8bb5-4151baa72406", + "metadata": {}, + "source": [ + "Once you have done that you are ready to define your datatype(s) which you would like to \"search\"." + ] + }, + { + "cell_type": "raw", + "id": "a5475216-6260-4674-890f-b3c990db79d9", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "" + ] + }, + { + "cell_type": "raw", + "id": "a1725b87-e687-46cc-a169-7cbe0baabe3f", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "" + ] + }, + { + "cell_type": "raw", + "id": "d661f375-f2f4-42b3-b4f1-50a2c0334a3d", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "" + ] + }, + { + "cell_type": "raw", + "id": "1a7bc500-461e-4e7c-95a2-0ff464d0dda7", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/hr/content/use_cases/classical_tasks/_category_.json b/docs/hr/content/use_cases/classical_tasks/_category_.json deleted file mode 100644 index 0eaf54def7..0000000000 --- a/docs/hr/content/use_cases/classical_tasks/_category_.json +++ /dev/null @@ -1,10 +0,0 @@ -{ - "label": "Classical ML", - "position": 4, - "collapsed": false, - "collapsible": false, - "link": { - "type": "generated-index", - "description": "Get started with classical ML with SuperDuperDB" - } -} \ No newline at end of file diff --git a/docs/hr/content/use_cases/classical_tasks/mnist_torch.md b/docs/hr/content/use_cases/classical_tasks/mnist_torch.md deleted file mode 100644 index a6e69e4403..0000000000 --- a/docs/hr/content/use_cases/classical_tasks/mnist_torch.md +++ /dev/null @@ -1,273 +0,0 @@ -# Training and Managing MNIST Predictions on MongoDB - -This notebook guides you through the implementation of a classic machine learning task: MNIST handwritten digit recognition. The twist? We perform the task directly in a database using SuperDuperDB. - -This example makes it easy to connect any of your image recognition -model directly to your database in real-time. With SuperDuperDB, you can -skip complicated MLOps pipelines. It's a new straightforward way to -integrate your AI model with your data, ensuring simplicity, efficiency -and speed. - -## Prerequisites - -Before diving into the implementation, ensure that you have the -necessary libraries installed by running the following commands: - -```python -!pip install superduperdb -!pip install torch torchvision matplotlib -``` - -## Connect to datastore - -First, we need to establish a connection to a MongoDB datastore via -SuperDuperDB. You can configure the `MongoDB_URI` based on your specific -setup. - -Here are some examples of MongoDB URIs: - -- For testing (default connection): `mongomock://test` -- Local MongoDB instance: `mongodb://localhost:27017` -- MongoDB with authentication: - `mongodb://superduper:superduper@mongodb:27017/documents` -- MongoDB Atlas: - `mongodb+srv://:@/` - -```python -from superduperdb import superduper -from superduperdb.backends.mongodb import Collection -import os - -mongodb_uri = os.getenv("MONGODB_URI","mongomock://test") - -# SuperDuperDB, now handles your MongoDB database -# It just super dupers your database -db = superduper(mongodb_uri) - -# Create a collection for MNIST -mnist_collection = Collection('mnist') -``` - -## Load Dataset - -After establishing a connection to MongoDB, the next step is to load the -MNIST dataset. SuperDuperDB's strength lies in handling diverse data -types, especially those that are challenging. To achieve this, we use an -`Encoder` in conjunction with `Document` wrappers. These components -allow Python dictionaries containing non-JSONable or bytes objects to be -seamlessly inserted into the underlying data infrastructure. - -```python -import torchvision -from superduperdb.ext.pillow import pil_image -from superduperdb import Document -from superduperdb.backends.mongodb import Collection - -import random - -# Load MNIST images as Python objects using the Python Imaging Library. -# Each MNIST item is a tuple (image, label) -mnist_data = list(torchvision.datasets.MNIST(root='./data', download=True)) - -# Create a list of Document instances from the MNIST data -# Each Document has an 'img' field (encoded using the Pillow library) and a 'class' field -document_list = [Document({'img': pil_image(x[0]), 'class': x[1]}) for x in mnist_data] - -# Shuffle the data and select a subset of 1000 documents -random.shuffle(document_list) -data = document_list[:1000] - -# Insert the selected data into the mnist_collection which we mentioned before like: mnist_collection = Collection('mnist') -db.execute( - mnist_collection.insert_many(data[:-100]), # Insert all but the last 100 documents - encoders=(pil_image,) # Encode images using the Pillow library. -) -``` - -Now that the images and their classes are inserted into the database, we can query the data in its original format. Particularly, we can use the `PIL.Image` instances to inspect the data. - -```python -# Get and display one of the images -r = db.execute(mnist_collection.find_one()) -r.unpack()['img'] -``` - -## Build Model - -Following that, we build our machine learning model. SuperDuperDB -conveniently supports various frameworks, and for this example, we opt -for PyTorch, a suitable choice for computer vision tasks. In this -instance, we combine `torch` with `torchvision`. - -To facilitate communication with the SuperDuperDB `Datalayer`, we design `postprocess` and `preprocess` functions. These functions are then encapsulated with the model, preprocessing, and postprocessing steps to create a native SuperDuperDB handler. - -```python -import torch - -# Define the LeNet-5 architecture for image classification -class LeNet5(torch.nn.Module): - def __init__(self, num_classes): - super().__init__() - # Layer 1 - self.layer1 = torch.nn.Sequential( - torch.nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=0), - torch.nn.BatchNorm2d(6), - torch.nn.ReLU(), - torch.nn.MaxPool2d(kernel_size=2, stride=2)) - # Layer 2 - self.layer2 = torch.nn.Sequential( - torch.nn.Conv2d(6, 16, kernel_size=5, stride=1, padding=0), - torch.nn.BatchNorm2d(16), - torch.nn.ReLU(), - torch.nn.MaxPool2d(kernel_size=2, stride=2)) - # Fully connected layers - self.fc = torch.nn.Linear(400, 120) - self.relu = torch.nn.ReLU() - self.fc1 = torch.nn.Linear(120, 84) - self.relu1 = torch.nn.ReLU() - self.fc2 = torch.nn.Linear(84, num_classes) - - def forward(self, x): - out = self.layer1(x) - out = self.layer2(out) - out = out.reshape(out.size(0), -1) - out = self.fc(out) - out = self.relu(out) - out = self.fc1(out) - out = self.relu1(out) - out = self.fc2(out) - return out - -# Postprocess function for the model output -def postprocess(x): - return int(x.topk(1)[1].item()) - -# Preprocess function for input data -def preprocess(x): - return torchvision.transforms.Compose([ - torchvision.transforms.Resize((32, 32 - -)), - torchvision.transforms.ToTensor(), - torchvision.transforms.Normalize(mean=(0.1307,), std=(0.3081,))] - )(x) - -# Create an instance of the LeNet-5 model -lenet_model = LeNet5(10) - -# Create a SuperDuperDB model with the LeNet-5 model, preprocess, and postprocess functions -# Specify 'preferred_devices' as ('cpu',) indicating CPU preference -model = superduper(lenet_model, preprocess=preprocess, postprocess=postprocess, preferred_devices=('cpu',)) -db.add(model) -``` - -## Train Model - -Now we are ready to "train" or "fit" the model. Trainable models in -SuperDuperDB come with a sklearn-like `.fit` method. - -```python -from torch.nn.functional import cross_entropy - -from superduperdb import Metric -from superduperdb import Dataset -from superduperdb.ext.torch.model import TorchTrainerConfiguration - -# Fit the model to the training data -job = model.fit( - X='img', # Feature matrix used as input data - y='class', # Target variable for training - db=db, # Database used for data retrieval - select=mnist_collection.find(), # Select the dataset from the 'mnist_collection' - configuration=TorchTrainerConfiguration( - identifier='my_configuration', # Unique identifier for the training configuration - objective=cross_entropy, # The objective function (cross-entropy in this case) - loader_kwargs={'batch_size': 10}, # DataLoader keyword arguments, batch size is set to 10 - max_iterations=10, # Maximum number of training iterations - validation_interval=5, # Interval for validation during training - ), - metrics=[Metric(identifier='acc', object=lambda x, y: sum([xx == yy for xx, yy in zip(x, y)]) / len(x))], # Define a custom accuracy metric for evaluation during training - validation_sets=[ - # Define a validation dataset using a subset of data with '_fold' equal to 'valid' - Dataset( - identifier='my_valid', - select=Collection('mnist').find({'_fold': 'valid'}), - ) - ], - distributed=False, # Set to True if distributed training is enabled -) -``` - -## Monitoring Training Efficiency - -You can monitor the training efficiency with visualization tools like -Matplotlib: - -```python -from matplotlib import pyplot as plt - -# Load the model from the database -model = db.load('model', model.identifier) - -# Plot the accuracy values -plt.plot(model.metric_values['my_valid/acc']) -plt.show() -``` - -## On-the-fly Predictions - -After training the model, you can continuously predict on new data as it arrives. By activating a `listener` for the database, the model can make predictions on incoming data changes without having to load all the data client-side. The listen toggle triggers the model to predict based on updates in the incoming data. - -```python -model.predict( - X='img', # Input feature - db=db, # Database used for data retrieval - select=mnist_collection.find(), # Select the dataset - listen=True, # Continuous predictions on incoming data - max_chunk_size=100, # Number of predictions to return at once -) -``` - -We can see that predictions are available in `_outputs.img.lenet5`. - -```python -# Execute find_one() to retrieve a single document from the 'mnist_collection'. -r = db.execute(mnist_collection.find_one({'_fold': 'valid'})) - -# Unpack the document and extract its content -r.unpack() -``` - -## Verification - -The models "activated" can be seen here: - -```python -# Show the status of the listener -db.show('listener') -``` - -We can verify that the model is activated, by inserting the rest of the -data: - -```python -# Iterate over the last 100 elements in the 'data' list -for r in data[-100:]: - # Update the 'update' field to True for each document - r['update'] = True - -# Insert the updated documents (with 'update' set to True) into the 'mnist_collection' -db.execute(mnist_collection.insert_many(data[-100:])) -``` - -You can see that the inserted data, are now also populated with -predictions: - -```python -# Execute find_one() to retrieve a single sample document from 'mnist_collection' -# where the 'update' field is True -sample_document = db.execute(mnist_collection.find_one({'update': True}))['_outputs'] - -# A sample document -print(sample_document) -``` diff --git a/docs/hr/content/use_cases/classical_tasks/resnet_features.md b/docs/hr/content/use_cases/classical_tasks/resnet_features.md deleted file mode 100644 index 94a8364cb9..0000000000 --- a/docs/hr/content/use_cases/classical_tasks/resnet_features.md +++ /dev/null @@ -1,184 +0,0 @@ - -# Image Feature-Store Using Torchvision on MongoDB - -```python -!pip install superduperdb -!pip install torchvision -``` - -In this example, we show how to utilize a pre-trained network from `torchvision` to produce image features. The images are automatically fetched and stored in MongoDB. We use a subset of the CoCo dataset (https://cocodataset.org/#home) to illustrate the process. - -Real-life use cases for creating a database of image features using a pre-trained network in `torchvision`: - -1. **Image Search and Retrieval:** - - **Use Case:** Enhance image search capabilities in e-commerce platforms. - - **How:** Generate image features for products using a pre-trained network. Store these features in a database for efficient image retrieval, making it easier for users to find similar products. - -2. **Content-Based Recommendation Systems:** - - **Use Case:** Improve content recommendations in media streaming services. - - **How:** Extract image features from movie or show frames. Store these features in a database to recommend content with similar visual characteristics to users based on their preferences. - -3. **Facial Recognition in Security Systems:** - - **Use Case:** Strengthen facial recognition systems in security applications. - - **How:** Utilize a pre-trained neural network to extract facial features from images. Store these features in a database for quick and accurate identification in security and surveillance scenarios. - -4. **Medical Image Analysis:** - - **Use Case:** Assist in medical diagnostics through image analysis. - - **How:** Extract features from medical images (X-rays, MRIs, etc.) using a pre-trained network. Store these features to aid in the development of diagnostic tools or systems for healthcare professionals. - -5. **Automated Image Tagging:** - - **Use Case:** Streamline image organization in photo libraries or social media platforms. - - **How:** Extract features from uploaded images using a pre-trained model. Use these features to automatically generate relevant tags, making it easier for users to search and categorize their photos. - -These use cases demonstrate how creating a database of image features using `torchvision` can be applied across various domains to enhance functionality and improve user experiences. Guess what, all can be done with `superduperdb` like this example. - -```python -# Download the zip file -!curl -O https://superduperdb-public.s3.eu-west-1.amazonaws.com/valsmall2014.zip - -# Unzip the contents of the zip file (assuming the file is already downloaded) -!unzip -qq valsmall2014.zip -``` - -## Connect to Datastore - -First, we need to establish a connection to a MongoDB datastore via SuperDuperDB. You can configure the `MongoDB_URI` based on your specific setup. - -Here are some examples of MongoDB URIs: - -- For testing (default connection): `mongomock://test` -- Local MongoDB instance: `mongodb://localhost:27017` -- MongoDB with authentication: `mongodb://superduper:superduper@mongodb:27017/documents` -- MongoDB Atlas: `mongodb+srv://:@/` - -```python -import os -from superduperdb import superduper -from superduperdb.backends.mongodb import Collection - -# Get the MongoDB URI from the environment variable or use a default value -mongodb_uri = os.getenv("MONGODB_URI", "mongomock://test") - -# SuperDuperDB, now handles your MongoDB database -# It just super dupers your database -db = superduper(mongodb_uri) - -# Specify a collection named 'coco' -collection = Collection('coco') -``` - -Next, we include all image URIs in MongoDB. These URIs may include a mix of local file paths (`file://...`), web URLs (`http...`), and S3 URIs (`s3://...`). Once the URIs are added, SuperDuperDB automatically loads their content into MongoDB without the need for extra overhead or job definitions. - -```python -import glob -import random - -from superduperdb import Document as D -from superduperdb.ext.pillow import pil_image as i - -# Get a list of file URIs for all JPEG images in the 'valsmall2014' directory -uris = [f'file://{x}' for x in glob.glob('valsmall2014/*.jpg')] - -# Insert documents into the 'coco' collection in the MongoDB database -db.execute(collection.insert_many([D({'img': i(uri=uri)}) for uri in uris], encoders=(i,))) # Here the image is encoded with pillow -``` - -To confirm the correct storage of images in the `Datalayer`, we can perform a verification check. - -```python -# Import the display function from the IPython.display module -from IPython.display import display - -# Define a lambda function for displaying images with resizing to avoid potential Jupyter crashes -display_image = lambda x: display(x.resize((round(x.size[0] * 0.5), round(x.size[1] * 0.5)))) - -# Retrieve the 'img' attribute from the result of collection.find_one() using db.execute() -# Note: This assumes that db is an instance of a database connection wrapped with superduperdb -x = db.execute(collection.find_one())['img'].x - -# Display the image using the previously defined lambda function -display_image(x) -``` - -Let's build the `torch` + `torchvision` model using the `TorchModel` wrapper from SuperDuperDB. This allows for the incorporation of custom pre- and post-processing steps along with the model's forward pass. - -```python -# Import necessary libraries and modules from torchvision and torch -from torchvision import transforms -import torch -import torch.nn as nn -import torchvision.models as models - -import warnings - -# Import custom modules -from superduperdb.ext.torch import TorchModel, tensor - -# Define a series of image transformations using torchvision.transforms.Compose -t = transforms.Compose([ - transforms.Resize((224, 224)), # Resize the input image to 224x224 pixels (must same as here) - transforms.CenterCrop((224, 224)), # Perform a center crop on the resized image - transforms.ToTensor(), # Convert the image to a PyTorch tensor - transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) # Normalize the tensor with specified mean and standard deviation -]) - -# Define a preprocess function that applies the defined transformations to an input image -def preprocess(x): - try: - return t(x) - except Exception as e: - # If an exception occurs during preprocessing, issue a warning and return a tensor of zeros - warnings.warn(str(e)) - return torch.zeros(3, 224, 224) - -# Load the pre-trained ResNet-50 model from torchvision -resnet50 = models.resnet50(pretrained=True) - -# Extract all layers of the ResNet-50 model except the last one -modules = list(resnet50.children())[:-1] -resnet50 = nn.Sequential(*modules) - -# Create a TorchModel instance with the Res - -Net-50 model, preprocessing function, and postprocessing lambda -model = TorchModel( - identifier='resnet50', - preprocess=preprocess, - object=resnet50, - postprocess=lambda x: x[:, 0, 0], # Postprocess by extracting the top-left element of the output tensor - encoder=tensor(torch.float, shape=(2048,)) # Specify the encoder configuration -) -``` - -To ensure the correctness of the `model`, let's test it on a single data point by setting `one=True`. - -```python -# Assuming x is an input tensor, you're making a prediction using the configured model -# with the one=True