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langchain/libs/experimental/poetry.lock

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Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "aiohttp"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "3.8.6"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Async http client/server framework (asyncio)"
optional = false
python-versions = ">=3.6"
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.dependencies]
aiosignal = ">=1.1.2"
async-timeout = ">=4.0.0a3,<5.0"
attrs = ">=17.3.0"
charset-normalizer = ">=2.0,<4.0"
frozenlist = ">=1.1.1"
multidict = ">=4.5,<7.0"
yarl = ">=1.0,<2.0"
[package.extras]
speedups = ["Brotli", "aiodns", "cchardet"]
[[package]]
name = "aiosignal"
version = "1.3.1"
description = "aiosignal: a list of registered asynchronous callbacks"
optional = false
python-versions = ">=3.7"
files = [
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]
[package.dependencies]
frozenlist = ">=1.1.0"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
[[package]]
name = "annotated-types"
version = "0.6.0"
description = "Reusable constraint types to use with typing.Annotated"
optional = false
python-versions = ">=3.8"
files = [
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]
[package.dependencies]
typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""}
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "anyio"
version = "3.7.1"
description = "High level compatibility layer for multiple asynchronous event loop implementations"
optional = false
python-versions = ">=3.7"
files = [
{file = "anyio-3.7.1-py3-none-any.whl", hash = "sha256:91dee416e570e92c64041bd18b900d1d6fa78dff7048769ce5ac5ddad004fbb5"},
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]
[package.dependencies]
exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
idna = ">=2.8"
sniffio = ">=1.1"
[package.extras]
doc = ["Sphinx", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme (>=1.2.2)", "sphinxcontrib-jquery"]
test = ["anyio[trio]", "coverage[toml] (>=4.5)", "hypothesis (>=4.0)", "mock (>=4)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
trio = ["trio (<0.22)"]
[[package]]
name = "appnope"
version = "0.1.3"
description = "Disable App Nap on macOS >= 10.9"
optional = false
python-versions = "*"
files = [
{file = "appnope-0.1.3-py2.py3-none-any.whl", hash = "sha256:265a455292d0bd8a72453494fa24df5a11eb18373a60c7c0430889f22548605e"},
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[[package]]
name = "argon2-cffi"
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
version = "23.1.0"
description = "Argon2 for Python"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
optional = false
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
python-versions = ">=3.7"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
files = [
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.dependencies]
argon2-cffi-bindings = "*"
[package.extras]
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
dev = ["argon2-cffi[tests,typing]", "tox (>4)"]
docs = ["furo", "myst-parser", "sphinx", "sphinx-copybutton", "sphinx-notfound-page"]
tests = ["hypothesis", "pytest"]
typing = ["mypy"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "argon2-cffi-bindings"
version = "21.2.0"
description = "Low-level CFFI bindings for Argon2"
optional = false
python-versions = ">=3.6"
files = [
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[package.dependencies]
cffi = ">=1.0.1"
[package.extras]
dev = ["cogapp", "pre-commit", "pytest", "wheel"]
tests = ["pytest"]
[[package]]
name = "arrow"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "1.3.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Better dates & times for Python"
optional = false
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
python-versions = ">=3.8"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
{file = "arrow-1.3.0-py3-none-any.whl", hash = "sha256:c728b120ebc00eb84e01882a6f5e7927a53960aa990ce7dd2b10f39005a67f80"},
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.dependencies]
python-dateutil = ">=2.7.0"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
types-python-dateutil = ">=2.8.10"
[package.extras]
doc = ["doc8", "sphinx (>=7.0.0)", "sphinx-autobuild", "sphinx-autodoc-typehints", "sphinx_rtd_theme (>=1.3.0)"]
test = ["dateparser (==1.*)", "pre-commit", "pytest", "pytest-cov", "pytest-mock", "pytz (==2021.1)", "simplejson (==3.*)"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "asttokens"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "2.4.1"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Annotate AST trees with source code positions"
optional = false
python-versions = "*"
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
{file = "asttokens-2.4.1-py2.py3-none-any.whl", hash = "sha256:051ed49c3dcae8913ea7cd08e46a606dba30b79993209636c4875bc1d637bc24"},
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.dependencies]
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
six = ">=1.12.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[package.extras]
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "async-lru"
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
version = "2.0.4"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Simple LRU cache for asyncio"
optional = false
python-versions = ">=3.8"
files = [
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
{file = "async-lru-2.0.4.tar.gz", hash = "sha256:b8a59a5df60805ff63220b2a0c5b5393da5521b113cd5465a44eb037d81a5627"},
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.dependencies]
typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
[[package]]
name = "async-timeout"
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
version = "4.0.3"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Timeout context manager for asyncio programs"
optional = false
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
python-versions = ">=3.7"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
files = [
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
{file = "async-timeout-4.0.3.tar.gz", hash = "sha256:4640d96be84d82d02ed59ea2b7105a0f7b33abe8703703cd0ab0bf87c427522f"},
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[[package]]
name = "attrs"
version = "23.1.0"
description = "Classes Without Boilerplate"
optional = false
python-versions = ">=3.7"
files = [
{file = "attrs-23.1.0-py3-none-any.whl", hash = "sha256:1f28b4522cdc2fb4256ac1a020c78acf9cba2c6b461ccd2c126f3aa8e8335d04"},
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]
[package.extras]
cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
dev = ["attrs[docs,tests]", "pre-commit"]
docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
tests = ["attrs[tests-no-zope]", "zope-interface"]
tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
[[package]]
name = "babel"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "2.13.1"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Internationalization utilities"
optional = false
python-versions = ">=3.7"
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
{file = "Babel-2.13.1-py3-none-any.whl", hash = "sha256:7077a4984b02b6727ac10f1f7294484f737443d7e2e66c5e4380e41a3ae0b4ed"},
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.dependencies]
pytz = {version = ">=2015.7", markers = "python_version < \"3.9\""}
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
setuptools = {version = "*", markers = "python_version >= \"3.12\""}
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
[package.extras]
dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "backcall"
version = "0.2.0"
description = "Specifications for callback functions passed in to an API"
optional = false
python-versions = "*"
files = [
{file = "backcall-0.2.0-py2.py3-none-any.whl", hash = "sha256:fbbce6a29f263178a1f7915c1940bde0ec2b2a967566fe1c65c1dfb7422bd255"},
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]
[[package]]
name = "beautifulsoup4"
version = "4.12.2"
description = "Screen-scraping library"
optional = false
python-versions = ">=3.6.0"
files = [
{file = "beautifulsoup4-4.12.2-py3-none-any.whl", hash = "sha256:bd2520ca0d9d7d12694a53d44ac482d181b4ec1888909b035a3dbf40d0f57d4a"},
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]
[package.dependencies]
soupsieve = ">1.2"
[package.extras]
html5lib = ["html5lib"]
lxml = ["lxml"]
[[package]]
name = "bleach"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "6.1.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "An easy safelist-based HTML-sanitizing tool."
