├── .github
    └── workflows
    │   ├── python-publish.yml
    │   ├── python-pytest.yml
    │   ├── python36-autodoc.yml
    │   └── upload-badges.yml
├── .gitignore
├── CONTRIBUTING.md
├── LICENSE
├── MANIFEST.in
├── README.md
├── assets
    ├── monthly_downloads.svg
    ├── total_downloads.svg
    └── weekly_downloads.svg
├── docs
    ├── .buildinfo
    ├── .nojekyll
    ├── FAQ.html
    ├── _images
    │   └── industry_ecommerce_46_0.jpeg
    ├── _sources
    │   ├── FAQ.rst.txt
    │   ├── analytics.rst.txt
    │   ├── array_dict_vectorizer.rst.txt
    │   ├── audio.rst.txt
    │   ├── client.rst.txt
    │   ├── cluster.rst.txt
    │   ├── custom_encodings_example.ipynb.txt
    │   ├── dimensionality_reduction.rst.txt
    │   ├── image.rst.txt
    │   ├── index.rst.txt
    │   ├── industry_ecommerce.ipynb.txt
    │   ├── industry_nba_players.ipynb.txt
    │   ├── intro.rst.txt
    │   ├── quickstart.ipynb.txt
    │   ├── read.rst.txt
    │   ├── text.rst.txt
    │   ├── vector_analytics_example.ipynb.txt
    │   ├── vector_search.rst.txt
    │   └── write.rst.txt
    ├── _static
    │   ├── basic.css
    │   ├── css
    │   │   ├── badge_only.css
    │   │   ├── fonts
    │   │   │   ├── Roboto-Slab-Bold.woff
    │   │   │   ├── Roboto-Slab-Bold.woff2
    │   │   │   ├── Roboto-Slab-Regular.woff
    │   │   │   ├── Roboto-Slab-Regular.woff2
    │   │   │   ├── fontawesome-webfont.eot
    │   │   │   ├── fontawesome-webfont.svg
    │   │   │   ├── fontawesome-webfont.ttf
    │   │   │   ├── fontawesome-webfont.woff
    │   │   │   ├── fontawesome-webfont.woff2
    │   │   │   ├── lato-bold-italic.woff
    │   │   │   ├── lato-bold-italic.woff2
    │   │   │   ├── lato-bold.woff
    │   │   │   ├── lato-bold.woff2
    │   │   │   ├── lato-normal-italic.woff
    │   │   │   ├── lato-normal-italic.woff2
    │   │   │   ├── lato-normal.woff
    │   │   │   └── lato-normal.woff2
    │   │   └── theme.css
    │   ├── doctools.js
    │   ├── documentation_options.js
    │   ├── file.png
    │   ├── jquery-3.5.1.js
    │   ├── jquery.js
    │   ├── js
    │   │   ├── badge_only.js
    │   │   ├── html5shiv-printshiv.min.js
    │   │   ├── html5shiv.min.js
    │   │   └── theme.js
    │   ├── language_data.js
    │   ├── minus.png
    │   ├── plus.png
    │   ├── pygments.css
    │   ├── searchtools.js
    │   └── underscore.js
    ├── analytics.html
    ├── array_dict_vectorizer.html
    ├── audio.html
    ├── client.html
    ├── cluster.html
    ├── custom_encodings_example.html
    ├── dimensionality_reduction.html
    ├── genindex.html
    ├── image.html
    ├── index.html
    ├── industry_ecommerce.html
    ├── industry_nba_players.html
    ├── intro.html
    ├── objects.inv
    ├── py-modindex.html
    ├── quickstart.html
    ├── read.html
    ├── search.html
    ├── searchindex.js
    ├── text.html
    ├── vector_analytics_example.html
    ├── vector_search.html
    └── write.html
├── docsrc
    ├── FAQ.rst
    ├── Makefile
    ├── README.md
    ├── analytics.rst
    ├── array_dict_vectorizer.rst
    ├── audio.rst
    ├── client.rst
    ├── cluster.rst
    ├── conf.py
    ├── dimensionality_reduction.rst
    ├── image.rst
    ├── images
    │   ├── 2d-cosine-similarity.png
    │   ├── dimensionality_reduced_vector_plot.png
    │   ├── vectordb-1d-plot-example-readme.png
    │   └── vectordb-plot-1d-cosine-similarity-comparison.png
    ├── index.rst
    ├── intro.rst
    ├── make.bat
    ├── read.rst
    ├── text.rst
    ├── vector_search.rst
    └── write.rst
├── examples
    ├── custom_encodings_example.ipynb
    ├── data
    │   ├── Corona_NLP_train.csv
    │   ├── nba_per_36.xlsx
    │   └── nba_per_game.xlsx
    ├── images
    │   ├── 2d-cosine-similarity.png
    │   ├── dimensionality_reduced_vector_plot.png
    │   ├── vectordb-1d-plot-example-readme.png
    │   └── vectordb-plot-1d-cosine-similarity-comparison.png
    ├── industry_ecommerce.ipynb
    ├── industry_nba_players.ipynb
    ├── quickstart.ipynb
    └── vector_analytics_example.ipynb
├── pytest.ini
├── requirements.txt
├── setup.py
├── tests
    ├── README.md
    ├── __init__.py
    ├── analytics
    │   ├── __init__.py
    │   ├── api
    │   │   ├── __init__.py
    │   │   └── test_comparator.py
    │   ├── scorer
    │   │   └── test_base_scorer.py
    │   ├── test_relational_documents.py
    │   ├── test_score.py
    │   ├── test_tables.py
    │   └── test_viz.py
    ├── conftest.py
    ├── test_client.py
    ├── test_doc_utils.py
    ├── test_error.py
    ├── test_models.py
    ├── test_read.py
    ├── test_search.py
    ├── test_write_collection_basics.py
    ├── test_write_deployed_models.py
    ├── test_write_documents.py
    ├── test_write_edit.py
    ├── test_write_insert.py
    ├── test_write_misc.py
    ├── test_write_multiprocessing.py
    ├── test_write_retrieve_and_encode.py
    └── utils.py
├── utils
    ├── automate_api.py
    └── download_badges.py
└── vectorai
    ├── __init__.py
    ├── analytics
        ├── __init__.py
        ├── analytics.py
        ├── api
        │   ├── __init__.py
        │   └── comparator.py
        ├── client.py
        ├── comparator.py
        ├── dimensionality_reduction.py
        ├── relational_documents.py
        ├── score.py
        ├── tables.py
        ├── utils.py
        └── viz.py
    ├── api
        ├── __init__.py
        ├── api.py
        ├── array_dict_vectorizer.py
        ├── audio.py
        ├── client.py
        ├── cluster.py
        ├── dimensionality_reduction.py
        ├── image.py
        ├── read.py
        ├── search.py
        ├── text.py
        ├── utils.py
        └── write.py
    ├── client.py
    ├── doc_utils.py
    ├── errors.py
    ├── models
        ├── __init__.py
        ├── base.py
        └── deployed
        │   ├── __init__.py
        │   ├── audio.py
        │   ├── base.py
        │   ├── image.py
        │   └── text.py
    ├── options.py
    ├── read.py
    ├── utils.py
    └── write.py


/.github/workflows/python-publish.yml:
--------------------------------------------------------------------------------
 1 | # # This workflows will upload a Python Package using Twine when a release is created
 2 | # # For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries
 3 | 
 4 | # name: Upload Python Package
 5 | 
 6 | # on:
 7 | #   schedule:
 8 | #     - cron: "0 0 * * *"
 9 | 
10 | # jobs:
11 | #   deploy:
12 | 
13 | #     runs-on: ubuntu-latest
14 | 
15 | #     steps:
16 | #     - uses: actions/checkout@v2
17 | #     - name: Set up Python
18 | #       uses: actions/setup-python@v2
19 | #       with:
20 | #         python-version: '3.x'
21 | #     - name: Install dependencies
22 | #       run: |
23 | #         python -m pip install --upgrade pip
24 | #         pip install setuptools wheel twine
25 | #     - name: Build and publish
26 | #       env:
27 | #         TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
28 | #         TWINE_PASSWORD: ${{ secrets.PYPI_PW }}
29 | #         IS_VECTORAI_NIGHTLY: True
30 | #       run: |
31 | #         python setup.py sdist bdist_wheel
32 | #         twine upload dist/*
33 | 


--------------------------------------------------------------------------------
/.github/workflows/python-pytest.yml:
--------------------------------------------------------------------------------
 1 | name: Run Parallelized Pytest
 2 | 
 3 | on:
 4 |   push:
 5 |     branches: 
 6 |       - master
 7 |     paths:
 8 |       - 'vectorai/**'
 9 |       - 'tests/**'
10 |       - 'setup.py'
11 |   pull_request:
12 |     branches:
13 |       - master
14 |     paths:
15 |       - 'vectorai/**'
16 |       - 'vectorai/api/**'
17 |       - 'tests/**'
18 |       - 'setup.py'
19 |       - '.github/workflows/python-pytest.yml'
20 | 
21 | jobs:
22 |   run_test:
23 |     runs-on: ubuntu-latest
24 |     strategy:
25 |       matrix:
26 |         python-version: [3.6, 3.7]
27 |         test-path: 
28 |           - tests/test_client.py
29 |           - tests/test_doc_utils.py
30 |           - tests/test_error.py
31 |           - tests/test_models.py
32 |           - tests/test_read.py
33 |           - tests/test_search.py
34 |           - tests/test_write_collection_basics.py
35 |           - tests/test_write_deployed_models.py
36 |           - tests/test_write_documents.py
37 |           - tests/test_write_edit.py
38 |           - tests/test_write_insert.py
39 |           - tests/test_write_misc.py
40 |           - tests/test_write_multiprocessing.py
41 |           - tests/test_write_retrieve_and_encode.py
42 |           - tests/analytics/api/test_comparator.py
43 |     steps:
44 |     - uses: actions/checkout@v2
45 |     - name: Set up Python ${{ matrix.python-version }}
46 |       uses: actions/setup-python@v2
47 |       with:
48 |         python-version: ${{ matrix.python-version }}
49 |     - name: Install dependencies
50 |       run: |
51 |         python -m pip install --upgrade pip
52 |         python -m pip install -e .['all']
53 |     - name: Run Tests
54 |       env:
55 |         USERNAME: ${{ secrets.USERNAME }}
56 |         API_KEY: ${{ secrets.API_KEY }}
57 |         VI_USERNAME: ${{ secrets.VI_USERNAME }}
58 |         VI_API_KEY: ${{ secrets.VI_API_KEY }}
59 |       run: python -m pytest ${{ matrix.test-path }} --use_client --reruns 5 --reruns-delay 1
60 | 


--------------------------------------------------------------------------------
/.github/workflows/python36-autodoc.yml:
--------------------------------------------------------------------------------
 1 | name: Run Autodoc
 2 | 
 3 | on:
 4 |   push:
 5 |     branches: 
 6 |       - master
 7 | 
 8 | jobs:
 9 |   run_test:
10 | 
11 |     runs-on: ubuntu-latest
12 | 
13 |     steps:
14 |     - uses: actions/checkout@v2
15 |     - name: Pytest 3.6
16 |       uses: actions/setup-python@v2
17 |       with:
18 |         python-version: '3.6'
19 |     - name: Install dependencies
20 |       run: |
21 |         python -m pip install --upgrade pip
22 |         pip install -e .['all']
23 |     - name: Updating docs
24 |       run: |
25 |         sudo apt-get install -y pandoc
26 |         cd docsrc
27 |         rm -rf _build
28 |         cp ../examples/*.ipynb .
29 |         rm -rf ../docs
30 |         python3 -m sphinx . ../docs
31 |         rm *.ipynb
32 |         touch ../docs/.nojekyll
33 |         echo ${{ github.ref }}
34 |         git add .
35 |         git config --local user.email "action@github.com"
36 |         git config --local user.name "GitHub Action"
37 |         git commit -m "ci: Automated build push" -a | exit 0
38 |     - name: Push changes
39 |       if: github.ref == 'refs/heads/master'
40 |       uses: ad-m/github-push-action@master
41 |       with:
42 |         github_token: ${{ secrets.SECRET_TOKEN }}
43 | 


--------------------------------------------------------------------------------
/.github/workflows/upload-badges.yml:
--------------------------------------------------------------------------------
 1 | name: Update Badges
 2 | 
 3 | on:
 4 |   schedule:
 5 |     - cron: "0 0 * * *"
 6 | 
 7 | jobs:
 8 |   run_test:
 9 |     runs-on: ubuntu-latest
10 |     steps:
11 |     - uses: actions/checkout@v2
12 |     - name: Pytest 3.7
13 |       uses: actions/setup-python@v2
14 |       with:
15 |         python-version: '3.7'
16 |     - name: Download badges
17 |       run: |
18 |         python -m pip install requests
19 |         python utils/download_badges.py
20 |         echo ${{ github.ref }}
21 |         git add .
22 |         git config --local user.email "action@github.com"
23 |         git config --local user.name "GitHub Action"
24 |         git commit -m "ci: Automated build push" -a | exit 0
25 |     - name: Push changes
26 |       if: github.ref == 'refs/heads/master'
27 |       uses: ad-m/github-push-action@master
28 |       with:
29 |         github_token: ${{ secrets.SECRET_TOKEN }}
30 |         branch: ${{ github.ref }}
31 | 


--------------------------------------------------------------------------------
/.gitignore:
--------------------------------------------------------------------------------
  1 | Example.ipynb
  2 | 
  3 | # Byte-compiled / optimized / DLL files
  4 | __pycache__/
  5 | *.py[cod]
  6 | *$py.class
  7 | 
  8 | # C extensions
  9 | *.so
 10 | 
 11 | # Distribution / packaging
 12 | .Python
 13 | build/
 14 | develop-eggs/
 15 | dist/
 16 | downloads/
 17 | eggs/
 18 | .eggs/
 19 | lib/
 20 | lib64/
 21 | parts/
 22 | sdist/
 23 | var/
 24 | wheels/
 25 | pip-wheel-metadata/
 26 | share/python-wheels/
 27 | *.egg-info/
 28 | .installed.cfg
 29 | *.egg
 30 | MANIFEST
 31 | 
 32 | # PyInstaller
 33 | #  Usually these files are written by a python script from a template
 34 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 35 | *.manifest
 36 | *.spec
 37 | 
 38 | # Installer logs
 39 | pip-log.txt
 40 | pip-delete-this-directory.txt
 41 | 
 42 | # Unit test / coverage reports
 43 | htmlcov/
 44 | .tox/
 45 | .nox/
 46 | .coverage
 47 | .coverage.*
 48 | .cache
 49 | nosetests.xml
 50 | coverage.xml
 51 | *.cover
 52 | *.py,cover
 53 | .hypothesis/
 54 | .pytest_cache/
 55 | 
 56 | # Translations
 57 | *.mo
 58 | *.pot
 59 | 
 60 | # Django stuff:
 61 | *.log
 62 | local_settings.py
 63 | db.sqlite3
 64 | db.sqlite3-journal
 65 | 
 66 | # Flask stuff:
 67 | instance/
 68 | .webassets-cache
 69 | 
 70 | # Scrapy stuff:
 71 | .scrapy
 72 | 
 73 | # Sphinx documentation
 74 | docs/_build/
 75 | _build/
 76 | 
 77 | # PyBuilder
 78 | target/
 79 | 
 80 | # Jupyter Notebook
 81 | .ipynb_checkpoints
 82 | 
 83 | # IPython
 84 | profile_default/
 85 | ipython_config.py
 86 | 
 87 | # pyenv
 88 | .python-version
 89 | 
 90 | # pipenv
 91 | #   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
 92 | #   However, in case of collaboration, if having platform-specific dependencies or dependencies
 93 | #   having no cross-platform support, pipenv may install dependencies that don't work, or not
 94 | #   install all needed dependencies.
 95 | #Pipfile.lock
 96 | 
 97 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow
 98 | __pypackages__/
 99 | 
100 | # Celery stuff
101 | celerybeat-schedule
102 | celerybeat.pid
103 | 
104 | # SageMath parsed files
105 | *.sage.py
106 | 
107 | # Environments
108 | .env
109 | .venv
110 | env/
111 | venv/
112 | ENV/
113 | env.bak/
114 | venv.bak/
115 | 
116 | # Spyder project settings
117 | .spyderproject
118 | .spyproject
119 | 
120 | # Rope project settings
121 | .ropeproject
122 | 
123 | # mkdocs documentation
124 | /site
125 | 
126 | # mypy
127 | .mypy_cache/
128 | .dmypy.json
129 | dmypy.json
130 | 
131 | # Pyre type checker
132 | .pyre/
133 | 
134 | # Ignore doctrees 
135 | .doctrees/
136 | doctrees/
137 | docsrc/_build/*
138 | docs/*.ipynb
139 | docsrc/*.ipynb
140 | 


--------------------------------------------------------------------------------
/CONTRIBUTING.md:
--------------------------------------------------------------------------------
  1 | # How to contribute to Vector AI?
  2 | 
  3 | Everyone is welcome to contribute, and we value everybody's contribution. Code
  4 | is thus not the only way to help the community. Answering questions, helping
  5 | others, reaching out and improving the documentations are immensely valuable to
  6 | the community.
  7 | 
  8 | It also helps us if you spread the word: reference the library from blog posts
  9 | on the awesome projects it made possible, shout out on Twitter/Reddit/Kaggle 
 10 | every time it has helped you, or simply star the repo to say "thank you".
 11 | 
 12 | ## You can contribute in so many ways!
 13 | 
 14 | There are 4 ways you can contribute to Vector AI:
 15 | * Fix outstanding issues with the existing code;
 16 | * Suggest new ways to improve current Vector methodologies.
 17 | * Contribute to examples/new data notebooks!
 18 | * Submitting issues related to bugs or desired new features.
 19 | 
 20 | **All are equally valuable to the community!**
 21 | 
 22 | ## Reporting A Bug
 23 | 
 24 | We would really appreciate it if you could **make sure the bug was not
 25 | already reported** (use the search bar on Github under Issues).
 26 | 
 27 | Did not find it? :( So we can act quickly on it, please follow these steps:
 28 | 
 29 | * Include your **OS type and version**;
 30 | * A short, self-contained, code snippet that allows us to reproduce the bug in
 31 |   less than 30s;
 32 | * Provide the *full* traceback if an exception is raised.
 33 | 
 34 | To help with this, we have several utilities to allow you to easily re-produce bugs. 
 35 | These can be done using the following:
 36 | 
 37 | ```
 38 | # Create 500 sample documnents to quickly feed into whatever function is required.
 39 | vi_client.create_sample_documents(500)
 40 | 
 41 | # Create vectors of length 500
 42 | vi_client.generate_vector(500)
 43 | 
 44 | ```
 45 | 
 46 | ### Did you have a new feature suggestion for Vector AI?
 47 | 
 48 | Please provide the following information:
 49 | 
 50 | * Short description of the model and link to the paper;
 51 | * Link to the implementation if it is open-source;
 52 | * Link to the model weights if they are available.
 53 | 
 54 | The best feature requests will provide:
 55 | 
 56 | 1. Motivation first:
 57 |   * Is it related to a problem/frustration with the library? If so, please explain
 58 |     why. Providing a code snippet that demonstrates the problem is best.
 59 |   * Is it related to something you would need for a project? We'd love to hear
 60 |     about it!
 61 |   * Is it something you worked on and think could benefit the community?
 62 |     Awesome! Tell us what problem it solved for you.
 63 | 2. Write a *full paragraph* describing the feature;
 64 | 3. Provide a **code snippet** that demonstrates its future use;
 65 | 4. In case this is related to a paper, please attach a link;
 66 | 5. Attach any additional information (drawings, screenshots, etc.) you think may help.
 67 | 
 68 | If your issue is well written we're already 80% of the way there by the time you
 69 | post it.
 70 | 
 71 | ### How To Make A Pull Request
 72 | 
 73 | We suggest the following method if you are interested in contributing code to Vector AI!
 74 | 
 75 | 1. Fork the repository. 
 76 | 2. Clone the forked repository. 
 77 | 3. Create a new branch. 
 78 | 4. Push changes to your repository. 
 79 | 5. Change remote to this repository.
 80 | 6. Create a PR to this public repository. 
 81 | 
 82 | For detailed instructions, , we recommend: [this detailed guide on making pull requests in open source projects](https://opensource.com/article/19/7/create-pull-request-github).
 83 | 
 84 | ### Running Tests
 85 | 
 86 | - When you are running tests, ensure that you have environment variables USERNAME and API_KEY set and that you run the following command if you are running tests using the client. 
 87 | 
 88 | ```
 89 | python3 -m pytest --use_client
 90 | ```
 91 | 
 92 | Alternatively, if you are adding functions, please use the following so we can save on compute: 
 93 | ```
 94 | python3 -m pytest
 95 | ```
 96 | 
 97 | ### Tests
 98 | 
 99 | Tests are incredibly important for our library to ensure that errors are fixed.
100 | Our main method of testing is running the notebooks and ensuring that their results are still viable. Therefore, please
101 | try to re-run the notebooks to ensure that your submission is not causing errors (which usually will not be the case!)
102 | 
103 | 
104 | ### Style guide
105 | 
106 | For documentation strings, `Vector AI` aims to follows the [google style](https://google.github.io/styleguide/pyguide.html) as closely as possible.
107 | For internal attributes, we use _ in front of the name of the attribute. 
108 | However, we will not reject PRs if they do not follow this style.
109 | 
110 | ### Running Automated API creation
111 | 
112 | To run the automated APi creation, simply run:
113 | ```
114 | python utils/automate_api.py
115 | ```
116 | 
117 | #### This guide was inspired by Transformers [transformers guide to contributing](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) which was influenced by Scikit-learn [scikit-learn guide to contributing](https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md).
118 | 


--------------------------------------------------------------------------------
/MANIFEST.in:
--------------------------------------------------------------------------------
1 | include LICENSE


--------------------------------------------------------------------------------
/assets/monthly_downloads.svg:
--------------------------------------------------------------------------------
1 | {"message":"Invalid color: black"}


--------------------------------------------------------------------------------
/assets/total_downloads.svg:
--------------------------------------------------------------------------------
1 | {"message":"Invalid color: black"}


--------------------------------------------------------------------------------
/assets/weekly_downloads.svg:
--------------------------------------------------------------------------------
1 | {"message":"Invalid color: black"}


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/docs/.buildinfo:
--------------------------------------------------------------------------------
1 | # Sphinx build info version 1
2 | # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
3 | config: 95aa15882f3570ba5ccd4a08d91cb6fb
4 | tags: 645f666f9bcd5a90fca523b33c5a78b7
5 | 


--------------------------------------------------------------------------------
/docs/.nojekyll:
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https://raw.githubusercontent.com/vector-ai/vectorai/7b1a0eb2bb06a82d85ac3633eea984604baf2ea6/docs/.nojekyll


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/docs/_images/industry_ecommerce_46_0.jpeg:
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https://raw.githubusercontent.com/vector-ai/vectorai/7b1a0eb2bb06a82d85ac3633eea984604baf2ea6/docs/_images/industry_ecommerce_46_0.jpeg


--------------------------------------------------------------------------------
/docs/_sources/FAQ.rst.txt:
--------------------------------------------------------------------------------
 1 | 
 2 | Frequently Asked Questions
 3 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 4 | 
 5 | Frequently Asked Questions
 6 | 
 7 | I got a JSONDecodeError. How do I resolve this?
 8 | #################################################
 9 | 
10 | A JSONDecodeError can be diagnosed in the following steps:
11 | 
12 | * Check that the field entries are the same as the type that is stated in the examples and the typehints.  
13 | * If the inputs are correct, message for help on the Discord chat! (Find link on README.)
14 | 
15 | Does the engine run on a nearest neighbor implementation? 
16 | ##################################################################
17 | 
18 | The neighbor runs an Exact Nearest Neighbors implementation and when the number of documents exceeds 
19 | 100k documents, the engine starts to run on ANN instead. 
20 | 
21 | When I insert a document with the same ID what happens to the document? 
22 | ###################################################################################
23 | 
24 | The document is over-written. In order to edit the document (ie – change a field or add a new field), 
25 | you can use the `edit_document` function. 
26 | 
27 | 
28 | How do I add a new field in a collection? 
29 | #################################################
30 | 
31 | Currently, the only way is to run edit_document.
32 | 
33 | How do I get more search results? 
34 | #################################################
35 | 
36 | To get more search results, you want to change the `page_size` parameter. To view the next 
37 | page of results, you want to use the cursor. 
38 | 
39 | Are there any limits to the API request calls? 
40 | #################################################
41 | 
42 | The API requests will time out after 400 seconds. There is no limit on the size of the request or anything. 
43 | 
44 | Is Vector AI able to store images and videos? 
45 | #################################################
46 | 
47 | We currently do not support storing images and videos but this can be fixed by using a link to the 
48 | video or image instead.  
49 | 
50 | 


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/docs/_sources/analytics.rst.txt:
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 1 | Visualisations
 2 | ^^^^^^^^^^^^^^^^^^
 3 | 
 4 | Visualisations
 5 | =======================================================
 6 | Visualisations
 7 | 
 8 | .. automodule:: vectorai.analytics.viz
 9 |     :members:
10 | 