parameter specifying that you expect a single prediction result. -model.predict(x, one=True) -``` - -Now that the model is prepared, we can apply it to the images stored in the `Datalayer`. - -```python -# Assuming X is the input data, in this case, images ('img') -prediction_results = model.predict( - X='img', # Specify the input data (images) - db=db, # Provide the database connection or object - select=collection.find(), # Specify the data to be used for prediction (fetch all data from the collection) - batch_size=10, # Set the batch size for making predictions - max_chunk_size=3000, # Set the maximum size of data chunks processed at once - in_memory=False, # Indicate that the data is not loaded entirely into memory, processed in chunks - listen=True # Enable listening mode, suggesting real-time or asynchronous prediction -) -``` - -To confirm that the features were stored in the `Datalayer`, you can examine them in the `_outputs.img.resnet50` field. - -```python -# Execute find_one() to retrieve a single document from the collection. -result = db.execute(collection.find_one()) - -# The purpose of unpack() is to extract or process the data -result.unpack() -``` diff --git a/docs/hr/content/use_cases/classical_tasks/sentiment_analysis_use_case.md b/docs/hr/content/use_cases/classical_tasks/sentiment_analysis_use_case.md deleted file mode 100644 index 53fe1c61dc..0000000000 --- a/docs/hr/content/use_cases/classical_tasks/sentiment_analysis_use_case.md +++ /dev/null @@ -1,185 +0,0 @@ -# Sentiment Analysis Using transformers on MongoDB - -```python -!pip install superduperdb -!pip install datasets -``` - -In this document, we're doing sentiment analysis using Hugging Face's `transformers` library. We demonstrate that you can perform this task seamlessly in SuperDuperDB, using MongoDB to store the data. - -Sentiment analysis has some real-life use cases: - -1. **Customer Feedback & Review Analysis:** Analyzing customer reviews and feedback to understand overall satisfaction, identify areas for improvement, and respond to customer concerns. It is used in the E-commerce industry frequently. - -2. **Brand Monitoring:** Monitoring social media, blogs, news articles, and online forums to gauge public sentiment towards a brand, product, or service. Addressing negative sentiment and capitalizing on positive feedback. - -Sentiment analysis plays a crucial role in understanding and responding to opinions and emotions expressed across various domains, contributing to better decision-making and enhanced user experiences. - -All of these can be done with your `existing database` and `SuperDuperDB`. You can integrate similar code into your ETL infrastructure as well. Let's see an example. - -```python -from datasets import load_dataset -import numpy -from transformers import AutoTokenizer, AutoModelForSequenceClassification - -# Import Document (aliased as D) and Dataset from the superduperdb module -from superduperdb import Document as D, Dataset -``` - -## Connect to datastore - -SuperDuperDB can work with MongoDB (one of many supported databases) as its database backend. To make this connection, we'll use the Python MongoDB client, pymongo, and "wrap" our database to transform it into a SuperDuper Datalayer. - -First, we need to establish a connection to a MongoDB datastore via SuperDuperDB. You can configure the `MongoDB_URI` based on your specific setup. - -Here are some examples of MongoDB URIs: - -- For testing (default connection): `mongomock://test` -- Local MongoDB instance: `mongodb://localhost:27017` -- MongoDB with authentication: `mongodb://superduper:superduper@mongodb:27017/documents` -- MongoDB Atlas: `mongodb+srv://:@/` - -```python -import os -from superduperdb.backends.mongodb import Collection -from superduperdb import superduper - -# Set an environment variable to enable PyTorch MPS fallback -os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' - -# Get the MongoDB URI from the environment variable "MONGODB_URI," defaulting to "mongomock://test" -mongodb_uri = os.getenv("MONGODB_URI","mongomock://test") - -# SuperDuperDB, now handles your MongoDB database -# It just super dupers your database -db = superduper(mongodb_uri) - -# Collection instance named 'imdb' in the database -collection = Collection('imdb') -``` - -We train the model using the IMDB dataset. - -```python -# Load the IMDb dataset using the load_dataset function from the datasets module -data = load_dataset("imdb") - -# Set the number of datapoints to be used for training and validation. Increase this number to do serious training -N_DATAPOINTS = 4 - -# Insert randomly selected training datapoints into the 'imdb' collection in the database -db.execute(collection.insert_many([ - # Insert training data into your database from the dataset. Create Document instances for each training datapoint, setting '_fold' to 'train' - D({'_fold': 'train', **data['train'][int(i)]}) for i in numpy.random.permutation(len(data['train']))[:N_DATAPOINTS] -])) - -# Insert randomly selected validation datapoints into the 'imdb' collection in the database -db.execute(collection.insert_many([ - # Insert validation data into your database from the dataset. Create Document instances for validation datapoint, setting '_fold' to 'valid' - D({'_fold': 'valid', **data['test'][int(i)]}) for i in numpy.random.permutation(len(data['test']))[:N_DATAPOINTS] -])) -``` - -Retrieve a sample from the database. - -```python -# Execute the find_one() method to retrieve a single document from the 'imdb' collection. To check if the database insertion is done okay. -r = db.execute(collection.find_one()) -r -``` - -Build a tokenizer and utilize it to create a data collator for batching inputs. - -```python -tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased") - -# Instantiate a sequence classification model for the 'distilbert-base-uncased' model with 2 labels -model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=2) - -# Create a Pipeline instance for sentiment analysis -# identifier: A unique identifier for the pipeline -# task: The type of task the pipeline is designed for, in this case, 'text-classification' -# preprocess: The tokenizer to use for preprocessing -# object: The model for text classification -# preprocess_kwargs: Additional keyword arguments for the tokenizer, e.g., truncation -model = Pipeline( - identifier='my-sentiment-analysis', - task='text-classification', - preprocess=tokenizer, - object=model, - preprocess_kwargs={'truncation': True}, -) -``` - -```python -# Assuming 'This is another test' is the input text for prediction -# You're making a prediction using the configured pipeline model -# with the one=True parameter specifying that you expect a single prediction result. -model.predict('This is another test', one=True) -``` - -We'll assess the model using a straightforward accuracy metric. This metric will be recorded in the model's metadata as part of the training process. - -```python -# Import TransformersTrainerConfiguration from the superduperdb.ext.transformers module -from superduperdb.ext.transformers import TransformersTrainerConfiguration - -# Create a configuration for training a transformer model -training_args = TransformersTrainerConfiguration( - identifier='sentiment-analysis', # A unique identifier for the training configuration - output_dir='sentiment-analysis', # The directory where model predictions will be saved - learning_rate=2e-5, # The learning rate for training the model - per_device_train_batch_size=2, # Batch size per GPU (or CPU) for training - per_device_eval_batch_size=2, # Batch size per GPU (or CPU) for evaluation - num_train_epochs=2, # The number of training epochs - weight_decay=0.01, # Weight decay for regularization - save_strategy="epoch", # Save model checkpoints after each epoch - use_cpu=True, # Use CPU for training (set to False if you want to use GPU) - evaluation_strategy='epoch', # Evaluate the model after each epoch - do_eval=True, # Perform evaluation during training -) -``` - -Now we're ready to train the model: - -```python -# Import the Metric class from the superduperdb module -from superduperdb import Metric - -# Fit the model using training data and specified configuration -model.fit( - X='text', # Input data (text) - y='label', # Target variable (label) - db=db, - - # Super Duper wrapped Database connection - select=collection.find(), # Specify the data to be used for training (fetch all data from the collection) - configuration=training_args, # Training configuration using the previously defined TransformersTrainerConfiguration - validation_sets=[ - # Define a validation dataset using a subset of data with '_fold' equal to 'valid' - Dataset( - identifier='my-eval', - select=collection.find({'_fold': 'valid'}), - ) - ], - data_prefetch=False, # Disable data prefetching during training - metrics=[ - # Define a custom accuracy metric for evaluation during training - Metric( - identifier='acc', - object=lambda x, y: sum([xx == yy for xx, yy in zip(x, y)]) / len(x) - ) - ] -) -``` - -We can confirm that the model produces sensible predictions by examining the output. If you are okay with the performance, you may predict it on your whole database and save it for future reference. All can be done on SuperDuperDB in real-time. - -```python -# Assuming "This movie sucks!" is the input text for sentiment analysis -# You're making a prediction using the configured pipeline model -# with the one=True parameter specifying that you expect a single prediction result. -sentiment_prediction = model.predict("This movie sucks!", one=True) - -print("Sentiment Prediction", sentiment_prediction) -``` diff --git a/docs/hr/content/use_cases/classical_tasks/transfer_learning.md b/docs/hr/content/use_cases/classical_tasks/transfer_learning.md deleted file mode 100644 index b34dbb514c..0000000000 --- a/docs/hr/content/use_cases/classical_tasks/transfer_learning.md +++ /dev/null @@ -1,151 +0,0 @@ -# Transfer-Learning Using Transformers and Scikit-Learn on MongoDB - -In this notebook, we will explore the process of transfer learning using SuperDuperDB. We will demonstrate how to connect to a MongoDB datastore, load a dataset, create a SuperDuperDB model based on Sentence Transformers, train a downstream model using Scikit-Learn, and apply the trained model to the database. Transfer learning is a powerful technique that can be used in various applications, such as vector search and downstream learning tasks. - -## Prerequisites - -Before diving into the implementation, ensure that you have the necessary libraries installed by running the following commands: - -```bash -!pip install superduperdb -!pip install ipython numpy datasets sentence-transformers -``` - -## Connect to Datastore - -First, we need to establish a connection to a MongoDB datastore via SuperDuperDB. You can configure the `MongoDB_URI` based on your specific setup. -Here are some examples of MongoDB URIs: - -- For testing (default connection): `mongomock://test` -- Local MongoDB instance: `mongodb://localhost:27017` -- MongoDB with authentication: `mongodb://superduper:superduper@mongodb:27017/documents` -- MongoDB Atlas: `mongodb+srv://:@/` - -```python -from superduperdb import superduper -from superduperdb.backends.mongodb import Collection -import os - -mongodb_uri = os.getenv("MONGODB_URI","mongomock://test") - -# SuperDuperDB, now handles your MongoDB database -# It just super dupers your database -db = superduper(mongodb_uri) - -# Reference a collection called transfer -collection = Collection('transfer') -``` - -## Load Dataset - -Transfer learning can be applied to any data that can be processed with SuperDuperDB models. -For our example, we will use a labeled textual dataset with sentiment analysis. We'll load a subset of the IMDb dataset. - -```python -import numpy -from datasets import load_dataset -from superduperdb import Document as D - -# Load IMDb dataset -data = load_dataset("imdb") - -# Set the number of data points for training (adjust as needed) -N_DATAPOINTS = 500 - -# Prepare training data -train_data = [ - D({'_fold': 'train', **data['train'][int(i)]}) - for i in numpy.random.permutation(len(data['train'])) -][:N_DATAPOINTS] - -# Prepare validation data -valid_data = [ - D({'_fold': 'valid', **data['test'][int(i)]}) - for i in numpy.random.permutation(len(data['test'])) -][:N_DATAPOINTS // 10] - -# Insert training data into the 'collection' SuperDuperDB collection -db.execute(collection.insert_many(train_data)) -``` - -## Run Model - -We'll create a SuperDuperDB model based on the `sentence_transformers` library. This demonstrates that you don't necessarily need a native SuperDuperDB integration with a model library to leverage its power. We configure the `Model wrapper` to work with the `SentenceTransformer class`. After configuration, we can link the model to a collection and daemonize the model with the `listen=True` keyword. - -```python -from superduperdb import Model -import sentence_transformers -from superduperdb.ext.numpy import array - -# Create a SuperDuperDB Model using Sentence Transformers -m = Model( - identifier='all-MiniLM-L6-v2', - object=sentence_transformers.SentenceTransformer('all-MiniLM-L6-v2'), - encoder=array('float32', shape=(384,)), - predict_method='encode', - batch_predict=True, -) - -# Make predictions on 'text' data from the 'collection' SuperDuperDB collection -m.predict( - X='text', - db=db, - select=collection.find(), - listen=True -) -``` - -## Train Downstream Model - -Now that we've created and added the model that computes features for the `"text"`, we can train a downstream model using Scikit-Learn. - -```python -# Import necessary modules and classes -from sklearn.svm import SVC -from superduperdb import superduper - -# Create a SuperDuperDB model with an SVC classifier -model = superduper( - SVC(gamma='scale', class_weight='balanced', C=100, verbose=True), - postprocess=lambda x: int(x) -) - -# Train the model on 'text' data with corresponding labels -model.fit( - X='text', - y='label', - db=db, - select=collection.find().featurize({'text': 'all-MiniLM-L6-v2'}), -) -``` - -## Run Downstream Model - -With the model trained, we can now apply it to the database. - -```python -# Make predictions on 'text' data with the trained SuperDuperDB model -model.predict( - X='text', - db=db - -, - select=collection.find().featurize({'text': 'all-MiniLM-L6-v2'}), - listen=True, -) -``` - -## Verification - -To verify that the process has worked, we can sample a few records to inspect the sanity of the predictions. - -```python -# Query a random document from the 'collection' SuperDuperDB collection -r = next(db.execute(collection.aggregate([{'$sample': {'size': 1}}]))) - -# Print a portion of the 'text' field from the random document -print(r['text'][:100]) - -# Print the prediction made by the SVC model stored in '_outputs' -print(r['_outputs']['text']['svc']) -``` diff --git a/docs/hr/content/use_cases/multimodal_vector_search.ipynb b/docs/hr/content/use_cases/multimodal_vector_search.ipynb new file mode 100644 index 0000000000..accbfbf428 --- /dev/null +++ b/docs/hr/content/use_cases/multimodal_vector_search.ipynb @@ -0,0 +1,366 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "38c1a328-fd86-4c5f-bd54-b8664f433608", + "metadata": {}, + "source": [ + "\n", + "# Multimodal vector search" + ] + }, + { + "cell_type": "markdown", + "id": "ec24191c-ed06-4264-9cd1-c8c0d7c23f0b", + "metadata": {}, + "source": [ + "The first step in any SuperDuperDB application is to connect to your data-backend with SuperDuperDB:" + ] + }, + { + "cell_type": "markdown", + "id": "32f8484d-2e35-472a-9b24-1a30ec1d144b", + "metadata": {}, + "source": [ + "\n", + "## Connect to SuperDuperDB" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "61976f44-8139-41c0-a73e-569c6d16c4b1", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "from superduperdb import superduper\n", + "\n", + "db = superduper('mongodb://localhost:27017/documents')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e981a457", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "from superduperdb import superduper\n", + "\n", + "db = superduper('sqlite://my_db.db')" + ] + }, + { + "cell_type": "markdown", + "id": "e40544ac-0d97-46df-8bb5-4151baa72406", + "metadata": {}, + "source": [ + "Once you have done that you are ready to define your datatype(s) which you would like to \"search\"." + ] + }, + { + "cell_type": "markdown", + "id": "b31257e4-06fa-4cc7-9626-bb4d03fdc029", + "metadata": {}, + "source": [ + "\n", + "## Create datatype" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "876cfb3b-8d2f-4950-86fb-4bf65accfb98", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e844c762-3391-401d-9047-ed8617a9c946", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "..." + ] + }, + { + "cell_type": "markdown", + "id": "55ad38e1", + "metadata": {}, + "source": [ + "\n", + "## Insert data\n", + "\n", + "In order to create data, we need create a `Schema` for encoding our special `Datatype` column(s) in the databackend." + ] + }, + { + "cell_type": "markdown", + "id": "3bd99c11", + "metadata": {}, + "source": [ + "Here's some sample data to work with:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ed28e4e5", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "!curl -O https://jupyter-sessions.s3.us-east-2.amazonaws.com/text.json\n", + "\n", + "import json\n", + "with open('text.json') as f:\n", + " data = json.