optional = false
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
python-versions = ">=3.8"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
{file = "bleach-6.1.0-py3-none-any.whl", hash = "sha256:3225f354cfc436b9789c66c4ee030194bee0568fbf9cbdad3bc8b5c26c5f12b6"},
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.dependencies]
six = ">=1.9.0"
webencodings = "*"
[package.extras]
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
css = ["tinycss2 (>=1.1.0,<1.3)"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[[package]]
name = "blis"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "0.7.11"
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
description = "The Blis BLAS-like linear algebra library, as a self-contained C-extension."
optional = true
python-versions = "*"
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
]
[package.dependencies]
numpy = [
{version = ">=1.15.0", markers = "python_version < \"3.9\""},
{version = ">=1.19.0", markers = "python_version >= \"3.9\""},
]
[[package]]
name = "catalogue"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "2.0.10"
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
description = "Super lightweight function registries for your library"
optional = true
python-versions = ">=3.6"
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "certifi"
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
version = "2023.7.22"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Python package for providing Mozilla's CA Bundle."
optional = false
python-versions = ">=3.6"
files = [
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[[package]]
name = "cffi"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "1.16.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Foreign Function Interface for Python calling C code."
optional = false
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
python-versions = ">=3.8"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.dependencies]
pycparser = "*"
[[package]]
name = "charset-normalizer"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "3.3.2"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
optional = false
python-versions = ">=3.7.0"
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Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[[package]]
name = "click"
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
version = "8.1.7"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Composable command line interface toolkit"
Install and use `ruff format` instead of black for code formatting. (#12585) Best to review one commit at a time, since two of the commits are 100% autogenerated changes from running `ruff format`: - Install and use `ruff format` instead of black for code formatting. - Output of `ruff format .` in the `langchain` package. - Use `ruff format` in experimental package. - Format changes in experimental package by `ruff format`. - Manual formatting fixes to make `ruff .` pass.
2023-10-31 14:53:12 +00:00
optional = true
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
python-versions = ">=3.7"
files = [
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
{file = "click-8.1.7-py3-none-any.whl", hash = "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28"},
{file = "click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de"},
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.dependencies]
colorama = {version = "*", markers = "platform_system == \"Windows\""}
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
[[package]]
name = "cloudpathlib"
version = "0.16.0"
description = "pathlib-style classes for cloud storage services."
optional = true
python-versions = ">=3.7"
files = [
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{file = "cloudpathlib-0.16.0.tar.gz", hash = "sha256:cdfcd35d46d529587d744154a0bdf962aca953b725c8784cd2ec478354ea63a3"},
]
[package.dependencies]
typing_extensions = {version = ">4", markers = "python_version < \"3.11\""}
[package.extras]
all = ["cloudpathlib[azure]", "cloudpathlib[gs]", "cloudpathlib[s3]"]
azure = ["azure-storage-blob (>=12)"]
gs = ["google-cloud-storage"]
s3 = ["boto3"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "colorama"
version = "0.4.6"
description = "Cross-platform colored terminal text."
optional = false
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
files = [
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]
[[package]]
name = "comm"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "0.2.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc."
optional = false
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
python-versions = ">=3.8"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
{file = "comm-0.2.0-py3-none-any.whl", hash = "sha256:2da8d9ebb8dd7bfc247adaff99f24dce705638a8042b85cb995066793e391001"},
{file = "comm-0.2.0.tar.gz", hash = "sha256:a517ea2ca28931c7007a7a99c562a0fa5883cfb48963140cf642c41c948498be"},
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.dependencies]
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
traitlets = ">=4"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[package.extras]
test = ["pytest"]
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[[package]]
name = "confection"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "0.1.3"
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
description = "The sweetest config system for Python"
optional = true
python-versions = ">=3.6"
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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{file = "confection-0.1.3.tar.gz", hash = "sha256:5a876d368a7698eec58791126757a75a3df16e26cc49653b52426e9ffd39f12f"},
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
]
[package.dependencies]
pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<3.0.0"
srsly = ">=2.4.0,<3.0.0"
[[package]]
name = "cymem"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "2.0.8"
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
description = "Manage calls to calloc/free through Cython"
optional = true
python-versions = "*"
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "dataclasses-json"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "0.6.1"
description = "Easily serialize dataclasses to and from JSON."