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/docs/_sources/array_dict_vectorizer.rst.txt:
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 1 | Array & Dictionary
 2 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 3 | 
 4 | Array & Dictionary
 5 | =======================================================
 6 | Array & Dictionary
 7 | 
 8 | .. automodule:: vectorai.api.array_dict_vectorizer
 9 |     :members:
10 | 


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/docs/_sources/audio.rst.txt:
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 1 | Audios
 2 | ^^^^^^^^^
 3 | 
 4 | Audios
 5 | =======================================================
 6 | Audios
 7 | 
 8 | .. automodule:: vectorai.api.audio
 9 |     :members:
10 |     


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/docs/_sources/client.rst.txt:
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 1 | Client
 2 | ^^^^^^^
 3 | 
 4 | Client
 5 | =======================================================
 6 | 
 7 | Documentation for Vector AI client goes here.
 8 | 
 9 | .. automodule:: vectorai.client
10 |     :members:
11 | 
12 | 
13 | 


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/docs/_sources/cluster.rst.txt:
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 1 | Cluster
 2 | ^^^^^^^
 3 | 
 4 | Cluster
 5 | =======================================================
 6 | 
 7 | Documentation for vector clustering goes here.
 8 | 
 9 | 
10 | .. automodule:: vectorai.api.cluster
11 |     :members:
12 | 


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/docs/_sources/dimensionality_reduction.rst.txt:
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 1 | Dimensionality Reduction
 2 | ^^^^^^^^^^^^^^^^^^^^^^^^^^
 3 | 
 4 | Dimensionality Reduction
 5 | =======================================================
 6 | 
 7 | .. automodule:: vectorai.api.dimensionality_reduction
 8 |     :members:
 9 | 
10 | 


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/docs/_sources/image.rst.txt:
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 1 | Images
 2 | ^^^^^^^^^
 3 | 
 4 | Images
 5 | =======================================================
 6 | Images
 7 | 
 8 | .. automodule:: vectorai.api.image
 9 |     :members:
10 |     


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/docs/_sources/index.rst.txt:
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  1 | .. vectorai documentation master file, created by
  2 |    sphinx-quickstart on Sat Sep 12 14:33:11 2020.
  3 |    You can adapt this file completely to your liking, but it should at least
  4 |    contain the root `toctree` directive.
  5 | 
  6 | Welcome to Vector AI's documentation!
  7 | ====================================
  8 | 
  9 | .. image:: https://getvectorai.com/assets/logo-with-text.png
 10 |   :width: 600
 11 |   :alt: Vector AI
 12 | 
 13 | Vector AI aims to store vectors alongside documents (text/audio/images/videos).
 14 | It is designed to be a light-weight library to create/manipulate/search and analyse the 
 15 | underlying vectors to power machine learning applications such as semantic
 16 | search, recommendations, etc.
 17 | 
 18 | - Our REST API documentation can be found here: https://api.vctr.ai/documentation   
 19 | - Our discord can be found here: https://discord.gg/CbwUxyD
 20 | 
 21 | Features: 
 22 | 
 23 |    - **Multimedia Data Vectorisation**: Image2Vec, Audio2Vec, etc (Any data can be turned into vectors through machine learning) 
 24 |    - **Vector Similarity Search**: Enable searching of vectors and rich multimedia with vector similarity search. The backbone of many popular A.I use cases like reverse image search, recommendations, personalisation, etc. 
 25 |    - **Vector Operations**: Flexible search with out of the box operations on vectors. e.g. mean, median, sum, etc. 
 26 |    - **Aggregation**: All the traditional aggregation you'd expect. e.g. group by mean, pivot tables, etc 
 27 |    - **Clustering**: Interpret your vectors and data by allocating them to buckets and get statistics about these different buckets based on data you provide. 
 28 |    - **Vector Analytics**: Get better understanding of your vectors by using out-of-the-box practical vector analytics, giving you better understanding of the quality of your vectors.
 29 | 
 30 | Why Vector AI compared to other Nearest Neighbor implementations?
 31 | -------------------------------------------------------------------
 32 | 
 33 | -  **Production Ready**: Our API is fully managed and can scale to power
 34 |    hundreds of millions of searches a day. Even at millions of searches
 35 |    it is blazing fast through edge caching, gpus and software
 36 |    optisation. So you never have to worry about scaling your
 37 |    infastructure as your use case scales.
 38 | -  **Richer understanding of your vectors and their properties**: Our
 39 |    library is designed to allow people to not just designed to obtain
 40 |    nearest neighbors but to actually use in production-ready search
 41 |    systems - allowing users to analyse, iterate, improve and
 42 |    productionise their vectors the moment they are added to the index.
 43 | -  **Simple to use. Quick to get started.**: One of our core design
 44 |    principles is that we focus a lot on how people can get started on
 45 |    using Vector AI as quickly as possible, while having a tonne of
 46 |    functionality and customisability options.
 47 | -  **Framework agnostic**: We are never going to force a specific
 48 |    framework on Vector AI. If you have a framework of choice, you can use
 49 |    it - as long as your documents are JSON-serializable!
 50 | -  **Store vector data with ease**: The document-orientated nature for
 51 |    Vector AI allows users to label, filter search and understand their
 52 |    vectors as much as possible. We think that other libraries that
 53 |    simply provide a nearest-neighbor implementation do not have as rich
 54 |    functionality.
 55 | 
 56 | 
 57 | How to install
 58 | ###############
 59 | 
 60 | To install vectorai, run the following
 61 | 
 62 | .. code-block:: RST
 63 | 
 64 |     pip install vectorai
 65 | 
 66 | 
 67 | To install from source, clone the repository and then run
 68 | 
 69 | .. code-block:: RST
 70 | 
 71 |     cd vectorai 
 72 |     pip install -e . 
 73 | 
 74 | Schema
 75 | ########
 76 | 
 77 | We have a very simple schema to follow to allow you to optimise functionality with vector search:
 78 | 
 79 | .. list-table:: Schema Rules
 80 |    :widths: 25 75
 81 |    :header-rows: 1
 82 | 
 83 |    * - Field
 84 |      - Purpose
 85 | 
 86 |    * - _id
 87 |      - ID of the document. These need to be unique for the document.
 88 | 
 89 |    * - _vector_
 90 |      - These are required to label the vectors for vector search.
 91 | 
 92 | .. toctree::
 93 |    :maxdepth: 2
 94 |    :caption: Contents
 95 | 
 96 |    intro
 97 |    quickstart
 98 | 
 99 | .. toctree::
100 |    :caption: Guides
101 |    
102 |    industry_ecommerce
103 |    vector_analytics_example
104 |    custom_encodings_example
105 | 
106 | .. toctree::
107 |    :caption: Case Studies
108 |    
109 |    industry_nba_players
110 | 
111 | .. toctree::
112 |    :caption: Frequently Asked Questions
113 | 
114 |    FAQ
115 | 
116 | 
117 | .. toctree::
118 |     :maxdepth: 2
119 |     :caption: Documentation
120 |     
121 |     client
122 |     read
123 |     write
124 |     cluster
125 |     array_dict_vectorizer
126 |     dimensionality_reduction
127 |     vector_search
128 |     image
129 |     text
130 |     audio
131 |     analytics
132 | 
133 | 
134 | Indices and tables
135 | ==================
136 | 
137 | * :ref:`genindex`
138 | * :ref:`modindex`
139 | * :ref:`search`
140 | 


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/docs/_sources/intro.rst.txt:
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 1 | 
 2 | Vector AI - Essentials
 3 | ^^^^^^^^^^^^^^^^^^^^^^
 4 | 
 5 | Vector AI is built to store vectors alongside documents (text/audio/images/videos). 
 6 | It is designed to be a light-weight library to create, manipulate, search and analyse vectors to power machine 
 7 | learning applications such as semantic search, recommendations, etc.
 8 | 
 9 | Important Terminologies
10 | =======================
11 | - **Vectors** (aka. embeddings, 1D arrays)
12 | 
13 | - **Models/Encoders** (aka. Embedders) Turns data into vectors e.g. Word2Vec turns words into vectors
14 | 
15 | - **Vector Similarity Search** (aka. Nearest Neighbor Search, Distance Search)
16 | 
17 | - **Collection** (aka. Index, Table) ~ a collection is made up of multiple documents
18 | 
19 | - **Documents** (aka. Json, Item, Dictionary, Row) ~ a document can contain vector + other important information
20 | 
21 | 
22 | .. code-block:: RST
23 |     e.g.
24 |     {
25 |         "_id" : "1", 
26 |         "description_vector__ ": [...], 
27 |         "description" : "This is a great idea"
28 |     }
29 | 
30 | Some important information: for predefined vectors use the suffix "_vector_" in the name like "description_vector_", for ids to do quick key value lookup use the name "_id"
31 | 
32 | Documents in Vector AI
33 | ========================
34 | 
35 | Documents (dictionaries) consists of fields (dictionary keys) and values.
36 | 
37 | 1. Vector AI is document orientated (dictionaries/jsons) which means you can have nested fields. This means that you have documents such as:
38 | 
39 |     .. code-block:: RST
40 | 
41 |         document_example = {
42 |             "car": {
43 |                 "wheels":
44 |                     {
45 |                         "number": 4
46 |                     }
47 |             }
48 |         }
49 | 
50 | then running vi_client.get_field("car.wheels.number") will return 4
51 | 
52 | 2. When uploading documents into VectorAi, it will infer the schema from the first document being inserted.
53 | 
54 | You are able to navigate the documents within the fields by using the functions below, allowing you to navigate through 
55 | nested documents if the fields are separated by .'s.
56 | 
57 | .. code-block:: python
58 | 
59 |     vi_client.set_field(field, doc, value)
60 |     vi_client.get_field(field, doc)
61 |     vi_client.set_field_across_documents(field, docs, values)
62 |     vi_client.get_field_across_documents(field, docs)
63 | 
64 | Models With Vector AI
65 | ========================
66 | 
67 | Vector AI has deployed models that we've handpicked and tuned to work nicely out of the box on most problems. 
68 | These models, however, may be changed over time. When they do we make sure that 
69 | previous models are still deployed and can be used.
70 | To prototype something quickly we highly recommend using these deployed models.
71 | 
72 | 
73 | **If you are working on a problem that requires highly customised or finetuned models, reach out to us 
74 | for enterprise services where we can fine tune these models for your use case or feel free to build your own.**
75 | 
76 | Currently, our deployed models are:
77 |     * ViText2Vec - our text to vector model
78 |     * ViImage2Vec - our image to vector model
79 |     * ViAudio2Vec - our audio to vector model
80 |     * dimensionality_reduction_job - perform dimensionality reduction on your vectors
81 |     * clustering_job - perform clustering on your vectors
82 |     * advanced_cluster_job - perform clustering with advanced options on your vectors
83 | 


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/docs/_sources/read.rst.txt:
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 1 | Read
 2 | ^^^^^^
 3 | 
 4 | Read
 5 | =======================================================
 6 | Read
 7 | 
 8 | .. automodule:: vectorai.api.read
 9 |     :members:
10 | 
11 | .. automodule:: vectorai.read
12 |     :members:
13 | 


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/docs/_sources/text.rst.txt:
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 1 | Texts
 2 | ^^^^^^^^
 3 | 
 4 | Texts
 5 | =======================================================
 6 | Texts
 7 | 
 8 | .. automodule:: vectorai.api.text
 9 |     :members:
10 | 


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/docs/_sources/vector_search.rst.txt:
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 1 | Search
 2 | ^^^^^^
 3 | 
 4 | Search
 5 | =======================================================
 6 | Search
 7 | 
 8 | .. automodule:: vectorai.api.search
 9 |     :members:
10 | 


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/docs/_sources/write.rst.txt:
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 1 | Write
 2 | ^^^^^
 3 | 
 4 | Write
 5 | =======================================================
 6 | Write
 7 | 
 8 | This is documentation for the Write API for Vector AI.
 9 | 
10 | .. automodule:: vectorai.write
11 |     :members:
12 |     :show-inheritance:
13 | 
14 | .. automodule:: vectorai.api.write
15 |     :members:
16 |     :show-inheritance:
17 | 


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 98 | <ul>
 99 | <li class="toctree-l1"><a class="reference internal" href="industry_ecommerce.html">Vector Search / Nearest Neighbors</a></li>
100 | <li class="toctree-l1"><a class="reference internal" href="industry_ecommerce.html#Search">Search</a></li>
101 | <li class="toctree-l1"><a class="reference internal" href="industry_ecommerce.html#Collection-Metadata">Collection Metadata</a></li>
102 | <li class="toctree-l1"><a class="reference internal" href="industry_ecommerce.html#Advanced-Search">Advanced Search</a></li>
103 | <li class="toctree-l1"><a class="reference internal" href="industry_ecommerce.html#Advanced-Vector-Search">Advanced Vector Search</a></li>
104 | <li class="toctree-l1"><a class="reference internal" href="industry_ecommerce.html#Vector-based-Recommendations-(Search-by-Id)">Vector based Recommendations (Search by Id)</a></li>
105 | <li class="toctree-l1"><a class="reference internal" href="industry_ecommerce.html#Vector-Analytics/Aggregation">Vector Analytics/Aggregation</a></li>
106 | <li class="toctree-l1"><a class="reference internal" href="vector_analytics_example.html">Clustering</a></li>
107 | <li class="toctree-l1"><a class="reference internal" href="vector_analytics_example.html#Dimensionality-Reduction">Dimensionality Reduction</a></li>
108 | <li class="toctree-l1"><a class="reference internal" href="vector_analytics_example.html#Visualisations-(Advanced)">Visualisations (Advanced)</a></li>
109 | <li class="toctree-l1"><a class="reference internal" href="custom_encodings_example.html">Custom Encodings</a></li>
110 | </ul>
111 | <p class="caption"><span class="caption-text">Case Studies</span></p>
112 | <ul>
113 | <li class="toctree-l1"><a class="reference internal" href="industry_nba_players.html">Example - Vector Recommendations With NBA Players</a></li>
114 | </ul>
115 | <p class="caption"><span class="caption-text">Frequently Asked Questions</span></p>
116 | <ul>
117 | <li class="toctree-l1"><a class="reference internal" href="FAQ.html">Frequently Asked Questions</a></li>
118 | </ul>
119 | <p class="caption"><span class="caption-text">Documentation</span></p>
120 | <ul>
121 | <li class="toctree-l1"><a class="reference internal" href="client.html">Client</a></li>
122 | <li class="toctree-l1"><a class="reference internal" href="read.html">Read</a></li>
123 | <li class="toctree-l1"><a class="reference internal" href="write.html">Write</a></li>
124 | <li class="toctree-l1"><a class="reference internal" href="cluster.html">Cluster</a></li>
125 | <li class="toctree-l1"><a class="reference internal" href="array_dict_vectorizer.html">Array &amp; Dictionary</a></li>
126 | <li class="toctree-l1"><a class="reference internal" href="dimensionality_reduction.html">Dimensionality Reduction</a></li>
127 | <li class="toctree-l1"><a class="reference internal" href="vector_search.html">Search</a></li>
128 | <li class="toctree-l1"><a class="reference internal" href="image.html">Images</a></li>
129 | <li class="toctree-l1"><a class="reference internal" href="text.html">Texts</a></li>
130 | <li class="toctree-l1"><a class="reference internal" href="audio.html">Audios</a></li>
131 | <li class="toctree-l1"><a class="reference internal" href="analytics.html">Visualisations</a></li>
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258 | </html>


--------------------------------------------------------------------------------
/docsrc/FAQ.rst:
--------------------------------------------------------------------------------
 1 | 
 2 | Frequently Asked Questions
 3 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 4 | 
 5 | Frequently Asked Questions
 6 | 
 7 | I got a JSONDecodeError. How do I resolve this?
 8 | #################################################
 9 | 
10 | A JSONDecodeError can be diagnosed in the following steps:
11 | 
12 | * Check that the field entries are the same as the type that is stated in the examples and the typehints.  
13 | * If the inputs are correct, message for help on the Discord chat! (Find link on README.)
14 | 
15 | Does the engine run on a nearest neighbor implementation? 
16 | ##################################################################
17 | 
18 | The neighbor runs an Exact Nearest Neighbors implementation and when the number of documents exceeds 
19 | 100k documents, the engine starts to run on ANN instead. 
20 | 
21 | When I insert a document with the same ID what happens to the document? 
22 | ###################################################################################
23 | 
24 | The document is over-written. In order to edit the document (ie – change a field or add a new field), 
25 | you can use the `edit_document` function. 
26 | 
27 | 
28 | How do I add a new field in a collection? 
29 | #################################################
30 | 
31 | Currently, the only way is to run edit_document.
32 | 
33 | How do I get more search results? 
34 | #################################################
35 | 
36 | To get more search results, you want to change the `page_size` parameter. To view the next 
37 | page of results, you want to use the cursor. 
38 | 
39 | Are there any limits to the API request calls? 
40 | #################################################
41 | 
42 | The API requests will time out after 400 seconds. There is no limit on the size of the request or anything. 
43 | 
44 | Is Vector AI able to store images and videos? 
45 | #################################################
46 | 
47 | We currently do not support storing images and videos but this can be fixed by using a link to the 
48 | video or image instead.  
49 | 
50 | 


--------------------------------------------------------------------------------
/docsrc/Makefile:
--------------------------------------------------------------------------------
 1 | # Minimal makefile for Sphinx documentation
 2 | #
 3 | 
 4 | # You can set these variables from the command line, and also
 5 | # from the environment for the first two.
 6 | SPHINXOPTS    ?=
 7 | SPHINXBUILD   ?= sphinx-build
 8 | SOURCEDIR     = .
 9 | BUILDDIR      = _build
10 | 
11 | # Put it first so that "make" without argument is like "make help".
12 | help:
13 | 	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
14 | 
15 | .PHONY: help Makefile
16 | 
17 | # Catch-all target: route all unknown targets to Sphinx using the new
18 | # "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
19 | %: Makefile
20 | 	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
21 | 
22 | docs:
23 | 	cp ../examples/*.ipynb .
24 | 	rm -rf _build
25 | 	python3 -m sphinx . _build -j3
26 | 	rm *.ipynb
27 | 
28 | docs-migrate:
29 | 	rm -rf _build
30 | 	cp ../examples/*.ipynb .
31 | 	rm -rf ../docs
32 | 	python3 -m sphinx . ../docs -j3
33 | 	rm *.ipynb
34 | 	touch ../docs/.nojekyll
35 | 


--------------------------------------------------------------------------------
/docsrc/README.md:
--------------------------------------------------------------------------------
 1 | ## Documentation 
 2 | 
 3 | To make documentation on Linux/Unix systems, (if you are on Windows, download WSL) and run:
 4 | 
 5 | ```
 6 | make docs-migrate
 7 | ```
 8 | 
 9 | or 
10 | 
11 | ```
12 | cp -r ../examples/*.ipynb .
13 | python3 -m sphinx . _build -j3
14 | # You can alter j above to the number of processes you want running in parallel. Afterwards, you can remove all notebooks from directory using:
15 | rm -f *.ipynb
16 | ```
17 | 
18 | or if you only want to make them and store them in the docsrc subdirectory, run:
19 | 
20 | ```
21 | make docs
22 | ```
23 | 
24 | The process for generating the documentation is that the notebooks are copied into this folder, the documentation then runs nbsphinx into ../docs/ folder which then hosts all the html files.
25 | 


--------------------------------------------------------------------------------
/docsrc/analytics.rst:
--------------------------------------------------------------------------------
 1 | Visualisations
 2 | ^^^^^^^^^^^^^^^^^^
 3 | 
 4 | Visualisations
 5 | =======================================================
 6 | Visualisations
 7 | 
 8 | .. automodule:: vectorai.analytics.viz
 9 |     :members:
10 | 


--------------------------------------------------------------------------------
/docsrc/array_dict_vectorizer.rst:
--------------------------------------------------------------------------------
 1 | Array & Dictionary
 2 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 3 | 
 4 | Array & Dictionary
 5 | =======================================================
 6 | Array & Dictionary
 7 | 
 8 | .. automodule:: vectorai.api.array_dict_vectorizer
 9 |     :members:
10 | 


--------------------------------------------------------------------------------
/docsrc/audio.rst:
--------------------------------------------------------------------------------
 1 | Audios
 2 | ^^^^^^^^^
 3 | 
 4 | Audios
 5 | =======================================================
 6 | Audios
 7 | 
 8 | .. automodule:: vectorai.api.audio
 9 |     :members:
10 |     


--------------------------------------------------------------------------------
/docsrc/client.rst:
--------------------------------------------------------------------------------
 1 | Client
 2 | ^^^^^^^
 3 | 
 4 | Client
 5 | =======================================================
 6 | 
 7 | Documentation for Vector AI client goes here.
 8 | 
 9 | .. automodule:: vectorai.client
10 |     :members:
11 | 
12 | 
13 | 


--------------------------------------------------------------------------------
/docsrc/cluster.rst:
--------------------------------------------------------------------------------
 1 | Cluster
 2 | ^^^^^^^
 3 | 
 4 | Cluster
 5 | =======================================================
 6 | 
 7 | Documentation for vector clustering goes here.
 8 | 
 9 | 
10 | .. automodule:: vectorai.api.cluster
11 |     :members:
12 | 


--------------------------------------------------------------------------------
/docsrc/conf.py:
--------------------------------------------------------------------------------
 1 | # Configuration file for the Sphinx documentation builder.
 2 | #
 3 | # This file only contains a selection of the most common options. For a full
 4 | # list see the documentation:
 5 | # https://www.sphinx-doc.org/en/master/usage/configuration.html
 6 | 
 7 | # -- Path setup --------------------------------------------------------------
 8 | 
 9 | # If extensions (or modules to document with autodoc) are in another directory,
10 | # add these directories to sys.path here. If the directory is relative to the
11 | # documentation root, use os.path.abspath to make it absolute, like shown here.
12 | #
13 | # import os
14 | # import sys
15 | # sys.path.insert(0, os.path.abspath('.'))
16 | 
17 | 
18 | # -- Project information -----------------------------------------------------
19 | 
20 | project = 'vectorai'
21 | copyright = '2020, OnSearch Pty Ltd'
22 | author = 'OnSearch Pty Ltd'
23 | 
24 | # The full ve ion, including alpha/beta/rc tags
25 | release = '0.1.0'
26 | 
27 | 
28 | # -- General configuration ---------------------------------------------------
29 | 
30 | # Add any Sphinx extension module names here, as strings. They can be
31 | # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
32 | # ones.
33 | extensions = [
34 |     'nbsphinx',
35 |     "sphinx.ext.autodoc",
36 |     "sphinx.ext.coverage",
37 |     "sphinx.ext.napoleon",
38 |      "sphinx_rtd_theme"
39 | ]
40 | 
41 | # Add any paths that contain templates here, relative to this directory.
42 | templates_path = ['_templates']
43 | 
44 | # List of patterns, relative to source directory, that match files and
45 | # directories to ignore when looking for source files.
46 | # This pattern also affects html_static_path and html_extra_path.
47 | exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
48 | 
49 | 
50 | # -- Options for HTML output -------------------------------------------------
51 | 
52 | # The theme to use for HTML and HTML Help pages.  See the documentation for
53 | # a list of builtin themes.
54 | #
55 | html_theme = "sphinx_rtd_theme"
56 | nbsphinx_execute = 'never'
57 | 
58 | # Add any paths that contain custom static files (such as style sheets) here,
59 | # relative to this directory. They are copied after the builtin static files,
60 | # so a file named "default.css" will overwrite the builtin "default.css".
61 | html_static_path = ['_static']
62 | autodoc_member_order = 'bysource'
63 | 


--------------------------------------------------------------------------------
/docsrc/dimensionality_reduction.rst:
--------------------------------------------------------------------------------
 1 | Dimensionality Reduction
 2 | ^^^^^^^^^^^^^^^^^^^^^^^^^^
 3 | 
 4 | Dimensionality Reduction
 5 | =======================================================
 6 | 
 7 | .. automodule:: vectorai.api.dimensionality_reduction
 8 |     :members:
 9 | 
10 | 


--------------------------------------------------------------------------------
/docsrc/image.rst:
--------------------------------------------------------------------------------
 1 | Images
 2 | ^^^^^^^^^
 3 | 
 4 | Images
 5 | =======================================================
 6 | Images
 7 | 
 8 | .. automodule:: vectorai.api.image
 9 |     :members:
10 |     


--------------------------------------------------------------------------------
/docsrc/images/2d-cosine-similarity.png:
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https://raw.githubusercontent.com/vector-ai/vectorai/7b1a0eb2bb06a82d85ac3633eea984604baf2ea6/docsrc/images/2d-cosine-similarity.png


--------------------------------------------------------------------------------
/docsrc/images/dimensionality_reduced_vector_plot.png:
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https://raw.githubusercontent.com/vector-ai/vectorai/7b1a0eb2bb06a82d85ac3633eea984604baf2ea6/docsrc/images/dimensionality_reduced_vector_plot.png


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/docsrc/images/vectordb-1d-plot-example-readme.png:
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https://raw.githubusercontent.com/vector-ai/vectorai/7b1a0eb2bb06a82d85ac3633eea984604baf2ea6/docsrc/images/vectordb-1d-plot-example-readme.png


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/docsrc/images/vectordb-plot-1d-cosine-similarity-comparison.png:
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https://raw.githubusercontent.com/vector-ai/vectorai/7b1a0eb2bb06a82d85ac3633eea984604baf2ea6/docsrc/images/vectordb-plot-1d-cosine-similarity-comparison.png