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "97f61e8c", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "!curl -O https://jupyter-sessions.s3.us-east-2.amazonaws.com/images.zip\n", + "!unzip images.zip\n", + "\n", + "import os\n", + "data = [{'image': f'file://image/{file}'} for file in os.listdir('./images')]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f806472", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "!curl -O https://jupyter-sessions.s3.us-east-2.amazonaws.com/audio.zip\n", + "!unzip audio.zip\n", + "\n", + "import os\n", + "data = [{'audio': f'file://audio/{file}'} for file in os.listdir('./audio')]" + ] + }, + { + "cell_type": "markdown", + "id": "431c1da8", + "metadata": {}, + "source": [ + "The next code-block is only necessary if you're working with a custom `DataType`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "06cca365", + "metadata": {}, + "outputs": [], + "source": [ + "from superduperdb import Schema, Document\n", + "\n", + "schema = Schema(\n", + " 'my_schema',\n", + " fields={\n", + " 'my_key': dt\n", + " }\n", + ")\n", + "\n", + "data = [\n", + " Document({'my_key': item}) for item in data\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "91f2ac56", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "from superduperdb.backends.mongodb import Collection\n", + "\n", + "collection = Collection('documents')\n", + "\n", + "db.execute(collection.insert_many(data))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "68687e87", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "from superduperdb.backends.ibis import Table\n", + "\n", + "table = Table(\n", + " 'my_table',\n", + " schema=schema,\n", + ")\n", + "\n", + "db.add(table)\n", + "db.execute(table.insert(data))" + ] + }, + { + "cell_type": "markdown", + "id": "254c96c1", + "metadata": {}, + "source": [ + "\n", + "## Build multimodal embedding models" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ef278e4", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "\n", + "..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4d0e6d9a", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "\n", + "..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ff8190c4", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "\n", + "..." + ] + }, + { + "cell_type": "markdown", + "id": "d0f93ed4", + "metadata": {}, + "source": [ + "\n", + "## Perform a vector search" + ] + }, + { + "cell_type": "markdown", + "id": "7acb5719", + "metadata": {}, + "source": [ + "- `item` is the item which is to be encoded\n", + "- `dt` is the `DataType` instance to apply" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "75771579", + "metadata": {}, + "outputs": [], + "source": [ + "from superduperdb import Document\n", + "\n", + "item = Document({'my_key': dt(item)})" + ] + }, + { + "cell_type": "markdown", + "id": "83e83a8a", + "metadata": {}, + "source": [ + "Once we have this search target, we can execute a search as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1bb722ca", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "from superduperdb.backends.mongodb import Collection\n", + "\n", + "collection = Collection('documents')\n", + "\n", + "select = collection.find().like(item)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e9832fe2", + "metadata": {}, + "outputs": [], + "source": [ + "# \n", + "\n", + "# Table was created earlier, before preparing vector-search\n", + "table = db.load('table', 'documents')\n", + "\n", + "select = table.like(item)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2913306c", + "metadata": {}, + "outputs": [], + "source": [ + "results = db.execute(select)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/hr/content/use_cases/multimodal_vector_search.md b/docs/hr/content/use_cases/multimodal_vector_search.md new file mode 100644 index 0000000000..bdd9205ee6 --- /dev/null +++ b/docs/hr/content/use_cases/multimodal_vector_search.md @@ -0,0 +1,191 @@ +--- +sidebar_label: Multimodal vector search +--- +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + + +# Multimodal vector search + +The first step in any SuperDuperDB application is to connect to your data-backend with SuperDuperDB: + + +## Connect to SuperDuperDB + + + + + ```python + from superduperdb import superduper + + db = superduper('mongodb://localhost:27017/documents') + ``` + + + ```python + from superduperdb import superduper + + db = superduper('sqlite://my_db.db') + ``` + + +Once you have done that you are ready to define your datatype(s) which you would like to "search". + + +## Create datatype + + + + + ```python + ... + ``` + + + ```python + ... + ``` + + + +## Insert data + +In order to create data, we need create a `Schema` for encoding our special `Datatype` column(s) in the databackend. + +Here's some sample data to work with: + + + + + ```python + !curl -O https://jupyter-sessions.s3.us-east-2.amazonaws.com/text.json + + import json + with open('text.json') as f: + data = json.load(f) + ``` + + + ```python + !curl -O https://jupyter-sessions.s3.us-east-2.amazonaws.com/images.zip + !unzip images.zip + + import os + data = [{'image': f'file://image/{file}'} for file in os.listdir('./images')] + ``` + + + ```python + !curl -O https://jupyter-sessions.s3.us-east-2.amazonaws.com/audio.zip + !unzip audio.zip + + import os + data = [{'audio': f'file://audio/{file}'} for file in os.listdir('./audio')] + ``` + + +The next code-block is only necessary if you're working with a custom `DataType`: + +```python +from superduperdb import Schema, Document + +schema = Schema( + 'my_schema', + fields={ + 'my_key': dt + } +) + +data = [ + Document({'my_key': item}) for item in data +] +``` + + + + + ```python + from superduperdb.backends.mongodb import Collection + + collection = Collection('documents') + + db.execute(collection.insert_many(data)) + ``` + + + ```python + from superduperdb.backends.ibis import Table + + table = Table( + 'my_table', + schema=schema, + ) + + db.add(table) + db.execute(table.insert(data)) + ``` + + + +## Build multimodal embedding models + + + + + ```python + + ... + ``` + + + ```python + + ... + ``` + + + ```python + + ... + ``` + + + +## Perform a vector search + +- `item` is the item which is to be encoded +- `dt` is the `DataType` instance to apply + +```python +from superduperdb import Document + +item = Document({'my_key': dt(item)}) +``` + +Once we have this search target, we can execute a search as follows: + + + + + ```python + from superduperdb.backends.mongodb import Collection + + collection = Collection('documents') + + select = collection.find().like(item) + ``` + + + ```python + + # Table was created earlier, before preparing vector-search + table = db.load('table', 'documents') + + select = table.like(item) + ``` + + +```python +results = db.execute(select) +``` + diff --git a/docs/hr/content/use_cases/productionization/_category_.json b/docs/hr/content/use_cases/productionization/_category_.json deleted file mode 100644 index a5561817c0..0000000000 --- a/docs/hr/content/use_cases/productionization/_category_.json +++ /dev/null @@ -1,10 +0,0 @@ -{ - "label": "Productionization", - "position": 6, - "collapsed": false, - "collapsible": false, - "link": { - "type": "generated-index", - "description": "Use SuperDuperDB in production contexts" - } -} \ No newline at end of file diff --git a/docs/hr/content/use_cases/productionization/sandbox-example.md b/docs/hr/content/use_cases/productionization/sandbox-example.md deleted file mode 100644 index 0a84c33584..0000000000 --- a/docs/hr/content/use_cases/productionization/sandbox-example.md +++ /dev/null @@ -1,233 +0,0 @@ -# SuperDuperDB: cluster usage - -SuperDuperDB allows developers, on the one hand to experiment and setup models quickly in scripts and notebooks, and on the other hand deploy persistent services, which are intended to "always" be on. These persistent services are: - -- Dask scheduler -- Dask workers -- Vector-searcher service -- Change-data-capture (CDC) service - -![](/img/light.png) - -To set up `superduperdb` to use this cluster mode, it's necessary to add explicit configurations -for each of these components. The following configuration does that, as well as enabling a pre-configured -community edition MongoDB database: - -```yaml -data_backend: mongodb://superduper:superduper@mongodb:27017/test_db -artifact_store: filesystem://./data -cluster: - cdc: http://cdc:8001 - compute: dask+tcp://scheduler:8786 - vector_search: http://vector-search:8000 -``` - -Add this configuration in `/.superduperdb/config.yaml`, where `/` is the root of your project. - -Once this configuration has been added, you're ready to use the `superduperdb` sandbox environment, which uses -`docker-compose` to deploy: - -- Standalone replica-set of MongoDB community edition -- Dask scheduler -- Dask workers -- Vector-searcher service -- Change-data-capture (CDC) service -- Jupyter notebook service - - -To build the `sandbox` image: - -```bash -make testenv_image -``` - -> If you want to install additional `pip` dependencies in the image, you have to list them in `requirements.txt`. -> -> The listed dependencies may refer to: -> 1. standalone packages (e.g `tensorflow>=2.15.0`) -> 2. dependency groups listed in `pyproject.toml` (e.g `.[dev]`) - -Then start the environment with: - -```bash -make testenv_init -``` - -This last command starts containers for each of the above services with `docker-compose`. You should see a bunch of logs for each service (mainly MongoDB). - -Once you have carried out these steps, you are ready to complete the rest of this notebook, which focuses on a implementing -a production style implementation of vector-search. - - -```python -import os - -# move to the root of the project (assumes starts in `/examples`) -os.chdir('../') - -from superduperdb import CFG - -# check that config has been properly set-up -assert CFG.data_backend == 'mongodb://superduper:superduper@mongodb:27017/test_db' -``` - -We'll be using MongoDB to store the vectors and data: - - -```python -from superduperdb.backends.mongodb import Collection -from superduperdb import superduper - -db = superduper() -doc_collection = Collection('documents') -``` - -We've already prepared some data which was scraped from the `pymongo` query API. You can download it -in the next cell: - - -```python -!curl -O https://superduperdb-public.s3.eu-west-1.amazonaws.com/pymongo.json - -import json - -with open('pymongo.json') as f: - data = json.load(f) - -data[0] -``` - -Let's insert this data: - - -```python -from superduperdb import Document - -out, G = db.execute( - doc_collection.insert_many([Document(r) for r in data[:-100]]) -) -``` - -We'll use a `sentence-transformers` model to calculate the embeddings. Here's how to wrap the model -so that it works with `superduperdb`: - - -```python -import sentence_transformers -from superduperdb import Model, vector - -model = Model( - identifier='all-MiniLM-L6-v2', - object=sentence_transformers.SentenceTransformer('all-MiniLM-L6-v2'), - encoder=vector(shape=(384,)), - predict_method='encode', # Specify the prediction method - postprocess=lambda x: x.tolist(), # Define postprocessing function - batch_predict=True, # Generate predictions for a set of observations all at once -) -``` - -Now let's create the vector-search component: - - -```python -from superduperdb import Listener, VectorIndex - -jobs, vi = db.add( - VectorIndex( - identifier=f'pymongo-docs-{model.identifier}', - indexing_listener=Listener( - select=doc_collection.find(), - key='value', - model=model, - predict_kwargs={'max_chunk_size': 1000}, - ), - ) -) -``` - -This command creates a job on `dask` to calculate the vectors and save them in the database. You can -follow the `stdout` of this job with this command: - - -```python -jobs[0].watch() -``` - -After a few moments, you'll be able to verify that the vectors have been saved in the documents: - - -```python -db.execute(doc_collection.find_one()) -``` - -Let's test a similarity/ vector search using the hybrid query-API of `superduperdb`. This search -dispatches one part off to the vector-search server (running on port 8001) and the other (classical) part to MongoDB -the results are combined by `superduperdb`: - - -```python -from IPython.display import Markdown - -result = sorted(db.execute( - doc_collection - .like(Document({'value': 'Aggregate'}), n=10, vector_index=f'pymongo-docs-{model.identifier}') - .find({}, {'_outputs': 0}) -), key=lambda r: -r['score']) - -# Display a horizontal line -display(Markdown('---')) - -# Iterate through the query results and display them -for r in result: - # Display the document's parent and res values in a formatted way - display(Markdown(f'### `{r["parent"] + "." if r["parent"] else ""}{r["res"]}`')) - - # Display the value of the document - display(Markdown(r['value'])) - - # Display a horizontal line - display(Markdown('---')) -``` - -One of the great things about this distributed setup, is that now allows data to be inserted into the service via other -MongoDB clients, even from other programming languages and applications. - -We show-case this here, by inserting the rest of the data using the official Python MongoDB driver `pymongo`. - -This cell will update the models, even if you restart the program: - - -```python -import pymongo - -coll = pymongo.MongoClient('mongodb://superduper:superduper@mongodb:27017/test_db').test_db.documents - -coll.insert_many(data[-100:]) -``` - -To get an idea what is happening, you can view the logs of the CDC container on -your host by executing this command in a terminal: - -```bash -docker logs -n 20 testenv_cdc_1 -``` - -Note this won't work inside this notebook since it's running in its own container. - -The CDC service should have captured the changes created with the `pymongo` insert, and has submitted a new job(s) -to the `dask` cluster. - -You can confirm that another job has been created and executed: - - -```python -db.metadata.show_jobs() -``` - -We can now check that all of the outputs (including those inserted via the `pymongo` client) have been populated -by the system. - - -```python -db.execute(doc_collection.count_documents({'_outputs': {'$exists': 1}})) -``` diff --git a/docs/hr/content/use_cases/question-answering/Chatbot.md b/docs/hr/content/use_cases/question-answering/Chatbot.md deleted file mode 100644 index 4b25b8cdde..0000000000 --- a/docs/hr/content/use_cases/question-answering/Chatbot.md +++ /dev/null @@ -1,281 +0,0 @@ - -# Q&A Assistant Using OpenAI on MongoDB (RAG) - -This notebook is designed to demonstrate how to implement a document Question-and-Answer (Q&A) task using SuperDuperDB in conjunction with OpenAI and MongoDB. It provides a step-by-step guide and explanation of each component involved in the process. - -Implementing a document Question-and-Answer (Q&A) system using SuperDuperDB, OpenAI, and MongoDB can find applications in various real-life scenarios: - -1. **Customer Support Chatbots:** Enable a chatbot to answer customer queries by extracting information from documents, manuals, or knowledge bases stored in MongoDB or any other SuperDuperDB supported database using Q&A. - -2. **Legal Document Analysis:** Facilitate legal professionals in quickly extracting relevant information from legal documents, statutes, and case laws, improving efficiency in legal research. - -3. **Medical Data Retrieval:** Assist healthcare professionals in obtaining specific information from medical documents, research papers, and patient records for quick reference during diagnosis and treatment. - -4. **Educational Content Assistance:** Enhance educational platforms by enabling students to ask questions related to course materials stored in a MongoDB database, providing instant and accurate responses. - -5. **Technical Documentation Search:** Support software developers and IT professionals in quickly finding solutions to technical problems by querying documentation and code snippets stored in MongoDB or any other database supported by SuperDuperDB. We did that! - -6. **HR Document Queries:** Simplify HR processes by allowing employees to ask questions about company policies, benefits, and procedures, with answers extracted from HR documents stored in MongoDB or any other database supported by SuperDuperDB. - -7. **Research Paper Summarization:** Enable researchers to pose questions about specific topics, automatically extracting relevant information from a MongoDB repository of research papers to generate concise summaries. - -8. **News Article Information Retrieval:** Empower users to inquire about specific details or background information from a database of news articles stored in MongoDB or any other database supported by SuperDuperDB, enhancing their understanding of current events. - -9. **Product Information Queries:** Improve e-commerce platforms by allowing users to ask questions about product specifications, reviews, and usage instructions stored in a MongoDB database. - -By implementing a document Q&A system with SuperDuperDB, OpenAI, and MongoDB, these use cases demonstrate the versatility and practicality of such a solution across different industries and domains. - -All is possible without zero friction with SuperDuperDB. Now back into the notebook. - -## Prerequisites - -Before starting the implementation, make sure you have the required libraries installed by running the following commands: - -```bash -!pip install superduperdb -!pip install ipython openai==1.1.2 -``` - -Additionally, ensure that you have set your OpenAI API key as an environment variable. You can uncomment the following code and add your API key: - -```python -import os - -# Add your OPEN_AI_API_KEY -# os.environ['OPENAI_API_KEY'] = 'sk-...' - -if 'OPENAI_API_KEY' not in os.environ: - raise Exception('Environment variable "OPENAI_API_KEY" not set') -``` - -## Connect to Datastore - -First, we need to establish a connection to a MongoDB datastore via SuperDuperDB. You can configure the `MongoDB_URI` based on your specific setup. - -Here are some examples of MongoDB URIs: - -- For testing (default connection): `mongomock://test` -- Local MongoDB instance: `mongodb://localhost:27017` -- MongoDB with authentication: `mongodb://superduper:superduper@mongodb:27017/documents` -- MongoDB Atlas: `mongodb+srv://:@/` - -```python -from superduperdb import superduper -from superduperdb.backends.mongodb import Collection -import os - -mongodb_uri = os.getenv("MONGODB_URI", "mongomock://test") - -# SuperDuperDB, now handles your MongoDB database -# It just super dupers your database -db = superduper(mongodb_uri) - -collection = Collection('questiondocs') -``` - -```python -db.metadata -``` - -## Load Dataset - -In this example, we use the internal textual data from the `superduperdb` project's API documentation. The objective is to create a chatbot that can offer information about the project. You can either load the data from your local project or use the provided data. - -If you have the SuperDuperDB project locally and want to load the latest version of the API, uncomment the following cell: - -```python -# import glob - -# ROOT = '../docs/hr/content/docs/' -# STRIDE = 3 # stride in numbers of lines -# WINDOW = 25 # length of window in numbers of