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
optional = false
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
python-versions = ">=3.7,<4.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.dependencies]
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
marshmallow = ">=3.18.0,<4.0.0"
typing-inspect = ">=0.4.0,<1"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "debugpy"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "1.8.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "An implementation of the Debug Adapter Protocol for Python"
optional = false
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
python-versions = ">=3.8"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[[package]]
name = "decorator"
version = "5.1.1"
description = "Decorators for Humans"
optional = false
python-versions = ">=3.5"
files = [
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{file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"},
]
[[package]]
name = "defusedxml"
version = "0.7.1"
description = "XML bomb protection for Python stdlib modules"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
files = [
{file = "defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61"},
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[[package]]
name = "exceptiongroup"
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
version = "1.1.3"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Backport of PEP 654 (exception groups)"
optional = false
python-versions = ">=3.7"
files = [
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
{file = "exceptiongroup-1.1.3-py3-none-any.whl", hash = "sha256:343280667a4585d195ca1cf9cef84a4e178c4b6cf2274caef9859782b567d5e3"},
{file = "exceptiongroup-1.1.3.tar.gz", hash = "sha256:097acd85d473d75af5bb98e41b61ff7fe35efe6675e4f9370ec6ec5126d160e9"},
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.extras]
test = ["pytest (>=6)"]
[[package]]
name = "executing"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "2.0.1"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Get the currently executing AST node of a frame, and other information"
optional = false
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
python-versions = ">=3.5"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
{file = "executing-2.0.1-py2.py3-none-any.whl", hash = "sha256:eac49ca94516ccc753f9fb5ce82603156e590b27525a8bc32cce8ae302eb61bc"},
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.extras]
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipython", "littleutils", "pytest", "rich"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[[package]]
name = "faker"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "19.13.0"
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
description = "Faker is a Python package that generates fake data for you."
optional = true
python-versions = ">=3.8"
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
{file = "Faker-19.13.0-py3-none-any.whl", hash = "sha256:da880a76322db7a879c848a0771e129338e0a680a9f695fd9a3e7a6ac82b45e1"},
{file = "Faker-19.13.0.tar.gz", hash = "sha256:14ccb0aec342d33aa3889a864a56e5b3c2d56bce1b89f9189f4fbc128b9afc1e"},
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
]
[package.dependencies]
python-dateutil = ">=2.4"
typing-extensions = {version = ">=3.10.0.1", markers = "python_version <= \"3.8\""}
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "fastjsonschema"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "2.18.1"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Fastest Python implementation of JSON schema"
optional = false
python-versions = "*"
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
{file = "fastjsonschema-2.18.1-py3-none-any.whl", hash = "sha256:aec6a19e9f66e9810ab371cc913ad5f4e9e479b63a7072a2cd060a9369e329a8"},
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.extras]
devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benchmark", "pytest-cache", "validictory"]
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[[package]]
name = "filelock"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "3.13.1"
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
description = "A platform independent file lock."
optional = true
python-versions = ">=3.8"
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
{file = "filelock-3.13.1-py3-none-any.whl", hash = "sha256:57dbda9b35157b05fb3e58ee91448612eb674172fab98ee235ccb0b5bee19a1c"},
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Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
]
[package.extras]
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.24)"]
testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"]
typing = ["typing-extensions (>=4.8)"]
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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name = "fqdn"
version = "1.5.1"
description = "Validates fully-qualified domain names against RFC 1123, so that they are acceptable to modern bowsers"
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update experimental poetry lock
2023-09-11 16:20:19 +00:00
[[package]]
name = "fsspec"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "2023.10.0"
update experimental poetry lock
2023-09-11 16:20:19 +00:00
description = "File-system specification"
optional = true
python-versions = ">=3.8"
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
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update experimental poetry lock
2023-09-11 16:20:19 +00:00
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[package.extras]
abfs = ["adlfs"]
adl = ["adlfs"]
arrow = ["pyarrow (>=1)"]
dask = ["dask", "distributed"]
devel = ["pytest", "pytest-cov"]
dropbox = ["dropbox", "dropboxdrivefs", "requests"]
full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
fuse = ["fusepy"]
gcs = ["gcsfs"]
git = ["pygit2"]
github = ["requests"]
gs = ["gcsfs"]
gui = ["panel"]
hdfs = ["pyarrow (>=1)"]
http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "requests"]
libarchive = ["libarchive-c"]
oci = ["ocifs"]
s3 = ["s3fs"]
sftp = ["paramiko"]
smb = ["smbprotocol"]
ssh = ["paramiko"]
tqdm = ["tqdm"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "greenlet"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "3.0.1"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Lightweight in-process concurrent programming"
optional = false
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
python-versions = ">=3.7"
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.extras]
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
docs = ["Sphinx"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
test = ["objgraph", "psutil"]
update experimental poetry lock
2023-09-11 16:20:19 +00:00
[[package]]
name = "huggingface-hub"
merge
2023-10-06 01:09:35 +00:00
version = "0.17.3"
update experimental poetry lock
2023-09-11 16:20:19 +00:00
description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
optional = true
python-versions = ">=3.8.0"
files = [
merge
2023-10-06 01:09:35 +00:00
{file = "huggingface_hub-0.17.3-py3-none-any.whl", hash = "sha256:545eb3665f6ac587add946e73984148f2ea5c7877eac2e845549730570c1933a"},
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update experimental poetry lock
2023-09-11 16:20:19 +00:00
]
[package.dependencies]
filelock = "*"
fsspec = "*"
packaging = ">=20.9"
pyyaml = ">=5.1"
requests = "*"
tqdm = ">=4.42.1"
typing-extensions = ">=3.7.4.3"
[package.extras]
all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "black (==23.7)", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (<2.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.0.241)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "urllib3 (<2.0)"]
cli = ["InquirerPy (==0.3.4)"]
dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "black (==23.7)", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (<2.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.0.241)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "urllib3 (<2.0)"]
docs = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "black (==23.7)", "gradio", "hf-doc-builder", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (<2.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.0.241)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "urllib3 (<2.0)", "watchdog"]
fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
inference = ["aiohttp", "pydantic (<2.0)"]
quality = ["black (==23.7)", "mypy (==1.5.1)", "ruff (>=0.0.241)"]
tensorflow = ["graphviz", "pydot", "tensorflow"]
testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "numpy", "pydantic (<2.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
torch = ["torch"]
typing = ["pydantic (<2.0)", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "idna"
version = "3.4"
description = "Internationalized Domain Names in Applications (IDNA)"
optional = false
python-versions = ">=3.5"
files = [
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{file = "idna-3.4.tar.gz", hash = "sha256:814f528e8dead7d329833b91c5faa87d60bf71824cd12a7530b5526063d02cb4"},
]
[[package]]
name = "importlib-metadata"
version = "6.8.0"
description = "Read metadata from Python packages"
optional = false
python-versions = ">=3.8"
files = [
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]
[package.dependencies]
zipp = ">=0.5"
[package.extras]
docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
perf = ["ipython"]
testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)", "pytest-ruff"]
[[package]]
name = "importlib-resources"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "6.1.1"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Read resources from Python packages"
optional = false
python-versions = ">=3.8"
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
{file = "importlib_resources-6.1.1-py3-none-any.whl", hash = "sha256:e8bf90d8213b486f428c9c39714b920041cb02c184686a3dee24905aaa8105d6"},
{file = "importlib_resources-6.1.1.tar.gz", hash = "sha256:3893a00122eafde6894c59914446a512f728a0c1a45f9bb9b63721b6bacf0b4a"},
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.dependencies]
zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
[package.extras]
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
testing = ["pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-ruff", "zipp (>=3.17)"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "iniconfig"
version = "2.0.0"
description = "brain-dead simple config-ini parsing"
optional = false
python-versions = ">=3.7"
files = [
{file = "iniconfig-2.0.0-py3-none-any.whl", hash = "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374"},
{file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
]
[[package]]
name = "ipykernel"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "6.26.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "IPython Kernel for Jupyter"
optional = false
python-versions = ">=3.8"
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
{file = "ipykernel-6.26.0-py3-none-any.whl", hash = "sha256:3ba3dc97424b87b31bb46586b5167b3161b32d7820b9201a9e698c71e271602c"},
{file = "ipykernel-6.26.0.tar.gz", hash = "sha256:553856658eb8430bbe9653ea041a41bff63e9606fc4628873fc92a6cf3abd404"},
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.dependencies]
appnope = {version = "*", markers = "platform_system == \"Darwin\""}
comm = ">=0.1.1"
debugpy = ">=1.6.5"
ipython = ">=7.23.1"
jupyter-client = ">=6.1.12"
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
matplotlib-inline = ">=0.1"
nest-asyncio = "*"
packaging = "*"
psutil = "*"
pyzmq = ">=20"
tornado = ">=6.1"
traitlets = ">=5.4.0"
[package.extras]
cov = ["coverage[toml]", "curio", "matplotlib", "pytest-cov", "trio"]
docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"]
pyqt5 = ["pyqt5"]
pyside6 = ["pyside6"]
test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio", "pytest-cov", "pytest-timeout"]
[[package]]
name = "ipython"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "8.12.3"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "IPython: Productive Interactive Computing"
optional = false
python-versions = ">=3.8"
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
{file = "ipython-8.12.3-py3-none-any.whl", hash = "sha256:b0340d46a933d27c657b211a329d0be23793c36595acf9e6ef4164bc01a1804c"},
{file = "ipython-8.12.3.tar.gz", hash = "sha256:3910c4b54543c2ad73d06579aa771041b7d5707b033bd488669b4cf544e3b363"},
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.dependencies]
appnope = {version = "*", markers = "sys_platform == \"darwin\""}
backcall = "*"
colorama = {version = "*", markers = "sys_platform == \"win32\""}
decorator = "*"
jedi = ">=0.16"
matplotlib-inline = "*"
pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
pickleshare = "*"
prompt-toolkit = ">=3.0.30,<3.0.37 || >3.0.37,<3.1.0"
pygments = ">=2.4.0"
stack-data = "*"
traitlets = ">=5"
typing-extensions = {version = "*", markers = "python_version < \"3.10\""}
[package.extras]
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black = ["black"]
doc = ["docrepr", "ipykernel", "matplotlib", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"]
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[[package]]
name = "ipywidgets"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "8.1.1"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Jupyter interactive widgets"
optional = false
python-versions = ">=3.7"
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.dependencies]
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
comm = ">=0.1.3"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
ipython = ">=6.1.0"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
jupyterlab-widgets = ">=3.0.9,<3.1.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
traitlets = ">=4.3.1"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
widgetsnbextension = ">=4.0.9,<4.1.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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description = "Operations with ISO 8601 durations"
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arrow = ">=0.15.0"
[[package]]
name = "jedi"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "0.19.1"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "An autocompletion tool for Python that can be used for text editors."
optional = false
python-versions = ">=3.6"
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.dependencies]
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
parso = ">=0.8.3,<0.9.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[package.extras]
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`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "jinja2"
version = "3.1.2"
description = "A very fast and expressive template engine."
optional = false
python-versions = ">=3.7"
files = [
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[package.dependencies]
MarkupSafe = ">=2.0"
[package.extras]
i18n = ["Babel (>=2.7)"]
update experimental poetry lock
2023-09-11 16:20:19 +00:00
[[package]]
name = "joblib"
version = "1.3.2"
description = "Lightweight pipelining with Python functions"
optional = true
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "json5"
version = "0.9.14"
description = "A Python implementation of the JSON5 data format."
optional = false
python-versions = "*"
files = [
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[package.extras]
dev = ["hypothesis"]
Tweak type hints to match dependency's behavior. (#11355) Needs #11353 to merge first, and a new `langchain` to be published with those changes.