--------------------------------------------------------------------------------
/docsrc/index.rst:
--------------------------------------------------------------------------------
  1 | .. vectorai documentation master file, created by
  2 |    sphinx-quickstart on Sat Sep 12 14:33:11 2020.
  3 |    You can adapt this file completely to your liking, but it should at least
  4 |    contain the root `toctree` directive.
  5 | 
  6 | Welcome to Vector AI's documentation!
  7 | ====================================
  8 | 
  9 | .. image:: https://getvectorai.com/assets/logo-with-text.png
 10 |   :width: 600
 11 |   :alt: Vector AI
 12 | 
 13 | Vector AI aims to store vectors alongside documents (text/audio/images/videos).
 14 | It is designed to be a light-weight library to create/manipulate/search and analyse the 
 15 | underlying vectors to power machine learning applications such as semantic
 16 | search, recommendations, etc.
 17 | 
 18 | - Our REST API documentation can be found here: https://api.vctr.ai/documentation   
 19 | - Our discord can be found here: https://discord.gg/CbwUxyD
 20 | 
 21 | Features: 
 22 | 
 23 |    - **Multimedia Data Vectorisation**: Image2Vec, Audio2Vec, etc (Any data can be turned into vectors through machine learning) 
 24 |    - **Vector Similarity Search**: Enable searching of vectors and rich multimedia with vector similarity search. The backbone of many popular A.I use cases like reverse image search, recommendations, personalisation, etc. 
 25 |    - **Vector Operations**: Flexible search with out of the box operations on vectors. e.g. mean, median, sum, etc. 
 26 |    - **Aggregation**: All the traditional aggregation you'd expect. e.g. group by mean, pivot tables, etc 
 27 |    - **Clustering**: Interpret your vectors and data by allocating them to buckets and get statistics about these different buckets based on data you provide. 
 28 |    - **Vector Analytics**: Get better understanding of your vectors by using out-of-the-box practical vector analytics, giving you better understanding of the quality of your vectors.
 29 | 
 30 | Why Vector AI compared to other Nearest Neighbor implementations?
 31 | -------------------------------------------------------------------
 32 | 
 33 | -  **Production Ready**: Our API is fully managed and can scale to power
 34 |    hundreds of millions of searches a day. Even at millions of searches
 35 |    it is blazing fast through edge caching, gpus and software
 36 |    optisation. So you never have to worry about scaling your
 37 |    infastructure as your use case scales.
 38 | -  **Richer understanding of your vectors and their properties**: Our
 39 |    library is designed to allow people to not just designed to obtain
 40 |    nearest neighbors but to actually use in production-ready search
 41 |    systems - allowing users to analyse, iterate, improve and
 42 |    productionise their vectors the moment they are added to the index.
 43 | -  **Simple to use. Quick to get started.**: One of our core design
 44 |    principles is that we focus a lot on how people can get started on
 45 |    using Vector AI as quickly as possible, while having a tonne of
 46 |    functionality and customisability options.
 47 | -  **Framework agnostic**: We are never going to force a specific
 48 |    framework on Vector AI. If you have a framework of choice, you can use
 49 |    it - as long as your documents are JSON-serializable!
 50 | -  **Store vector data with ease**: The document-orientated nature for
 51 |    Vector AI allows users to label, filter search and understand their
 52 |    vectors as much as possible. We think that other libraries that
 53 |    simply provide a nearest-neighbor implementation do not have as rich
 54 |    functionality.
 55 | 
 56 | 
 57 | How to install
 58 | ###############
 59 | 
 60 | To install vectorai, run the following
 61 | 
 62 | .. code-block:: RST
 63 | 
 64 |     pip install vectorai
 65 | 
 66 | 
 67 | To install from source, clone the repository and then run
 68 | 
 69 | .. code-block:: RST
 70 | 
 71 |     cd vectorai 
 72 |     pip install -e . 
 73 | 
 74 | Schema
 75 | ########
 76 | 
 77 | We have a very simple schema to follow to allow you to optimise functionality with vector search:
 78 | 
 79 | .. list-table:: Schema Rules
 80 |    :widths: 25 75
 81 |    :header-rows: 1
 82 | 
 83 |    * - Field
 84 |      - Purpose
 85 | 
 86 |    * - _id
 87 |      - ID of the document. These need to be unique for the document.
 88 | 
 89 |    * - _vector_
 90 |      - These are required to label the vectors for vector search.
 91 | 
 92 | .. toctree::
 93 |    :maxdepth: 2
 94 |    :caption: Contents
 95 | 
 96 |    intro
 97 |    quickstart
 98 | 
 99 | .. toctree::
100 |    :caption: Guides
101 |    
102 |    industry_ecommerce
103 |    vector_analytics_example
104 |    custom_encodings_example
105 | 
106 | .. toctree::
107 |    :caption: Case Studies
108 |    
109 |    industry_nba_players
110 | 
111 | .. toctree::
112 |    :caption: Frequently Asked Questions
113 | 
114 |    FAQ
115 | 
116 | 
117 | .. toctree::
118 |     :maxdepth: 2
119 |     :caption: Documentation
120 |     
121 |     client
122 |     read
123 |     write
124 |     cluster
125 |     array_dict_vectorizer
126 |     dimensionality_reduction
127 |     vector_search
128 |     image
129 |     text
130 |     audio
131 |     analytics
132 | 
133 | 
134 | Indices and tables
135 | ==================
136 | 
137 | * :ref:`genindex`
138 | * :ref:`modindex`
139 | * :ref:`search`
140 | 


--------------------------------------------------------------------------------
/docsrc/intro.rst:
--------------------------------------------------------------------------------
 1 | 
 2 | Vector AI - Essentials
 3 | ^^^^^^^^^^^^^^^^^^^^^^
 4 | 
 5 | Vector AI is built to store vectors alongside documents (text/audio/images/videos). 
 6 | It is designed to be a light-weight library to create, manipulate, search and analyse vectors to power machine 
 7 | learning applications such as semantic search, recommendations, etc.
 8 | 
 9 | Important Terminologies
10 | =======================
11 | - **Vectors** (aka. embeddings, 1D arrays)
12 | 
13 | - **Models/Encoders** (aka. Embedders) Turns data into vectors e.g. Word2Vec turns words into vectors
14 | 
15 | - **Vector Similarity Search** (aka. Nearest Neighbor Search, Distance Search)
16 | 
17 | - **Collection** (aka. Index, Table) ~ a collection is made up of multiple documents
18 | 
19 | - **Documents** (aka. Json, Item, Dictionary, Row) ~ a document can contain vector + other important information
20 | 
21 | 
22 | .. code-block:: RST
23 |     e.g.
24 |     {
25 |         "_id" : "1", 
26 |         "description_vector__ ": [...], 
27 |         "description" : "This is a great idea"
28 |     }
29 | 
30 | Some important information: for predefined vectors use the suffix "_vector_" in the name like "description_vector_", for ids to do quick key value lookup use the name "_id"
31 | 
32 | Documents in Vector AI
33 | ========================
34 | 
35 | Documents (dictionaries) consists of fields (dictionary keys) and values.
36 | 
37 | 1. Vector AI is document orientated (dictionaries/jsons) which means you can have nested fields. This means that you have documents such as:
38 | 
39 |     .. code-block:: RST
40 | 
41 |         document_example = {
42 |             "car": {
43 |                 "wheels":
44 |                     {
45 |                         "number": 4
46 |                     }
47 |             }
48 |         }
49 | 
50 | then running vi_client.get_field("car.wheels.number") will return 4
51 | 
52 | 2. When uploading documents into VectorAi, it will infer the schema from the first document being inserted.
53 | 
54 | You are able to navigate the documents within the fields by using the functions below, allowing you to navigate through 
55 | nested documents if the fields are separated by .'s.
56 | 
57 | .. code-block:: python
58 | 
59 |     vi_client.set_field(field, doc, value)
60 |     vi_client.get_field(field, doc)
61 |     vi_client.set_field_across_documents(field, docs, values)
62 |     vi_client.get_field_across_documents(field, docs)
63 | 
64 | Models With Vector AI
65 | ========================
66 | 
67 | Vector AI has deployed models that we've handpicked and tuned to work nicely out of the box on most problems. 
68 | These models, however, may be changed over time. When they do we make sure that 
69 | previous models are still deployed and can be used.
70 | To prototype something quickly we highly recommend using these deployed models.
71 | 
72 | 
73 | **If you are working on a problem that requires highly customised or finetuned models, reach out to us 
74 | for enterprise services where we can fine tune these models for your use case or feel free to build your own.**
75 | 
76 | Currently, our deployed models are:
77 |     * ViText2Vec - our text to vector model
78 |     * ViImage2Vec - our image to vector model
79 |     * ViAudio2Vec - our audio to vector model
80 |     * dimensionality_reduction_job - perform dimensionality reduction on your vectors
81 |     * clustering_job - perform clustering on your vectors
82 |     * advanced_cluster_job - perform clustering with advanced options on your vectors
83 | 


--------------------------------------------------------------------------------
/docsrc/make.bat:
--------------------------------------------------------------------------------
 1 | @ECHO OFF
 2 | 
 3 | pushd %~dp0
 4 | 
 5 | REM Command file for Sphinx documentation
 6 | 
 7 | if "%SPHINXBUILD%" == "" (
 8 | 	set SPHINXBUILD=sphinx-build
 9 | )
10 | set SOURCEDIR=.
11 | set BUILDDIR=_build
12 | 
13 | if "%1" == "" goto help
14 | 
15 | xcopy ..\examples\*.ipynb .
16 | 
17 | %SPHINXBUILD% >NUL 2>NUL
18 | if errorlevel 9009 (
19 | 	echo.
20 | 	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
21 | 	echo.installed, then set the SPHINXBUILD environment variable to point
22 | 	echo.to the full path of the 'sphinx-build' executable. Alternatively you
23 | 	echo.may add the Sphinx directory to PATH.
24 | 	echo.
25 | 	echo.If you don't have Sphinx installed, grab it from
26 | 	echo.http://sphinx-doc.org/
27 | 	exit /b 1
28 | )
29 | 
30 | %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
31 | 
32 | del -rf *.ipynb
33 | 
34 | goto end
35 | 
36 | :help
37 | %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
38 | 
39 | :end
40 | popd
41 | 


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/docsrc/read.rst:
--------------------------------------------------------------------------------
 1 | Read
 2 | ^^^^^^
 3 | 
 4 | Read
 5 | =======================================================
 6 | Read
 7 | 
 8 | .. automodule:: vectorai.api.read
 9 |     :members:
10 | 
11 | .. automodule:: vectorai.read
12 |     :members:
13 | 


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/docsrc/text.rst:
--------------------------------------------------------------------------------
 1 | Texts
 2 | ^^^^^^^^
 3 | 
 4 | Texts
 5 | =======================================================
 6 | Texts
 7 | 
 8 | .. automodule:: vectorai.api.text
 9 |     :members:
10 | 


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/docsrc/vector_search.rst:
--------------------------------------------------------------------------------
 1 | Search
 2 | ^^^^^^
 3 | 
 4 | Search
 5 | =======================================================
 6 | Search
 7 | 
 8 | .. automodule:: vectorai.api.search
 9 |     :members:
10 | 


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/docsrc/write.rst:
--------------------------------------------------------------------------------
 1 | Write
 2 | ^^^^^
 3 | 
 4 | Write
 5 | =======================================================
 6 | Write
 7 | 
 8 | This is documentation for the Write API for Vector AI.
 9 | 
10 | .. automodule:: vectorai.write
11 |     :members:
12 |     :show-inheritance:
13 | 
14 | .. automodule:: vectorai.api.write
15 |     :members:
16 |     :show-inheritance:
17 | 


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/examples/data/Corona_NLP_train.csv:
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https://raw.githubusercontent.com/vector-ai/vectorai/7b1a0eb2bb06a82d85ac3633eea984604baf2ea6/examples/data/Corona_NLP_train.csv


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/examples/data/nba_per_36.xlsx:
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/examples/data/nba_per_game.xlsx:
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/examples/images/2d-cosine-similarity.png:
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/examples/images/dimensionality_reduced_vector_plot.png:
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/examples/images/vectordb-1d-plot-example-readme.png:
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/examples/images/vectordb-plot-1d-cosine-similarity-comparison.png:
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/pytest.ini:
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1 | [pytest]
2 | markers =
3 |     use_client: marks tests as those that use the client
4 | 


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/requirements.txt:
--------------------------------------------------------------------------------
1 | requests
2 | numpy
3 | pandas
4 | appdirs>=1.4.4
5 | plotly>=4.0.0
6 | tqdm>=4.27.0


--------------------------------------------------------------------------------
/setup.py:
--------------------------------------------------------------------------------
 1 | #!/usr/bin/env python
 2 | # -*- coding: utf-8 -*-
 3 | 
 4 | from setuptools import setup, find_packages
 5 | import os
 6 | 
 7 | core_req = ["requests", "numpy", "pandas", "appdirs>=1.4.4", "tqdm>=4.27.0", "plotly>=4.0.0"]
 8 | extras_req = {
 9 |     "dev" : ["twine", "black", "pytest", "pytest-cov", "vectorai", "openapi-to-sdk"],
10 |     "test" : ["pytest", "pytest-cov", "pytest-rerunfailures"],
11 |     "docs" : ["sphinx-rtd-theme>=0.5.0", "nbsphinx>=0.7.1"]
12 | }
13 | extras_req["all"] = [p for r in extras_req.values() for p in r]
14 | 
15 | version = '0.2.5'
16 | if 'IS_VECTORAI_NIGHTLY' in os.environ.keys():
17 |     from datetime import datetime
18 |     name = 'vectorai-nightly'
19 |     version = version + '.' + datetime.today().date().__str__().replace('-', '.') 
20 | else:
21 |     name = 'vectorai'
22 | 
23 | setup(
24 |     name=name,
25 |     version=version,
26 |     author="OnSearch Pty Ltd",
27 |     author_email="dev@vctr.ai",
28 |     description="A Python framework for building vector based applications. Encode, query and analyse data using vectors.",
29 |     long_description=open("README.md", "r", encoding="utf-8").read(),
30 |     long_description_content_type="text/markdown",
31 |     keywords="vector, embeddings, machinelearning, ai, artificialintelligence, nlp, tensorflow, pytorch, nearestneighbors, search, analytics, clustering, dimensionalityreduction",
32 |     url="https://github.com/vector-ai/vectorai",
33 |     license="Apache",
34 |     packages=find_packages(exclude=["tests*"]),
35 |     python_requires=">=3",
36 |     install_requires=core_req,
37 |     extras_require=extras_req,
38 |     classifiers=[
39 |         "Development Status :: 5 - Production/Stable",
40 |         "Intended Audience :: Developers",
41 |         "Intended Audience :: Education",
42 |         "Intended Audience :: Science/Research",
43 |         "Intended Audience :: Information Technology",
44 |         "Intended Audience :: Financial and Insurance Industry",
45 |         "Intended Audience :: Healthcare Industry",
46 |         "Intended Audience :: Manufacturing",
47 |         "License :: OSI Approved :: Apache Software License",
48 |         "Operating System :: OS Independent",
49 |         "Programming Language :: Python",
50 |         "Programming Language :: Python :: 3",
51 |         "Programming Language :: Python :: 3.4",
52 |         "Programming Language :: Python :: 3.5",
53 |         "Programming Language :: Python :: 3.6",
54 |         "Programming Language :: Python :: 3.7",
55 |         "Programming Language :: Python :: Implementation :: PyPy",
56 |         "Topic :: Database",
57 |         "Topic :: Internet :: WWW/HTTP :: Indexing/Search",
58 |         "Topic :: Multimedia :: Sound/Audio :: Conversion",
59 |         "Topic :: Multimedia :: Video :: Conversion",
60 |         "Topic :: Scientific/Engineering :: Artificial Intelligence",
61 |         "Topic :: Scientific/Engineering :: Image Recognition",
62 |         "Topic :: Scientific/Engineering :: Information Analysis",
63 |         "Topic :: Scientific/Engineering :: Visualization",
64 |         "Topic :: Software Development :: Libraries :: Application Frameworks",
65 |     ],
66 | )
67 | 


--------------------------------------------------------------------------------
/tests/README.md:
--------------------------------------------------------------------------------
1 | 
2 | ## Writing Testing For Package
3 | 
4 | ```
5 | pytest --cov=vectorai tests/*
6 | ```
7 | 
8 | If you would like to contribute code or tests, we suggest adhering to the pylint style guide and typehint as much as possible.
9 | 


--------------------------------------------------------------------------------
/tests/__init__.py:
--------------------------------------------------------------------------------
1 | """Testing suite for the library. 
2 | """
3 | 


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/tests/analytics/__init__.py:
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https://raw.githubusercontent.com/vector-ai/vectorai/7b1a0eb2bb06a82d85ac3633eea984604baf2ea6/tests/analytics/__init__.py


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/tests/analytics/api/__init__.py:
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https://raw.githubusercontent.com/vector-ai/vectorai/7b1a0eb2bb06a82d85ac3633eea984604baf2ea6/tests/analytics/api/__init__.py


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/tests/analytics/api/test_comparator.py:
--------------------------------------------------------------------------------
 1 | """Smoke tests for the API - Ensure that these do not error out!
 2 | """
 3 | import pytest
 4 | import time
 5 | from ...utils import TempClientWithDocs
 6 | 
 7 | @pytest.mark.use_client
 8 | def test_smoke_compare_ranks(test_analytics_client, document_vector_fields):
 9 |     with TempClientWithDocs(test_analytics_client) as client:
10 |         time.sleep(2)
11 |         results = test_analytics_client.random_compare_search_by_id(
12 |             collection_name=client.collection_name,
13 |             vector_fields=[document_vector_fields[0], document_vector_fields[1]]
14 |         )
15 | 
16 | # @pytest.mark.use_client
17 | # def test_smoke_compare_ranks_vector(test_analytics_client, document_vector_fields):
18 |     # with TempClientWithDocs(test_analytics_client) as client:
19 |         # time.sleep(2)
20 |         # results = test_analytics_client.random_compare_search(
21 |             # collection_name=client.collection_name,
22 |             # vector_fields=document_vector_fields
23 |         # )
24 | 


--------------------------------------------------------------------------------
/tests/analytics/scorer/test_base_scorer.py:
--------------------------------------------------------------------------------
1 | # """
2 | #     Test Base Scorer In Analytics
3 | # """
4 | # from vectorai.analytics.scorer
5 | 
6 | # class test_scorer():
7 | 
8 | 


--------------------------------------------------------------------------------
/tests/analytics/test_relational_documents.py:
--------------------------------------------------------------------------------
 1 | """
 2 |     Tests for relational documents.
 3 | """
 4 | from vectorai.analytics.relational_documents import *
 5 | from vectorai.utils import UtilsMixin
 6 | import pytest
 7 | 
 8 | def test_vector_operation():
 9 |     assert (vector_operation([1, 2, 3], [3, 2, 1]) == [2, 2, 2])
10 | 
11 | @pytest.mark.parametrize("test_operation, expected_output", [("minus", [0, 0, 0])])
12 | def test_relational_document_creation(test_operation, expected_output):
13 |     mixin_utils = UtilsMixin()
14 |     doc_1 = {'_vector_': [1, 2, 3], 'country': 'Australia'}
15 |     doc_2 = {'_vector_': [1, 2, 3], 'country': 'New Zealand'}
16 |     relational_doc = create_relational_document(doc_1, doc_2, vector_fields=['_vector_'], 
17 |     label_field='country', operation=test_operation)
18 |     assert relational_doc['_vector_'] == expected_output
19 | 


--------------------------------------------------------------------------------
/tests/analytics/test_score.py:
--------------------------------------------------------------------------------
 1 | """
 2 | Testing module for analytics scoring.
 3 | """
 4 | def test_cosine_similarity(test_client):
 5 |     """
 6 |         Testing cosine similarity function works.
 7 |     """
 8 |     test_client.calculate_cosine_similarity(test_client.generate_vector(10), 
 9 |     test_client.generate_vector(10))
10 |     assert True
11 | 


--------------------------------------------------------------------------------
/tests/analytics/test_tables.py:
--------------------------------------------------------------------------------
 1 | import time
 2 | import pytest
 3 | 
 4 | class TestCompare:
 5 |     @pytest.mark.use_client
 6 |     def test_setup(self, test_client, test_collection_name):
 7 |         """
 8 |             Test Setup.
 9 |         """
10 |         num_of_docs = 50
11 |         if test_collection_name in test_client.list_collections():
12 |             test_client.delete_collection(test_collection_name)
13 |         documents = test_client.create_sample_documents(num_of_docs)
14 |         test_client.set_field_across_documents('color_vector_',
15 |         [test_client.generate_vector(50, num_of_constant_values=49) for x in range(num_of_docs)], documents)
16 |         test_client.set_field_across_documents('color_2_vector_',
17 |         [test_client.generate_vector(50, num_of_constant_values=49) for x in range(num_of_docs)], documents)
18 |         results = test_client.insert_documents(test_collection_name, documents)
19 |         time.sleep(10)
20 |         assert results['inserted_successfully'] == num_of_docs
21 |     
22 |     @pytest.mark.use_client
23 |     @pytest.mark.parametrize("test_vector_fields", [("color_vector_"), ("color_2_vector_")])
24 |     def test_compare_tables_simple(self, test_client, test_collection_name, test_vector_fields):
25 |         """
26 |             Test compare a simple table.
27 |         """
28 |         time.sleep(10)
29 |         id_document = test_client.random_documents(test_collection_name, 1)['documents'][0]
30 |         print(id_document)
31 |         df = test_client.compare_vector_search_results(test_collection_name,
32 |         vector_fields=[test_vector_fields], id_document=id_document, label='color')
33 |         assert df.shape[0] > 0
34 | 
35 |     @pytest.mark.use_client
36 |     def test_compare_tables_2_columns(self, test_client, test_collection_name):
37 |         """
38 |             Test compare a simple table.
39 |         """
40 |         id_document = test_client.random_documents(test_collection_name, 1)['documents'][0]
41 |         df = test_client.compare_vector_search_results(test_collection_name,
42 |         vector_fields=["color_vector_", "color_2_vector_"], id_document=id_document, label='color')
43 |         assert df.shape[0] > 0
44 |         assert df.shape[1] == 2
45 |     @pytest.mark.use_client
46 |     def test_teardown(self, test_client, test_collection_name):
47 |         """
48 |             Teardown.
49 |         """
50 |         test_client.delete_collection(test_collection_name)
51 |         time.sleep(5)
52 |         assert test_collection_name not in test_client.list_collections()
53 | 


--------------------------------------------------------------------------------
/tests/analytics/test_viz.py:
--------------------------------------------------------------------------------
 1 | """
 2 |     Test visualisations
 3 | """
 4 | import plotly.graph_objects as go
 5 | 
 6 | def test_radar_plot_across_documents(test_client):
 7 |     """
 8 |         Test radar plots across documents
 9 |     """
10 |     docs = test_client.create_sample_documents(5)
11 |     fig = test_client.plot_radar_across_documents(docs, anchor_documents=docs[0:2], 
12 |         vector_field='color_vector_', label_field='color')
13 |     assert isinstance(fig, go.Figure)
14 | 
15 | def test_radar_plot_across_vector_fields(test_client):
16 |     """
17 |         Test radar plots across documents.
18 |     """
19 |     docs = test_client.create_sample_documents(5)
20 |     fig = test_client.plot_radar_across_vector_fields(docs, anchor_document=docs[0], 
21 |     vector_fields=['color_vector_', 'color_2_vector_'], label_field='country')
22 |     assert isinstance(fig, go.Figure)
23 | 