lines - -# files = sorted(glob.glob(f'{ROOT}/*.md') + glob.glob(f'{ROOT}/*.mdx')) - -# content = sum([open(file).read().split('\n') for file in files], []) -# chunks = ['\n'.join(content[i: i + WINDOW]) for i in range(0, len(content), STRIDE)] -``` - -Otherwise, you can load the data from an external source. The text chunks include code snippets and explanations, which will be utilized to construct the document Q&A chatbot. - -```python -from IPython.display import * - -# Assuming 'chunks' is a list or iterable containing markdown content -Markdown(chunks[20]) -``` - -```python -# Use !curl to download the 'superduperdb_docs.json' file -!curl -O https://superduperdb-public.s3.eu-west-1.amazonaws.com/superduperdb_docs.json - -import json -from IPython.display import Markdown - -# Open the downloaded JSON file and load its contents into the 'chunks' variable -with open('superduperdb_docs.json') as f: - chunks = json.load(f) -``` - -The chunks of text contain both code snippets and explanations, making them valuable for constructing a document Q&A chatbot. The combination of code and explanations enables the chatbot to provide comprehensive and context-aware responses to user queries. - -As usual, we insert the data. The `Document` wrapper allows `superduperdb` to handle records with special data types such as images, video, and custom data-types. - -```python -from superduperdb import Document - -# Insert multiple documents into the collection -db.execute(collection.insert_many([Document({'txt': chunk}) for chunk in chunks])) -``` - -## Create a Vector-Search Index - -To enable question-answering over your documents, set up a standard `superduperdb` vector-search index using `openai` (other options include `torch`, `sentence_transformers`, `transformers`, etc.). - -A `Model` is a wrapper around a self-built or ecosystem model, such as `torch`, `transformers`, `openai`. - -```python -from superduperdb.ext.openai import OpenAIEmbedding - -# Create an instance of the OpenAIEmbedding model with the specified identifier ('text-embedding-ada-002') -model = OpenAIEmbedding(identifier= 'text-embedding-ada-002', model='text-embedding-ada-002') -``` - -```python -model.predict('This is a test', one=True) -``` - -A `Listener` essentially deploys a `Model` to "listen" to incoming data, computes outputs, and then saves the results in the database via - - `db`. - -```python -# Import the Listener class from the superduperdb module -from superduperdb import Listener - -# Create a Listener instance with the specified model, key, and selection criteria -listener = Listener( - model=model, # The model to be used for listening - key='txt', # The key field in the documents to be processed by the model - select=collection.find() # The selection criteria for the documents -) -``` - -A `VectorIndex` wraps a `Listener`, allowing its outputs to be searchable. - -```python -# Import the VectorIndex class from the superduperdb module -from superduperdb import VectorIndex - -# Add a VectorIndex to the SuperDuperDB database with the specified identifier and indexing listener -db.add( - VectorIndex( - identifier='my-index', # Unique identifier for the VectorIndex - indexing_listener=listener # Listener to be used for indexing documents - ) -) -``` - -```python -# Execute a find_one operation on the SuperDuperDB collection -db.execute(collection.find_one()) -``` - -```python -from superduperdb.backends.mongodb import Collection -from superduperdb import Document as D -from IPython.display import * - -# Define the query for the search -query = 'Code snippet how to create a `VectorIndex` with a torchvision model' - -# Execute a search using SuperDuperDB to find documents containing the specified query -result = db.execute( - collection - .like(D({'txt': query}), vector_index='my-index', n=5) - .find() -) - -# Display a horizontal rule to separate results -display(Markdown('---')) - -# Display each document's 'txt' field and separate them with a horizontal rule -for r in result: - display(Markdown(r['txt'])) - display(Markdown('---')) -``` - -## Create a Chat-Completion Component - -In this step, a chat-completion component is created and added to the system. This component is essential for the Q&A functionality: - -```python -# Import the OpenAIChatCompletion class from the superduperdb.ext.openai module -from superduperdb.ext.openai import OpenAIChatCompletion - -# Define the prompt for the OpenAIChatCompletion model -prompt = ( - 'Use the following description and code snippets about SuperDuperDB to answer this question about SuperDuperDB\n' - 'Do not use any other information you might have learned about other python packages\n' - 'Only base your answer on the code snippets retrieved\n' - '{context}\n\n' - 'Here\'s the question:\n' -) - -# Create an instance of OpenAIChatCompletion with the specified model and prompt -chat = OpenAIChatCompletion(model='gpt-3.5-turbo', prompt=prompt) - -# Add the OpenAIChatCompletion instance -db.add(chat) - -# Print information about the models in the SuperDuperDB database -print(db.show('model')) -``` - -## Ask Questions to Your Docs - -Finally, you can ask questions about the documents. You can target specific queries and use the power of MongoDB for vector-search and filtering rules. Here's an example of asking a question: - -```python -from superduperdb import Document -from IPython.display import Markdown - -# Define the search parameters -search_term = 'Can you give me a code-snippet to set up a `VectorIndex`?' -num_results = 5 - -# Use the SuperDuperDB model to generate a response based on the search term and context -output, context = db.predict( - model_name='gpt-3.5-turbo', - input=search_term, - context_select=( - collection - .like(Document({'txt': search_term}), vector_index='my-index', n=num_results) - .find() - ), - context_key='txt', -) - -# Display the generated response using Markdown -Markdown(output.content) -``` - -## Reset the Demo - -```python -# Remove a VectorIndex with the identifier 'my-index' -db.remove('vector_index', 'my-index', force=True) - -# Remove a Listener associated with the 'text-embedding-ada-002/txt' key -db.remove('listener', 'text-embedding-ada-002/txt', force=True) - -# Remove a model with the identifier 'text-embedding-ada-002' -db.remove('model', 'text-embedding-ada-002', force=True) -``` - -## Now you can build an API as well just like we did - -### FastAPI Question the Docs Apps Tutorial - -This tutorial will guide you through setting up a basic FastAPI application for handling questions with documentation. The tutorial covers both local development and deployment to the Fly.io platform. -[https://github.com/SuperDuperDB/chat-with-your-docs-backend](https://github.com/SuperDuperDB/chat-with-your-docs-backend) \ No newline at end of file diff --git a/docs/hr/content/use_cases/question-answering/_category_.json b/docs/hr/content/use_cases/question-answering/_category_.json deleted file mode 100644 index b1673224e2..0000000000 --- a/docs/hr/content/use_cases/question-answering/_category_.json +++ /dev/null @@ -1,10 +0,0 @@ -{ - "label": "Question answering", - "position": 3, - "collapsed": false, - "collapsible": false, - "link": { - "type": "generated-index", - "description": "Get started with Question and Answer with SuperDuperDB" - } -} \ No newline at end of file diff --git a/docs/hr/content/use_cases/question-answering/chat_with_your_database.md b/docs/hr/content/use_cases/question-answering/chat_with_your_database.md deleted file mode 100644 index a013c4ec79..0000000000 --- a/docs/hr/content/use_cases/question-answering/chat_with_your_database.md +++ /dev/null @@ -1,289 +0,0 @@ -# Chat with your Snowflake Database Using OpenAI (RAG) - - - -Imagine chatting with your database using just a few lines of code. Sounds unbelievable, right? Well, believe it! We'll show you how you can effortlessly chat with a huge database containing 10 million business records—all with just a few lines of SuperDuperDB code. - -Here is the behemoth 10M dataset: [FREE COMPANY DATASET](https://app.snowflake.com/marketplace/listing/GZSTZRRVYL2/people-data-labs-free-company-dataset) - -Here is the [Google Colab Notebook](https://colab.research.google.com/drive/1YXzAuuQdqkWEQKchglxUuAMzNTKLu5rC#scrollTo=0Zf4Unc_fNBp) for you to quickstart! - -Chatting with this type of massive dataset using the standard RAG pipeline is next to impossible due to the cost and scale. However, with SuperDuperDB, you can achieve the same functionality with just a few lines of code. - -You can control the low level code while enjoying writing the high level code! So that, you can increase the capacity of it! Whether you're using `Snowflake` or `any other SQL dataset`, we've got your back. - -Here's the simplicity of it: -1. Connect using your URI (works with any SQL Database). -2. Specify your Database/Table Name. -3. Craft a query in plain English. - -You'll not only get results but also clear explanations! - -Let SuperDuperDB and OpenAI do the heavy lifting—all within a single prompt. Stay tuned for more exciting features, including prompt chaining! - -Bring the power of AI into your database effortlessly! - -Let's bring AI into your database! 🚀 - -```python -# Only one dependency -# !pip install superduperdb openai - -# Import SuperDuperDB and connect your database -from superduperdb import superduper -from superduperdb.backends.ibis.query import RawSQL -``` - -### Import SuperDuperDB and connect your database - -Here we have connected with a mega database from `Snowflake` but it works with all other `SQL` database. - -```python -user = "superduperuser" -password = "superduperpassword" -account = "XXXX-XXXX" # ORGANIZATIONID-USERID -database = "FREE_COMPANY_DATASET/PUBLIC" # DATABASE/SCHEMA - -# Here we are using Snowflake FREE_COMPANY_DATASET with 10 million company data -snowflake_uri = f"snowflake://{user}:{password}@{account}/{database}" - -# Let's superduper your database -db = superduper( - snowflake_uri, # It could be any SQL Database - metadata_store='sqlite:///your_database_name.db', # We need a persistent metadata store to store important infos like job. It could be anything including your own database. Here we are using a SQLite database. You can use your same database as well. In that case, you don't have to add metadata_store; we will use the same database as metadata store -) -``` - -### Add OpenAI API Key - -If you don't have any, call Sam Altman! - -```python -import os -from superduperdb.ext.openai import OpenAIChatCompletion -from IPython.display import Markdown - -# Add your OPEN_AI_API_KEY or keep it in your os.environ, we will pick it up from the environment -os.environ['OPENAI_API_KEY'] = 'sk-XXX_SAM_ALTMAN_IS_NOT_FIRED_XXX' -``` - -### Create a helper function chat with your database - -Here you can tweak the prompts or you can leave here as it is! - -```python -def chat_with_your_database(table_name, query, limit=5): - # Define the search parameters - search_term = f'Write me a SQL query for the table named {table_name}. The query is: {query}' - - # Define the prompt for the OpenAIChatCompletion model - prompt = ( - 'Act as a database administrator, and expert in SQL. You will be helping me write complex SQL queries. I will explain you my needs, you will generate SQL queries against my database. The database is a Snowflake database, please take it into consideration when generating SQL.' - f' I will provide you with a description of the structure of my tables. You must remember them and use them for generating SQL queries.\n' - 'Here are the tables in CSV format: {context}\n\n' - f'Generate only the SQL query. Always write "regex_pattern" in every "WHERE" query. Integrate a "LIMIT {limit}" clause into the query. Exclude any text other than the SQL query itself. Do not include markdown "```" or "```sql" at the start or end.' - 'Here\'s the CSV file:\n' - ) - - # Add the OpenAIChatCompletion instance to the database - db.add(OpenAIChatCompletion(model='gpt-3.5-turbo', prompt=prompt)) - - # Use the OpenAIChatCompletion model to predict the next query based on the provided context - output, context = db.predict( - model_name='gpt-3.5-turbo', - input=search_term, - context_select=db.execute(RawSQL(f'DESCRIBE {table_name}')).as_pandas().to_csv() - # context_select=db.execute(RawSQL(f'SELECT * FROM {table_name} LIMIT 10')).as_pandas().to_csv() # Use in case of some other SQL databases like Postgres where `DESCRIBE` is not supported. - ) - - try: - # Attempt to execute the predicted SQL query and retrieve the result as a pandas DataFrame - # print(output.content) - query_result = db.execute(RawSQL(output.content)).as_pandas() - - if query_result.empty: - query_result = "No result found. Please edit your query based on the database. Be specific. Like keep everything in lowercase. Use regex etc. Run the same thing multiple times. Always." - except: - # If an exception occurs, provide a message to guide the user on adjusting their query - query_result = "Please edit your query based on the database so that we can find you a suitable result. Please check your table schema if you encounter issues. Run again, if necessary." - - return query_result -``` - -### Create another helper function to explain the result - -This function will be used to explain the result - -```python -def explain_the_result(query_result): - # Define the search parameters - try: - search_term = f'Find business insights from it {query_result.to_csv()}' - except: - return "No result found. Run again. Please edit your query. Be specific. And always run again. LLM will catch the error and will show you the perfect result in multiple attempts." - - # Define the prompt for the OpenAIChatCompletion model - prompt = ( - f'Assume the role of a database analyst. Your objective is to provide accurate business insights based on the provided CSV content. Avoid reproducing the same CSV file or rewriting the SQL query. Conclude your response with a summary.\n' - 'Context: {context}' - 'Here\'s the CSV file for you to analyze:\n' - ) - - # Add the OpenAIChatCompletion instance to the database - db.add(OpenAIChatCompletion(model='gpt-3.5-turbo', prompt=prompt)) - - # Use the OpenAIChatCompletion model to predict insights based on the provided context - output, context = db.predict( - model_name='gpt-3.5-turbo', - input=search_term, - ) - - try: - # Attempt to format the predicted output as Markdown - query_result = Markdown(output.content) - except: - # If an exception occurs, provide a message to guide the user on adjusting their input - query_result = "Please edit your input based on the dataset so that we can find you a suitable output. Please check your data if you encounter issues." - - return query_result -``` - -### Now let's start chatting with your database. - -Run this multiple times as it will keep its context. Here you just edit the `table_name` and `query` to see the final result. - -```python -# If you see no result, Run this codeblock multiple times to make the gpt-3.5-turbo work better and change your query as well. Idea: start with a simple query. Then make it gradually complex. - -table_name = "FREECOMPANYDATASET" -query = "Find me some company in Germany in Berlin in Dortmund in automotive industry. Keep all in lowercase" - -result = chat_with_your_database(table_name, query) - -result -``` - -Result: -| COUNTRY | FOUNDED | ID | INDUSTRY | LINKEDIN_URL | LOCALITY | NAME | REGION | SIZE | WEBSITE | -|---------|---------|----------------------------|------------|------------------------------------------------|------------------------|--------------------------------|--------|-------|-------------------------------| -| germany | None | theaterscoutings-berlin | automotive | [linkedin.com/company/theaterscoutings-berlin](https://linkedin.com/company/theaterscoutings-berlin) | None | theaterscoutings berlin | berlin | 1-10 | [theaterscoutings-berlin.de](https://theaterscoutings-berlin.de) | -| germany | None | v8-garage | automotive | [linkedin.com/company/v8-garage](https://linkedin.com/company/v8-garage) | None | v8 garage | berlin | 1-10 | [v8-garage.de](https://v8-garage.de) | -| germany | None | dipsales-gmbh | automotive | [linkedin.com/company/dipsales-gmbh](https://linkedin.com/company/dipsales-gmbh) | berlin | dipsales gmbh | berlin | 1-10 | [dipsales.com](https://dipsales.com) | -| germany | None | autohaus-koschnick-gmbh | automotive | [linkedin.com/company/autohaus-koschnick-gmbh](https://linkedin.com/company/autohaus-koschnick-gmbh) | berlin | autohaus koschnick gmbh | berlin | 1-10 | [autohaus-koschnick.de](https://autohaus-koschnick.de) | -| germany | None | samoconsult-gmbh | automotive | [linkedin.com/company/samoconsult-gmbh](https://linkedin.com/company/samoconsult-gmbh) | None | samoconsult gmbh | berlin | 1-10 | [samoconsult.de](https://samoconsult.de) | - -### Let's call the `explain_the_result` function to find insights - -Call the explain_the_result function to analyze and explain the business insights - -```python -# Run multiple times if no result shown -explain_the_result(query_result=result) -``` - -```text -# Result: -1. The automotive industry is well-represented in Berlin, Germany, with multiple companies listed in this dataset. - -2. The companies listed are mostly small, with a size range of 1-10 employees. - -3. The companies' LinkedIn profiles provide an opportunity for networking and connecting with industry professionals. - -4. Theaterscoutings Berlin, v8 garage, dipsales gmbh, autohaus koschnick gmbh, and samoconsult gmbh are some of the automotive companies operating in Berlin. - -5. These companies primarily focus on automotive-related services, such as theater scouting, garage services, sales, and consultation. - -6. The websites of these companies can be visited for further information on their offerings and services. - -7. The locality of these companies is primarily Berlin, indicating a concentration of automotive businesses in this area. - -Further analysis could be done by examining the specific offerings and competitive landscape of these companies within the automotive industry in Berlin. -``` - -### Let's generate result on the fly by model chaining - -Now you can do model-chaining as well, if you only care about the explanations. Here we found from the dataset about the company - -```python -# Run multiple times