2023-10-05 02:36:58 +00:00
[[package]]
name = "jsonpatch"
version = "1.33"
description = "Apply JSON-Patches (RFC 6902)"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
files = [
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[package.dependencies]
jsonpointer = ">=1.9"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "jsonpointer"
version = "2.4"
description = "Identify specific nodes in a JSON document (RFC 6901)"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
files = [
{file = "jsonpointer-2.4-py2.py3-none-any.whl", hash = "sha256:15d51bba20eea3165644553647711d150376234112651b4f1811022aecad7d7a"},
Resolve: VectorSearch enabled SQLChain? (#10177) Squashed from #7454 with updated features We have separated the `SQLDatabseChain` from `VectorSQLDatabseChain` and put everything into `experimental/`. Below is the original PR message from #7454. ------- We have been working on features to fill up the gap among SQL, vector search and LLM applications. Some inspiring works like self-query retrievers for VectorStores (for example [Weaviate](https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/weaviate_self_query.html) and [others](https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/self_query.html)) really turn those vector search databases into a powerful knowledge base! 🚀🚀 We are thinking if we can merge all in one, like SQL and vector search and LLMChains, making this SQL vector database memory as the only source of your data. Here are some benefits we can think of for now, maybe you have more 👀: With ALL data you have: since you store all your pasta in the database, you don't need to worry about the foreign keys or links between names from other data source. Flexible data structure: Even if you have changed your schema, for example added a table, the LLM will know how to JOIN those tables and use those as filters. SQL compatibility: We found that vector databases that supports SQL in the marketplace have similar interfaces, which means you can change your backend with no pain, just change the name of the distance function in your DB solution and you are ready to go! ### Issue resolved: - [Feature Proposal: VectorSearch enabled SQLChain?](https://github.com/hwchase17/langchain/issues/5122) ### Change made in this PR: - An improved schema handling that ignore `types.NullType` columns - A SQL output Parser interface in `SQLDatabaseChain` to enable Vector SQL capability and further more - A Retriever based on `SQLDatabaseChain` to retrieve data from the database for RetrievalQAChains and many others - Allow `SQLDatabaseChain` to retrieve data in python native format - Includes PR #6737 - Vector SQL Output Parser for `SQLDatabaseChain` and `SQLDatabaseChainRetriever` - Prompts that can implement text to VectorSQL - Corresponding unit-tests and notebook ### Twitter handle: - @MyScaleDB ### Tag Maintainer: Prompts / General: @hwchase17, @baskaryan DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev ### Dependencies: No dependency added
2023-09-07 00:08:12 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[[package]]
name = "jsonschema"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "4.19.2"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "An implementation of JSON Schema validation for Python"
optional = false
python-versions = ">=3.8"
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
{file = "jsonschema-4.19.2-py3-none-any.whl", hash = "sha256:eee9e502c788e89cb166d4d37f43084e3b64ab405c795c03d343a4dbc2c810fc"},
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
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attrs = ">=22.2.0"
fqdn = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
idna = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
importlib-resources = {version = ">=1.4.0", markers = "python_version < \"3.9\""}
isoduration = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
jsonpointer = {version = ">1.13", optional = true, markers = "extra == \"format-nongpl\""}
jsonschema-specifications = ">=2023.03.6"
pkgutil-resolve-name = {version = ">=1.3.10", markers = "python_version < \"3.9\""}
referencing = ">=0.28.4"
rfc3339-validator = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
rfc3986-validator = {version = ">0.1.0", optional = true, markers = "extra == \"format-nongpl\""}
rpds-py = ">=0.7.1"
uri-template = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
webcolors = {version = ">=1.11", optional = true, markers = "extra == \"format-nongpl\""}
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format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=1.11)"]
[[package]]
name = "jsonschema-specifications"
version = "2023.7.1"
description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
optional = false
python-versions = ">=3.8"
files = [
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importlib-resources = {version = ">=1.4.0", markers = "python_version < \"3.9\""}
referencing = ">=0.28.0"
[[package]]
name = "jupyter"
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description = "Jupyter metapackage. Install all the Jupyter components in one go."
optional = false
python-versions = "*"
files = [
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ipykernel = "*"
ipywidgets = "*"
jupyter-console = "*"
nbconvert = "*"
notebook = "*"
qtconsole = "*"
[[package]]
name = "jupyter-client"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "8.6.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Jupyter protocol implementation and client libraries"
optional = false
python-versions = ">=3.8"
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
{file = "jupyter_client-8.6.0-py3-none-any.whl", hash = "sha256:909c474dbe62582ae62b758bca86d6518c85234bdee2d908c778db6d72f39d99"},
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.dependencies]
importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
python-dateutil = ">=2.8.2"
pyzmq = ">=23.0"
tornado = ">=6.2"
traitlets = ">=5.3"
[package.extras]
docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx (>=4)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pytest", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"]
[[package]]
name = "jupyter-console"
version = "6.6.3"
description = "Jupyter terminal console"
optional = false
python-versions = ">=3.7"
files = [
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]
[package.dependencies]
ipykernel = ">=6.14"
ipython = "*"
jupyter-client = ">=7.0.0"
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
prompt-toolkit = ">=3.0.30"
pygments = "*"
pyzmq = ">=17"
traitlets = ">=5.4"
[package.extras]
test = ["flaky", "pexpect", "pytest"]
[[package]]
name = "jupyter-core"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "5.5.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Jupyter core package. A base package on which Jupyter projects rely."