--------------------------------------------------------------------------------
/tests/conftest.py:
--------------------------------------------------------------------------------
  1 | """
  2 | Global testing variables.
  3 | """
  4 | import pytest
  5 | import os
  6 | from vectorai.client import ViClient
  7 | from vectorai.analytics.client import ViAnalyticsClient
  8 | from vectorai.models.deployed import ViText2Vec
  9 | import random
 10 | import string
 11 | 
 12 | def pytest_addoption(parser):
 13 |     parser.addoption(
 14 |         "--use_client", action="store_true", default=False, help="run slow tests"
 15 |     )
 16 | 
 17 | 
 18 | def pytest_configure(config):
 19 |     config.addinivalue_line("markers", "slow: mark test as slow to run")
 20 | 
 21 | 
 22 | def pytest_collection_modifyitems(config, items):
 23 |     if config.getoption("--use_client"):
 24 |         # --runslow given in cli: do not skip slow tests
 25 |         return
 26 |     skip_slow = pytest.mark.skip(reason="need --use_client option to run")
 27 |     for item in items:
 28 |         if "use_client" in item.keywords:
 29 |             item.add_marker(skip_slow)
 30 | 
 31 | def get_random_string(length):
 32 |     # Random string with the combination of lower and upper case
 33 |     letters = 'abcdefghijklmnopqrstuvwxyz'
 34 |     return ''.join(random.choice(letters) for i in range(length))
 35 | 
 36 | @pytest.fixture
 37 | def test_username():
 38 |     return os.environ['VI_USERNAME']
 39 | 
 40 | 
 41 | @pytest.fixture
 42 | def test_api_key():
 43 |     return os.environ['VI_API_KEY']
 44 | 
 45 | 
 46 | @pytest.fixture
 47 | def test_client(test_username, test_api_key):
 48 |     """Testing for the client login.
 49 |     """
 50 |     return ViClient(username=test_username, api_key=test_api_key,
 51 |         url="https://vectorai-development-api-vectorai-test-api.azurewebsites.net/")
 52 | 
 53 | @pytest.fixture(scope='class')
 54 | def test_collection_name():
 55 |     return "test_colour_col_" + str(get_random_string(3))
 56 | 
 57 | # @pytest.fixture
 58 | # def test_collection_client(test_username, test_api_key, test_collection_name):
 59 | #     """Testing for the client login.
 60 | #     """
 61 | #     client = ViCollectionClient(username=test_username, api_key=test_api_key, collection_name=test_collection_name)
 62 | #     return client
 63 | 
 64 | @pytest.fixture
 65 | def test_analytics_client(test_username, test_api_key):
 66 |     return ViClient(username=test_username, api_key=test_api_key)
 67 | 
 68 | @pytest.fixture
 69 | def test_vector_field():
 70 |     return "item_vector_"
 71 | 
 72 | @pytest.fixture
 73 | def document_vector_fields():
 74 |     return ['color_vector_', 'color_2_vector_']
 75 | 
 76 | @pytest.fixture
 77 | def test_id_field():
 78 |     return "_id"
 79 | 
 80 | @pytest.fixture
 81 | def sample_documents():
 82 |     sample_documents = [
 83 |         {
 84 |             "name": "Bob",
 85 |             "color": "Orange",
 86 |             "team": "los angeles lakers"
 87 |         },
 88 |         {
 89 |             "name": "William",
 90 |             "color": "Yellow",
 91 |             "team": "miami heat"
 92 |         },
 93 |         {
 94 |             "name": "James Patterson",
 95 |             "color": "Blue",
 96 |             "team": "Charlotte Bobcats"
 97 |         }
 98 |     ]
 99 |     return sample_documents
100 | 
101 | 
102 | @pytest.fixture
103 | def test_text_encoder():
104 |     """
105 |         Text Encoder
106 |     """
107 |     model = ViText2Vec(os.environ['VI_USERNAME'], os.environ['VI_API_KEY'])
108 |     return model
109 | 


--------------------------------------------------------------------------------
/tests/test_client.py:
--------------------------------------------------------------------------------
 1 | """Testing the client.
 2 | """
 3 | 
 4 | from vectorai import *
 5 | 
 6 | 
 7 | def test_client_login_works(test_username, test_api_key):
 8 |     """Testing for the client login.
 9 |     """
10 |     client = ViClient(username=test_username, api_key=test_api_key)
11 |     assert True
12 | 


--------------------------------------------------------------------------------
/tests/test_doc_utils.py:
--------------------------------------------------------------------------------
 1 | """Testing for document utilities.
 2 | """
 3 | import pytest
 4 | from vectorai.errors import MissingFieldError
 5 | 
 6 | def test_set_field(test_client):
 7 |     sample = {}
 8 |     test_client.set_field("simple", doc=sample, value=[0, 2])
 9 |     assert test_client.get_field("simple", sample) == [0, 2]
10 | 
11 | def test_set_field_nested(test_client):
12 |     sample = {}
13 |     test_client.set_field('simple.weird.strange', sample, value=3)
14 |     assert test_client.get_field('simple.weird.strange', sample) == 3
15 |     assert sample['simple']['weird']['strange'] == 3
16 | 
17 | def test_get_field_chunk(test_client):
18 |     sample = {
19 |         'kfc': [{'food': 'chicken'}, {'food': 'prawns'}]}
20 |     assert test_client.get_field('kfc.0.food', sample) == 'chicken'
21 |     assert test_client.get_field('kfc.1.food', sample) == 'prawns'
22 | 
23 | def test_get_field_chunk_error(test_client):
24 |     sample = {
25 |         'kfc': [{'food': 'chicken'}, {'food': 'prawns'}]}
26 |     with pytest.raises(MissingFieldError):
27 |         test_client.get_field('kfc.food', sample, missing_treatment='raise_error')
28 | 
29 | def test_get_fields(test_client):
30 |     doc = test_client.create_sample_documents(1)[0]
31 |     assert len(test_client.get_fields(['size.cm', 'size.feet'], doc)) == 2
32 | 
33 | def test_get_field_across_documents(test_client):
34 |     docs = test_client.create_sample_documents(2)
35 |     values = test_client.get_field_across_documents('color', docs)
36 |     assert len(values) == 2
37 | 
38 | def test_set_and_get_field_across_documents(test_client):
39 |     docs = test_client.create_sample_documents(5)
40 |     test_client.set_field_across_documents('size.inches', list(range(5)), docs)
41 |     for i, doc in enumerate(docs):
42 |         assert test_client.get_field('size.inches', doc) == i
43 | 
44 | def test_is_field(test_client):
45 |     """
46 |         Test if it is a field
47 |     """
48 |     docs = test_client.create_sample_documents(10)
49 |     assert test_client.is_field("size", docs[0])
50 |     assert not test_client.is_field("hfueishfuie", docs[0])
51 |     assert test_client.is_field("size.cm", docs[0])
52 |     assert not test_client.is_field("size.bafehui", docs[0])
53 | 


--------------------------------------------------------------------------------
/tests/test_error.py:
--------------------------------------------------------------------------------
 1 | """
 2 |     Testing for Errors
 3 | """
 4 | import pytest
 5 | from vectorai.errors import APIError
 6 | 
 7 | def test_api_error(test_client):
 8 |     response = {'status': "error", "message": "This is a test error."}
 9 |     with pytest.raises(APIError):
10 |         test_client._raise_error(response)
11 | 


--------------------------------------------------------------------------------
/tests/test_models.py:
--------------------------------------------------------------------------------
 1 | """
 2 |     Test for models.
 3 | """
 4 | 
 5 | import pytest
 6 | from vectorai.models import ViDeployedModel
 7 | 
 8 | def test_operations_sum(test_text_encoder):
 9 |     vectors = [[1, 2], [2, 3]]
10 |     assert [3, 5] == test_text_encoder._vector_operation(vectors, vector_operation="sum")
11 | 
12 | def test_operations_minus(test_text_encoder):
13 |     vectors = [[1, 2], [2, 3]]
14 |     assert [-1, -1] == test_text_encoder._vector_operation(vectors, vector_operation="minus")
15 | 
16 | def test_operations_mean(test_text_encoder):
17 |     vectors = [[1, 2], [2, 3]]
18 |     assert [1.5, 2.5] == test_text_encoder._vector_operation(vectors, vector_operation="mean")
19 | 
20 | def test_operations_max(test_text_encoder):
21 |     vectors = [[1, 2], [2, 3]]
22 |     assert [2, 3] == test_text_encoder._vector_operation(vectors, vector_operation="max")
23 | 
24 | def test_operations_min(test_text_encoder):
25 |     vectors = [[1, 2], [2, 3]]
26 |     assert [1, 2] == test_text_encoder._vector_operation(vectors, vector_operation="min")
27 | 
28 | def test_operations_min_with_error(test_text_encoder):
29 |     with pytest.raises(ValueError):
30 |         vectors = vectors = [[1, 2], [2, 3], [2, 4]]
31 |         test_text_encoder._vector_operation(vectors, vector_operation='minus')
32 | 


--------------------------------------------------------------------------------
/tests/test_read.py:
--------------------------------------------------------------------------------
  1 | """Testing the various read functions for Vi
  2 | """
  3 | import pytest
  4 | import time
  5 | from vectorai.errors import MissingFieldWarning, MissingFieldError
  6 | from .utils import TempClientWithDocs
  7 | 
  8 | class TestRead:
  9 |     @pytest.mark.use_client
 10 |     def test_setup_for_read(self, test_client, test_collection_name):
 11 |         """Test Setup for Read Operations"""
 12 |         if test_collection_name in test_client.list_collections():
 13 |             test_client.delete_collection(test_collection_name)
 14 |         documents = test_client.create_sample_documents(5)
 15 |         test_client.insert_documents(
 16 |             collection_name=test_collection_name, documents=documents
 17 |         )
 18 |         time.sleep(10)
 19 |         assert True
 20 | 
 21 |     @pytest.mark.use_client
 22 |     def test_get_item_by_id(self, test_client, test_collection_name):
 23 |         return_item = test_client.id(collection_name=test_collection_name, document_id="0")
 24 |         for var in ['color', 'number', 'color_vector_', 'insert_date_']:
 25 |             assert var in return_item
 26 | 
 27 |     @pytest.mark.use_client
 28 |     def test_advanced_search_by_id(self, test_client, test_collection_name):
 29 |         filter_query = [
 30 |             {'field': 'color',
 31 |             'filter_type': 'text',
 32 |             'condition_value': 'red',
 33 |             'condition': '=='}
 34 |         ]
 35 |         results = test_client.advanced_search_by_id(test_collection_name, 
 36 |         document_id=test_client.random_documents(test_collection_name)['documents'][0]['_id'],
 37 |         search_fields={'color_vector_':1}, filters=filter_query)
 38 |         assert len(results) > 0
 39 | 
 40 |     @pytest.mark.use_client
 41 |     def test_get_document_by_bulk_id(self, test_client, test_collection_name):
 42 |         return_documents = test_client.bulk_id(
 43 |             collection_name=test_collection_name, document_ids=["0", "1"]
 44 |         )
 45 |         assert len(return_documents) == 2
 46 | 
 47 | 
 48 |     @pytest.mark.use_client
 49 |     def test_cleanup_for_read(self, test_client, test_collection_name):
 50 |         """Test Setup for Read Operations"""
 51 |         test_client.delete_collection(collection_name=test_collection_name)
 52 |         assert True
 53 | 
 54 | def test_get_field(test_client):
 55 |     """Test for accessing the document field.
 56 |     """
 57 |     test_dict = {"kfc": {"item": "chickens"}}
 58 |     assert test_client.get_field("kfc.item", doc=test_dict) == "chickens"
 59 | 
 60 | def test_get_empty_field(test_client):
 61 |     with pytest.raises(MissingFieldError):
 62 |         docs = test_client.create_sample_documents(10)
 63 |         test_client.get_field_across_documents('_id_', docs)
 64 | 
 65 | def test_check_schema(test_client):
 66 |     """Testing a nested dictionary to ensure it can detected a nested vector field
 67 |     """
 68 |     with pytest.warns(None) as record:
 69 |         nested_schema = {}
 70 |         test_client._check_schema(nested_schema)
 71 |         # nested_schema = {'chk': {'chk_vector_': [0, 2, 3]}}
 72 |     assert len(record) == 2
 73 |     assert test_client._check_schema(nested_schema) == (True, True)
 74 | 
 75 | def test_check_schema_with_vector_field(test_client):
 76 |     """Testing a nested dictionary to ensure it can detected a nested vector field
 77 |     """
 78 |     with pytest.warns(None) as record:
 79 |         nested_schema = {'chk': {'chk_vector_': [0, 2, 3]}}
 80 |         test_client._check_schema(nested_schema)
 81 |     assert len(record) == 1
 82 |     assert test_client._check_schema(nested_schema) == (True, False)
 83 | 
 84 | def test_check_schema_id_field(test_client):
 85 |     with pytest.warns(None) as record:
 86 |         nested_schema = {'_id': "text"}
 87 |         test_client._check_schema(nested_schema)
 88 |     assert len(record) == 1
 89 |     assert test_client._check_schema(nested_schema) == (False, True)
 90 | 
 91 | def test_check_schema_both(test_client):
 92 |     with pytest.warns(None) as record:
 93 |         nested_schema = {'_id': "text", "chk_vector_":[0, 1, 2]}
 94 |     assert len(record) == 0
 95 |     assert test_client._check_schema(nested_schema) == (False, False)
 96 | 
 97 | @pytest.mark.use_client
 98 | def test_search_collections(test_client):
 99 |     """
100 |         Smoke test for searching collections
101 |     """
102 |     cn = 'example_collection_123y8io'
103 |     if cn not in test_client.list_collections():
104 |         test_client.create_collection(cn)
105 |         time.sleep(2)
106 |     assert len(test_client.search_collections('123y8io')) > 0, "Not searching collections properly."
107 |     test_client.delete_collection(cn)
108 | 
109 | @pytest.mark.use_client
110 | def test_random_recommendation_smoke_test(test_client, test_collection_name):
111 |     """
112 |         Smoke test for recommending random ID.
113 |     """
114 |     with TempClientWithDocs(test_client, test_collection_name):
115 |         time.sleep(2)
116 |         results = test_client.random_recommendation(
117 |             test_collection_name, 
118 |             search_field='color_vector_')
119 |         assert len(results['results']) > 0, "Random recommendation fails."
120 | 
121 | @pytest.mark.use_client
122 | def test_random_documents_with_filters(test_client, test_collection_name):
123 |     """
124 |         Random documents with filters.
125 |     """
126 |     with TempClientWithDocs(test_client, test_collection_name, num_of_docs=20):
127 |         time.sleep(2)
128 |         filter_query = [{'field': 'country', 
129 |         'filter_type': 'category',
130 |         'condition_value': 'Italy', 
131 |         'condition': '=='}]
132 |         docs = test_client.random_documents_with_filters(
133 |             test_collection_name, filters=filter_query, page_size=20)
134 |         print(filter_query)
135 |         for doc in docs['documents']:
136 |             assert doc['country'] == 'Italy'
137 | 
138 | @pytest.mark.use_client
139 | def test_search_with_filters(test_client, test_collection_name):
140 |     with TempClientWithDocs(test_client, test_collection_name, num_of_docs=100):
141 |         time.sleep(2)
142 |         filter_query = [{'field': 'country', 
143 |         'filter_type': 'category',
144 |         'condition_value': 'Italy', 
145 |         'condition': '=='}]
146 |         docs = test_client.search_with_filters(
147 |             test_collection_name, vector=test_client.generate_vector(30),
148 |             field=['color_vector_'],
149 |             filters=filter_query, page_size=20)
150 |         for doc in docs['results']:
151 |             assert doc['country'] == 'Italy'
152 | 
153 | @pytest.mark.use_client
154 | def test_hybrid_search_with_filters(test_client, test_collection_name):
155 |     with TempClientWithDocs(test_client, test_collection_name, num_of_docs=100):
156 |         time.sleep(2)
157 |         filter_query = [{'field': 'country', 
158 |         'filter_type': 'category',
159 |         'condition_value': 'Italy', 
160 |         'condition': '=='}]
161 |         docs = test_client.hybrid_search_with_filters(
162 |             test_collection_name, 
163 |             vector=test_client.generate_vector(30),
164 |             text="red", 
165 |             text_fields=['color'],
166 |             fields=['color_vector_'],
167 |             filters=filter_query, page_size=20)
168 |         for doc in docs['results']:
169 |             assert doc['country'] == 'Italy'
170 | 


--------------------------------------------------------------------------------
/tests/test_search.py:
--------------------------------------------------------------------------------
 1 | """
 2 |     Testing for search functions
 3 | """
 4 | import numpy as np
 5 | import pytest
 6 | import time
 7 | 
 8 | @pytest.mark.skip(reason="Chunk Search being altered.")
 9 | @pytest.mark.use_client
10 | def test_chunk_search(test_client, test_collection_name):
11 |     if test_collection_name in test_client.list_collections():
12 |         test_client.delete_collection(test_collection_name)
13 |     test_client.insert_documents(test_collection_name, 
14 |     test_client.create_sample_documents(10, include_chunks=True))
15 |     time.sleep(5)
16 |     vec = np.random.rand(1, 30).tolist()[0]
17 |     results = test_client.chunk_search(
18 |         test_collection_name,
19 |         vector=vec,
20 |         search_fields=['chunk.color_chunkvector_'],
21 |         )
22 |     assert 'error' not in results.keys()
23 | 


--------------------------------------------------------------------------------
/tests/test_write_collection_basics.py:
--------------------------------------------------------------------------------
  1 | """Test the write database.
  2 | """
  3 | import json
  4 | import pytest
  5 | import os
  6 | import time
  7 | import numpy as np
  8 | from vectorai.models.deployed import ViText2Vec
  9 | from vectorai.write import ViWriteClient
 10 | from vectorai.errors import APIError, MissingFieldError, MissingFieldWarning, CollectionNameError
 11 | from vectorai.client import ViClient
 12 | from .utils import TempClientWithDocs
 13 | 
 14 | class TestCollectionBasics:
 15 |     @pytest.mark.use_client
 16 |     def test_create_collection(self, test_client, test_collection_name, test_vector_field):
 17 |         collection_name = test_collection_name
 18 |         if collection_name in test_client.list_collections():
 19 |             test_client.delete_collection(collection_name)
 20 |         response = test_client.create_collection(
 21 |             collection_name=collection_name, collection_schema={test_vector_field: 512}
 22 |         )
 23 |         assert response is None
 24 | 
 25 |     @pytest.mark.use_client
 26 |     def test_prevent_collection_overwrite(self, test_client, test_collection_name):
 27 |         """
 28 |             Test prevention of the overwriting of the collections.
 29 |         """
 30 |         if test_collection_name not in test_client.list_collections():
 31 |             test_client.create_collection(test_collection_name)
 32 |         with pytest.raises(APIError):
 33 |             response = test_client.create_collection(collection_name=test_collection_name)
 34 | 
 35 |     @pytest.mark.use_client
 36 |     def test_list_collections(self, test_collection_name, test_client):
 37 |         response = test_client.list_collections()
 38 |         assert response.count(test_collection_name) == 1
 39 | 
 40 |     @pytest.mark.use_client
 41 |     def test_delete_collection(self, test_client, test_collection_name):
 42 |         response = test_client.delete_collection(collection_name=test_collection_name)
 43 |         assert response['status'] == 'complete'
 44 | 
 45 | def test_dummy_vector(test_client):
 46 |     """
 47 |         Test the dummy vector
 48 |     """
 49 |     assert len(test_client.dummy_vector(512)) == 512
 50 | 
 51 | def test_set_field_on_new_field(test_client):
 52 |     """
 53 |         Assert when set on new field.
 54 |     """
 55 |     doc = {}
 56 |     test_client.set_field('balls', doc, 3)
 57 |     assert doc['balls'] == 3
 58 | 
 59 | def test_set_field_on_new_dict(test_client):
 60 |     doc = {}
 61 |     test_client.set_field('check.balls', doc, 3)
 62 |     assert test_client.get_field('check.balls', doc) == 3
 63 | 
 64 | def test_vector_name(test_client):
 65 |     text_encoder = ViText2Vec(os.environ['VI_USERNAME'], os.environ['VI_API_KEY'])
 66 |     test_client.set_name(text_encoder, 'vectorai_text')
 67 |     vector_name = test_client._get_vector_name_for_encoding("color", text_encoder, model_list=[text_encoder])
 68 |     assert vector_name == "color_vectorai_text_vector_"
 69 | 
 70 | def test_vector_name_2(test_client):
 71 |     text_encoder = ViText2Vec(os.environ['VI_USERNAME'], os.environ['VI_API_KEY'])
 72 |     text_encoder_2 = ViText2Vec(os.environ['VI_USERNAME'], os.environ['VI_API_KEY'])
 73 |     test_client.set_name(text_encoder, "vectorai")
 74 |     test_client.set_name(text_encoder_2, "vectorai_2")
 75 |     vector_name = test_client._get_vector_name_for_encoding("color", text_encoder, model_list=[text_encoder, text_encoder_2])
 76 |     assert vector_name == "color_vectorai_vector_"
 77 |     vector_name = test_client._get_vector_name_for_encoding("color", text_encoder_2, model_list=[text_encoder, text_encoder_2])
 78 |     assert vector_name == 'color_vectorai_2_vector_'
 79 | 
 80 | def test_vector_name_same_name(test_client):
 81 |     text_encoder = ViText2Vec(os.environ['VI_USERNAME'], os.environ['VI_API_KEY'])
 82 |     with pytest.raises(ValueError):
 83 |         vector_name = test_client._check_if_multiple_models_have_same_name(models={'color':[text_encoder, text_encoder]})
 84 | 
 85 | def test_encode_documents_With_models_using_encode(test_client):
 86 |     docs = test_client.create_sample_documents(5)
 87 |     text_encoder = ViText2Vec(os.environ['VI_USERNAME'], os.environ['VI_API_KEY'])
 88 |     test_client.set_name(text_encoder, "vectorai_text")
 89 |     test_client.encode_documents_with_models_using_encode(docs, models={'color': [text_encoder]})
 90 |     assert 'color_vectorai_text_vector_' in docs[0].keys()
 91 | 
 92 | @pytest.mark.use_client
 93 | def test_raises_warning_if_no_id(test_client, test_collection_name):
 94 |     docs = test_client.create_sample_documents(10)
 95 |     {x.pop('_id') for x in docs}
 96 |     with pytest.warns(MissingFieldWarning) as record:
 97 |         test_client.insert_documents(test_collection_name, docs)
 98 |     assert len(record) > 1
 99 |     assert record[1].message.args[0] == test_client.NO_ID_WARNING_MESSAGE
100 | 
101 | @pytest.mark.use_client
102 | def test_raises_warning_if_only_one_id_is_present(test_client, test_collection_name):
103 |     docs = test_client.create_sample_documents(10)
104 |     {x.pop('_id') for x in docs[1:]}
105 |     with pytest.warns(MissingFieldWarning) as record:
106 |         test_client.insert_documents(test_collection_name, docs)
107 |     assert record[0].message.args[0] == test_client.NO_ID_WARNING_MESSAGE
108 | 
109 | @pytest.mark.use_client
110 | def test_retrieve_and_encode_simple(test_client, test_collection_name):
111 |     """Test retrieving documents and encoding them with vectors.
112 |     """
113 |     VECTOR_LENGTH = 100
114 |     def fake_encode(x):
115 |         return test_client.generate_vector(VECTOR_LENGTH)
116 |     with TempClientWithDocs(test_client, test_collection_name, 100) as client:
117 |         results = client.retrieve_and_encode(test_collection_name,
118 |         models={'country': fake_encode})
119 |         assert list(client.collection_schema(test_collection_name)['country_vector_'].keys())[0] == 'vector'
120 |         assert len(results['failed_document_ids']) == 0
121 |         assert 'country_vector_' in client.collection_schema(test_collection_name)
122 |         docs = client.retrieve_documents(test_collection_name)['documents']
123 |         assert len(docs[0]['country_vector_']) == VECTOR_LENGTH
124 | 
125 | @pytest.mark.parametrize('collection_name',['HIUFE', 'HUIF_;', 'fheuwiHF'])
126 | def test_collection_name_error(test_client, collection_name):
127 |     with pytest.raises(CollectionNameError):
128 |         test_client._typecheck_collection_name(collection_name)
129 | 
130 | @pytest.mark.parametrize('collection_name', ['fehwu'])
131 | def test_collection_name_not_error(test_client, collection_name):
132 |     test_client._typecheck_collection_name(collection_name)
133 |     assert True
134 | 


--------------------------------------------------------------------------------
/tests/test_write_deployed_models.py:
--------------------------------------------------------------------------------
 1 | """Test the write database.
 2 | """
 3 | import json
 4 | import pytest
 5 | import os
 6 | import time
 7 | import numpy as np
 8 | from vectorai.models.deployed import ViText2Vec
 9 | from vectorai.write import ViWriteClient
10 | from vectorai.errors import APIError, MissingFieldError, MissingFieldWarning, CollectionNameError
11 | from vectorai.client import ViClient
12 | from .utils import TempClientWithDocs
13 | 
14 | @pytest.mark.use_client
15 | def test_encode_documents_with_deployed_model(test_client, test_text_encoder):
16 |     """
17 |         Test single encoding method for models.
18 |     """
19 |     documents = test_client.create_sample_documents(10)
20 |     test_client.encode_documents_with_models(documents, models={'color': [test_text_encoder]}, use_bulk_encode=False)
21 |     assert 'color_vector_' in documents[0].keys()
22 |     assert len(documents[0]['color_vector_']) > 0
23 | 
24 | @pytest.mark.use_client
25 | def test_bulk_encode_documents_with_deployed_model(test_client, test_text_encoder):
26 |     """
27 |         Test bulk encoding method for models.
28 |     """
29 |     # Test when model key input is a list
30 |     documents = test_client.create_sample_documents(10)
31 |     test_client.encode_documents_with_models(documents, models={'color': [test_text_encoder]}, use_bulk_encode=True)
32 |     assert 'color_vector_' in documents[0].keys()
33 |     assert len(documents[0]['color_vector_']) > 0
34 |     del documents
35 |     documents = test_client.create_sample_documents(10)
36 |     test_client.encode_documents_with_models(documents, models={'color': test_text_encoder}, use_bulk_encode=True)
37 |     assert 'color_vector_' in documents[0].keys()
38 |     assert len(documents[0]['color_vector_']) > 0
39 | 