if no result shown -table_name = "FREECOMPANYDATASET" -query = "Find me information about BMW company in Germany. Keep all in lowercase." - -# The result is generated from your dataset. Tweak limit params if you want specific results. -explain_the_result(chat_with_your_database(table_name, query, limit=1)) -``` - -```txt -## Result -#### Based on the given information, here are some possible business insights: - -- **Company Name:** BMW -- **Country:** Germany -- **Founded:** 1916 -- **Industry:** Automotive -- **Region:** Bavaria -- **LinkedIn URL:** linkedin.com/company/abrockman -- **Locality:** Munich -- **Size:** 10,001+ -- **Website:** N/A (Not provided in the given data) - -BMW is one of the oldest automotive companies, founded in 1916 in Germany. This long history indicates its established presence and experience in the industry. - -The company has a significant presence in Bavaria, specifically in Munich. This location may serve as its headquarters or a key operational hub. - -The size of the company is mentioned as 10,001+, which implies that the company has a large workforce or a substantial number of employees. - -The LinkedIn URL provided links to the company's profile on the platform. This indicates that BMW has an active presence on LinkedIn, where it may engage with professionals and potentially recruit talent. - -No website is listed for BMW in the given data, which could mean that the company's website was not included or omitted in the dataset. - -Please note that these insights are based solely on the information provided and further research is recommended for a comprehensive understanding of the business and its insights. -``` - -## Let's chat realtime. - -### Ask questions, get result. - -We just boiled the whole thing in one function. - -Rerun this for new questions. Don't worry, it is keeping the context! - -Let's have one simple interface. Where you just write your query and see the result. Simple. - -```python -# Run multiple times if no result shown -table_name = "FREECOMPANYDATASET" - -# Be innovative and specific here -query = "Find me information about Volkswagen company in Germany. Keep all in lowercase." - -def integrated_result(table_name, query): - queried_result = chat_with_your_database(table_name, query) - explained_result = explain_the_result(queried_result) - - display(queried_result, explained_result) - -# Showing the result here -integrated_result(table_name, query) -``` - -Result: - -| COUNTRY | FOUNDED | ID | INDUSTRY | LINKEDIN_URL | LOCALITY | NAME | REGION | SIZE | WEBSITE | -|---------|---------|-------------|------------|------------------------------------------------|------------|------------|--------------|--------|--------------------------------------| -| germany | None | volkswagen | automotive | [linkedin.com/company/volkswagen](https://linkedin.com/company/volkswagen) | wolfsburg | volkswagen | niedersachsen | 10001+ | [volkswagen-newsroom.com](https://volkswagen-newsroom.com) | - -```text -# The business is Volkswagen, a German automotive company founded in Wolfsburg, Germany. -# The company has a LinkedIn profile at linkedin.com/company/volkswagen. -# It is located in the region of Niedersachsen in Germany. -# Volkswagen is classified as a large company, with a size of 10001+ employees. -# The company's official website is volkswagen-newsroom.com. -``` - -## Voila! You just had a conversation with your database. Let's take it from here. - -This is just the beginning – feel free to customize prompts for your dataset. One secret tip: Mentioning your database schema in the chat_your_database function enhances accuracy by a few miles. Another one is giving more data to it. Anyway, it's yours. Play with it. The better you prompt, the better result you get. This prompt of us is just a simple one works for everything! Your journey with SuperDuperDB is in your hands now. Let the exploration begin! - -#### Give us a star. We will release more update in this example like visualization, fine tuning etc. - diff --git a/docs/hr/content/use_cases/question-answering/voice_memos.md b/docs/hr/content/use_cases/question-answering/voice_memos.md deleted file mode 100644 index 201bc5d4e4..0000000000 --- a/docs/hr/content/use_cases/question-answering/voice_memos.md +++ /dev/null @@ -1,226 +0,0 @@ -# Voice-Memo Assistant on MongoDB (RAG) - -## Cataloguing voice-memos for a self managed personal assistant - -Explore the capabilities of SuperDuperDB by effortlessly integrating models across various data modalities, including audio and text. This project aims to develop sophisticated data-based applications with minimal code complexity. - -### Objectives: - -1. Manage a database of audio recordings. -2. Index the content of these audio recordings. -3. Perform searches and queries on the content of these audio recordings. - -### Our approach involves: - -* Using a transformers model from Facebook's AI team for audio-to-text transcription. -* Applying an OpenAI vectorization model to index the transcribed text. -* Combining the OpenAI ChatGPT model with relevant recordings to query the audio database. - -Real-life use cases encompass personal note-taking, voice diaries, meeting transcriptions, language learning, task reminders, podcast indexing, knowledge base creation, journalism interviews, storytelling archives, and music catalog searches. - -In this example, we'll organize and catalog voice memos for a self-managed personal assistant using SuperDuperDB. - -## Prerequisites - -Before diving into the implementation, ensure that you have the necessary libraries installed by running the following commands: - -```bash -!pip install superduperdb -!pip install transformers soundfile torchaudio librosa openai -!pip install -U datasets -``` - -Additionally, ensure that you have set your openai API key as an environment variable. You can uncomment the following code and add your API key: - -```python -import os - -#os.environ['OPENAI_API_KEY'] = 'sk-XXXX' -if 'OPENAI_API_KEY' not in os.environ: - raise Exception('Environment variable "OPENAI_API_KEY" not set') -``` - -## Connect to datastore - -First, we need to establish a connection to a MongoDB datastore via SuperDuperDB. You can configure the `MongoDB_URI` based on your specific setup. Here are some examples of MongoDB URIs: - -* For testing (default connection): `mongomock://test` -* Local MongoDB instance: `mongodb://localhost:27017` -* MongoDB with authentication: `mongodb://superduper:superduper@mongodb:27017/documents` -* MongoDB Atlas: `mongodb+srv://:@/` - -```python -from superduperdb import superduper -from superduperdb.backends.mongodb import Collection -import os - -mongodb_uri = os.getenv("MONGODB_URI","mongomock://test") - -# Superdupers your database -db = superduper(mongodb_uri) - -# Create a collection for Voice memos -voice_collection = Collection('voice-memos') -``` - -## Load Dataset - -In this example, we use the `LibriSpeech` dataset as our voice recording dataset, containing around 1000 hours of read English speech. Similar functionality can be achieved with any audio source, including audio hosted on the web or in an `s3` bucket. For instance, repositories of audio from conference calls or memos can be indexed in the same way. - -```python -from datasets import load_dataset -from superduperdb.ext.numpy import array -from superduperdb import Document - -# Load the LibriSpeech ASR demo data from Hugging Face datasets -data = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation") - -# Create an `Encoder` for audio data -enc = array('float64', shape=(None,)) - -# Add the encoder to the SuperDuperDB instance -db.add(enc) - -# Insert audio data into the MongoDB collection 'voice_collection' -db.execute(voice_collection.insert_many([ - # Create a SuperDuperDB Document for each audio sample - Document({'audio': enc(r['audio']['array'])}) for r in data -])) -``` - -## Install Pre-Trained Model (LibriSpeech) into Database - -Apply a pre-trained `transformers` model to the data: - -```python -from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration -from superduperdb.ext.transformers import Pipeline - -# Load the pre-trained Speech2Text model and processor from Facebook's library -model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr") -processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr") - -# Define the sampling rate for the audio data -SAMPLING_RATE = 16000 - -# Create a SuperDuperDB pipeline for speech-to-text transcription -transcriber = Pipeline( - identifier='transcription', - object=model, # The pre-trained Speech2Text model - preprocess=processor, # The processor for handling input audio data - preprocess_kwargs={'sampling_rate': SAMPLING_RATE, 'return_tensors': 'pt', 'padding': True}, # Preprocessing configurations - postprocess=lambda x: processor.batch_decode(x, skip_special_tokens=True), # Postprocessing to convert model output to text - predict_method='generate', # Specify the prediction method - preprocess_type='other', # Specify the type of preprocessing -) -``` - -## Run Predictions on All Recordings in the Collection - -Apply the `Pipeline` to all audio recordings: - -```python -transcriber.predict( - X='audio', # Specify the input feature name as 'audio' - db=db, # Provide the SuperDuperDB instance - select=voice_collection.find(), # Specify the collection of audio data to transcribe - max_chunk_size=10 # Set the maximum chunk size for processing audio data -) -``` - -## Ask Questions to Your Voice Assistant - -Interact with your voice assistant by asking questions, leveraging the capabilities of MongoDB for vector-search and filtering rules: - -```python -from superduperdb import VectorIndex, Listener -from superduperdb.ext.openai import OpenAIEmbedding - -# Create a VectorIndex with OpenAI embedding for audio transcriptions -db.add( - VectorIndex( - identifier='my-index', # Set a unique identifier for the VectorIndex - indexing_listener=Listener( - model=OpenAIEmbedding(identifier= 'text-embedding-ada-002', model='text-embedding-ada-002'), # Use OpenAIEmbedding for audio transcriptions - key='_outputs.audio.transcription', # Specify the key for indexing the transcriptions in the output - select=voice_collection.find(), # Select the collection of audio data to index - ), - ) -) -``` - -Let's verify the functionality by searching for the term "royal cavern." - -```python -# Define the search parameters -search_term = 'royal cavern' # Set the search term for audio transcriptions -num_results = 2 # Set the number of desired search results - -# Execute a search query using the VectorIndex 'my-index' -# Search for audio transcriptions similar to the specified search term -# and retrieve the specified number of results -search_results = list( - db.execute( - voice_collection.like( - {'_outputs.audio.transcription': search_term}, - n=num_results, - vector_index='my-index', # Use the 'my-index' VectorIndex for similarity search - ).find({}, {'_outputs.audio.transcription': 1}) # Retrieve only the 'transcription' field in the results - ) -) -``` - -## Enrich with Chat-Completion - -Connect the previous steps with gpt-3.5.turbo, a chat-completion model on OpenAI. The goal is to enhance completions by seeding them with the most relevant audio recordings, determined by their textual transcriptions. Retrieve these transcriptions using the previously configured `VectorIndex`. - -```python -# Import the OpenAIChatCompletion module from superduperdb.ext.openai -from superduperdb.ext.openai import OpenAIChatCompletion - -# Create an instance of OpenAIChatCompletion with the GPT-3.5-turbo model -chat = OpenAIChatCompletion( - model='gpt-3.5-turbo', - prompt=( - 'Use the following facts to answer this question\n' - '{context}\n\n' - 'Here\'s the question:\n' - ), -) - -# Add the OpenAIChatCompletion instance to the database -db.add(chat) - -# Display the details of the added model in the database -print(db.show('model')) -``` - -## Full Voice-Assistant Experience - -Evaluate the complete model by asking a question related to a specific fact mentioned in the audio recordings. The model will retrieve the most relevant recordings and utilize them to formulate its answer: - -```python -from superduperdb import Document - -# Define a question to ask the chat completion model -question = 'Is anything really Greek?' - -# Use the db.predict method to get a response from the GPT-3.5-turbo model -response = db.predict( - model_name='gpt-3.5-turbo', - - # Input the question to the chat completion model - input=question, - - # Select relevant context for the model from the SuperDuperDB collection of audio transcriptions - context_select=voice_collection.like( - Document({'_outputs.audio.transcription': question}), vector_index='my-index' - ).find(), - - # Specify the key in the context used by the model - context_key='_outputs.audio.transcription', -)[0].content - -# Print the response obtained from the chat completion model -print(response) -``` \ No newline at end of file diff --git a/docs/hr/content/use_cases/retrieval_augmented_generation.ipynb b/docs/hr/content/use_cases/retrieval_augmented_generation.ipynb new file mode 100644 index 0000000000..d0741499cd --- /dev/null +++ b/docs/hr/content/use_cases/retrieval_augmented_generation.ipynb @@ -0,0 +1 @@ +{"metadata": {"kernelspec": {"display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.7"}}, "nbformat": 4, "nbformat_minor": 5, "cells": [{"cell_type": "markdown", "id": "38c1a328-fd86-4c5f-bd54-b8664f433608", "metadata": {}, "source": ["\n", "# Retrieval augmented generation"]}, {"cell_type": "markdown", "id": "ec24191c-ed06-4264-9cd1-c8c0d7c23f0b", "metadata": {}, "source": ["The first step in any SuperDuperDB application is to connect to your data-backend with SuperDuperDB:"]}, {"cell_type": "markdown", "id": "32f8484d-2e35-472a-9b24-1a30ec1d144b", "metadata": {}, "source": ["\n", "## Connect to SuperDuperDB"]}, {"cell_type": "code", "execution_count": null, "id": "61976f44-8139-41c0-a73e-569c6d16c4b1", "metadata": {}, "outputs": [], "source": ["# \n", "from superduperdb import superduper\n", "\n", "db = superduper('mongodb://localhost:27017/documents')"]}, {"cell_type": "code", "execution_count": null, "id": "e981a457", "metadata": {}, "outputs": [], "source": ["# \n", "from superduperdb import superduper\n", "\n", "db = superduper('sqlite://my_db.db')"]}, {"cell_type": "markdown", "id": "e40544ac-0d97-46df-8bb5-4151baa72406", "metadata": {}, "source": ["Once you have done that you are ready to define your datatype(s) which you would like to \"search\"."]}, {"cell_type": "markdown", "metadata": {}, "source": ["\n", "## Insert data\n", "\n", "In order to create data, we need create a `Schema` for encoding our special `Datatype` column(s) in the databackend."]}, {"cell_type": "markdown", "metadata": {}, "source": ["Here's some sample data to work with:"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["# \n", "!curl -O https://jupyter-sessions.s3.us-east-2.amazonaws.com/text.json\n", "\n", "import json\n", "with open('text.json') as f:\n", " data = json.load(f)"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["# \n", "!curl -O https://jupyter-sessions.s3.us-east-2.amazonaws.com/images.zip\n", "!unzip images.zip\n", "\n", "import os\n", "data = [{'image': f'file://image/{file}'} for file in os.listdir('./images')]"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["# \n", "!curl -O https://jupyter-sessions.s3.us-east-2.amazonaws.com/audio.zip\n", "!unzip audio.zip\n", "\n", "import os\n", "data = [{'audio': f'file://audio/{file}'} for file in os.listdir('./audio')]"]}, {"cell_type": "markdown", "metadata": {}, "source": ["The next code-block is only necessary if you're working with a custom `DataType`:"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["from superduperdb import Schema, Document\n", "\n", "schema = Schema(\n", " 'my_schema',\n", " fields={\n", " 'my_key': dt\n", " }\n", ")\n", "\n", "data = [\n", " Document({'my_key': item}) for item in data\n", "]"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["# \n", "from superduperdb.backends.mongodb import Collection\n", "\n", "collection = Collection('documents')\n", "\n", "db.execute(collection.insert_many(data))"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["# \n", "from superduperdb.backends.ibis import Table\n", "\n", "table = Table(\n", " 'my_table',\n", " schema=schema,\n", ")\n", "\n", "db.add(table)\n", "db.execute(table.insert(data))"]}, {"cell_type": "markdown", "id": "c9a2cd87-723f-4cee-87c7-9b8181c9e54b", "metadata": {}, "source": ["\n", "## Build text embedding model"]}, {"cell_type": "code", "execution_count": null, "id": "a9b1f538-65ca-499e-b6d0-2dd733f81723", "metadata": {}, "outputs": [], "source": ["# \n", "..."]}, {"cell_type": "code", "execution_count": null, "id": "e83facd8-8823-492f-a2c6-659f38d8e6ec", "metadata": {}, "outputs": [], "source": ["# \n", "..."]}, {"cell_type": "code", "execution_count": null, "id": "3b4a9a60-41df-461d-b165-1d136ee25694", "metadata": {}, "outputs": [], "source": ["# \n", "..."]}, {"cell_type": "code", "execution_count": null, "id": "b1219380-13ce-4301-90e6-6ede2eee1497", "metadata": {}, "outputs": [], "source": ["# \n", "..."]}, {"cell_type": "markdown", "metadata": {}, "source": ["\n", "## Perform a vector search"]}, {"cell_type": "markdown", "metadata": {}, "source": ["- `item` is the item which is to be encoded\n", "- `dt` is the `DataType` instance to apply"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["from superduperdb import Document\n", "\n", "item = Document({'my_key': dt(item)})"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Once we have this search target, we can execute a search as follows:"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["# \n", "from superduperdb.backends.mongodb import Collection\n", "\n", "collection = Collection('documents')\n", "\n", "select = collection.find().like(item)"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["# \n", "\n", "# Table was created earlier, before preparing vector-search\n", "table = db.load('table', 'documents')\n", "\n", "select = table.like(item)"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["results = db.execute(select)"]}, {"cell_type": "markdown", "id": "1179a67b-4e40-496b-9851-98f32d42faa0", "metadata": {}, "source": ["\n", "## Build LLM"]}, {"cell_type": "code", "execution_count": null, "id": "f98e5ff4", "metadata": {}, "outputs": [], "source": ["# \n", "\n", "..."]