optional = false
python-versions = ">=3.8"
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
{file = "jupyter_core-5.5.0-py3-none-any.whl", hash = "sha256:e11e02cd8ae0a9de5c6c44abf5727df9f2581055afe00b22183f621ba3585805"},
{file = "jupyter_core-5.5.0.tar.gz", hash = "sha256:880b86053bf298a8724994f95e99b99130659022a4f7f45f563084b6223861d3"},
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.dependencies]
platformdirs = ">=2.5"
pywin32 = {version = ">=300", markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\""}
traitlets = ">=5.3"
[package.extras]
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
docs = ["myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"]
Harrison/move experimental (#8084)
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test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
[[package]]
name = "jupyter-events"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "0.9.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Jupyter Event System library"
optional = false
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
python-versions = ">=3.8"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
{file = "jupyter_events-0.9.0-py3-none-any.whl", hash = "sha256:d853b3c10273ff9bc8bb8b30076d65e2c9685579db736873de6c2232dde148bf"},
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.dependencies]
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
jsonschema = {version = ">=4.18.0", extras = ["format-nongpl"]}
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
python-json-logger = ">=2.0.4"
pyyaml = ">=5.3"
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
referencing = "*"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
rfc3339-validator = "*"
rfc3986-validator = ">=0.1.1"
traitlets = ">=5.3"
[package.extras]
cli = ["click", "rich"]
docs = ["jupyterlite-sphinx", "myst-parser", "pydata-sphinx-theme", "sphinxcontrib-spelling"]
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
test = ["click", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "pytest-console-scripts", "rich"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "jupyter-lsp"
version = "2.2.0"
description = "Multi-Language Server WebSocket proxy for Jupyter Notebook/Lab server"
optional = false
python-versions = ">=3.8"
files = [
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[package.dependencies]
importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
jupyter-server = ">=1.1.2"
[[package]]
name = "jupyter-server"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "2.10.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
optional = false
python-versions = ">=3.8"
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
{file = "jupyter_server-2.10.0-py3-none-any.whl", hash = "sha256:dde56c9bc3cb52d7b72cc0f696d15d7163603526f1a758eb4a27405b73eab2a5"},
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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argon2-cffi = "*"
jinja2 = "*"
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`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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overrides = "*"
packaging = "*"
prometheus-client = "*"
pywinpty = {version = "*", markers = "os_name == \"nt\""}
pyzmq = ">=24"
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
send2trash = ">=1.8.2"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
terminado = ">=0.8.3"
tornado = ">=6.2.0"
traitlets = ">=5.6.0"
websocket-client = "*"
[package.extras]
docs = ["ipykernel", "jinja2", "jupyter-client", "jupyter-server", "myst-parser", "nbformat", "prometheus-client", "pydata-sphinx-theme", "send2trash", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-openapi (>=0.8.0)", "sphinxcontrib-spelling", "sphinxemoji", "tornado", "typing-extensions"]
test = ["flaky", "ipykernel", "pre-commit", "pytest (>=7.0)", "pytest-console-scripts", "pytest-jupyter[server] (>=0.4)", "pytest-timeout", "requests"]
[[package]]
name = "jupyter-server-terminals"
version = "0.4.4"
description = "A Jupyter Server Extension Providing Terminals."
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python-versions = ">=3.8"
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pywinpty = {version = ">=2.0.3", markers = "os_name == \"nt\""}
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docs = ["jinja2", "jupyter-server", "mistune (<3.0)", "myst-parser", "nbformat", "packaging", "pydata-sphinx-theme", "sphinxcontrib-github-alt", "sphinxcontrib-openapi", "sphinxcontrib-spelling", "sphinxemoji", "tornado"]
test = ["coverage", "jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-cov", "pytest-jupyter[server] (>=0.5.3)", "pytest-timeout"]
[[package]]
name = "jupyterlab"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "4.0.8"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "JupyterLab computational environment"
optional = false
python-versions = ">=3.8"
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
{file = "jupyterlab-4.0.8-py3-none-any.whl", hash = "sha256:2ff5aa2a51eb21df241d6011c236e88bd1ff9a5dbb75bebc54472f9c18bfffa4"},
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.dependencies]
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importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
importlib-resources = {version = ">=1.4", markers = "python_version < \"3.9\""}
ipykernel = "*"
jinja2 = ">=3.0.3"
jupyter-core = "*"
jupyter-lsp = ">=2.0.0"
jupyter-server = ">=2.4.0,<3"
jupyterlab-server = ">=2.19.0,<3"
notebook-shim = ">=0.2"
packaging = "*"
tomli = {version = "*", markers = "python_version < \"3.11\""}
tornado = ">=6.2.0"
traitlets = "*"
[package.extras]
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
dev = ["black[jupyter] (==23.10.1)", "build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.0.292)"]
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-tornasync", "sphinx (>=1.8,<7.2.0)", "sphinx-copybutton"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
docs-screenshots = ["altair (==5.0.1)", "ipython (==8.14.0)", "ipywidgets (==8.0.6)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.0.post0)", "matplotlib (==3.7.1)", "nbconvert (>=7.0.0)", "pandas (==2.0.2)", "scipy (==1.10.1)", "vega-datasets (==0.9.0)"]
test = ["coverage", "pytest (>=7.0)", "pytest-check-links (>=0.7)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter (>=0.5.3)", "pytest-timeout", "pytest-tornasync", "requests", "requests-cache", "virtualenv"]
[[package]]
name = "jupyterlab-pygments"
version = "0.2.2"
description = "Pygments theme using JupyterLab CSS variables"
optional = false
python-versions = ">=3.7"
files = [
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]
[[package]]
name = "jupyterlab-server"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "2.25.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "A set of server components for JupyterLab and JupyterLab like applications."