--------------------------------------------------------------------------------
/tests/test_write_documents.py:
--------------------------------------------------------------------------------
 1 | """Test the write database.
 2 | """
 3 | import json
 4 | import pytest
 5 | import os
 6 | import time
 7 | import numpy as np
 8 | from vectorai.models.deployed import ViText2Vec
 9 | from vectorai.write import ViWriteClient
10 | from vectorai.errors import APIError, MissingFieldError, MissingFieldWarning, CollectionNameError
11 | from vectorai.client import ViClient
12 | from .utils import TempClientWithDocs
13 | 
14 | def test__write_document_nested_field():
15 |     sample = {"this": {}}
16 |     ViWriteClient.set_field("this.is", doc=sample, value=[0, 2])
17 |     assert sample["this"]["is"] == [0, 2]
18 | 
19 | def test__write_document_nested_field_2():
20 |     sample = {"this": {"is": {}}}
21 |     ViWriteClient.set_field("this.is", doc=sample, value=[0, 2])
22 |     assert sample["this"]["is"] == [0, 2]
23 | 
24 | @pytest.mark.use_client
25 | def test_encode_documents_with_deployed_model(test_client, test_text_encoder):
26 |     """
27 |         Test single encoding method for models.
28 |     """
29 |     documents = test_client.create_sample_documents(10)
30 |     test_client.encode_documents_with_models(documents, models={'color': [test_text_encoder]}, use_bulk_encode=False)
31 |     assert 'color_vector_' in documents[0].keys()
32 |     assert len(documents[0]['color_vector_']) > 0
33 | 
34 | @pytest.mark.use_client
35 | def test_bulk_encode_documents_with_deployed_model(test_client, test_text_encoder):
36 |     """
37 |         Test bulk encoding method for models.
38 |     """
39 |     # Test when model key input is a list
40 |     documents = test_client.create_sample_documents(10)
41 |     test_client.encode_documents_with_models(documents, models={'color': [test_text_encoder]}, use_bulk_encode=True)
42 |     assert 'color_vector_' in documents[0].keys()
43 |     assert len(documents[0]['color_vector_']) > 0
44 |     del documents
45 |     documents = test_client.create_sample_documents(10)
46 |     test_client.encode_documents_with_models(documents, models={'color': test_text_encoder}, use_bulk_encode=True)
47 |     assert 'color_vector_' in documents[0].keys()
48 |     assert len(documents[0]['color_vector_']) > 0
49 | 
50 | def test_dummy_vector(test_client):
51 |     """
52 |         Test the dummy vector
53 |     """
54 |     assert len(test_client.dummy_vector(512)) == 512
55 | 
56 | def test_set_field_on_new_field(test_client):
57 |     """
58 |         Assert when set on new field.
59 |     """
60 |     doc = {}
61 |     test_client.set_field('balls', doc, 3)
62 |     assert doc['balls'] == 3
63 | 
64 | def test_set_field_on_new_dict(test_client):
65 |     doc = {}
66 |     test_client.set_field('check.balls', doc, 3)
67 |     assert test_client.get_field('check.balls', doc) == 3
68 | 
69 | def test_vector_name(test_client):
70 |     text_encoder = ViText2Vec(os.environ['VI_USERNAME'], os.environ['VI_API_KEY'])
71 |     test_client.set_name(text_encoder, 'vectorai_text')
72 |     vector_name = test_client._get_vector_name_for_encoding("color", text_encoder, model_list=[text_encoder])
73 |     assert vector_name == "color_vectorai_text_vector_"
74 | 
75 | def test_vector_name_2(test_client):
76 |     text_encoder = ViText2Vec(os.environ['VI_USERNAME'], os.environ['VI_API_KEY'])
77 |     text_encoder_2 = ViText2Vec(os.environ['VI_USERNAME'], os.environ['VI_API_KEY'])
78 |     test_client.set_name(text_encoder, "vectorai")
79 |     test_client.set_name(text_encoder_2, "vectorai_2")
80 |     vector_name = test_client._get_vector_name_for_encoding("color", text_encoder, model_list=[text_encoder, text_encoder_2])
81 |     assert vector_name == "color_vectorai_vector_"
82 |     vector_name = test_client._get_vector_name_for_encoding("color", text_encoder_2, model_list=[text_encoder, text_encoder_2])
83 |     assert vector_name == 'color_vectorai_2_vector_'
84 | 
85 | def test_vector_name_same_name(test_client):
86 |     text_encoder = ViText2Vec(os.environ['VI_USERNAME'], os.environ['VI_API_KEY'])
87 |     with pytest.raises(ValueError):
88 |         vector_name = test_client._check_if_multiple_models_have_same_name(models={'color':[text_encoder, text_encoder]})
89 | 
90 | def test_encode_documents_With_models_using_encode(test_client):
91 |     docs = test_client.create_sample_documents(5)
92 |     text_encoder = ViText2Vec(os.environ['VI_USERNAME'], os.environ['VI_API_KEY'])
93 |     test_client.set_name(text_encoder, "vectorai_text")
94 |     test_client.encode_documents_with_models_using_encode(docs, models={'color': [text_encoder]})
95 |     assert 'color_vectorai_text_vector_' in docs[0].keys()
96 | 


--------------------------------------------------------------------------------
/tests/test_write_edit.py:
--------------------------------------------------------------------------------
  1 | """Test the write database.
  2 | """
  3 | import json
  4 | import pytest
  5 | import os
  6 | import time
  7 | import numpy as np
  8 | from vectorai.models.deployed import ViText2Vec
  9 | from vectorai.write import ViWriteClient
 10 | from vectorai.errors import APIError, MissingFieldError, MissingFieldWarning, CollectionNameError
 11 | from vectorai.client import ViClient
 12 | from .utils import TempClientWithDocs
 13 | 
 14 | class TestEdit:
 15 |     @pytest.mark.use_client
 16 |     def test_setup_for_read(self, test_client, test_collection_name):
 17 |         """Test Setup for Read Operations"""
 18 |         if test_collection_name in test_client.list_collections():
 19 |             test_client.delete_collection(collection_name=test_collection_name)
 20 |         documents = [
 21 |             {
 22 |                 "_id": "2",
 23 |                 "document_vector_": test_client.generate_vector(vector_length=512),
 24 |                 "attribute": "red",
 25 |             },
 26 |             {
 27 |                 "_id": "1",
 28 |                 "document_vector_": test_client.generate_vector(vector_length=512),
 29 |                 "attribute": "blue",
 30 |             },
 31 |         ]
 32 | 
 33 |         test_client.insert_documents(
 34 |             collection_name=test_collection_name, documents=documents
 35 |         )
 36 |         time.sleep(10)
 37 |         assert True
 38 | 
 39 | 
 40 |     @pytest.mark.use_client
 41 |     def test_edit_document(self, test_client, test_collection_name):
 42 |         with TempClientWithDocs(test_client, test_collection_name) as client:
 43 |             edits = {
 44 |                 "_id": "1",
 45 |                 "location": "Paris"
 46 |             }
 47 |             client.edit_document(
 48 |                 collection_name=test_collection_name, edits=edits, document_id=edits['_id']
 49 |             )
 50 |             time.sleep(2)
 51 |             doc = client.id(collection_name=test_collection_name, document_id="1")
 52 |             assert doc["location"] == "Paris"
 53 | 
 54 |     @pytest.mark.use_client
 55 |     def test_create_filter(self, test_client, test_collection_name):
 56 |         with TempClientWithDocs(test_client, test_collection_name) as client:
 57 |             doc = {
 58 |                 'location': "Paris"
 59 |             }
 60 |             client.insert(test_collection_name, doc)
 61 |             results = test_client.filters(
 62 |                 test_collection_name,
 63 |                 test_client.create_filter_query(test_collection_name, 'location', 'contains', 'Paris'))
 64 |             assert len(results) > 0
 65 | 
 66 |     @pytest.mark.use_client
 67 |     def test_create_filter_2(self, test_client, test_collection_name):
 68 |         with TempClientWithDocs(test_client, test_collection_name) as client:
 69 |             doc = {
 70 |                 'location': "Paris"
 71 |             }
 72 |             client.insert(test_collection_name, doc)
 73 |             results = test_client.filters(
 74 |                 test_collection_name,
 75 |                 test_client.create_filter_query(
 76 |                     test_collection_name, 'location', 'exact_match', 'Paris'))
 77 |             assert len(results) > 0
 78 | 
 79 |     @pytest.mark.use_client
 80 |     def test_create_filter_3(self, test_client, test_collection_name):
 81 |         with TempClientWithDocs(test_client, test_collection_name) as client:
 82 |             results = test_client.filters(test_collection_name,
 83 |             test_client.create_filter_query(test_collection_name, 'size.feet', '<=', '31'))
 84 |             assert len(results) > 0
 85 | 
 86 |     @pytest.mark.use_client
 87 |     def test_create_filter_4(self, test_client, test_collection_name):
 88 |         with TempClientWithDocs(test_client, test_collection_name):
 89 |             results = test_client.filters(test_collection_name,
 90 |             test_client.create_filter_query(test_collection_name, 'insert_date_', '>=', '2020-01-01'))
 91 |             assert len(results) > 0
 92 | 
 93 |     @pytest.mark.use_client
 94 |     def test_edit_documents(self, test_client, test_collection_name):
 95 |         """Test adding of an attribute
 96 |         """
 97 |         with TempClientWithDocs(test_client, test_collection_name):
 98 |             edits = [
 99 |                 {"_id": "2", "location": "Sydney",},
100 |                 {"_id": "1", "location": "New York",},
101 |             ]
102 |             test_client.edit_documents(test_collection_name, edits)
103 |             doc = test_client.id(collection_name=test_collection_name, document_id="2")
104 |             assert doc["location"] == "Sydney"
105 |             doc = test_client.id(collection_name=test_collection_name, document_id="1")
106 |             assert doc['location'] == 'New York'
107 | 
108 | @pytest.mark.use_client
109 | def test_edit_documents(test_client, test_collection_name):
110 |     with TempClientWithDocs(test_client, test_collection_name, 100) as client:
111 |         edits = test_client.create_sample_documents(100)
112 |         {x.update({'favorite_singer': 'billie eilish'}) for x in edits}
113 |         response = client.edit_documents(test_collection_name, edits)
114 |         assert response['edited_successfully'] == len(edits)
115 |         # Retrieve the documents 
116 |         docs = client.retrieve_documents(test_collection_name, 
117 |         include_fields=['favorite_singer'], page_size=1)['documents']
118 |         for doc in docs:
119 |             assert doc['favorite_singer'] == 'billie eilish' 
120 | 


--------------------------------------------------------------------------------
/tests/test_write_insert.py:
--------------------------------------------------------------------------------
  1 | """Test the write database.
  2 | """
  3 | import json
  4 | import pytest
  5 | import os
  6 | import time
  7 | import numpy as np
  8 | from vectorai.models.deployed import ViText2Vec
  9 | from vectorai.write import ViWriteClient
 10 | from vectorai.errors import APIError, MissingFieldError, MissingFieldWarning, CollectionNameError
 11 | from vectorai.client import ViClient
 12 | from .utils import TempClientWithDocs
 13 | 
 14 | class TestInsert:
 15 |     @pytest.mark.use_client
 16 |     def test_insert_documents_simple_and_collection_stats_match(self, test_client, 
 17 |     test_collection_name):
 18 |         """
 19 |             Testing for simple document insertion
 20 |         """
 21 |         if test_collection_name in test_client.list_collections():
 22 |             test_client.delete_collection(test_collection_name)
 23 |         sample_documents = test_client.create_sample_documents(10)
 24 |         test_client.insert_documents(test_collection_name, sample_documents)
 25 |         time.sleep(10)
 26 |         assert test_client.collection_stats(test_collection_name)['number_of_documents'] == 10
 27 |         test_client.delete_collection(test_collection_name)
 28 |         time.sleep(3)
 29 | 
 30 |     @pytest.mark.use_client
 31 |     def test_inserting_documents_without_id_fields(self, test_client, test_collection_name):
 32 |         """
 33 |             Test inserting documents if they do not have an ID field.
 34 |         """
 35 |         if test_collection_name in test_client.list_collections():
 36 |             test_client.delete_collection(test_collection_name)
 37 |         sample_documents = test_client.create_sample_documents(10)
 38 |         # Remove the ID fields
 39 |         {x.pop('_id') for x in sample_documents}
 40 |         test_client.insert_documents(test_collection_name, sample_documents)
 41 |         time.sleep(10)
 42 |         assert test_client.collection_stats(test_collection_name)['number_of_documents'] == 10
 43 |         test_client.delete_collection(test_collection_name)
 44 |         time.sleep(3)
 45 | 
 46 |     @pytest.mark.use_client
 47 |     def test_inserting_documents_without_id_fields_with_overwrite(self, test_client, 
 48 |     test_collection_name):
 49 |         """
 50 |             Test inserting documents if they do not have an ID field.
 51 |         """
 52 |         if test_collection_name in test_client.list_collections():
 53 |             test_client.delete_collection(test_collection_name)
 54 |         sample_documents = test_client.create_sample_documents(10)
 55 |         # Remove the ID fields
 56 |         {x.pop('_id') for x in sample_documents}
 57 |         with pytest.warns(MissingFieldWarning):
 58 |             test_client.insert_documents(test_collection_name, sample_documents, overwrite=True)
 59 |         time.sleep(10)
 60 |         assert test_client.collection_stats(test_collection_name)['number_of_documents'] == 10
 61 |         test_client.delete_collection(test_collection_name)
 62 |         time.sleep(3)
 63 | 
 64 |     @pytest.mark.use_client
 65 |     def test_inserting_documents_when_id_is_not_a_string(self, test_client, test_collection_name):
 66 |         """
 67 |             Test inserting documents when ID is not a string
 68 |         """
 69 |         if test_collection_name in test_client.list_collections():
 70 |             test_client.delete_collection(test_collection_name)
 71 |         sample_documents = test_client.create_sample_documents(10)
 72 |         # Create integer IDs strings
 73 |         {x.update({'_id': int(x['_id'])}) for x in sample_documents}
 74 |         test_client.insert_documents(test_collection_name, sample_documents, overwrite=False)
 75 |         time.sleep(10)
 76 |         assert test_client.collection_stats(test_collection_name)['number_of_documents'] == 10
 77 |         test_client.delete_collection(test_collection_name)
 78 |         time.sleep(3)
 79 | 
 80 |     @pytest.mark.use_client
 81 |     def test_inserting_documents_when_id_is_not_a_string_with_overwrite(self, test_client, 
 82 |     test_collection_name):
 83 |         """
 84 |             Test inserting documents when ID is not a string
 85 |         """
 86 |         if test_collection_name in test_client.list_collections():
 87 |             test_client.delete_collection(test_collection_name)
 88 |         sample_documents = test_client.create_sample_documents(10)
 89 |         # Create integer IDs strings
 90 |         {x.update({'_id': int(x['_id'])}) for x in sample_documents}
 91 |         test_client.insert_documents(test_collection_name, sample_documents, overwrite=True)
 92 |         time.sleep(10)
 93 |         assert test_client.collection_stats(test_collection_name)['number_of_documents'] == 10
 94 |         test_client.delete_collection(test_collection_name)
 95 |         time.sleep(3)
 96 | 
 97 |     @pytest.mark.use_client
 98 |     def test_insert_single_document(self, test_client, test_collection_name):
 99 |         if test_collection_name not in test_client.list_collections():
100 |             test_client.create_collection(test_collection_name)
101 |         # document = {"sample_vector_": test_client.generate_vector(20), "sample_name": "hi"}
102 |         document = test_client.create_sample_document(1)
103 |         response = test_client.insert(
104 |             collection_name=test_collection_name, document=document
105 |         )
106 |         assert response['status'] == 'success'
107 | 
108 |     @pytest.mark.use_client
109 |     def test_insert_single_document_error(self, test_client, test_collection_name):
110 |         """Trigger an insert fail error
111 |         """
112 |         with pytest.raises(APIError):
113 |             if test_collection_name not in test_client.list_collections():
114 |                 test_client.create_collection(test_collection_name)
115 |             document = {
116 |                 "sample_vectors_": [test_client.generate_vector(20)] + [np.nan],
117 |                 "samplename": [["hi"]],
118 |             }
119 |             response = test_client.insert(
120 |                 collection_name=test_collection_name, document=document
121 |             )
122 | 
123 | 
124 |     @pytest.mark.use_client
125 |     def test_clean_up(self, test_client, test_collection_name):
126 |         """Remove a collection if it is there.
127 |         """
128 |         if test_collection_name in test_client.list_collections():
129 |             test_client.delete_collection(test_collection_name)
130 |         assert test_collection_name not in test_client.list_collections()
131 | 


--------------------------------------------------------------------------------
/tests/test_write_misc.py:
--------------------------------------------------------------------------------
 1 | """Test the write database.
 2 | """
 3 | import json
 4 | import pytest
 5 | import os
 6 | import time
 7 | import numpy as np
 8 | from vectorai.models.deployed import ViText2Vec
 9 | from vectorai.write import ViWriteClient
10 | from vectorai.errors import APIError, MissingFieldError, MissingFieldWarning, CollectionNameError
11 | from vectorai.client import ViClient
12 | from .utils import TempClientWithDocs
13 | 
14 | def test_vector_name_same_name(test_client):
15 |     text_encoder = ViText2Vec(os.environ['VI_USERNAME'], os.environ['VI_API_KEY'])
16 |     with pytest.raises(ValueError):
17 |         vector_name = test_client._check_if_multiple_models_have_same_name(models={'color':[text_encoder, text_encoder]})
18 | 
19 | def test_encode_documents_With_models_using_encode(test_client):
20 |     docs = test_client.create_sample_documents(5)
21 |     text_encoder = ViText2Vec(os.environ['VI_USERNAME'], os.environ['VI_API_KEY'])
22 |     test_client.set_name(text_encoder, "vectorai_text")
23 |     test_client.encode_documents_with_models_using_encode(docs, models={'color': [text_encoder]})
24 |     assert 'color_vectorai_text_vector_' in docs[0].keys()
25 | 
26 | @pytest.mark.use_client
27 | def test_raises_warning_if_no_id(test_client, test_collection_name):
28 |     docs = test_client.create_sample_documents(10)
29 |     {x.pop('_id') for x in docs}
30 |     with pytest.warns(MissingFieldWarning) as record:
31 |         test_client.insert_documents(test_collection_name, docs)
32 |     assert len(record) > 1
33 |     assert record[1].message.args[0] == test_client.NO_ID_WARNING_MESSAGE
34 | 
35 | @pytest.mark.use_client
36 | def test_raises_warning_if_only_one_id_is_present(test_client, test_collection_name):
37 |     docs = test_client.create_sample_documents(10)
38 |     {x.pop('_id') for x in docs[1:]}
39 |     with pytest.warns(MissingFieldWarning) as record:
40 |         test_client.insert_documents(test_collection_name, docs)
41 |     assert record[0].message.args[0] == test_client.NO_ID_WARNING_MESSAGE
42 | 


--------------------------------------------------------------------------------
/tests/test_write_multiprocessing.py:
--------------------------------------------------------------------------------
  1 | """Test the write database.
  2 | """
  3 | import json
  4 | import pytest
  5 | import os
  6 | import time
  7 | import numpy as np
  8 | from vectorai.models.deployed import ViText2Vec
  9 | from vectorai.write import ViWriteClient
 10 | from vectorai.errors import APIError, MissingFieldError, MissingFieldWarning, CollectionNameError
 11 | from vectorai.client import ViClient
 12 | from .utils import TempClientWithDocs
 13 | 
 14 | @pytest.mark.use_client
 15 | def test_multiprocess_insert(test_client, test_collection_name):
 16 |     NUM_OF_DOCUMENTS_INSERTED = 10
 17 |     if test_collection_name in test_client.list_collections():
 18 |         test_client.delete_collection(test_collection_name)
 19 |         time.sleep(10)
 20 |     documents = test_client.create_sample_documents(NUM_OF_DOCUMENTS_INSERTED)
 21 |     results = test_client.insert_documents(test_collection_name, documents, workers=5, overwrite=False)
 22 |     time.sleep(10)
 23 |     assert len(results['failed_document_ids']) == 0
 24 |     assert test_collection_name in test_client.list_collections()
 25 |     assert test_client.collection_stats(test_collection_name)['number_of_documents'] == NUM_OF_DOCUMENTS_INSERTED
 26 |     test_client.delete_collection(test_collection_name)
 27 | 
 28 | @pytest.mark.use_client
 29 | def test_multiprocess_insert_with_error(test_client, test_collection_name):
 30 |     NUM_OF_DOCUMENTS_INSERTED = 100
 31 |     if test_collection_name in test_client.list_collections():
 32 |         test_client.delete_collection(test_collection_name)
 33 |     documents = test_client.create_sample_documents(NUM_OF_DOCUMENTS_INSERTED)
 34 |     documents.append({
 35 |         '_id': '9993',
 36 |         'color': np.nan
 37 |     })
 38 | 
 39 |     # This should result in 1 failure
 40 |     results = test_client.insert_documents(test_collection_name, documents, workers=5, overwrite=False)
 41 |     time.sleep(10)
 42 |     assert len(results['failed_document_ids']) == 1
 43 |     assert test_collection_name in test_client.list_collections()
 44 |     assert test_client.collection_stats(test_collection_name)['number_of_documents'] > 0
 45 |     test_client.delete_collection(test_collection_name)
 46 | 
 47 | @pytest.mark.use_client
 48 | def test_multiprocess_insert_with_error_with_overwrite(test_client, test_collection_name):
 49 |     NUM_OF_DOCUMENTS_INSERTED = 100
 50 |     if test_collection_name in test_client.list_collections():
 51 |         test_client.delete_collection(test_collection_name)
 52 |         time.sleep(5)
 53 |     documents = test_client.create_sample_documents(NUM_OF_DOCUMENTS_INSERTED)
 54 |     documents.append({
 55 |         '_id': '9993',
 56 |         'color': np.nan
 57 |     })
 58 | 
 59 |     # This should result in 1 failure
 60 |     results = test_client.insert_documents(test_collection_name, documents, workers=5, overwrite=True)
 61 |     time.sleep(10)
 62 |     assert len(results['failed_document_ids']) == 1
 63 |     assert test_collection_name in test_client.list_collections()
 64 |     assert test_client.collection_stats(test_collection_name)['number_of_documents'] > 0
 65 |     test_client.delete_collection(test_collection_name)
 66 | 
 67 | @pytest.mark.use_client
 68 | def test_multiprocess_with_overwrite(test_client, test_collection_name):
 69 |     if test_collection_name in test_client.list_collections():
 70 |         test_client.delete_collection(test_collection_name)
 71 |         time.sleep(5)
 72 |     NUM_OF_DOCS = 10
 73 |     docs = test_client.create_sample_documents(NUM_OF_DOCS)
 74 |     test_client.insert_documents(test_collection_name, docs[0:5], workers=1, overwrite=False)
 75 |     response = test_client.insert_documents(test_collection_name, docs[3:5], workers=1,
 76 |     overwrite=True)
 77 |     assert response['inserted_successfully'] == 2
 78 | 
 79 | @pytest.mark.use_client
 80 | def test_multiprocess_with_overwrite_insert(test_client, test_collection_name):
 81 |     if test_collection_name in test_client.list_collections():
 82 |         test_client.delete_collection(test_collection_name)
 83 |         time.sleep(5)
 84 |     NUM_OF_DOCS = 10
 85 |     docs = test_client.create_sample_documents(NUM_OF_DOCS)
 86 |     test_client.insert_documents(test_collection_name, docs[0:5], workers=1, overwrite=False)
 87 |     response = test_client.insert_documents(test_collection_name, docs[3:5], workers=1,
 88 |     overwrite=True)
 89 |     assert response['inserted_successfully'] == 2
 90 | 
 91 | @pytest.mark.use_client
 92 | def test_multiprocess_overwrite(test_client, test_collection_name):
 93 |     if test_collection_name in test_client.list_collections():
 94 |         test_client.delete_collection(test_collection_name)
 95 |         time.sleep(5)
 96 |     NUM_OF_DOCS = 100
 97 |     docs = test_client.create_sample_documents(NUM_OF_DOCS)
 98 |     test_client.insert_documents(test_collection_name, docs[0:5], workers=1, overwrite=False)
 99 |     # For document with id '3'
100 |     TEST_ID = '3'
101 |     id_document = test_client.id(collection_name=test_collection_name, document_id=TEST_ID)
102 |     test_client.set_field('test.field', id_document, 'stranger')
103 |     docs[3] = id_document
104 |     print(docs[3])
105 |     docs[3].update({'_id': '3'})
106 |     response = test_client.insert_documents(test_collection_name, docs[3:5], workers=1,
107 |     overwrite=True)
108 |     id_document = test_client.id(collection_name=test_collection_name, document_id=TEST_ID)
109 |     assert test_client.get_field('test.field', id_document) == 'stranger'
110 |     time.sleep(5)
111 |     test_client.delete_collection(test_collection_name)
112 | 
113 | @pytest.mark.use_client
114 | def test_multiprocess_not_overwrite(test_client, test_collection_name):
115 |     if test_collection_name in test_client.list_collections():
116 |         test_client.delete_collection(test_collection_name)
117 |         time.sleep(5)
118 |     NUM_OF_DOCS = 100
119 |     docs = test_client.create_sample_documents(NUM_OF_DOCS)
120 |     test_client.insert_documents(test_collection_name, docs[0:5], workers=1, overwrite=False)
121 |     # For document with id '3'
122 |     TEST_ID = '3'
123 |     id_document = test_client.id(collection_name=test_collection_name, document_id=TEST_ID)
124 |     test_client.set_field('test.field', id_document, 'stranger')
125 |     docs[3] = id_document
126 |     docs[3].update({'_id': '3'})
127 |     response = test_client.insert_documents(test_collection_name, docs[3:5], workers=1,
128 |     overwrite=False)
129 |     id_document = test_client.id(collection_name=test_collection_name, document_id=TEST_ID)
130 |     with pytest.raises(MissingFieldError):
131 |         test_client.get_field('test.field', id_document)
132 |     time.sleep(5)
133 |     test_client.delete_collection(test_collection_name)
134 | 