}, {"cell_type": "code", "execution_count": null, "id": "9bf39c47", "metadata": {}, "outputs": [], "source": ["# \n", "\n", "..."]}, {"cell_type": "code", "execution_count": null, "id": "95e48deb", "metadata": {}, "outputs": [], "source": ["# \n", "\n", "..."]}, {"cell_type": "code", "execution_count": null, "id": "fe4ac344", "metadata": {}, "outputs": [], "source": ["# \n", "\n", "..."]}, {"cell_type": "code", "execution_count": null, "id": "29f98dca", "metadata": {}, "outputs": [], "source": ["# \n", "\n", "..."]}, {"cell_type": "code", "execution_count": null, "id": "3778ab16", "metadata": {}, "outputs": [], "source": ["llm.predict_one(X='Tell me about SuperDuperDB')"]}, {"cell_type": "code", "execution_count": null, "id": "f9c9689c", "metadata": {}, "outputs": [], "source": ["# \n", "from superduperdb.components.model import QueryModel\n", "from superduperdb import Variable, Document\n", "\n", "query_model = QueryModel(\n", " select=collection.find().like(Document({'my_key': Variable('item')}))\n", ")"]}, {"cell_type": "code", "execution_count": null, "id": "c28fdaf6", "metadata": {}, "outputs": [], "source": ["from superduperdb.components.graph import Graph, Input\n", "from superduperdb import superduper\n", "\n", "\n", "@superduper\n", "class PromptBuilder:\n", " def __init__(self, initial_prompt, post_prompt, key):\n", " self.inital_prompt = initial_prompt\n", " self.post_prompt = post_prompt\n", " self.key = key\n", "\n", " def __call__(self, X, context):\n", " return (\n", " self.initial_prompt + '\\n\\n'\n", " + [r[self.key] for r in context]\n", " + self.post_prompt + '\\n\\n'\n", " + X\n", " )\n", "\n", "\n", "prompt_builder = PromptBuilder(\n", " initial_prompt='Answer the following question based on the following facts:',\n", " post_prompt='Here\\'s the question:',\n", " key='my_key',\n", ")\n", "\n", "with Graph() as G:\n", " input = Input('X')\n", " query_results = query_model(item=input)\n", " prompt = prompt_builder(X=input, context=query_results)\n", " output = llm(X=prompt)"]}]} \ No newline at end of file diff --git a/docs/hr/content/use_cases/retrieval_augmented_generation.md b/docs/hr/content/use_cases/retrieval_augmented_generation.md new file mode 100644 index 0000000000..fb83d4c65f --- /dev/null +++ b/docs/hr/content/use_cases/retrieval_augmented_generation.md @@ -0,0 +1,264 @@ +--- +sidebar_label: Retrieval augmented generation +--- +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + + +# Retrieval augmented generation + +The first step in any SuperDuperDB application is to connect to your data-backend with SuperDuperDB: + + +## Connect to SuperDuperDB + + + + + ```python + from superduperdb import superduper + + db = superduper('mongodb://localhost:27017/documents') + ``` + + + ```python + from superduperdb import superduper + + db = superduper('sqlite://my_db.db') + ``` + + +Once you have done that you are ready to define your datatype(s) which you would like to "search". + + +## Insert data + +In order to create data, we need create a `Schema` for encoding our special `Datatype` column(s) in the databackend. + +Here's some sample data to work with: + + + + + ```python + !curl -O https://jupyter-sessions.s3.us-east-2.amazonaws.com/text.json + + import json + with open('text.json') as f: + data = json.load(f) + ``` + + + ```python + !curl -O https://jupyter-sessions.s3.us-east-2.amazonaws.com/images.zip + !unzip images.zip + + import os + data = [{'image': f'file://image/{file}'} for file in os.listdir('./images')] + ``` + + + ```python + !curl -O https://jupyter-sessions.s3.us-east-2.amazonaws.com/audio.zip + !unzip audio.zip + + import os + data = [{'audio': f'file://audio/{file}'} for file in os.listdir('./audio')] + ``` + + +The next code-block is only necessary if you're working with a custom `DataType`: + +```python +from superduperdb import Schema, Document + +schema = Schema( + 'my_schema', + fields={ + 'my_key': dt + } +) + +data = [ + Document({'my_key': item}) for item in data +] +``` + + + + + ```python + from superduperdb.backends.mongodb import Collection + + collection = Collection('documents') + + db.execute(collection.insert_many(data)) + ``` + + + ```python + from superduperdb.backends.ibis import Table + + table = Table( + 'my_table', + schema=schema, + ) + + db.add(table) + db.execute(table.insert(data)) + ``` + + + +## Build text embedding model + + + + + ```python + ... + ``` + + + ```python + ... + ``` + + + ```python + ... + ``` + + + ```python + ... + ``` + + + +## Perform a vector search + +- `item` is the item which is to be encoded +- `dt` is the `DataType` instance to apply + +```python +from superduperdb import Document + +item = Document({'my_key': dt(item)}) +``` + +Once we have this search target, we can execute a search as follows: + + + + + ```python + from superduperdb.backends.mongodb import Collection + + collection = Collection('documents') + + select = collection.find().like(item) + ``` + + + ```python + + # Table was created earlier, before preparing vector-search + table = db.load('table', 'documents') + + select = table.like(item) + ``` + + +```python +results = db.execute(select) +``` + + +## Build LLM + + + + + ```python + + ... + ``` + + + ```python + + ... + ``` + + + ```python + + ... + ``` + + + ```python + + ... + ``` + + + ```python + + ... + ``` + + +```python +llm.predict_one(X='Tell me about SuperDuperDB') +``` + + + + + ```python + from superduperdb.components.model import QueryModel + from superduperdb import Variable, Document + + query_model = QueryModel( + select=collection.find().like(Document({'my_key': Variable('item')})) + ) + ``` + + +```python +from superduperdb.components.graph import Graph, Input +from superduperdb import superduper + + +@superduper +class PromptBuilder: + def __init__(self, initial_prompt, post_prompt, key): + self.inital_prompt = initial_prompt + self.post_prompt = post_prompt + self.key = key + + def __call__(self, X, context): + return ( + self.initial_prompt + '\n\n' + + [r[self.key] for r in context] + + self.post_prompt + '\n\n' + + X + ) + + +prompt_builder = PromptBuilder( + initial_prompt='Answer the following question based on the following facts:', + post_prompt='Here\'s the question:', + key='my_key', +) + +with Graph() as G: + input = Input('X') + query_results = query_model(item=input) + prompt = prompt_builder(X=input, context=query_results) + output = llm(X=prompt) +``` + diff --git a/docs/hr/content/use_cases/vector_search/_category_.json b/docs/hr/content/use_cases/vector_search/_category_.json deleted file mode 100644 index 367abecfb1..0000000000 --- a/docs/hr/content/use_cases/vector_search/_category_.json +++ /dev/null @@ -1,10 +0,0 @@ -{ - "label": "Vector search", - "position": 1, - "collapsed": false, - "collapsible": false, - "link": { - "type": "generated-index", - "description": "Get started with Vector Search with SuperDuperDB" - } -} \ No newline at end of file diff --git a/docs/hr/content/use_cases/vector_search/chunked_vector_search.md b/docs/hr/content/use_cases/vector_search/chunked_vector_search.md deleted file mode 100644 index 373ce1207b..0000000000 --- a/docs/hr/content/use_cases/vector_search/chunked_vector_search.md +++ /dev/null @@ -1,231 +0,0 @@ ---- -sidebar_position: 4 ---- - -# Vector-Search Using Chunked Data on MongoDB - -Let's find specific text within documents using vector-search. In this -example, we show how to do vector-search. But here, we want to go one -step further. Let's search for smaller pieces of text within larger -documents. For instance, a developer may store entire documents but -wants to find specific parts or references inside those documents. - -Here we will show you an example with Wikipedia dataset. Implementing -this kind of search is usually more complex, but with `superduperdb`, -it's just one extra command. - -Real-life use cases for the described problem of searching for specific -text within documents using vector-search with smaller text units -include: - -1. **Legal Document Analysis:** Lawyers could store entire legal - documents and search for specific clauses, references, or terms - within those documents. - -2. **Scientific Research Papers:** Researchers might want to find and - extract specific information or references within scientific papers. - -3. **Code Search in Version Control Systems:** Developers could store - entire code files and search for specific functions, classes, or - code snippets within those files. - -4. **Content Management Systems:** Content managers may store complete - articles and search for specific paragraphs or keywords within those - articles. - -5. **Customer Support Ticket Analysis:** Support teams might store - entire support tickets and search for specific issues or resolutions - mentioned within the tickets. - -In each of these scenarios, the ability to efficiently search for and -retrieve smaller text units within larger documents can significantly -enhance data analysis and retrieval capabilities. - - -## Connect to datastore - -First, we need to establish a connection to a MongoDB datastore via -SuperDuperDB. You can configure the `MongoDB_URI` based on your specific -setup. - -Here are some examples of MongoDB URIs: - -- For testing (default connection): `mongomock://test` -- Local MongoDB instance: `mongodb://localhost:27017` -- MongoDB with authentication: - `mongodb://superduper:superduper@mongodb:27017/documents` -- MongoDB Atlas: - `mongodb+srv://:@/` - - -``` python -import os -from superduperdb import superduper - -mongodb_uri = os.getenv("MONGODB_URI", "mongomock://test") - -# SuperDuperDB, now handles your MongoDB database -# It just super dupers your database -db = superduper(mongodb_uri) -``` - -To demonstrate this search technique with larger text units, we'll use a Wikipedia sample. Run this command to fetch the data. - -``` python -# Downloading the Wikipedia sample JSON file -!curl -O https://superduperdb-public.s3.eu-west-1.amazonaws.com/wikipedia-sample.json -``` - -Just like before, we insert the data using a syntax similar to -`pymongo`: - -``` python -import json -from superduperdb.backends.mongodb import Collection -from superduperdb import Document as D - -# Read the first 100 records from a JSON file ('wikipedia-sample.json') -with open('wikipedia-sample.json') as f: - data = json.load(f)[:100] - -# Connect to the database and insert the data into the 'wikipedia' collection. 'D(r)' converts each record 'r' into a 'Document' object before insertion -db.execute(Collection('wikipedia').insert_many([D(r) for r in data])) -``` - -Let's take a look at a document - -``` python -# Executing a find_one query on the 'wikipedia' collection and unpacking the result -r = db.execute(Collection('wikipedia').find_one()).unpack() - -# Displaying the result -r -``` - -To create the search functionality, we establish a straightforward model designed to break down the raw text into segments. These segments are then stored in another collection: - -``` python -from superduperdb import Model - -# Define a function 'splitter' to split the 'abstract' field of a document into chunks. -def splitter(r): - # Initialize the output list with the document title - out = [r['title']] - # Split the 'abstract' field into chunks of 5 words - split = r['abstract'].split(' ') - # Iterate over the chunks and add them to the output list - for i in range(0, len(split) - 5, 5): - out.append(' '.join(split[i: i + 5])) - # Filter out empty strings from the output list - out = [x for x in out if x] - return out - -# Create a 'Model' instance named 'splitter' with the defined 'splitter' function -model = Model( - identifier='splitter', # Identifier for the model - object=splitter, # The function to be used as a model - flatten=True, # Flatten the output into a single list - model_update_kwargs={'document_embedded': False}, # Model update arguments -) - -# Use the 'predict' method of the model to get predictions for the input 'r'. one=true indicates that we only want one output to check! -model.predict(r, one=True) -``` - -Let's utilize this model across the entire input collection: - -``` python -# Use the 'predict' method of the model -model.predict( - X='_base', # Input data used by the model - db=db, # Database instance (assuming 'db' is defined earlier in your code) - select=Collection('wikipedia').find() # MongoDB query to select documents from the 'wikipedia' collection -) -``` -Now let's look at the split data: - -``` python -# Using the 'execute' method to execute a MongoDB query -# Finding one document in the collection '_outputs._base.splitter' -db.execute(Collection('_outputs._base.splitter').find_one()) -``` - -We can perform a search on this data in a manner similar to the previous -example: - - -``` python -from superduperdb import VectorIndex, Listener -from superduperdb.ext.openai import OpenAIEmbedding - -# Create an instance of the OpenAIEmbedding model with 'text-embedding-ada-002' -model = OpenAIEmbedding(identifier= 'text-embedding-ada-002', model='text-embedding-ada-002') - - -# Add a VectorIndex to the database -db.add( - VectorIndex( - identifier=f'chunked-documents', # Identifier for the VectorIndex - indexing_listener=Listener( - model=model, # Embedding model used for indexing - key='_outputs._base.splitter', # Key to access the embeddings in the database - select=Collection('_outputs._base.splitter').find(), # MongoDB query to select documents for indexing - predict_kwargs={'max_chunk_size': 1000}, # Additional parameters for the model's predict method like chunk size - ), - compatible_listener=Listener( - model=model, # Embedding model used for compatibility checking - key='_base', - select=None, # No specific MongoDB query for Listener - active=False, - ) - ) -) -``` - -Now we can search through the split-text collection and retrieve the -full original documents, highlighting which text was found to be -relevant: - -``` python -from superduperdb.backends.mongodb import Collection -from superduperdb import Document as D -from IPython.display import * - -# Define the query -query = 'politics' - -# Specify the shingle and main collections -shingle_collection = Collection('_outputs._base.splitter') -main_collection = Collection('wikipedia') - -# Execute a search using superduperdb -result = db.execute( - shingle_collection - .like(D({'_base': query}), vector_index='chunked-documents', n=5) - .find({}, {'_outputs._base.text-embedding-ada-002': 0}) -) - -# Display the search results -display(Markdown(f'---')) - -# Iterate over the search results -for shingle in result: - # Retrieve the original document from the main collection - original = db.execute(main_collection.find_one({'_id': shingle['_source']})) - - # Display the title of the original document - display(Markdown(f'# {original["title"]}"')) - - # Highlight the shingle in the abstract of the original document - start = original['abstract'].find(shingle['_outputs']['_base']['splitter']) - - to_format = ( - original["abstract"][:start] + '**' + '' + - shingle["_outputs"]["_base"]["splitter"].upper() + '**' + '' + - original["abstract"][start + len(shingle["_outputs"]["_base"]["splitter"]):] - ) - - # Display the formatted abstract - display(Markdown(to_format)) - display(Markdown(f'---')) -``` diff --git a/docs/hr/content/use_cases/vector_search/multi-modal-duckdb.md b/docs/hr/content/use_cases/vector_search/multi-modal-duckdb.md deleted file mode 100644 index 3680965ea8..0000000000 --- a/docs/hr/content/use_cases/vector_search/multi-modal-duckdb.md +++ /dev/null @@ -1,212 +0,0 @@ ---- -sidebar_position: 5 ---- - -# Multimodel Vector-Search on DuckDB - - -SuperDuperDB offers the flexibility to connect to various SQL databases. Check out range of supported SQL databases [here](../../docs/data_integrations/) - -In this example, we showcase how to implement multimodal vector-search with DuckDB. This is an extension of multimodal vector-search with MongoDB, which is just slightly easier to set up (see [here](https://docs.superduperdb.com/docs/use_cases/items/multimodal_image_search_clip)). Everything demonstrated here applies equally to any of the supported SQL databases mentioned above, as well as to tabular data formats on disk, such as `pandas`. - -Real life use cases could be vectorizing diverse things like images, texts and searching it efficiently with SuperDuperDB. - -## Prerequisites - -Before proceeding with this use-case, ensure that you have installed the necessary software requirements: - -```bash -!pip install superduperdb -``` - -## Connect to Datastore - -The initial step in any `superduperdb` workflow is to connect to your datastore. To connect to a different datastore, simply add a different `URI`, for example, `postgres://...