optional = false
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
python-versions = ">=3.8"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
{file = "jupyterlab_server-2.25.0-py3-none-any.whl", hash = "sha256:c9f67a98b295c5dee87f41551b0558374e45d449f3edca153dd722140630dcb2"},
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.dependencies]
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importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
jinja2 = ">=3.0.3"
json5 = ">=0.9.0"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
jsonschema = ">=4.18.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
jupyter-server = ">=1.21,<3"
packaging = ">=21.3"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
requests = ">=2.31"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[package.extras]
docs = ["autodoc-traits", "jinja2 (<3.2.0)", "mistune (<4)", "myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-copybutton", "sphinxcontrib-openapi (>0.8)"]
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
openapi = ["openapi-core (>=0.18.0,<0.19.0)", "ruamel-yaml"]
test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-validator (>=0.6.0,<0.7.0)", "pytest (>=7.0)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "requests-mock", "ruamel-yaml", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "jupyterlab-widgets"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "3.0.9"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Jupyter interactive widgets for JupyterLab"
optional = false
python-versions = ">=3.7"
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
{file = "jupyterlab_widgets-3.0.9-py3-none-any.whl", hash = "sha256:3cf5bdf5b897bf3bccf1c11873aa4afd776d7430200f765e0686bd352487b58d"},
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[[package]]
name = "langchain"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "0.0.331"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Building applications with LLMs through composability"
optional = false
python-versions = ">=3.8.1,<4.0"
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
{file = "langchain-0.0.331-py3-none-any.whl", hash = "sha256:64e6e1a57b8deafc1c4e914820b2b8e22a5eed60d49432cadc3b8cca9d613694"},
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.dependencies]
aiohttp = ">=3.8.3,<4.0.0"
Tweak type hints to match dependency's behavior. (#11355) Needs #11353 to merge first, and a new `langchain` to be published with those changes.
2023-10-05 02:36:58 +00:00
anyio = "<4.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
async-timeout = {version = ">=4.0.0,<5.0.0", markers = "python_version < \"3.11\""}
Tweak type hints to match dependency's behavior. (#11355) Needs #11353 to merge first, and a new `langchain` to be published with those changes.
2023-10-05 02:36:58 +00:00
dataclasses-json = ">=0.5.7,<0.7"
jsonpatch = ">=1.33,<2.0"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
langsmith = ">=0.0.52,<0.1.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
numpy = ">=1,<2"
Tweak type hints to match dependency's behavior. (#11355) Needs #11353 to merge first, and a new `langchain` to be published with those changes.
2023-10-05 02:36:58 +00:00
pydantic = ">=1,<3"
PyYAML = ">=5.3"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
requests = ">=2,<3"
SQLAlchemy = ">=1.4,<3"
tenacity = ">=8.1.0,<9.0.0"
[package.extras]
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
all = ["O365 (>=2.0.26,<3.0.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "amadeus (>=8.1.0)", "arxiv (>=1.4,<2.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "awadb (>=0.3.9,<0.4.0)", "azure-ai-formrecognizer (>=3.2.1,<4.0.0)", "azure-ai-vision (>=0.11.1b1,<0.12.0)", "azure-cognitiveservices-speech (>=1.28.0,<2.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "beautifulsoup4 (>=4,<5)", "clarifai (>=9.1.0)", "clickhouse-connect (>=0.5.14,<0.6.0)", "cohere (>=4,<5)", "deeplake (>=3.8.3,<4.0.0)", "docarray[hnswlib] (>=0.32.0,<0.33.0)", "duckduckgo-search (>=3.8.3,<4.0.0)", "elasticsearch (>=8,<9)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "google-api-python-client (==2.70.0)", "google-auth (>=2.18.1,<3.0.0)", "google-search-results (>=2,<3)", "gptcache (>=0.1.7)", "html2text (>=2020.1.16,<2021.0.0)", "huggingface_hub (>=0,<1)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "lancedb (>=0.1,<0.2)", "langkit (>=0.0.6,<0.1.0)", "lark (>=1.1.5,<2.0.0)", "librosa (>=0.10.0.post2,<0.11.0)", "lxml (>=4.9.2,<5.0.0)", "manifest-ml (>=0.0.1,<0.0.2)", "marqo (>=1.2.4,<2.0.0)", "momento (>=1.10.1,<2.0.0)", "nebula3-python (>=3.4.0,<4.0.0)", "neo4j (>=5.8.1,<6.0.0)", "networkx (>=2.6.3,<4)", "nlpcloud (>=1,<2)", "nltk (>=3,<4)", "nomic (>=1.0.43,<2.0.0)", "openai (>=0,<1)", "openlm (>=0.0.5,<0.0.6)", "opensearch-py (>=2.0.0,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pexpect (>=4.8.0,<5.0.0)", "pgvector (>=0.1.6,<0.2.0)", "pinecone-client (>=2,<3)", "pinecone-text (>=0.4.2,<0.5.0)", "psycopg2-binary (>=2.9.5,<3.0.0)", "pymongo (>=4.3.3,<5.0.0)", "pyowm (>=3.3.0,<4.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pytesseract (>=0.3.10,<0.4.0)", "python-arango (>=7.5.9,<8.0.0)", "pyvespa (>=0.33.0,<0.34.0)", "qdrant-client (>=1.3.1,<2.0.0)", "rdflib (>=6.3.2,<7.0.0)", "redis (>=4,<5)", "requests-toolbelt (>=1.0.0,<2.0.0)", "sentence-transformers (>=2,<3)", "singlestoredb (>=0.7.1,<0.8.0)", "tensorflow-text (>=2.11.0,<3.0.0)", "tigrisdb (>=1.0.0b6,<2.0.0)", "tiktoken (>=0.3.2,<0.6.0)", "torch (>=1,<3)", "transformers (>=4,<5)", "weaviate-client (>=3,<4)", "wikipedia (>=1,<2)", "wolframalpha (==5.0.0)"]
Tweak type hints to match dependency's behavior. (#11355) Needs #11353 to merge first, and a new `langchain` to be published with those changes.