--------------------------------------------------------------------------------
/tests/test_write_retrieve_and_encode.py:
--------------------------------------------------------------------------------
 1 | """Test the write database.
 2 | """
 3 | import json
 4 | import pytest
 5 | import os
 6 | import time
 7 | import numpy as np
 8 | from vectorai.models.deployed import ViText2Vec
 9 | from vectorai.write import ViWriteClient
10 | from vectorai.errors import APIError, MissingFieldError, MissingFieldWarning, CollectionNameError
11 | from vectorai.client import ViClient
12 | from .utils import TempClientWithDocs
13 | 
14 | @pytest.mark.use_client
15 | def test_retrieve_and_encode_simple(test_client, test_collection_name):
16 |     """Test retrieving documents and encoding them with vectors.
17 |     """
18 |     VECTOR_LENGTH = 100
19 |     def fake_encode(x):
20 |         return test_client.generate_vector(VECTOR_LENGTH)
21 |     # with TempClientWithDocs(test_client, test_collection_name, 100) as client:
22 |     test_client.insert_documents(test_collection_name, test_client.create_sample_documents(100))
23 |     results = test_client.retrieve_and_encode(test_collection_name,
24 |     models={'country': fake_encode})
25 |     assert list(test_client.collection_schema(test_collection_name)['country_vector_'].keys())[0] == 'vector'
26 |     assert len(results['failed_document_ids']) == 0
27 |     assert 'country_vector_' in test_client.collection_schema(test_collection_name)
28 |     docs = test_client.retrieve_documents(test_collection_name)['documents']
29 |     assert len(docs[0]['country_vector_']) == VECTOR_LENGTH
30 | 


--------------------------------------------------------------------------------
/tests/utils.py:
--------------------------------------------------------------------------------
 1 | import time
 2 | import random
 3 | import string
 4 | from vectorai import ViClient
 5 | 
 6 | class TempClient:
 7 |     def __init__(self, client, collection_name: str=None):
 8 |         self.client = client
 9 |         if isinstance(client, ViClient):
10 |             self.collection_name = collection_name
11 |         # elif isinstance(client, ViCollectionClient):
12 |         #     self.collection_name = self.client.collection_name
13 | 
14 |     def teardown_collection(self):
15 |         if self.collection_name in self.client.list_collections():
16 |             time.sleep(2)
17 |             if isinstance(self.client, ViClient):
18 |                 self.client.delete_collection(self.collection_name)
19 |             elif isinstance(self.client, ViCollectionClient):
20 |                 self.client.delete_collection()
21 | 
22 |     def __enter__(self):
23 |         self.teardown_collection()
24 |         return self.client
25 | 
26 |     def __exit__(self, *exc):
27 |         self.teardown_collection()
28 | 
29 | class TempClientWithDocs(TempClient):
30 |     """
31 |         Temporary Client With Documents already inserted.
32 |     """
33 |     def __init__(self, client, collection_name: str=None, num_of_docs: int=10):
34 |         self.client = client
35 |         if hasattr(self.client, 'collection_name'):
36 |             self.collection_name = collection_name
37 |         else:
38 |             if collection_name is None:
39 |                 collection_name = self.generate_random_collection_name()
40 |             self.collection_name = collection_name
41 |             self.client.collection_name = collection_name
42 |         self.num_of_docs = num_of_docs
43 |         self.teardown_collection()
44 |         self.client.insert_documents(self.collection_name,
45 |         self.client.create_sample_documents(self.num_of_docs))
46 | 
47 |     def generate_random_collection_name(self):
48 |         return self.generate_random_string(20)
49 | 
50 |     def generate_random_string(self, num_of_letters):
51 |         letters = string.ascii_lowercase
52 |         return '_delete_'.join(random.choice(letters) for i in range(num_of_letters))
53 | 
54 |     def __enter__(self):
55 |         # self.teardown_collection()
56 |         # self.client.insert_documents(self.collection_name,
57 |         # self.client.create_sample_documents(self.num_of_docs))
58 |         return self.client
59 | 


--------------------------------------------------------------------------------
/utils/automate_api.py:
--------------------------------------------------------------------------------
 1 | if __name__=="__main__":
 2 |     import os
 3 |     from openapi_to_sdk.sdk_automation import PythonSDKBuilder
 4 | 
 5 |     url="https://vectorai-development-api.azurewebsites.net"
 6 |     url="https://vectorai-development-api.azurewebsites.net"
 7 |     # url = "https://api.vctr.ai"
 8 |     sdk = PythonSDKBuilder(
 9 |         url=url,
10 |         # url="https://vectorai-development-api.azurewebsites.net",
11 |         # url='https://vecdb-aueast-api.azurewebsites.net',
12 |         inherited_properties=['username', 'api_key', 'url'],
13 |         decorators=[
14 |             'retry()',
15 |             "return_curl_or_response('json')"],
16 |         override_param_defaults=dict(
17 |             min_score=None,
18 |             cursor=None,
19 |             # url='https://vecdb-aueast-api.azurewebsites.net',
20 |             url=url,
21 |             # sort=False,
22 |             sort_by_created_at_date=False,
23 |         ),
24 |         internal_functions=[
25 |             "list_collections",
26 |             "create_collection",
27 |             "search",
28 |             "delete_collection",
29 |             "create_collection_from_document"
30 |         ],
31 |     )
32 |     sdk.to_python_file(
33 |         class_name="_ViAPIClient",
34 |         filename='vectorai/api/api.py',
35 |         import_strings=['import requests', 'from vectorai.api.utils import retry, return_curl_or_response'],
36 |         include_response_parsing=False,
37 |     )
38 | 
39 |     from vectorai.api.api import _ViAPIClient
40 |     vi = _ViAPIClient(os.environ['VI_USERNAME'], os.environ['VI_API_KEY'], url=url)
41 |     print(vi._list_collections())
42 | 
43 | 


--------------------------------------------------------------------------------
/utils/download_badges.py:
--------------------------------------------------------------------------------
 1 | import requests
 2 | def download_image(url, output_image_file):
 3 |     r = requests.get(url)
 4 |     with open(output_image_file, 'w') as f:
 5 |         if isinstance(r.content, bytes):
 6 |             content = r.content.decode()
 7 |         else:
 8 |             content = r.content
 9 |         f.write(content)
10 | 
11 | if __name__=="__main__":
12 |     
13 |     download_image("https://static.pepy.tech/personalized-badge/vectorai-nightly?period=total&units=none&left_color=black&right_color=purple&left_text=Total%20Downloads",
14 |     "assets/total_downloads.svg")
15 |     download_image("https://static.pepy.tech/personalized-badge/vectorai-nightly?period=week&units=none&left_color=black&right_color=purple&left_text=Weekly%20Downloads",
16 |     "assets/weekly_downloads.svg")
17 |     download_image("https://static.pepy.tech/personalized-badge/vectorai-nightly?period=month&units=none&left_color=black&right_color=purple&left_text=Monthly%20Downloads",
18 |     "assets/monthly_downloads.svg")
19 | 


--------------------------------------------------------------------------------
/vectorai/__init__.py:
--------------------------------------------------------------------------------
1 | """Vecdb Client
2 | """
3 | __version__ = "0.2.2"
4 | 
5 | from .api import *
6 | from .client import *
7 | from .read import *
8 | from .write import *
9 | 


--------------------------------------------------------------------------------
/vectorai/analytics/__init__.py:
--------------------------------------------------------------------------------
1 | """SUbmodule for Vector Analytics.
2 | """
3 | 
4 | from .analytics import ViAnalyticsMixin
5 | from .client import ViAnalyticsClient
6 | 


--------------------------------------------------------------------------------
/vectorai/analytics/analytics.py:
--------------------------------------------------------------------------------
 1 | """Mixin class for analytics submodule containing vector analytics tools.
 2 | """
 3 | 
 4 | from .dimensionality_reduction import *
 5 | from .viz import *
 6 | from .tables import *
 7 | 
 8 | class ViAnalyticsMixin(VizMixin, TableMixin):
 9 |     """
10 |     Vi Analytics Mixin.
11 |     Currently includes visualisation mixin. 
12 |     """
13 | 
14 |     pass
15 | 


--------------------------------------------------------------------------------
/vectorai/analytics/api/__init__.py:
--------------------------------------------------------------------------------
1 | from .comparator import ComparatorAPI


--------------------------------------------------------------------------------
/vectorai/analytics/api/comparator.py:
--------------------------------------------------------------------------------
 1 | import requests
 2 | from typing import List, Dict, Optional
 3 | from ...api.utils import retry, return_curl_or_response
 4 | 
 5 | class ComparatorAPI:
 6 |     def __init__(self, username: str=None, api_key: str=None, 
 7 |     url: str = "https://api.vctr.ai", analytics_url="https://vector-analytics.vctr.ai"):
 8 |         self.username = username
 9 |         self.api_key = api_key
10 |         self.url = url
11 |         self.analytics_url = analytics_url
12 | 
13 | 
14 |     @return_curl_or_response('content')
15 |     @retry()
16 |     def _compare_ranks(
17 |         self, 
18 |         ranked_list_1: List[Dict],
19 |         ranked_list_2: List[Dict],
20 |         fields_to_display: List[str]=None,
21 |         image_fields: List[str]=[],
22 |         audio_fields: List[str]=[],
23 |         column_titles: Optional[List[str]] = None,
24 |         x_axis_title: str = 'Fields',
25 |         y_axis_title: str = 'Comparing: ',
26 |         header: str = "<h1>Top-K Ranking Comparator</h1>",
27 |         subheader: str = "<h2>Compare ranks in the different lists.</h2>",
28 |         colors: List[str]=['#ccff99', 'powderblue', '#ffc2b3'],
29 |         return_curl: bool=False,
30 |         **kwargs
31 |         ):
32 |         """
33 |         Compare Top-K Lists.
34 |         Args:
35 |             ranked_list_1: A list of results as a dictionary containing the required fields.
36 |             ranked_list_2: Another list of results
37 |             fields_to_display: The fields required for displaying the object
38 |             image_fields: The fields which are images 
39 |             audio_fields: The fields which are audio
40 |             column_titles: The name of the columns for the differnt rank fields
41 |             x_axis_title: The title of the x axis 
42 |             y_axis_title: The title of the y axis
43 |             header: The name of the graph 
44 |             subheader: The sub-header of the graph
45 |         """
46 | 
47 |         params={
48 |             "username": self.username,
49 |             "api_key": self.api_key,
50 |             "ranked_list_1": ranked_list_1,
51 |             "ranked_list_2": ranked_list_2,
52 |             "fields_to_display": fields_to_display,
53 |             "image_fields": image_fields,
54 |             "audio_fields": audio_fields,
55 |             "column_titles": column_titles,
56 |             "x_axis_title": x_axis_title,
57 |             "y_axis_title": y_axis_title,
58 |             "header": header,
59 |             "subheader": subheader,
60 |             "colors": colors,
61 |         }
62 |         params.update(kwargs)
63 |         return requests.post(
64 |             url= f"{self.analytics_url}/comparator/compare_ranks/",
65 |             json=params)
66 | 


--------------------------------------------------------------------------------
/vectorai/analytics/client.py:
--------------------------------------------------------------------------------
 1 | import os
 2 | from .comparator import ComparatorClient
 3 | from .viz import VizMixin
 4 | 
 5 | class ViAnalyticsClient(ComparatorClient, VizMixin):
 6 |     def __init__(self, username: str=None, api_key: str=None,
 7 |     url: str = "https://api.vctr.ai", analytics_url="https://vector-analytics.vctr.ai"):
 8 |         self.username = username if username is not None else os.environ['VI_USERNAME']
 9 |         self.api_key = api_key if api_key is not None else os.environ['VI_API_KEY']
10 |         self.url = url
11 |         self.analytics_url = analytics_url
12 | 


--------------------------------------------------------------------------------
/vectorai/analytics/comparator.py:
--------------------------------------------------------------------------------
  1 | from typing import List, Dict, Optional
  2 | # from ..read import ViReadClient
  3 | # from ..client import ViClient
  4 | from .api.comparator import ComparatorAPI
  5 | 
  6 | class ComparatorClient(ComparatorAPI):
  7 |     def __init__(self, username: str=None, api_key: str=None,
  8 |     url: str = "https://api.vctr.ai",
  9 |     analytics_url="https://vector-analytics.vctr.ai"):
 10 |         self.username = username
 11 |         self.api_key = api_key
 12 |         self.url = url
 13 |         self.analytics_url = analytics_url
 14 | 
 15 |     def write_to_html(self, content, file_name: str):
 16 |         with open(file_name, 'w') as f:
 17 |             f.write(content)
 18 | 
 19 |     def output(self, content, html_file: str=None):
 20 |         if html_file is None:
 21 |             if self.is_in_notebook():
 22 |                 from IPython.display import HTML
 23 |                 return HTML(content.decode())
 24 |             return content
 25 |         self.write_to_html(content)
 26 |         print(f"Written to {html_file}.")
 27 |         return content
 28 | 
 29 |     def compare_ranks(
 30 |         self,
 31 |         ranked_list_1: List[Dict],
 32 |         ranked_list_2: List[Dict],
 33 |         fields_to_display: List[str]=None,
 34 |         image_fields: List[str]=[],
 35 |         audio_fields: List[str]=[],
 36 |         column_titles: Optional[List[str]] = None,
 37 |         x_axis_title: str = 'Fields',
 38 |         y_axis_title: str = 'Comparing: ',
 39 |         header: str = "<h1>Top-K Ranking Comparator</h1>",
 40 |         subheader: str = "<h2>Compare ranks in the different lists.</h2>",
 41 |         colors: List[str]=['#ccff99', 'powderblue', '#ffc2b3'],
 42 |         html_file: str=None
 43 |     ):
 44 |         """
 45 |         Compare Top-K Lists.
 46 |         Args:
 47 |             ranked_list_1: A list of results as a dictionary containing the required fields.
 48 |             ranked_list_2: Another list of results
 49 |             fields_to_display: The fields required for displaying the object
 50 |             image_fields: The fields which are images
 51 |             audio_fields: The fields which are audio
 52 |             column_titles: The name of the columns for the differnt rank fields
 53 |             x_axis_title: The title of the x axis
 54 |             y_axis_title: The title of the y axis
 55 |             header: The name of the graph
 56 |             subheader: The sub-header of the graph
 57 |         """
 58 |         content = self._compare_ranks(ranked_list_1, ranked_list_2,
 59 |             column_titles=column_titles, fields_to_display=fields_to_display,
 60 |             image_fields=image_fields, audio_fields=audio_fields,
 61 |             x_axis_title=x_axis_title, y_axis_title=y_axis_title,
 62 |             header=header, subheader=subheader, colors=colors)
 63 |         return self.output(content)
 64 | 
 65 |     def compare_search(
 66 |         self,
 67 |         collection_name: str,
 68 |         vector_fields: List[str],
 69 |         vector : List[float],
 70 |         fields_to_display: List[str]=None,
 71 |         image_fields: List[str]=[],
 72 |         audio_fields: List[str]=[],
 73 |         x_axis_title: str = 'Fields',
 74 |         y_axis_title: str = 'Vector fields',
 75 |         header: str = "<h1>Top-K Ranking Comparator</h1>",
 76 |         subheader: str = "<h2>Compare ranks in the different lists.</h2>",
 77 |         colors: List[str]=['#ccff99', 'powderblue', '#ffc2b3']
 78 |     ):
 79 |         """
 80 |         Compare Searching By ID
 81 |         """
 82 |         ranked_list_1 = self.search(
 83 |             collection_name,
 84 |             vector=vector,
 85 |             search_field=vector_fields[0])
 86 |         ranked_list_2 = self.search_by_id(
 87 |             collection_name,
 88 |             vector=vector,
 89 |             search_field=vector_fields[1])
 90 |         return self.compare_ranks(
 91 |             ranked_list_1,
 92 |             ranked_list_2,
 93 |             column_titles=vector_fields,
 94 |             fields_to_display=fields_to_display,
 95 |             image_fields=image_fields,
 96 |             audio_fields=audio_fields,
 97 |             x_axis_title=x_axis_title,
 98 |             y_axis_title=y_axis_title,
 99 |             header=header,
100 |             subheader=subheader,
101 |             colors=colors
102 |         )
103 | 
104 |     def random_compare_search_by_id(
105 |         self,
106 |         collection_name: str,
107 |         vector_fields: List[str],
108 |         fields_to_display: List[str]=None,
109 |         image_fields: List[str]=[],
110 |         audio_fields: List[str]=[],
111 |         x_axis_title: str = 'Fields',
112 |         y_axis_title: str = 'Vector fields',
113 |         header: str = "<h1>Top-K Ranking Comparator</h1>",
114 |         subheader: str = "<h2>Compare ranks in the different lists.</h2>",
115 |         colors: List[str]=['#ccff99', 'powderblue', '#ffc2b3'],
116 |         page_size=15,
117 |     ):
118 |         fields_to_include = ['_id'] + vector_fields
119 |         random_docs = self.random_documents(collection_name, page_size=1,
120 |         include_fields=fields_to_include)['documents']
121 |         random_id = random_docs[0]['_id']
122 |         ranked_list_1 = self.search_by_id(
123 |             document_id=random_id,
124 |             collection_name=collection_name,
125 |             search_field=vector_fields[0],
126 |             page_size=page_size)['results']
127 |         ranked_list_2 = self.search_by_id(
128 |             document_id=random_id,
129 |             collection_name=collection_name,
130 |             search_field=vector_fields[1],
131 |             page_size=page_size)['results']
132 |         return self.compare_ranks(
133 |             ranked_list_1,
134 |             ranked_list_2,
135 |             column_titles=vector_fields,
136 |             fields_to_display=fields_to_display,
137 |             image_fields=image_fields,
138 |             audio_fields=audio_fields,
139 |             x_axis_title=x_axis_title,
140 |             y_axis_title=y_axis_title,
141 |             header=header,
142 |             subheader=subheader,
143 |             colors=colors
144 |         )
145 | 
146 |     def compare_search_by_id(
147 |         self,
148 |         collection_name: str,
149 |         vector_fields: List[str],
150 |         document_id: str,
151 |         fields_to_display: List[str]=None,
152 |         image_fields: List[str]=[],
153 |         audio_fields: List[str]=[],
154 |         x_axis_title: str = 'Fields',
155 |         y_axis_title: str = 'Vector fields',
156 |         header: str = "<h1>Top-K Ranking Comparator</h1>",
157 |         subheader: str = "<h2>Compare ranks in the different lists.</h2>",
158 |         colors: List[str]=['#ccff99', 'powderblue', '#ffc2b3']
159 |     ):
160 |         """
161 |         Compare Searching By ID
162 |         """
163 |         ranked_list_1 = self.search_by_id(
164 |             collection_name,
165 |             document_id,
166 |             search_field=vector_fields[0])
167 |         ranked_list_2 = self.search_by_id(
168 |             collection_name,
169 |             document_id,
170 |             search_field=vector_fields[1])
171 |         return self.compare_ranks(
172 |             ranked_list_1,
173 |             ranked_list_2,
174 |             column_titles=vector_fields,
175 |             fields_to_display=fields_to_display,
176 |             image_fields=image_fields,
177 |             audio_fields=audio_fields,
178 |             x_axis_title=x_axis_title,
179 |             y_axis_title=y_axis_title,
180 |             header=header,
181 |             subheader=subheader,
182 |             colors=colors
183 |         )
184 | 


--------------------------------------------------------------------------------
/vectorai/analytics/dimensionality_reduction.py:
--------------------------------------------------------------------------------
  1 | """Vi Dimensionality Reduction
  2 | """
  3 | import numpy as np
  4 | import pandas as pd
  5 | from typing import List, Dict, Any
  6 | from ..write import ViWriteClient
  7 | 
  8 | 
  9 | class ViDimensionalityReductionBase(ViWriteClient):
 10 |     """
 11 |     Base class for dimensionality reduction.
 12 |     
 13 |     Example:
 14 |         >>> class IVISDimensionalityReduction(ViDimensionalityReductionBase):
 15 |         >>>     def __init__(self, batch_size=120, k=15, embedding_dims=2):
 16 |         >>>         self.scaler = MinMaxScaler()
 17 |         >>>         self.model = Ivis(embedding_dims=embedding_dims, k=k, batch_size=batch_size)
 18 |         >>>     def fit_transform(self, documents, field_vector):
 19 |         >>>           if isinstance(documents[0], dict):
 20 |         >>>             if 'documents' in documents[0].keys():
 21 |         >>>                vectors = [self.get_field(field_vector, document) for document in documents['documents']]
 22 |         >>>            else:
 23 |         >>>                vectors = [self.get_field(field_vector, document) for document in documents]
 24 |         >>>            
 25 |         >>>            X = np.stack(vectors)
 26 |         >>>            X_scaled = self.scaler.fit_transform(X)
 27 |         >>>            return self.model.fit_transform(X_scaled)
 28 |         >>>    
 29 |         >>>        def transform(self, X):
 30 |         >>>            if isinstance(documents[0], dict):
 31 |         >>>                if 'documents' in documents[0].keys():
 32 |         >>>                    vectors = [document[field_vector] for document in documents['documents']]
 33 |         >>>                else:
 34 |         >>>                    vectors = [document[field_vector] for document in documents]
 35 |         >>>            X_scaled = self.scaler.transform(vectors)
 36 |         >>>            return self.model.transform(X_scaled)
 37 |     """
 38 |     
 39 |     def fit_transform(self, documents: list, vector_field: str):
 40 |         """
 41 |         Fit and transform
 42 | 
 43 |         Args:
 44 |             documents:
 45 |                 A list of lists to flatten to make into 1 list.
 46 |             vector_field:
 47 |                 The vector field to fit and transform
 48 |         Returns:
 49 |             A dimensionality reduced array:
 50 |                 A numpy array with less dimensions than the original.
 51 | 
 52 |         Example:
 53 | 
 54 |         >>> class IVISDimensionalityReduction(ViDimensionalityReductionBase):
 55 |         >>>     def __init__(self, batch_size=120, k=15, embedding_dims=2):
 56 |         >>>         self.scaler = MinMaxScaler()
 57 |         >>>         self.model = Ivis(embedding_dims=embedding_dims, k=k, batch_size=batch_size)
 58 |         >>>     def fit_transform(self, documents, field_vector):
 59 |         >>>           if isinstance(documents[0], dict):
 60 |         >>>             if 'documents' in documents[0].keys():
 61 |         >>>                vectors = [self.get_field(field_vector, document) for document in documents['documents']]
 62 |         >>>            else:
 63 |         >>>                vectors = [self.get_field(field_vector, document) for document in documents]
 64 |         >>>            
 65 |         >>>            X = np.stack(vectors)
 66 |         >>>            X_scaled = self.scaler.fit_transform(X)
 67 |         >>>            return self.model.fit_transform(X_scaled)
 68 |         >>>    
 69 |         >>>        def transform(self, X):
 70 |         >>>            if isinstance(documents[0], dict):
 71 |         >>>                if 'documents' in documents[0].keys():
 72 |         >>>                    vectors = [document[field_vector] for document in documents['documents']]
 73 |         >>>                else:
 74 |         >>>                    vectors = [document[field_vector] for document in documents]
 75 |         >>>            X_scaled = self.scaler.transform(vectors)
 76 |         >>>            return self.model.transform(X_scaled)
 77 |         """
 78 |         pass
 79 |     
 80 |     def transform(self, documents: List[Dict[str, Any]]):
 81 |         """
 82 |         Transform documents
 83 | 
 84 |         Args:
 85 |             documents:
 86 |                 A list of lists to flatten to make into 1 list.
 87 |         
 88 |         Returns:
 89 |             transformed array:
 90 |                 A transformed numpy array
 91 | 
 92 |         Example:
 93 |         
 94 |         >>> class IVISDimensionalityReduction(ViDimensionalityReductionBase):
 95 |         >>>     def __init__(self, batch_size=120, k=15, embedding_dims=2):
 96 |         >>>         self.scaler = MinMaxScaler()
 97 |         >>>         self.model = Ivis(embedding_dims=embedding_dims, k=k, batch_size=batch_size)
 98 |         >>>     def fit_transform(self, documents, field_vector):
 99 |         >>>           if isinstance(documents[0], dict):
100 |         >>>             if 'documents' in documents[0].keys():
101 |         >>>                vectors = [self.get_field(field_vector, document) for document in documents['documents']]
102 |         >>>            else:
103 |         >>>                vectors = [self.get_field(field_vector, document) for document in documents]
104 |         >>>            
105 |         >>>            X = np.stack(vectors)
106 |         >>>            X_scaled = self.scaler.fit_transform(X)
107 |         >>>            return self.model.fit_transform(X_scaled)
108 |         >>>    
109 |         >>>        def transform(self, X):
110 |         >>>            if isinstance(documents[0], dict):
111 |         >>>                if 'documents' in documents[0].keys():
112 |         >>>                    vectors = [document[field_vector] for document in documents['documents']]
113 |         >>>                else:
114 |         >>>                    vectors = [document[field_vector] for document in documents]
115 |         >>>            X_scaled = self.scaler.transform(vectors)
116 |         >>>            return self.model.transform(X_scaled)
117 |         """
118 |         pass
119 | 