`. - -```python -import os -from superduperdb import superduper - -os.makedirs('.superduperdb', exist_ok=True) - -# Let's super duper your SQL database -db = superduper('duckdb://.superduperdb/test.ddb') -``` - -## Load Dataset - -Now that you're connected, add some data to the datastore: - -```python -# Download the coco_sample.zip file -!curl -O https://superduperdb-public.s3.eu-west-1.amazonaws.com/coco_sample.zip - -# Download the captions_tiny.json file -!curl -O https://superduperdb-public.s3.eu-west-1.amazonaws.com/captions_tiny.json - -# Unzip the contents of coco_sample.zip -!unzip coco_sample.zip - -# Create a directory named 'data/coco' -!mkdir -p data/coco - -# Move the 'images_small' directory to 'data/coco/images' -!mv images_small data/coco/images -``` - -```python -# Import necessary libraries -import json -import pandas as pd -from PIL import Image - -# Open the 'captions_tiny.json' file and load its contents -with open('captions_tiny.json') as f: - data = json.load(f)[:500] - -# Create a DataFrame from a list comprehension with image paths and captions -data = pd.DataFrame([ - { - 'image': r['image']['_content']['path'], - 'captions': r['captions'] - } for r in data -]) - -# Add an 'id' column to the DataFrame -data['id'] = pd.Series(data.index).apply(str) - -# Create a DataFrame with 'id' and 'image' columns -images_df = data[['id', 'image']] - -# Open each image using PIL.Image -images_df['image'] = images_df['image'].apply(Image.open) - -# Create a DataFrame with 'id' and 'captions' columns, exploding the 'captions' column -captions_df = data[['id', 'captions']].explode('captions') -``` - -## Define Schema - -For this use-case, you need a table with images and another table with text. SuperDuperDB extends standard SQL functionality, allowing developers to define their own data types through the `Encoder` abstraction. - -```python -from superduperdb.backends.ibis.query import Table -from superduperdb.backends.ibis.field_types import dtype -from superduperdb.ext.pillow import pil_image -from superduperdb import Schema - -# Define the 'captions' table -captions = Table( - 'captions', - primary_id='id', - schema=Schema( - 'captions-schema', - fields={'id': dtype(str), 'captions': dtype(str)}, - ) -) - -# Define the 'images' table -images = Table( - 'images', - primary_id='id', - schema=Schema( - 'images-schema', - fields={'id': dtype(str), 'image': pil_image}, - ) -) - -# Add the 'captions' and 'images' tables to the SuperDuperDB database -db.add(captions) -db.add(images) -``` - -## Add data to the datastore - -```python -# Insert data from the 'images_df' DataFrame into the 'images' table -_ = db.execute(images.insert(images_df)) - -# Insert data from the 'captions_df' DataFrame into the 'captions' table -_ = db.execute(captions.insert(captions_df)) -``` - -## Build SuperDuperDB `Model` Instances - -This use-case utilizes the `superduperdb.ext.torch` extension. Both models used output `torch` tensors, which are encoded with `tensor`: - -```python -import clip -import torch -from superduperdb.ext.torch import TorchModel, tensor - -# Load the CLIP model -model, preprocess = clip.load("RN50", device='cpu') - -# Define a tensor type -t = tensor(torch.float, shape=(1024,)) - -# Create a TorchModel for text encoding -text_model = TorchModel( - identifier='clip_text', - object=model, - preprocess=lambda x: clip.tokenize(x)[0], - encoder=t, - forward_method='encode_text', -) - -# Create a TorchModel for visual encoding -visual_model = TorchModel( - identifier='clip_image', - object=model.visual, - preprocess=preprocess, - encoder=t, -) -``` - -## Create a Vector-Search Index - -Define a multimodal search index based on the imported models. The `visual_model` is applied to the images, making the `images` table searchable. - -```python -from superduperdb import VectorIndex, Listener - -# Add a VectorIndex -db.add( - VectorIndex( - 'my-index', - indexing_listener=Listener( - model=visual_model, - key='image', - select=images, - ), - compatible_listener=Listener( - model=text_model, - key='captions', - active=False, - select=None, - ) - ) -) -``` - -## Search Images Using Text - -Now, let's demonstrate how to search for images using text queries: - -```python -from superduperdb import Document - -# Execute a query to find images with captions containing 'dog catches frisbee' -res = db.execute( - images - .like(Document({'captions': 'dog catches frisbee'}), vector_index='my-index', n=10) - .limit(10) -) -``` - -Display images - -```python -# Display the image data from the fourth result in the search -res[3]['image'].x -``` diff --git a/docs/hr/content/use_cases/vector_search/multimodal_image_search_clip.md b/docs/hr/content/use_cases/vector_search/multimodal_image_search_clip.md deleted file mode 100644 index 5a4a81752d..0000000000 --- a/docs/hr/content/use_cases/vector_search/multimodal_image_search_clip.md +++ /dev/null @@ -1,207 +0,0 @@ ---- -sidebar_position: 2 ---- - - -# Multimodal Vector-Search Using CLIP on MongoDB - -This notebook demonstrates how SuperDuperDB can perform multimodal searches using the `VectorIndex`. It highlights SuperDuperDB's flexibility in integrating different models for vectorizing diverse queries during search and inference. In this example, we utilize the [CLIP multimodal architecture](https://openai.com/research/clip). - -Real life use cases could be vectorizing diverse things like images and searching it efficiently. - -## Prerequisites - -Before starting, make sure you have the required libraries installed. Run the following commands: - -```bash -!pip install superduperdb -!pip install ipython openai-clip -!pip install -U datasets -``` - -## Connect to datastore - -First, we need to establish a connection to a MongoDB datastore via SuperDuperDB. You can configure the `MongoDB_URI` based on your specific setup. -Here are some examples of MongoDB URIs: - -- For testing (default connection): `mongomock://test` -- Local MongoDB instance: `mongodb://localhost:27017` -- MongoDB with authentication: `mongodb://superduper:superduper@mongodb:27017/documents` -- MongoDB Atlas: `mongodb+srv://:@/` - -```python -import os -from superduperdb import superduper -from superduperdb.backends.mongodb import Collection - -mongodb_uri = os.getenv("MONGODB_URI", "mongomock://test") -db = superduper(mongodb_uri, artifact_store='filesystem://./models/') - -# SuperDuperDB, now handles your MongoDB database -# It just super dupers your database -db = superduper(mongodb_uri, artifact_store='filesystem://.data') - -collection = Collection('multimodal') -``` - -## Load Dataset - -For simplicity and interactivity, we'll use a subset of the [Tiny-Imagenet dataset](https://paperswithcode.com/dataset/tiny-imagenet). The processes shown here can be applied to larger datasets with higher-resolution images. If working with larger datasets, especially with high-resolution images, it's recommended to use a machine with a GPU for efficiency. - -To insert images into the database, we'll use the `Encoder`-`Document` framework. This framework allows saving Python class instances as blobs in the `Datalayer` and retrieving them as Python objects. SuperDuperDB comes with built-in support for `PIL.Image` instances, making it easy to integrate Python AI models with the datalayer. If needed, you can also create custom encoders. - -```bash -!curl -O https://superduperdb-public.s3.eu-west-1.amazonaws.com/coco_sample.zip -!unzip coco_sample.zip -``` - -```python -from superduperdb import Document -from superduperdb.ext.pillow import pil_image as i -import glob -import random - -# Use glob to get a list of image file paths in the 'images_small' directory -images = glob.glob('images_small/*.jpg') - -# Create a list of SuperDuperDB Document instances with image data -# Note: The 'uri' parameter is set to the file URI using the 'file://' scheme -# The list is limited to the first 500 images for demonstration purposes -documents = [Document({'image': i(uri=f'file://{img}')}) for img in images][:500] -``` - -Access a random Document in the documents list, just to check: - -```python -# Access a random Document in the documents list, just to check -documents[1] -``` - -The wrapped python dictionaries may be inserted directly to the `Datalayer`: - -```python -# Insert the list of Document instances into a collection using SuperDuperDB -# Specify the 'i' encoder (pil_image) for the 'image' field -db.execute(collection.insert_many(documents), encoders=(i,)) -``` - -You can verify that the images are correctly stored as follows: - -```python -x = db.execute(imagenet_collection.find_one()).unpack()['image'] - -# Resize the image for display while maintaining the aspect ratio and Display the resized image -display(x.resize((300, 300 * int(x.size[1] / x.size[0])))) -``` - -## Build Models - -Now, let's prepare the CLIP model for multimodal search. This involves two components: `text encoding` and `visual encoding`. Once both components are installed, you can perform searches using both images and text to find matching items. - -```python -import clip -from superduperdb import vector -from superduperdb.ext.torch import TorchModel - -# Load the CLIP model and obtain the preprocessing function -model, preprocess = clip.load("RN50", device='cpu') - -# Define a vector with shape (1024,) -e = vector(shape=(1024,)) - -# Create a TorchModel for text encoding -text_model = TorchModel( - identifier='clip_text', # Unique identifier for the model - object=model, # CLIP model - preprocess=lambda x: clip.tokenize(x)[0], # Model input preprocessing using CLIP - postprocess=lambda x: x.tolist(), # Convert the model output to a list - encoder=e, # Vector encoder with shape (1024,) - forward_method='encode_text', # Use the 'encode_text' method for forward pass -) - -# Create a TorchModel for visual encoding -visual_model = TorchModel( - identifier='clip_image', # Unique identifier for the model - object=model.visual, # Visual part of the CLIP model - preprocess=preprocess, # Visual preprocessing using CLIP - postprocess=lambda x: x.tolist(), # Convert the output to a list - encoder=e, # Vector encoder with shape (1024,) -) -``` - -## Create a Vector-Search Index - -Now, let's create the index for vector-based searching. We'll register both models with the index simultaneously. Specify that the `visual_model` will be responsible for creating vectors in the database (`indexing_listener`). The `compatible_listener` indicates how an alternative model can be used to search the vectors, allowing multimodal search with models expecting different types of indexes. - -```python -from superduperdb import VectorIndex -from superduperdb import Listener - -# Create a VectorIndex and add it to the database -db.add( - VectorIndex( - 'my-index', # Unique identifier for the VectorIndex - indexing_listener=Listener( - model=visual_model, # Visual model for embeddings - key='image', # Key field in documents for embeddings - select=collection.find(), # Select the documents for indexing - predict_kwargs={'batch_size': 10}, # Prediction arguments for the indexing model - ), - compatible_listener=Listener( - # Create a listener to listen upcoming changes in databases - model=text_model, - key='text', - active=False, - select=None, - ) - ) -) -``` - -## Search Images Using Text - -Now we can demonstrate searching for images using text queries: - -```python -from IPython.display import display -from superduperdb import Document - -query_string = 'sports' - -# Execute the 'like' query using the VectorIndex 'my-index' and find the top 3 results -out = db.execute( - collection.like(Document - -({'text': query_string}), vector_index='my-index', n=3).find({}) -) - -# Display the images from the search results -for r in search_results: - x = r['image'].x - display(x.resize((300, int(300 * x.size[1] / x.size[0])))) -``` - -Let's dig further: - -```python -img = db.execute(collection.find_one({}))['image'] -img.x -``` - -## Now let's try Similarity search - -Perform a similarity search using the vector index 'my-index' -Find the top 3 images similar to the input image 'img' -Finally displaying the retrieved images while resizing them for better visualization. - -```python -# Execute the 'like' query using the VectorIndex 'my-index' to find similar images to the specified 'img' -similar_images = db.execute( - collection.like(Document({'image': img}), vector_index='my-index', n=3).find({}) -) - -# Display the similar images from the search results -for i in similar_images: - x = i['image'].x - display(x.resize((300, int(300 * x.size[1] / x.size[0])))) -``` \ No newline at end of file diff --git a/docs/hr/content/use_cases/vector_search/plain_vector_search.md b/docs/hr/content/use_cases/vector_search/plain_vector_search.md deleted file mode 100644 index bec25442c2..0000000000 --- a/docs/hr/content/use_cases/vector_search/plain_vector_search.md +++ /dev/null @@ -1,198 +0,0 @@ ---- -sidebar_position: 1 ---- - -# Vanilla Text Vector-Search on MongoDB - -This guide shows how to use SuperDuperDB for vector search, a powerful technique to find similar documents. We'll cover the setup and demonstrate searching a dataset of documents. Vector search with SuperDuperDB is a useful tool in various situations: - -1. **Efficient Content Search:** Forget old full-text search. Now, use vector search to quickly find what you need in your content. - -2. **Visual Product Discovery:** Users discover similar products by uploading images, making shopping easier. - -3. **Smart Recommendations:** Get recommendations based on both visual and text features, making user experiences better. - -4. **Content Analysis:** Identify similar content and images for thorough fact-checking and reporting. - -5. **RAG Chatbots:** Essential for RAG chatbots in language models, making them more effective. - -These examples show how vector search makes tasks more efficient and improves user experiences in different areas. - -One of the remarkable features of SuperDuperDB is its ability to pull data from any type of database, vectorize it, and perform vector searches. Here's an example. - -## Prerequisites - -Before diving into the implementation, ensure that you have the necessary libraries installed by running the following commands: - -```bash -!pip install superduperdb -!pip install ipython openai==1.1.2 -``` - -Additionally, ensure that you have set your OpenAI API key as an environment variable. You can uncomment the following code and add your API key: - -```python -import os - -#os.environ['OPENAI_API_KEY'] = 'sk-...' - -if 'OPENAI_API_KEY' not in os.environ: - raise Exception('You need to set an OpenAI key as environment variable: "export OPEN_API_KEY=sk-..."') -``` - -## Connect to datastore - -First, we need to establish a connection to a MongoDB datastore via SuperDuperDB. You can configure the `MongoDB_URI` based on your specific setup. -Here are some examples of MongoDB URIs: - -- For testing (default connection): `mongomock://test` -- Local MongoDB instance: `mongodb://localhost:27017` -- MongoDB with authentication: `mongodb://superduper:superduper@mongodb:27017/documents` -- MongoDB Atlas: `mongodb+srv://:@/` - -```python -from superduperdb import superduper -from superduperdb.backends.mongodb import Collection -import os - -mongodb_uri = os.getenv("MONGODB_URI","mongomock://test") - -# SuperDuperDB, now handles your MongoDB database -# It just super dupers your database by initializing a SuperDuperDB datalayer instance with a MongoDB backend and filesystem-based artifact store -db = superduper(mongodb_uri, artifact_store='filesystem://./data/') - -# Reference collection named 'documents' -doc_collection = Collection('documents') -``` - -```python -# Overall metadata information -db.metadata -``` - -## Load Dataset - -We have prepared a dataset, which is the inline documentation of the pymongo API. Let's load this dataset: - -```python -# Download the 'pymongo.json' file using curl -!curl -O https://superduperdb-public.s3.eu-west-1.amazonaws.com/pymongo.json - -# Import the json module to work with JSON data -import json - -# Load the content of the 'pymongo.json' file into the 'data' variable -with open('pymongo.json') as f: - data = json.load(f) -``` - -As usual, we insert the data: - -```python -from superduperdb import Document - -# Insert multiple documents into the 'documents' collection -db.execute(doc_collection.insert_many([Document(r) for r in data])) -``` - -```python -# Execute a query to find one document in the 'documents' collection -result = db.execute(Collection('documents').find_one()) - -# Print the result -print(result) -``` - -## Create Vectors - -In the remainder of the notebook, you can choose between using the `openai` or `sentence_transformers` libraries to perform vector search. After instantiating the model wrappers, the rest of the notebook remains identical. - -For OpenAI vectors: - -```python -# Import the OpenAIEmbedding model from SuperDuperDB -from superduperdb.ext.openai.model import OpenAIEmbedding - -# Initialize an instance of the OpenAIEmbedding model with the 'text-embedding-ada-002' model -model = OpenAIEmbedding(identifier= 'text-embedding-ada-002', model='text-embedding-ada-002') -``` - -For Sentence-Transformers vectors, uncomment the following section: - -```python -#import sentence_transformers -#from superduperdb import Model, vector - -#model = Model( -# identifier='all-MiniLM-L6-v2', -# object=sentence_transformers.SentenceTransformer('all-MiniLM-L6-v2'), -# encoder=vector(shape=(384,)), -# predict_method='encode', # Specify the prediction method -# postprocess=lambda x: x.tolist(), # Define postprocessing function -# batch_predict=True, # Generate predictions for a set of observations all at once -#) -``` - -## Index Vectors - -Now we can configure the Atlas vector-search index. This command saves and sets up a - - model to `listen` to a particular subfield (or the whole document) for new text, converts it on the fly to vectors, and then indexes these vectors using Atlas vector-search. - -```python -from superduperdb import Listener, VectorIndex - -# Add a VectorIndex to the SuperDuperDB instance -db.add( - VectorIndex( - # Use a dynamic identifier based on the model's identifier - identifier=f'pymongo-docs-{model.identifier}', - - # Specify an indexing listener with MongoDB collection and model - indexing_listener=Listener( - select=doc_collection.find(), # MongoDB collection query - key='value', # Key for the documents - model=model, # Specify the model for processing - predict_kwargs={'max_chunk_size': 1000}, # Additional prediction arguments - ), - ) -) - -# Display the vector indexes in the SuperDuperDB instance -db.show('vector_index') -``` - -## Perform Vector Search - -Now that the index is set up, we can use it in a query. SuperDuperDB provides some syntactic sugar for the `aggregate` search pipelines, which can be helpful. It also handles all the conversion of inputs to vectors under the hood. - -```python -# Import necessary classes -from superduperdb import Document -from IPython.display import * - -# Define the search parameters -search_term = 'Query the database' -num_results = 5 - -# Execute the query -result = db.execute( - doc_collection - .like(Document({'value': search_term}), vector_index=f'pymongo-docs-{model.identifier}', n=num_results) - .find() -) - -# Display a horizontal line -display(Markdown('---')) - -# Iterate through the query results and display them -for r in result: - # Display the document's parent and res values in a formatted way - display(Markdown(f'### `{r["parent"] + "." if r["parent"] else ""}{r["res"]}`')) - - # Display the value of the document - display(Markdown(r['value'])) - - # Display a horizontal line - display(Markdown('---')) -``` diff --git a/docs/hr/content/use_cases/vector_search/snowflake-example.md b/docs/hr/content/use_cases/vector_search/snowflake-example.md deleted file mode 100644 index 8bcd11d538..0000000000 --- a/docs/hr/content/use_cases/vector_search/snowflake-example.md +++ /dev/null @@ -1,205 +0,0 @@ -# Vector-Search on Snowflake - -In this use-case we describe how to implement vector-search using `superduperdb` on Snowflake. - -Here is the notebook [notebook](https://github.com/SuperDuperDB/superduperdb/blob/main/examples/snowflake-example.ipynb) for you to run. - -### Configure `superduperdb` to work with Snowflake - -The first step in doing this is -to connect to your snowflake account. When you log in it should look something like this: - -![](/img/snowflake-login.png) - -The important thing you need to get from this login is the **organization-id** and **user-id** from the menu in the bottom right (annotated on the image). You will set these values in the cell below. - - - -```python -import os - -# We set this value, since Snowflake via `ibis` doesn't support `bytes` directly. -os.environ['SUPERDUPERDB_BYTES_ENCODING'] = 'Str' - -from superduperdb import superduper, CFG - -user = "" -password = "" -account = "WSWZPKW-LN66790" # ORGANIZATIONID-USERID - -def make_uri(database): - return f"snowflake://{user}:{password}@{account}/{database}" -``` - -## Set up sample data to test vector-search - -We're going to use some of the Snowflake sample data in this example, namely the `FREE_COMPANY_DATASET`. You -can find the `FREE_COMPANY_DATASET` on [this link](https://app.snowflake.com/marketplace/listing/GZSTZRRVYL2/people-data-labs-free-company-dataset). - -Since the database where this data is hosted is read-only, we copy a sample of the data to our own dataset, and work with the data there. - - -```python -from superduperdb.backends.ibis.query import RawSQL - -db = superduper( - make_uri("FREE_COMPANY_DATASET/PUBLIC"), - metadata_store='sqlite:///.testdb.db' -) - -sample = db.execute(RawSQL('SELECT * FROM FREECOMPANYDATASET SAMPLE (5000 ROWS);')).as_pandas() -``` - -### Connect to your dedicated vector-search database - -We use the connection we created to get the snapshot, to also create the dataset we are going to work with: - - -```python -db.databackend.conn.create_database('SUPERDUPERDB_EXAMPLE', force=True) -``` - -Now we are ready to connect to this database with `superduperdb`: - - -```python -from superduperdb.backends.ibis.query import RawSQL - -db = superduper( - make_uri("SUPERDUPERDB_EXAMPLE/PUBLIC"), - metadata_store='sqlite:///.testdb.db' -) -``` - -Since `superduperdb` implements extra features on top of your classical database/ datalake, it's necessary -to add the tables you wish to work with to the system. You'll notice we are creating a schema as well; that allows -us to implement "interesting" data-types on top of Snowflake, such as images or audio. - - -```python -from superduperdb.backends.ibis.query import Table -from superduperdb.backends.ibis.field_types import dtype -from superduperdb import Schema - -_, t = db.add( - Table( - 'MYTABLE', - primary_id='ID', - schema=Schema( - 'MYSCHEMA', - fields={ - k: dtype('str') - for k in sample.columns - } - ) - ) -) -``` - -If you log back into Snowflake now it should look like this: - -![](/img/snowflake-table.png) - -You'll see that the database and table have been created. - -### Insert data into the vector-search table - -Before inserting the data, we'll do a little bit of cleaning. The reason for this, is we want to have clean ids which uniquely define -the rows which we are working with: - - -```python -import random -sample.ID = sample.ID.str.replace('[^A-Za-z0-9\-]', '', regex=True).str.replace('[-]+', '-', regex=True) -sample[sample.isnull()] = None -del sample['FOUNDED'] - -random_id = lambda: str(random.randint(100000, 999999)) -sample.ID = sample.ID.apply(lambda x: x + '-' + random_id()) -``` - - -```python -t = db.load('table', 'MYTABLE') -t -``` - -Now that we've created the table we want to work with, we can insert the sample data - - -```python -db.execute(t.insert(sample)) -``` - -Let's check this was successful by fetching some data: - - -```python -list(db.execute(t.limit(5))) -``` - -In the next step, we're going to port a model from `sentence_transformers` to `superduperdb` and use this model in searching the rows -of the table with vector-search. You can see that, in-addition to the `sentence_transformers` model, `superduperdb` allows -developers to specify a preprocessing (and postprocessing) function in their `Model` instances. In this case, -the `preprocess` argument tells the model how to convert with the individual lines of data (dictionaries) to strings, so that the model can understand these lines: - - -```python -from superduperdb.ext.sentence_transformers import SentenceTransformer -from superduperdb.ext.numpy import array - -model = SentenceTransformer( - identifier='all-MiniLM-L6-v2', - preprocess=lambda r: '; '.join([f'{k}={v}' for k, v in r.items()]), - encoder=array(dtype='float32', shape=(384,)), - predict_method='encode', - batch_predict=True, - device='mps', -) -``` - -This model is then sent to the `VectorIndex` component via `Listener` and registered with the system -with `db.add`: - - -```python -from superduperdb import Listener, VectorIndex - -db.add( - VectorIndex( - identifier='my-index', - indexing_listener=Listener( - select=t, - key='_base', - model=model, - predict_kwargs={'max_chunk_size': 500, 'batch_size': 30}, - ), - ) -) -``` - -This step will take a few moments (unless you have a GPU to hand). - -:::important -**Once this step is finished you can -search Snowflake with vector-search!** -::: - -### Execute a vector-search query with `.like` - -```python -from superduperdb import Document - -cur = db.execute( - t - .like(Document({'description': 'A management consultancy company based in the USA'}), vector_index='my-index', n = 3) - .limit(3) -) -``` - -We can view the results as a dataframe: - - -```python -cur.as_pandas() -``` diff --git a/docs/hr/content/use_cases/vector_search/video_search.md b/docs/hr/content/use_cases/vector_search/video_search.md deleted file mode 100644 index ef77928dea..0000000000 --- a/docs/hr/content/use_cases/vector_search/video_search.md +++ /dev/null @@ -1,288 +0,0 @@ ---- -sidebar_position: 3 ---- - - -# Video Vector-Search with Text on MongoDB - -This notebook guides you through the process of searching for specific textual information within videos and retrieving relevant video segments. To achieve this, we leverage various libraries and techniques, including: -* clip: A library for vision and language understanding. -* PIL: Python Imaging Library for image processing. -* torch: The PyTorch library for deep learning. - -Searching within videos with text has practical applications in various domains: - -1. **Video Indexing:** People can find specific topics within videos, enhancing search experiences. - -2. **Content Moderation:** Social media platforms use text-based searches to identify and moderate content violations. - -3. **Content Discovery:** Users search for specific scenes or moments within movies or TV shows using text queries. Security personnel can search within video footage for specific incidents or individuals. - -Your imagination is your limit. Basically, all this example is doing is making the video like a blog post and searchable as well! - -## Prerequisites - -Before diving into the implementation, ensure that you have the necessary libraries installed by running the following commands: - -```bash -!pip install superduperdb -!pip install ipython opencv-python pillow openai-clip -``` - -## Connect to datastore - -First, we need to establish a connection to a MongoDB datastore via SuperDuperDB. You can configure the `MongoDB_URI` based on your specific setup. -Here are some examples of MongoDB URIs: - -* For testing (default connection): `mongomock://test` -* Local MongoDB instance: `mongodb://localhost:27017` -* MongoDB with authentication: `mongodb://superduper:superduper@mongodb:27017/documents` -* MongoDB Atlas: `mongodb+srv://:@/` - -```python -from superduperdb import superduper, Collection, CFG -import os - -# Set configuration options for downloads -CFG.downloads.hybrid = True -CFG.downloads.root = './' - -# Define the MongoDB URI, with a default value if not provided -mongodb_uri = os.getenv("MONGODB_URI", "mongomock://test") - -# SuperDuperDB, now handles your MongoDB database -# It just super dupers your database by initializing a SuperDuperDB datalayer instance with a MongoDB backend and filesystem-based artifact store -db = superduper(mongodb_uri, artifact_store='filesystem://./data/') - -# Create a collection named 'videos' -video_collection = Collection('videos') -``` - -## Load Dataset - -We'll begin by configuring a video encoder. - -```python -from superduperdb import Encoder - -# Create an instance of the Encoder with the identifier 'video_on_file' and load_hybrid set to False -vid_enc = Encoder( - identifier='video_on_file', - load_hybrid=False, -) - -# Add the Encoder instance to the SuperDuperDB instance -db.add(vid_enc) -``` - -Let's fetch a sample video from the internet and insert it into our collection. - -```python -from superduperdb.base.document import Document - -# Insert a video document into the 'videos' collection -db.execute( - video_collection.insert_one( - Document({'video': vid_enc(uri='https://superduperdb-public.s3.eu-west-1.amazonaws.com/animals_excerpt.mp4')}) # Encodes the video - ) -) - -# Display the list of videos in the 'videos' collection -list(db.execute(Collection('videos').find())) -``` - -## Register Encoders - -Now, let's set up encoders to process videos and extract frames. These encoders will assist in converting videos into individual frames. - -```python -import cv2 -import tqdm -from PIL import Image -from superduperdb.ext.pillow import pil_image -from superduperdb import Model, Schema - -# Define a function to convert a video file into a list of images -def video2images(video_file): - # Set the sampling frequency for frames - sample_freq = 10 - - # Open the video file using OpenCV - cap = cv2.VideoCapture(video_file) - - # Initialize variables - frame_count = 0 - fps = cap.get(cv2.CAP_PROP_FPS) - extracted_frames = [] - progress = tqdm.tqdm() - - # Iterate through video frames - while True: - ret, frame = cap.read() - if not ret: - break - - # Get the current timestamp based on frame count and FPS - current_timestamp = frame_count // fps - - # Sample frames based on the specified frequency - if frame_count % sample_freq == 0: - extracted_frames.append({ - 'image': Image.fromarray(frame[:,:,::-1]), # Convert BGR to RGB - 'current_timestamp': current_timestamp, - }) - frame_count += 1 - progress.update(1) - - # Release resources - cap.release() - cv2.destroyAllWindows() - - # Return the list of extracted frames - return extracted_frames - -# Create a SuperDuperDB model for the video2images function -video2images_model = Model( - identifier='video2images', - object=video2images, - flatten=True, - model_update_kwargs={'document_embedded': False}, - output_schema=Schema(identifier='myschema', fields={'image': pil_image}) -) -``` - -Additionally, we'll configure a listener to continually download video URLs and store the best frames in another collection. - -```python -from superduperdb import Listener - -# Add a listener to process videos using the video2images model -db.add( - Listener( - model=video2images, # Assuming video2images is your SuperDuperDB model - select=video_collection.find(), - key='video', - ) -) - -# Get the unpacked outputs of the video2images process for a specific video -outputs = db.execute(Collection('_outputs.video.video2images').find_one()).unpack() - -# Display the image output from the processed video -image_output = outputs['_outputs']['video']['video2images']['image'] -``` - -## Create CLIP Model - -Now, let's establish a model for CLIP (Contrastive Language-Image Pre-training), serving for both visual and textual analysis. - -```python -import clip -from superduperdb import vector -from superduperdb.ext.torch import TorchModel - -# Load the CLIP model and define a tensor type -model, preprocess = clip.load("RN50", device='cpu') -t = vector(shape=(1024,)) - -# Create a TorchModel for visual encoding -visual_model = TorchModel( - identifier='clip_image', - preprocess=preprocess, - object=model.visual, - encoder=t, - postprocess=lambda x: x.tolist(), -) - -# Create a TorchModel for text encoding -text_model = TorchModel( - identifier='clip_text', - object=model, - preprocess=lambda x: clip.tokenize(x)[0], - forward_method='encode_text', - encoder=t, - device='cpu', # Specify the device for text encoding - preferred_devices=None, # Specify preferred devices for model execution - postprocess=lambda x: x.tolist(), -) -``` - -## Create VectorIndex - -We'll now establish a VectorIndex to index and search the video frames based on both visual and textual content. This includes creating an indexing listener for visual data and a compatible listener for textual data. - -```python -from - - superduperdb import Listener, VectorIndex -from superduperdb.backends.mongodb import Collection - -# Add a VectorIndex for video search -db.add( - VectorIndex( - identifier='video_search_index', - indexing_listener=Listener( - model=visual_model, # Visual model for image processing - key='_outputs.video.video2images.image', # Visual model for image processing - select=Collection('_outputs.video.video2images').find(), # Collection containing video image data - ), - compatible_listener=Listener( - model=text_model, # Text model for processing associated text data - key='text', - select=None, - active=False - ) - ) -) -``` - -## Query Text Against Saved Frames - -Now, let's search for something that happened during the video: - -```python -# Define the search parameters -search_term = 'Some ducks' -num_results = 1 - -# Execute the search and get the next result -r = next(db.execute( - Collection('_outputs.video.video2images') - .like(Document({'text': search_term}), vector_index='video_search_index', n=num_results) - .find() -)) - -# Extract the timestamp from the search result -search_timestamp = r['_outputs']['video']['video2images']['current_timestamp'] - -# Retrieve the back reference to the original video using the '_source' field -video = db.execute(Collection('videos').find_one({'_id': r['_source']})) -``` - -## Start the Video from the Resultant Timestamp - -Finally, we can display and play the video starting from the timestamp where the searched text is found. - -```python -from IPython.display import display, HTML - -# Create HTML code for the video player with a specified source and controls -video_html = f""" - - -""" - -# Display the HTML code in the notebook -display(HTML(video_html)) -``` diff --git a/docs/hr/package-lock.json b/docs/hr/package-lock.json index e794cd2e25..f055032612 100644 --- a/docs/hr/package-lock.json +++ b/docs/hr/package-lock.json @@ -1,7 +1,7 @@ { "name": "newdocs", "version": "0.0.1", - 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// items: [{ type: 'autogenerated', dirName: 'use_cases' }], + items: [{ type: 'autogenerated', dirName: 'use_cases' }], link: { type: 'generated-index', description: 'Common and useful use-cases implemented in SuperDuperDB with a walkthrough', // slug: 'use-cases', }, - items: [ - { - type: 'category', - label: 'Vector Search', - link: { - type: 'generated-index', - description: 'Go through our vector search use cases below ', - }, - items: [ - 'use_cases/vector_search/plain_vector_search', - 'use_cases/vector_search/multimodal_image_search_clip', - 'use_cases/vector_search/video_search', - 'use_cases/vector_search/chunked_vector_search', - 'use_cases/vector_search/multi-modal-duckdb', - 'use_cases/vector_search/snowflake-example', - ] - }, - { - type: 'category', - label: 'Question Answering', - link: { - type: 'generated-index', - description: 'Go through our Question-Answering application use cases below ', - }, - items: [ - 'use_cases/question-answering/Chatbot', - 'use_cases/question-answering/chat_with_your_database', - 'use_cases/question-answering/voice_memos', - ] - }, - { - type: 'category', - label: 'Classical ML Applications', - link: { - type: 'generated-index', - description: 'Go through our Classical ML application use cases below ', - }, - items: [ - 'use_cases/classical_tasks/mnist_torch', - 'use_cases/classical_tasks/resnet_features', - 'use_cases/classical_tasks/sentiment_analysis_use_case', - 'use_cases/classical_tasks/transfer_learning', - ] - }, - { - type: 'category', - label: 'Productionization', - link: { - type: 'generated-index', - description: 'Use SuperDuperDB in production contexts', - }, - items: [ - 'use_cases/productionization/sandbox-example', - ] - }, - ], + // items: [ + // { + // type: 'category', + // label: 'Vector Search', + // link: { + // type: 'generated-index', + // description: 'Go through our vector search use cases below ', + // }, + // items: [ + // 'use_cases/vector_search/plain_vector_search', + // 'use_cases/vector_search/multimodal_image_search_clip', + // 'use_cases/vector_search/video_search', + // 'use_cases/vector_search/chunked_vector_search', + // 'use_cases/vector_search/multi-modal-duckdb', + // 'use_cases/vector_search/snowflake-example', + // ] + // }, + // { + // type: 'category', + // label: 'Question Answering', + // link: { + // type: 'generated-index', + // description: 'Go through our Question-Answering application use cases below ', + // }, + // items: [ + // 'use_cases/question-answering/Chatbot', + // 'use_cases/question-answering/chat_with_your_database', + // 'use_cases/question-answering/voice_memos', + // ] + // }, + // { + // type: 'category', + // label: 'Classical ML Applications', + // link: { + // type: 'generated-index', + // description: 'Go through our Classical ML application use cases below ', + // }, + // items: [ + // 'use_cases/classical_tasks/mnist_torch', + // 'use_cases/classical_tasks/resnet_features', + // 'use_cases/classical_tasks/sentiment_analysis_use_case', + // 'use_cases/classical_tasks/transfer_learning', + // ] + // }, + // { + // type: 'category', + // label: 'Productionization', + // link: { + // type: 'generated-index', + // description: 'Use SuperDuperDB in production contexts', + // }, + // items: [ + // 'use_cases/productionization/sandbox-example', + // ] + // }, + // ], },