--------------------------------------------------------------------------------
/vectorai/analytics/relational_documents.py:
--------------------------------------------------------------------------------
 1 | """
 2 |     Utilities for relational documents.
 3 | """
 4 | from ..models.deployed.base import ViDeployedModel
 5 | import numpy as np
 6 | from itertools import permutations
 7 | from typing import List, Dict
 8 | 
 9 | def vector_operation(vector_1: list, vector_2: list, operation: str='mean', axis: int=0):
10 |     """
11 |         Perform vector operation between 2 vectors.
12 |         Args:
13 |             vector_1: First vector
14 |             vector_2: Second vector
15 |             Operation: The operation that can be used (mean/minus/sum/min/max)
16 |             Axis: Axis on which to perform the vector operation
17 |         Example:
18 |             >>> vector_operation(np.array([1, 2, 3]), np.array([2, 3, 4]))
19 |     """
20 |     vectors = [vector_1, vector_2]
21 |     if operation == "mean":
22 |         return np.mean(vectors, axis=axis).tolist()
23 |     elif operation == 'minus':
24 |         return np.subtract(vector_1, vector_2).tolist()
25 |     elif operation == "sum":
26 |         return np.sum(vectors, axis=axis).tolist()
27 |     elif operation == "min":
28 |         return np.min(vectors, axis=axis).tolist()
29 |     elif operation == "max":
30 |         return np.max(vectors, axis=axis).tolist()
31 |     else:
32 |         raise ValueError("Vector operation needs to be one of minus/mean/sum/min/max.")
33 | 
34 | def create_relational_document(doc_1: Dict, doc_2: Dict, vector_fields: List[str], label_field: str, operation="minus"):
35 |     """
36 |         Args:
37 |             Operation: minus/sum
38 |             Vector_fields: The name of the vector fields
39 |             label: The entity of the vector
40 |         Example:
41 |             >>> from vectorai.utils import UtilsMixin
42 |             >>> mixin_utils = UtilsMixin()
43 |             >>> doc = mixin_utils.create_sample_document()
44 |             >>> doc_2 = mixin_utils.create_sample_document()
45 |             >>> create_relational_document(doc_1, doc_2, vector_fields=['color_vector_'], label_field='country')
46 |     """
47 |     # Create a new document
48 |     new_doc = {}
49 |     for vector in vector_fields:
50 |         new_vector = vector_operation(doc_1[vector], doc_2[vector], operation=operation)
51 |         if operation.lower() == 'minus':
52 |             new_label = doc_1[label_field] + " - " + doc_2[label_field]
53 |         elif operation.lower() == 'sum':
54 |             new_label = doc_1[label_field] + " + " + doc_2[label_field]
55 |         elif operation.lower() == 'mean':
56 |             new_label = "avg(" + doc_1[label_field] + ", " + doc_2[label_field] + ")"
57 |         elif operation.lower() == 'max': 
58 |             new_label = "max(" + doc_1[label_field] + ", " + doc_2[label_field] + ")"
59 |         elif operation.lower() == 'min':
60 |             new_label = "min(" + doc_1[label_field] + ", " + doc_2[label_field] + ")"
61 |         new_doc.update({label_field: new_label, vector : new_vector})
62 |     new_doc.update({'doc_1': doc_1, 'doc_2': doc_2})
63 |     return new_doc
64 | 
65 | def create_relational_collection(docs: list, label_field: str, vector_fields: List[str], operation: str='minus'):
66 |     """
67 |         Create relational collection that.
68 |         It should return n*(n-1) documents (for every permutation of the documents.
69 |         Args:
70 |             docs: The documents
71 |             label_field: The field of the labels
72 |             vector_fields: The vector fields in the document 
73 |             operation: Must be one of minus/mean/max/min/sum.
74 |         Example:
75 |             create_relational_collection(docs)
76 |     """
77 |     perm = list(permutations(docs, 2))
78 |     print("Returning n * (n - 1) documents. Insert and search to explore what relationships you end up with!")
79 |     return [create_relational_document(*c, vector_fields=vector_fields, operation=operation, label_field=label_field) for i, c in enumerate(perm)]
80 | 


--------------------------------------------------------------------------------
/vectorai/analytics/score.py:
--------------------------------------------------------------------------------
 1 | """Scoring Functions to compare for analytics.
 2 | """
 3 | from typing import Dict, Any, List
 4 | from ..read import ViReadClient
 5 | from numpy import inner
 6 | from numpy.linalg import norm
 7 | 
 8 | class ViScore(ViReadClient):
 9 |     @staticmethod
10 |     def calculate_cosine_similarity(a, b):
11 |         return inner(a, b) / (norm(a) * norm(b))
12 |     
13 |     def get_cosine_similarity_scores(
14 |         self,
15 |         other_documents: List[Dict[str, Any]],
16 |         anchor_document: Dict[str, Any],
17 |         vector_field: str,
18 |     ) -> List[float]:
19 |         """
20 |         Compare scores based on cosine similarity
21 | 
22 |         Args:
23 |             other_documents:
24 |                 List of documents (Python Dictionaries)
25 |             anchor_document:
26 |                 Document to compare all the other documents with.
27 |             vector_field:
28 |                 The field in the documents to compare
29 |         
30 |         Example:
31 |             >>> documents = [{...}]
32 |             >>> ViClient.get_cosine_similarity_scores(documents[1:10], documents[0])
33 |         """
34 |         similarity_scores = []
35 |         for i, doc in enumerate(other_documents):
36 |             similarity_score = self.calculate_cosine_similarity(
37 |                 self.get_field(vector_field, doc),
38 |                 self.get_field(vector_field, anchor_document)
39 |             )
40 |             similarity_scores.append(similarity_score)
41 |         return similarity_scores
42 | 


--------------------------------------------------------------------------------
/vectorai/analytics/tables.py:
--------------------------------------------------------------------------------
 1 | """
 2 | The Table Mixin 
 3 | """
 4 | import pandas as pd
 5 | from typing import List
 6 | from ..utils import get_random_int
 7 | from ..errors import APIError
 8 | 
 9 | class TableMixin:
10 |     """
11 |         Mixin For Tables For Easy Vector Comparison.
12 |     """
13 |     def _return_vector_search_results(self, collection_name: str, vector_field: str, id_value: str, label: str, 
14 |     num_rows: int=10):
15 |         """
16 |             Return the results from a Pandas DataFrame
17 |             vector_field: the vector field to compare with 
18 |             id_value: the id value of the document 
19 |             label: label to compare with 
20 |             collection_name: collection name
21 |             num_rows: The number of rows
22 |         """
23 |         results = self.advanced_search_by_id(collection_name, document_id=id_value, 
24 |                                 fields={vector_field:1}, page_size=num_rows)
25 |         if 'results' not in results.keys():
26 |             raise APIError(results)
27 |         values = self.get_field_across_documents(label, results['results'])
28 |         return values
29 | 
30 |     def compare_vector_search_results(self, collection_name: str, vector_fields: List[str], label: str,
31 |     id_document: str=None, id_value: str=None, num_rows=10):
32 |         """
33 |             Compare vector results
34 |             Args:
35 |                 vector_fields: The list of vectors
36 |                 id_value: The value of the ID of the document
37 |                 id_document: The document with the id_value in it
38 |                 label: The label for the vector
39 |                 num_rows: The number of rows to compare search results for
40 |             Example:
41 |                 compare_vector_search_results(collection_name, vector_fields)
42 |         """
43 |         if id_value is None:
44 |             print("Using a random document unless the id_value is specified.")
45 |             id_document = self.random_documents(collection_name, page_size=1, seed=get_random_int())['documents'][0]
46 |         if isinstance(id_document, dict):
47 |             id_value = id_document['_id']
48 |         values = {}
49 |         for f in vector_fields:
50 |             values[f] = self._return_vector_search_results(collection_name=collection_name, vector_field=f, 
51 |         id_value=id_value, label=label, num_rows=num_rows)
52 |         return pd.DataFrame.from_dict(values)
53 | 


--------------------------------------------------------------------------------
/vectorai/analytics/utils.py:
--------------------------------------------------------------------------------
  1 | """Various utilites for analytics
  2 | """
  3 | from typing import List, Dict, Any, Tuple
  4 | from ..read import ViReadClient
  5 | 
  6 | 
  7 | class ViAnalyticsUtils(ViReadClient):
  8 |     def sort_documents_by_value(self, documents: List[Dict[str, Any]], sort_by: str, reverse=False):
  9 |         """
 10 |         Sort documents based on a specific value
 11 | 
 12 |         Args:
 13 |             documents:
 14 |                 list of documents.
 15 |             sort_by:
 16 |                 the field by which to sort
 17 |         
 18 |         Example:
 19 |             >>> from vectorai.analytics import ViAnalyticsUtils
 20 |             >>> ViAnalyticsUtils.sort_documents_by_values(documents, sort_by='random_field.here')
 21 |         """
 22 | 
 23 |         docs = sorted(
 24 |             documents,
 25 |             key=lambda i: self.get_field(sort_by, i),
 26 |             reverse=reverse,
 27 |         )
 28 |         return docs
 29 | 
 30 | 
 31 | class MeanDict:
 32 |     """
 33 |     A special kind of dictionary that lets you take the average of floats as you add them to a dictionary.
 34 |     
 35 |     Example:
 36 |         >>> from vectorai.analytics.utils import MeanDict
 37 |         >>> mean_dict = MeanDict()
 38 |         >>> mean_dict['a'] = 12
 39 |         >>> mean_dict['a'] = 24
 40 |         >>> print(mean_dict['a'])
 41 |         18
 42 |     """
 43 | 
 44 |     def __init__(self):
 45 |         self._dict = {}
 46 |         self._counter = {}
 47 | 
 48 |     def __setitem__(self, k: str, item: float):
 49 |         """
 50 |         What number to add to an item in the meandict.
 51 | 
 52 |         Args:
 53 |             k:
 54 |                 Key for mean dict
 55 |             item:
 56 |                 the item value for the key.
 57 |         
 58 |         Example:
 59 |             >>> from vectorai.analytics.utils import MeanDict
 60 |             >>> mean_dict = MeanDict()
 61 |             >>> mean_dict['a'] = 12
 62 |             >>> mean_dict['a'] = 24
 63 |             >>> print(mean_dict['a'])
 64 |         """
 65 |         if k not in self._dict.keys():
 66 |             self._dict[k] = item
 67 |             self._counter[k] = 1
 68 |         else:
 69 |             total = self._dict[k] * self._counter[k]
 70 |             self._counter[k] = self._counter[k] + 1
 71 |             self._dict[k] = (total + item) / self._counter[k]
 72 | 
 73 |     def __getitem__(self, k):
 74 |         """
 75 |         Return the value from the meandict
 76 |         Args:
 77 |             k:
 78 |                 Key for mean dict
 79 | 
 80 |         Example:
 81 |             >>> from vectorai.analytics.utils import MeanDict
 82 |             >>> mean_dict = MeanDict()
 83 |             >>> mean_dict['a'] = 12
 84 |             >>> mean_dict['a'] = 24
 85 |             >>> print(mean_dict['a'])
 86 |         """
 87 |         return self._dict[k]
 88 | 
 89 |     def get_x_y(self) -> Tuple[List, List]:
 90 |         """
 91 |         Return the keys and values as 2 lists.
 92 | 
 93 |         Example:
 94 |             >>> from vectorai.analytics.utils import MeanDict
 95 |             >>> mean_dict = MeanDict()
 96 |             >>> mean_dict['a'] = 12
 97 |             >>> mean_dict['a'] = 24
 98 |             >>> mean_dict.get_x_y()
 99 |             ['a'], [18]
100 |         """
101 |         return_dict = {
102 |             k: v for k, v in sorted(self._dict.items(), key=lambda item: item[1])
103 |         }
104 |         return list(return_dict.keys()), list(return_dict.values())
105 | 


--------------------------------------------------------------------------------
/vectorai/api/__init__.py:
--------------------------------------------------------------------------------
1 | from .client import *


--------------------------------------------------------------------------------
/vectorai/api/client.py:
--------------------------------------------------------------------------------
 1 | from .api import _ViAPIClient
 2 | from .cluster import *
 3 | from .text import *
 4 | from .audio import *
 5 | from .image import *
 6 | from .dimensionality_reduction import *
 7 | 
 8 | class ViAPIClient(_ViAPIClient, ViDimensionalityReductionClient, ViClusterClient):
 9 |     def __init__(self, username, api_key, url="https://api.vctr.ai"):
10 |         self.username = username
11 |         self.api_key = api_key
12 |         self.url = url
13 | 


--------------------------------------------------------------------------------
/vectorai/api/dimensionality_reduction.py:
--------------------------------------------------------------------------------
  1 | import requests
  2 | from typing import List
  3 | from .utils import retry, return_curl_or_response
  4 | 
  5 | class ViDimensionalityReductionClient:
  6 |     """
  7 |     Dimensionality Reduction
  8 |     """
  9 |     def __init__(self, username: str, api_key: str, url=None):
 10 |         self.username = username
 11 |         self.api_key = api_key
 12 |         if url:
 13 |             self.url = url
 14 |         else:
 15 |             self.url = "https://api.vctr.ai"
 16 | 
 17 |     @retry()
 18 |     def dimensionality_reduce(
 19 |         self,
 20 |         collection_name: str,
 21 |         vectors: List[List[float]],
 22 |         vector_field: str,
 23 |         n_components: int,
 24 |         alias: str = "default",
 25 |         return_curl: bool=False,
 26 |         **kwargs
 27 |     ):
 28 |         """
 29 | Trains a Dimensionality Reduction model on the collection
 30 | 
 31 | Dimensionality reduction allows your vectors to be reduced down to any dimensions greater than 0 using unsupervised machine learning. This is useful for even faster search and visualising the vectors.
 32 |         
 33 | Args:
 34 | 	vector_field:
 35 | 		Vector field to perform dimensionality reduction on
 36 | 	alias:
 37 | 		Alias is used to name the dimensionality reduced vectors
 38 | 	n_components:
 39 | 		The size/length to reduce the vector down to. If 0 is set then highest possible is of components is set, when this is done you can get reduction on demand of any length.
 40 | 	refresh:
 41 | 		Whether to refresh the whole collection and retrain the dimensionality reduction model
 42 | 	collection_name:
 43 | 		Name of Collection
 44 | """
 45 |         params = {
 46 |             "username": self.username,
 47 |             "api_key": self.api_key,
 48 |             "collection_name": collection_name,
 49 |             "vectors": vectors,
 50 |             "vector_field": vector_field,
 51 |             "alias": alias,
 52 |             "n_components": n_components,
 53 |         }
 54 |         params.update(kwargs)
 55 |         response = requests.get(
 56 |             url="{}/collection/dimensionality_reduce".format(self.url),
 57 |             params=params
 58 |         )
 59 |         return return_curl_or_response(response, 'json', return_curl=return_curl)
 60 | 
 61 |     @retry()
 62 |     def dimensionality_reduction_job(
 63 |         self,
 64 |         collection_name: str,
 65 |         vector_field: str,
 66 |         n_components: int = 0,
 67 |         alias: str = "default",
 68 |         refresh: bool = True,
 69 |         return_curl: bool=False,
 70 |         **kwargs
 71 |     ):
 72 |         """
 73 | Trains a Dimensionality Reduction model on the collection
 74 | 
 75 | Dimensionality reduction allows your vectors to be reduced down to any dimensions greater than 0 using unsupervised machine learning. This is useful for even faster search and visualising the vectors.
 76 |         
 77 | Args:
 78 | 	vector_field:
 79 | 		Vector field to perform dimensionality reduction on
 80 | 	alias:
 81 | 		Alias is used to name the dimensionality reduced vectors
 82 | 	n_components:
 83 | 		The size/length to reduce the vector down to. If 0 is set then highest possible is of components is set, when this is done you can get reduction on demand of any length.
 84 | 	refresh:
 85 | 		Whether to refresh the whole collection and retrain the dimensionality reduction model
 86 | 	collection_name:
 87 | 		Name of Collection
 88 | """
 89 |         params = {
 90 |             "username": self.username,
 91 |             "api_key": self.api_key,
 92 |             "collection_name": collection_name,
 93 |             "vector_field": vector_field,
 94 |             "alias": alias,
 95 |             "n_components": n_components,
 96 |             "refresh": refresh,
 97 |         }
 98 |         params.update(kwargs)
 99 |         response = requests.get(
100 |             url="{}/collection/jobs/dimensionality_reduction".format(self.url),
101 |             params=params
102 |         )
103 |         return return_curl_or_response(response, 'json', return_curl=return_curl)
104 | 


--------------------------------------------------------------------------------
/vectorai/api/text.py:
--------------------------------------------------------------------------------
  1 | import io
  2 | import base64
  3 | import requests
  4 | from typing import Dict, List
  5 | from .utils import retry, return_curl_or_response
  6 | 
  7 | class ViTextClient:
  8 |     """
  9 |     Search and Encoding of Texts
 10 |     """
 11 |     def __init__(self, username, api_key, url=None):
 12 |         self.username = username
 13 |         self.api_key = api_key
 14 |         if url:
 15 |             self.url = url
 16 |         else:
 17 |             self.url = "https://api.vctr.ai"
 18 | 
 19 |     @return_curl_or_response('json')
 20 |     @retry()
 21 |     def search_text(
 22 |         self,
 23 |         collection_name: str,
 24 |         text,
 25 |         fields: List,
 26 |         metric: str = "cosine",
 27 |         min_score=None,
 28 |         page: int = 1,
 29 |         page_size: int = 10,
 30 |         include_vector:bool=False,
 31 |         include_count:bool=True,
 32 |         asc:bool=False,
 33 |         return_curl: bool=False,
 34 |         **kwargs
 35 |     ):
 36 |         """
 37 | Search a text field with text using Vector Search with text directly.
 38 | 
 39 | For example: "product_description" represents the description of a product::
 40 | 
 41 |     "AirPods deliver effortless, all-day audio on the go. And AirPods Pro bring Active Noise Cancellation to an in-ear headphone — with a customisable fit"
 42 | 
 43 |     -> <Encode the text to vector> ->
 44 | 
 45 |     i.e. text vector, "product_description_vector_": [0.794617772102356, 0.3581121861934662, 0.21113917231559753, 0.24878688156604767, 0.9741804003715515 ...]
 46 | 
 47 |     -> <Vector Search> ->
 48 | 
 49 |     Search Results: {...}
 50 | 
 51 | Args:
 52 | 	text:
 53 | 		Text to encode into vector and vector search with
 54 | 	collection_name:
 55 | 		Name of Collection
 56 | 	search_fields:
 57 | 		Vector fields to search through
 58 | 	approx:
 59 | 		Used for approximate search
 60 | 	sum_fields:
 61 | 		Whether to sum the multiple vectors similarity search score as 1 or seperate
 62 | 	page_size:
 63 | 		Size of each page of results
 64 | 	page:
 65 | 		Page of the results
 66 | 	metric:
 67 | 		Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
 68 | 	min_score:
 69 | 		Minimum score for similarity metric
 70 | 	include_vector:
 71 | 		Include vectors in the search results
 72 | 	include_count:
 73 | 		Include count in the search results
 74 | 	hundred_scale:
 75 | 		Whether to scale up the metric by 100
 76 |     asc:
 77 |         Whether to sort the score by ascending order (default is false, for getting most similar results)
 78 | """
 79 |         params = {
 80 |                 "username": self.username,
 81 |                 "api_key": self.api_key,
 82 |                 "collection_name": collection_name,
 83 |                 "text": text,
 84 |                 "search_fields": fields,
 85 |                 "metric": metric,
 86 |                 "page": page,
 87 |                 "page_size": page_size,
 88 |                 "include_vector": include_vector,
 89 |                 "include_count": include_count,
 90 |                 "asc": asc
 91 |         }
 92 |         params.update(kwargs)
 93 |         return requests.post(
 94 |             url="{}/collection/search_with_text".format(self.url),
 95 |             json=params
 96 |         )
 97 | 
 98 |     @return_curl_or_response
 99 |     @retry()
100 |     def encode_text(self, collection_name: str, text, return_curl: bool=False, **kwargs):
101 |         """
102 | Encode text into a vector
103 | 
104 | For example: a text field "product_description" represents the description of a product::
105 | 
106 |     "AirPods deliver effortless, all-day audio on the go. And AirPods Pro bring Active Noise Cancellation to an in-ear headphone — with a customisable fit"
107 | 
108 |     -> <Encode the text to vector> ->
109 | 
110 |     text vector: [0.794617772102356, 0.3581121861934662, 0.21113917231559753, 0.24878688156604767, 0.9741804003715515 ...]
111 | 
112 | Args:
113 | 	text:
114 | 		Text to encode into vector
115 | 	collection_name:
116 | 		Name of Collection
117 | """
118 |         params = {
119 |             "username": self.username,
120 |             "api_key": self.api_key,
121 |             "collection_name": collection_name,
122 |             "text": text,
123 |         }
124 |         params.update(kwargs)
125 |         return requests.get(
126 |             url="{}/collection/encode_text".format(self.url),
127 |             params=params
128 |         )
129 | 
130 |     @return_curl_or_response('json')
131 |     @retry()
132 |     def encode_text_job(
133 |         self, collection_name: str, text_field: str, refresh: bool = False, **kwargs
134 |     ):
135 |         """
136 | Encode all texts in a field into vectors
137 | 
138 | Within a collection encode the specified text field in every document into vectors.
139 | 
140 | For example, a text field "product_description" represents the description of a product::
141 | 
142 |     document 1 text field: {"product_description" : "AirPods deliver effortless, all-day audio on the go. And AirPods Pro bring Active Noise Cancellation to an in-ear headphone — with a customisable fit."
143 | 
144 |     document 2 text field: {"product_description" : "MacBook Pro elevates the notebook to a whole new level of performance and portability. Wherever your ideas take you, you’ll get there faster than ever with high‑performance processors and memory, advanced graphics, blazing‑fast storage and more — all in a compact package."
145 | 
146 |     -> <Encode the texts to vectors> ->
147 | 
148 |     document 1 text vector: {"product_description_vector_": [0.794617772102356, 0.3581121861934662, 0.21113917231559753, 0.24878688156604767, 0.9741804003715515 ...]}
149 | 
150 |     document 2 text vector: {"product_description_vector_": [0.8364648222923279, 0.6280597448348999, 0.8112713694572449, 0.36105549335479736, 0.005313870031386614 ...]}
151 | 
152 | Args:
153 | 	text_field:
154 | 		The text field to encode into vectors
155 | 	refresh:
156 | 		Whether to refresh the whole collection and re-encode all to vectors
157 | 	collection_name:
158 | 		Name of Collection
159 | """
160 |         params={
161 |                 "username": self.username,
162 |                 "api_key": self.api_key,
163 |                 "collection_name": collection_name,
164 |                 "text_field": text_field,
165 |                 "refresh": refresh,
166 |         }
167 |         params.update(kwargs)
168 |         return requests.get(
169 |             url="{}/collection/jobs/encode_text_field".format(self.url),
170 |             params=params
171 |         )
172 | 


--------------------------------------------------------------------------------
/vectorai/api/utils.py:
--------------------------------------------------------------------------------
 1 | """
 2 |     Add default retry to inserting
 3 | """
 4 | import time
 5 | import os
 6 | import sys
 7 | if sys.version_info.major >= 3:
 8 |     from shlex import quote
 9 | else:
10 |     from pipes import quote 
11 | from functools import wraps
12 | from ..options import get_option
13 | from ..errors import APIError
14 | 
15 | def retry(num_of_retries=3, timeout=5):
16 |     """
17 |     Allows the function to retry upon failure. 
18 |     Args:
19 |         num_of_retries: The number of times the function should retry
20 |         timeout: The number of seconds to wait between each retry
21 |     """
22 |     num_of_retries = get_option('maximum_num_of_http_retries')
23 |     timeout = get_option('maximum_http_timeout')
24 | 
25 |     def _retry(func):
26 |         @wraps(func)
27 |         def function_wrapper(*args, **kwargs):
28 |             for i in range(num_of_retries):
29 |                 try:
30 |                     return func(*args, **kwargs)
31 |                 # Using general error to avoid any possible error dependencies.
32 |                 except ConnectionError as error:
33 |                     time.sleep(timeout)
34 |                     print("Retrying...")
35 |                     if i == num_of_retries - 1:
36 |                         raise error
37 |                     continue
38 |                 break
39 |         return function_wrapper
40 |     return _retry
41 | 
42 | def return_response(response, return_type='json'):
43 |     """
44 |     Return error response if the status code != 200.
45 |     """
46 |     if response.status_code != 200:
47 |         content = response.content
48 |         if isinstance(content, bytes):
49 |             content = content.decode()
50 |         raise APIError(content)
51 |     if return_type is None:
52 |         return response
53 |     elif return_type == 'json':
54 |         return response.json()
55 |     elif return_type == 'content':
56 |         return response.content
57 |     return response
58 | 
59 | def dict_to_params(data_dict):
60 |     data_request = ''
61 |     for i, (k, v) in enumerate(a.items()):
62 |         data_request += str(k) + '=' + str(v)
63 |         if i != len(a.items()) - 1:
64 |             data_request += '&'
65 |     return data_request
66 | 
67 | def _return_curl(response):
68 |     req = response.request
69 |     command = "curl -X {method} -H {headers} -d '{data}' '{uri}'"
70 |     method = req.method
71 |     uri = req.url
72 |     data = req.body
73 |     headers = ['"{0}: {1}"'.format(k, v) for k, v in req.headers.items()]
74 |     headers = " -H ".join(headers)
75 |     return command.format(method=method, headers=headers, data=data, uri=uri).replace('-H "Accept-Encoding: gzip, deflate"', '')
76 | 
77 | def _return_curl_or_response(response, return_type='json', return_curl=False):
78 |     if return_curl: return _return_curl(response)
79 |     return return_response(response, return_type=return_type)
80 | 
81 | def return_curl_or_response(return_type):
82 |     """
83 |     Return a curl or response once the request is received
84 |     Args:
85 |         num_of_retries: The number of times the function should retry
86 |         timeout: The number of seconds to wait between each retry
87 |     """
88 |     RETURN_CURL = get_option('return_curl')
89 |     def _return_api_call(func):
90 |         @wraps(func)
91 |         def function_wrapper(*args, **kwargs):
92 |             return _return_curl_or_response(func(*args, **kwargs), 
93 |             return_type=return_type, return_curl=RETURN_CURL)
94 |         return function_wrapper
95 |     return _return_api_call
96 | 


--------------------------------------------------------------------------------
/vectorai/client.py:
--------------------------------------------------------------------------------
  1 | import io
  2 | import base64
  3 | import requests
  4 | import pandas as pd
  5 | import os
  6 | import warnings
  7 | from .api.utils import return_curl_or_response
  8 | from .write import ViWriteClient
  9 | from .analytics.client import ViAnalyticsClient
 10 | from .utils import decorate_functions_by_argument, set_default_collection
 11 | from .errors import LoginError, APIError
 12 | 
 13 | class ViClient(ViWriteClient, ViAnalyticsClient):
 14 |     """
 15 |         The main Vi client with most of the available read and write methods available to it.
 16 | 
 17 |         Parameters:
 18 |             username:
 19 |                 your username for accessing vectorai
 20 |             api_key:
 21 |                 your api key for accessing vectorai
 22 |             url:
 23 |                 url of the deployed vectorai database
 24 | 
 25 |         Example:
 26 |             >>> from vectorai.client import ViClient
 27 |             >>> vi_client = ViClient(username, api_key, vectorai_url)
 28 |             >>> vi_client.list_collections()
 29 |     """
 30 | 
 31 |     def __init__(self,
 32 |         username: str=None,
 33 |         api_key: str=None,
 34 |         # Old API URL: https://vecdb-aueast-api.azurewebsites.net
 35 |         # url: str="https://vectorai-development-api-vectorai-test-api.azurewebsites.net/",
 36 |         url: str="https://vectorai-development-api.azurewebsites.net",
 37 |         analytics_url="https://vector-analytics.vctr.ai", verbose: bool = True) -> None:
 38 |         super().__init__(username, api_key, url)
 39 |         if username is None:
 40 |             if 'VI_USERNAME' not in os.environ.keys():
 41 |                 raise APIError("Specify username of set VI_USERNAME as an environment variable.")
 42 |             username = os.environ['VI_USERNAME']
 43 | 
 44 |         if api_key is None:
 45 |             if 'VI_API_KEY' not in os.environ.keys():
 46 |                 raise APIError("Specify VectorAI API key VI_API_KEY as an environment variable.")
 47 |             api_key = os.environ['VI_API_KEY']
 48 | 
 49 |         self.username = username
 50 |         self.api_key = api_key
 51 |         self.url = url
 52 |         self.analytics_url = analytics_url
 53 | 
 54 |         if verbose:
 55 |             self.check_login_details()
 56 |             print(
 57 |                 f"Logged in. Welcome {self.username}. To view list of available collections, call list_collections() method."
 58 |             )
 59 | 
 60 |     def check_login_details(self):
 61 |         try:
 62 |             self.list_collections()
 63 |         except:
 64 |             raise LoginError("Username, api key or url is incorrect.")
 65 | 
 66 | def request_api_key(self,email, description, referral_code="api_referred", **kwargs):
 67 | 		"""Request an api key
 68 | Make sure to save the api key somewhere safe. If you have a valid referral code, you can recieve the api key more quickly.
 69 |     
 70 | Args
 71 | ========
 72 | username: Username you'd like to create, lowercase only
 73 | email: Email you are using to sign up
 74 | description: Description of your intended use case
 75 | referral_code: The referral code you've been given to allow you to register for an api key before others
 76 | 
 77 | """
 78 | 		return requests.post(
 79 | 			url='https://api.vctr.ai/project/request_api_key',
 80 | 			json=dict(
 81 | 				username=self.username,
 82 | 				email=email, 
 83 | 				description=description, 
 84 | 				**kwargs)).json()
 85 | 
 86 | @decorate_functions_by_argument(set_default_collection, 'collection_name')
 87 | class ViCollectionClient(ViClient):
 88 |     """
 89 |         The Vi client when you are mainly working with 1 client.
 90 | 
 91 |         Args:
 92 |             username:
 93 |                 your username for accessing vecdb
 94 |             api_key:
 95 |                 your api key for accessing vecdb
 96 |             url:
 97 |                 url of the deployed vecdb database
 98 |             collection_name:
 99 |                 The name of the collection
100 | 
101 |         Example:
102 |             >>> from vectorai.client import ViClient
103 |             >>> vi_client = ViClient(username, api_key, collection_name, vectorai_url)
104 |             >>> vi_client.insert_documents(documents)
105 |     """
106 |     def __init__(self, collection_name: str, username: str, api_key: str, url: str="https://api.vctr.ai", verbose: bool=True) -> None:
107 |         warnings.warn("ViCollectionClient is no longer supported and will be deprecated in the near future." + \
108 |             "Stick to using ViClient in the future.")
109 |         if username is None:
110 |             if 'VI_USERNAME' not in os.environ.keys():
111 |                 raise APIError("Specify username of set VI_USERNAME as an environment variable.")
112 |             username = os.environ['VI_USERNAME']
113 | 
114 |         if api_key is None:
115 |             if 'VI_API_KEY' not in os.environ.keys():
116 |                 raise APIError("Specify VectorAI API key VI_API_KEY as an environment variable.")
117 |             api_key = os.environ['VI_API_KEY']
118 | 
119 |         self.username = username
120 |         self.api_key = api_key
121 |         self._collection_name = collection_name
122 |         self.url = url
123 |         if verbose:
124 |             self.check_login_details()
125 |             print(
126 |                 f"Logged in. Welcome {self.username}. To view list of available collections, call list_collections() method."
127 |             )
128 |             setattr(self, 'decorator_called', False)
129 | 
130 |     @property
131 |     def collection_name(self) -> str:
132 |         return self._collection_name
133 | 
134 |     @collection_name.setter
135 |     def collection_name(self, value: str) -> None:
136 |         self._collection_name = value
137 | 
138 |     @collection_name.getter
139 |     def collection_name(self) -> str:
140 |         return self._collection_name
141 | 


--------------------------------------------------------------------------------
/vectorai/errors.py:
--------------------------------------------------------------------------------
 1 | """Errors
 2 | """
 3 | class APIError(Exception):
 4 |     """Base error class for all errors in library
 5 |     """
 6 | 
 7 |     def __init__(self, response_message: str=None):
 8 |         """
 9 |         The main Vi  base error.
10 | 
11 |         Args:
12 |             response_message: THe error message
13 | 
14 |         Example:
15 |             >>> raise APIError("Missing ____.")
16 |         """
17 |         self.response_message = response_message
18 | 
19 | class MissingFieldWarning(APIError, UserWarning):
20 |     """
21 |         Warning for missing field. Used for checking collection schema
22 |         upon insertion.
23 |     """
24 |     pass
25 | 
26 | class MissingFieldError(APIError):
27 |     """
28 |         Error in case the field is missing from a document.
29 |         Used for when a specific field is missing.
30 |     """
31 |     pass
32 | 
33 | class LoginError(APIError):
34 |     """
35 |     Login Error
36 |     """
37 |     pass
38 | 
39 | class CollectionNameError(APIError):
40 |     """
41 |     Collection Name Error
42 |     """
43 |     
44 |     def __init__(self, message="Collection names must contain only lower case A-Z and numbers and less than 240 characters"):
45 |         self.message = message
46 | 


--------------------------------------------------------------------------------
/vectorai/models/__init__.py:
--------------------------------------------------------------------------------
1 | """Module For Storing The Embed Functions
2 | """
3 | from .base import *
4 | from .deployed import *
5 | 


--------------------------------------------------------------------------------
/vectorai/models/base.py:
--------------------------------------------------------------------------------
  1 | """Base class for models
  2 | """
  3 | from abc import ABC, abstractmethod
  4 | from typing import List, Dict, Union
  5 | import json
  6 | import gc
  7 | import os
  8 | from appdirs import *
  9 | from ..client import ViWriteClient
 10 | 
 11 | class _Vi2Vec(ViWriteClient):
 12 |     """
 13 |     Abstract class for text models. We inherit from the base class to make use of the ability to write 
 14 |     to nested dictionaries and other utilities that will be helpful.
 15 |     """
 16 | 
 17 |     def chunk(self, documents, chunk_size=20):
 18 |         return ViWriteClient.chunk(documents, chunk_size)
 19 | 
 20 |     def init_tpu(self):
 21 |         """
 22 |             Initialise TPU for training a model.
 23 | 
 24 |             Example::
 25 | 
 26 |                 >>> from vectorai.client import Transformer2Vec
 27 |                 >>> model = Transformer2Vec
 28 |                 >>> model.init_tpu()
 29 |         """
 30 |         try:
 31 |             tpu = tf.distribute.cluster_resolver.TPUClusterResolver()  # TPU detection
 32 |             print("Running on TPU ", tpu.cluster_spec().as_dict()["worker"])
 33 |             IS_TPU = True
 34 |             tf.config.experimental_connect_to_cluster(tpu)
 35 |             tf.tpu.experimental.initialize_tpu_system(tpu)
 36 |             self.tpu_strategy = tf.distribute.experimental.TPUStrategy(tpu)
 37 |         except:
 38 |             pass
 39 |     
 40 |     @staticmethod
 41 |     def is_json_serializable(document: dict):
 42 |         """
 43 |             Check to ensure a document is JSON serializable.
 44 |             
 45 |             Example::
 46 | 
 47 |                 >>> from vectorai.client import Transformer2Vec
 48 |                 >>> sample_doc = {'test': 'hi'}
 49 |                 >>> Transformer2Vec.is_json_serializable(sample_doc)
 50 |                 >>> model.init_tpu()
 51 |         """
 52 |         assert isinstance(document, dict), "Check only 1 item!"
 53 |         test_json = user_data_dir("test_vectorai", "test.json")
 54 | 
 55 |         with open(test_json, "w"):
 56 |             json.dump(test_json)
 57 | 
 58 |         with open(test_json) as f:
 59 |             check_json = json.load(f)
 60 |         os.remove(test_json)
 61 |         
 62 |         assert check_json == document, (
 63 |             "This will not upload correctly. Please ensure all items "
 64 |             + "in the dictionary are lists/floats/ints/strings."
 65 |         )
 66 |         print("Checked! Feel free to upload!")
 67 | 
 68 |     @abstractmethod
 69 |     def encode(x):
 70 |         """An abstract method to specify the encode method.
 71 |         """
 72 |         pass
 73 | 
 74 | 
 75 | class ViText2Vec(_Vi2Vec):
 76 |     """Abstract class for text models.
 77 |     """
 78 | 
 79 |     @abstractmethod
 80 |     def encode_text(self, text: Union[str, Dict]) -> List[float]:
 81 |         """Encodes either string or a document.
 82 |         """
 83 |         pass
 84 | 
 85 |     def encode(self, text: str):
 86 |         """
 87 |             Text to vector.
 88 |             
 89 |             Args:
 90 |                 text:
 91 |                     Text to encode.
 92 |             
 93 |             Example::
 94 | 
 95 |                 >>> from vectorai.client import Transformer2Vec
 96 |                 >>> sample_doc = {'test': 'hi'}
 97 |                 >>> model_transformer = Transformer2Vec('distilbert')
 98 |                 >>> model_transformer.encode("Riemann Sums.")
 99 |         """
100 |         return self.encode_text(text=text)
101 | 
102 |     def bulk_encode(self, *args, **kwargs):
103 |         return self.bulk_encode_text(*args, **kwargs)
104 | 
105 |     def bulk_encode_text(
106 |         self,
107 |         documents: Union[List[str], List[Dict]],
108 |         document_fields: str = None,
109 |         vector_output_field: str = None,
110 |         chunk_size: int = 10,
111 |     ) -> List[float]:
112 |         """
113 |             Encodes either a list of strings or a list of documents. This can be over-ridden if bulk-encoding is supported
114 |             outside of list comprehension.
115 |             Currently only supports 1 input text field.
116 |             Bulk encoding text assumes that bulk-encoding is best done via list comprehension of encode_text.
117 | 
118 |             Args:
119 |                 documents:
120 |                     A list of Python dictionaries
121 |                 document_fields
122 |                     The fields to encode
123 |                 vector_output_field:
124 |                     The name of the vector output
125 |                 chunk_size (batch size): 
126 |                     The number of documents to encode.
127 | 
128 |             Example::
129 | 
130 |                 >>> from vectorai.client import Transformer2Vec
131 |                 >>> sample_docs = [{'name': 'bert', 'age": 10}, {'name': 'elmo', 'age': 15}]
132 |                 >>> model_transformer = Transformer2Vec('distilbert')
133 |                 >>> model_transformer.bulk_encode(sample_docs, "name")
134 |         
135 |         """
136 |         if vector_output_field is None:
137 |             vector_output_field = document_fields + '_vector_'
138 |         
139 |         if isinstance(documents[0], str):
140 |             assert (
141 |                 document_fields is None
142 |             ), "You cannot have an input text field if you are just feeding in a list of documents."
143 |             assert (
144 |                 vector_output_field is None
145 |             ), "You cannot have a vector output field if you are feeding i- Overfitting does not seem to provide good results. "
146 |             # typechecking to ensure correct input is fed in.
147 |             all_vectors = []
148 |             for chunk in self.chunk(documents, chunk_size=chunk_size):
149 |                 all_vectors.append(self.encode_text(chunk))
150 |             return all_vectors
151 | 
152 |         if isinstance(documents[0], dict):
153 |             assert (
154 |                 document_fields is not None
155 |             ), "You need a text input field if you are not feeding in at a document level."
156 |             assert (
157 |                 vector_output_field is not None
158 |             ), "You need a vector output field if you are feeding in at a document level."
159 |             for chunk in self.chunk(documents, chunk_size=chunk_size):
160 |                 # List comprehension of the text encoding methodology.
161 |                 vectors = [
162 |                     self.encode_text(document=x, document_fields=document_fields)
163 |                     for x in chunk
164 |                 ]
165 |                 [
166 |                     x.update({vector_output_field: vector})
167 |                     for x, vector in list(zip(chunk, vectors))
168 |                 ]
169 |                 del vectors
170 |                 gc.collect()
171 |             print("Finished updating documents with additional field.")
172 |             return
173 |         raise ValueError(
174 |             "Unsure of how to bulk encode. Please write custom encoding methodology"
175 |         )
176 | 
177 | 
178 | class ViImage2Vec(_Vi2Vec):
179 |     """Abstract class for image models.
180 |     """
181 | 
182 |     @abstractmethod
183 |     def encode_image(self, image) -> List[float]:
184 |         pass
185 | 
186 |     @abstractmethod
187 |     def bulk_encode_image(self, images):
188 |         pass
189 | 
190 | 
191 | class ViAudio2Vec(_Vi2Vec):
192 |     """Abstract class for audio models.
193 |     """
194 | 
195 |     @abstractmethod
196 |     def encode_audio(self, audio) -> List[float]:
197 |         pass
198 | 
199 |     @abstractmethod
200 |     def bulk_encode_audio(self, audio):
201 |         pass
202 | 


--------------------------------------------------------------------------------
/vectorai/models/deployed/__init__.py:
--------------------------------------------------------------------------------
1 | from .text import *
2 | from .audio import *
3 | from .image import *
4 | 


--------------------------------------------------------------------------------
/vectorai/models/deployed/audio.py:
--------------------------------------------------------------------------------
 1 | import io
 2 | import base64
 3 | import requests
 4 | from .base import ViDeployedModel
 5 | 
 6 | 
 7 | class ViAudio2Vec(ViDeployedModel):
 8 |     def encode(self, audio):
 9 |         return requests.get(
10 |             url="{}/collection/encode_audio".format(self.url),
11 |             params={
12 |                 "username": self.username,
13 |                 "api_key": self.api_key,
14 |                 "collection_name": self.collection_name,
15 |                 "audio_url": audio,
16 |             },
17 |         ).json()
18 | 
19 |     @property
20 |     def __name__(self):
21 |         if self._name is None:
22 |             return "vectorai_audio"
23 |         return self._name
24 | 
25 |     @__name__.setter
26 |     def __name__(self, value):
27 |         self._name = value
28 | 
29 | 
30 | class ViAudioArray2Vec(ViDeployedModel):
31 |     def __init__(
32 |         self,
33 |         username,
34 |         api_key,
35 |         url=None,
36 |         collection_name="base",
37 |         vector_operation: str = "mean",
38 |     ):
39 |         self.username = username
40 |         self.api_key = api_key
41 |         if url:
42 |             self.url = url
43 |         else:
44 |             self.url = "https://api.vctr.ai"
45 |         self.collection_name = collection_name
46 | 
47 |     def encode(self, audios):
48 |         return _vector_operation(
49 |             [
50 |                 requests.get(
51 |                     url="{}/collection/encode_audio".format(self.url),
52 |                     params={
53 |                         "username": self.username,
54 |                         "api_key": self.api_key,
55 |                         "collection_name": self.collection_name,
56 |                         "audio_url": audio,
57 |                     },
58 |                 ).json()
59 |                 for audio in audios
60 |             ],
61 |             vector_operation=self.vector_operation,
62 |         )
63 | 
64 |     @property
65 |     def __name__(self):
66 |         if self._name is None:
67 |             return "vectorai_audio_array"
68 |         return self._name
69 | 
70 |     @__name__.setter
71 |     def __name__(self, value):
72 |         self._name = value
73 | 


--------------------------------------------------------------------------------
/vectorai/models/deployed/base.py:
--------------------------------------------------------------------------------
 1 | import numpy as np
 2 | from abc import abstractmethod
 3 | 
 4 | 
 5 | class ViDeployedModel:
 6 |     def __init__(self, username, api_key, url="https://api.vctr.ai", collection_name="base"):
 7 |         self.username = username
 8 |         self.api_key = api_key
 9 |         self.url = url
10 |         self.collection_name = collection_name
11 |         self._name = "default"
12 |     
13 |     def _vector_operation(self, vectors, vector_operation: str = "mean"):
14 |         """
15 |             Creates a vector operation based on the model
16 |         """
17 |         vector_operation = vector_operation.lower()
18 |         if vector_operation == "mean" or vector_operation=="average":
19 |             return np.mean(vectors, axis=0).tolist()
20 |         elif vector_operation == 'minus':
21 |             if len(vectors) > 2:
22 |                 raise ValueError("More than 2 vectors.")
23 |             return np.subtract(vectors[0], vectors[1]).tolist()
24 |         elif vector_operation == "sum":
25 |             return np.sum(vectors, axis=0).tolist()
26 |         elif vector_operation == "min":
27 |             return np.min(vectors, axis=0).tolist()
28 |         elif vector_operation == "max":
29 |             return np.max(vectors, axis=0).tolist()
30 |         else:
31 |             return np.mean(vectors, axis=0).tolist()
32 | 
33 |     @abstractmethod
34 |     def __name__(self):
35 |         pass
36 | 


--------------------------------------------------------------------------------
/vectorai/models/deployed/image.py:
--------------------------------------------------------------------------------
 1 | import io
 2 | import base64
 3 | import requests
 4 | from .base import ViDeployedModel
 5 | from typing import List
 6 | 
 7 | class ViImage2Vec(ViDeployedModel):
 8 |     def encode(self, image):
 9 |         return requests.get(
10 |             url="{}/collection/encode_image".format(self.url),
11 |             params={
12 |                 "username": self.username,
13 |                 "api_key": self.api_key,
14 |                 "collection_name": self.collection_name,
15 |                 "image_url": image,
16 |             },
17 |         ).json()
18 | 
19 |     def bulk_encode(self, images: List[str]):
20 |         """
21 |             Bulk convert text to vectors
22 |         """
23 |         return requests.get(
24 |             url="{}/collection/bulk_encode_image".format(self.url),
25 |             params={
26 |                 "username": self.username,
27 |                 "api_key": self.api_key,
28 |                 "collection_name": self.collection_name,
29 |                 "image_urls": images,
30 |             },
31 |         ).json()
32 | 
33 |     @property
34 |     def __name__(self):
35 |         if self._name is None:
36 |             return "vectorai_image"
37 |         return self._name
38 | 
39 |     @__name__.setter
40 |     def __name__(self, value):
41 |         self._name = value
42 | 
43 | 
44 | class ViImageArray2Vec(ViDeployedModel):
45 |     def __init__(
46 |         self,
47 |         username,
48 |         api_key,
49 |         url=None,
50 |         collection_name="base",
51 |         vector_operation: str = "mean",
52 |     ):
53 |         self.username = username
54 |         self.api_key = api_key
55 |         if url:
56 |             self.url = url
57 |         else:
58 |             self.url = "https://api.vctr.ai"
59 |         self.collection_name = collection_name
60 | 
61 |     def encode(self, images):
62 |         return self._vector_operation(
63 |             requests.get(
64 |                 url="{}/collection/bulk_encode_image".format(self.url),
65 |                 params={
66 |                     "username": self.username,
67 |                     "api_key": self.api_key,
68 |                     "collection_name": self.collection_name,
69 |                     "image_urls": images,
70 |                 },
71 |             ).json(),
72 |             vector_operation=self.vector_operation,
73 |         )
74 | 
75 |     @property
76 |     def __name__(self):
77 |         if self._name is None:
78 |             return "vectorai_image_array"
79 |         return self._name
80 | 
81 |     @__name__.setter
82 |     def __name__(self, value):
83 |         self._name = value
84 | 


--------------------------------------------------------------------------------
/vectorai/models/deployed/text.py:
--------------------------------------------------------------------------------
 1 | import io
 2 | import base64
 3 | import requests
 4 | import numpy as np
 5 | from typing import List
 6 | from .base import ViDeployedModel
 7 | 
 8 | 
 9 | class ViText2Vec(ViDeployedModel):
10 |     def encode(self, text: str):
11 |         """
12 |             Convert text to vectors.
13 |         """
14 |         return requests.get(
15 |             url="{}/collection/encode_text".format(self.url),
16 |             params={
17 |                 "username": self.username,
18 |                 "api_key": self.api_key,
19 |                 "collection_name": self.collection_name,
20 |                 "text": text,
21 |             },
22 |         ).json()
23 | 
24 |     def bulk_encode(self, texts: List[str]):
25 |         """
26 |             Bulk convert text to vectors
27 |         """
28 |         return requests.get(
29 |             url="{}/collection/bulk_encode_text".format(self.url),
30 |             params={
31 |                 "username": self.username,
32 |                 "api_key": self.api_key,
33 |                 "collection_name": self.collection_name,
34 |                 "texts": texts,
35 |             }
36 |         ).json()
37 | 
38 |     @property
39 |     def __name__(self):
40 |         if self._name is None:
41 |             return "vectorai_text"
42 |         return self._name
43 | 
44 |     @__name__.setter
45 |     def __name__(self, value):
46 |         self._name = value
47 | 
48 | 
49 | class ViTextArray2Vec(ViDeployedModel):
50 |     def __init__(
51 |         self,
52 |         username,
53 |         api_key,
54 |         url=None,
55 |         collection_name="base",
56 |         vector_operation: str = "mean",
57 |     ):
58 |         self.username = username
59 |         self.api_key = api_key
60 |         if url:
61 |             self.url = url
62 |         else:
63 |             self.url = "https://api.vctr.ai"
64 |         self.collection_name = collection_name
65 |         self.vector_operation = vector_operation
66 | 
67 |     def encode(self, texts):
68 |         return self._vector_operation(
69 |             requests.get(
70 |                 url="{}/collection/bulk_encode_text".format(self.url),
71 |                 params={
72 |                     "username": self.username,
73 |                     "api_key": self.api_key,
74 |                     "collection_name": self.collection_name,
75 |                     "texts": texts,
76 |                 }
77 |             ).json(),
78 |             vector_operation=self.vector_operation,
79 |         )
80 | 
81 |     @property
82 |     def __name__(self):
83 |         if self._name is None:
84 |             return "vectorai_text_array"
85 |         return self._name
86 | 
87 |     @__name__.setter
88 |     def __name__(self, value):
89 |         self._name = value
90 | 


--------------------------------------------------------------------------------
/vectorai/options.py:
--------------------------------------------------------------------------------
 1 | OPTIONS = {
 2 |     'return_curl': False,
 3 |     'maximum_num_of_http_retries': 3,
 4 |     'maximum_http_timeout': 5
 5 | }
 6 | 
 7 | def get_option(option_field):
 8 |     return OPTIONS[option_field]
 9 | 
10 | def set_option(option_field, value):
11 |     OPTIONS[option_field]= value
12 | 


--------------------------------------------------------------------------------