2023-10-05 02:36:58 +00:00
azure = ["azure-ai-formrecognizer (>=3.2.1,<4.0.0)", "azure-ai-vision (>=0.11.1b1,<0.12.0)", "azure-cognitiveservices-speech (>=1.28.0,<2.0.0)", "azure-core (>=1.26.4,<2.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "azure-search-documents (==11.4.0b8)", "openai (>=0,<1)"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
clarifai = ["clarifai (>=9.1.0)"]
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
cli = ["typer (>=0.9.0,<0.10.0)"]
Tweak type hints to match dependency's behavior. (#11355) Needs #11353 to merge first, and a new `langchain` to be published with those changes.
2023-10-05 02:36:58 +00:00
cohere = ["cohere (>=4,<5)"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
docarray = ["docarray[hnswlib] (>=0.32.0,<0.33.0)"]
embeddings = ["sentence-transformers (>=2,<3)"]
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "dashvector (>=1.0.1,<2.0.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.6.0,<0.7.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "html2text (>=2020.1.16,<2021.0.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "lxml (>=4.9.2,<5.0.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "openai (>=0,<1)", "openapi-pydantic (>=0.3.2,<0.4.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "upstash-redis (>=0.15.0,<0.16.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
javascript = ["esprima (>=4.0.1,<5.0.0)"]
Tweak type hints to match dependency's behavior. (#11355) Needs #11353 to merge first, and a new `langchain` to be published with those changes.
2023-10-05 02:36:58 +00:00
llms = ["clarifai (>=9.1.0)", "cohere (>=4,<5)", "huggingface_hub (>=0,<1)", "manifest-ml (>=0.0.1,<0.0.2)", "nlpcloud (>=1,<2)", "openai (>=0,<1)", "openlm (>=0.0.5,<0.0.6)", "torch (>=1,<3)", "transformers (>=4,<5)"]
openai = ["openai (>=0,<1)", "tiktoken (>=0.3.2,<0.6.0)"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
qdrant = ["qdrant-client (>=1.3.1,<2.0.0)"]
text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[[package]]
name = "langcodes"
version = "3.3.0"
description = "Tools for labeling human languages with IETF language tags"
optional = true
python-versions = ">=3.6"
files = [
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "langsmith"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "0.0.60"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
optional = false
python-versions = ">=3.8.1,<4.0"
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.dependencies]
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
pydantic = ">=1,<3"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
requests = ">=2,<3"
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name = "marshmallow"
version = "3.20.1"
description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
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python-versions = ">=3.8"
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lint = ["flake8 (==6.0.0)", "flake8-bugbear (==23.7.10)", "mypy (==1.4.1)", "pre-commit (>=2.4,<4.0)"]
tests = ["pytest", "pytz", "simplejson"]
[[package]]
name = "matplotlib-inline"
version = "0.1.6"
description = "Inline Matplotlib backend for Jupyter"
optional = false
python-versions = ">=3.5"
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[[package]]
name = "mistune"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "3.0.2"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "A sane and fast Markdown parser with useful plugins and renderers"
optional = false
python-versions = ">=3.7"
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
update experimental poetry lock
2023-09-11 16:20:19 +00:00
[[package]]
name = "mpmath"
version = "1.3.0"
description = "Python library for arbitrary-precision floating-point arithmetic"
optional = true
python-versions = "*"
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docs = ["sphinx"]
gmpy = ["gmpy2 (>=2.1.0a4)"]
tests = ["pytest (>=4.6)"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[[package]]
name = "murmurhash"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "1.0.10"
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
description = "Cython bindings for MurmurHash"
optional = true
python-versions = ">=3.6"
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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typing-extensions = ">=3.10"
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dmypy = ["psutil (>=4.0)"]
install-types = ["pip"]
python2 = ["typed-ast (>=1.4.0,<2)"]
reports = ["lxml"]
[[package]]
name = "mypy-extensions"
version = "1.0.0"
description = "Type system extensions for programs checked with the mypy type checker."
optional = false
python-versions = ">=3.5"
files = [
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[[package]]
name = "nbclient"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "0.9.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
optional = false
python-versions = ">=3.8.0"
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
{file = "nbclient-0.9.0-py3-none-any.whl", hash = "sha256:a3a1ddfb34d4a9d17fc744d655962714a866639acd30130e9be84191cd97cd15"},
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.dependencies]
jupyter-client = ">=6.1.12"
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
nbformat = ">=5.1"
traitlets = ">=5.4"
[package.extras]
dev = ["pre-commit"]
docs = ["autodoc-traits", "mock", "moto", "myst-parser", "nbclient[test]", "sphinx (>=1.7)", "sphinx-book-theme", "sphinxcontrib-spelling"]
test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=7.0.0)", "pytest (>=7.0)", "pytest-asyncio", "pytest-cov (>=4.0)", "testpath", "xmltodict"]
[[package]]
name = "nbconvert"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "7.11.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Converting Jupyter Notebooks"
optional = false
python-versions = ">=3.8"
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
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bleach = "!=5.0.0"
defusedxml = "*"
importlib-metadata = {version = ">=3.6", markers = "python_version < \"3.10\""}
jinja2 = ">=3.0"
jupyter-core = ">=4.7"
jupyterlab-pygments = "*"
markupsafe = ">=2.0"
mistune = ">=2.0.3,<4"
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packaging = "*"
pandocfilters = ">=1.4.1"
pygments = ">=2.4.1"
tinycss2 = "*"
traitlets = ">=5.1"
[package.extras]
all = ["nbconvert[docs,qtpdf,serve,test,webpdf]"]
docs = ["ipykernel", "ipython", "myst-parser", "nbsphinx (>=0.2.12)", "pydata-sphinx-theme", "sphinx (==5.0.2)", "sphinxcontrib-spelling"]
qtpdf = ["nbconvert[qtpng]"]
qtpng = ["pyqtwebengine (>=5.15)"]
serve = ["tornado (>=6.1)"]
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
test = ["flaky", "ipykernel", "ipywidgets (>=7)", "pytest"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
webpdf = ["playwright"]
[[package]]
name = "nbformat"
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
version = "5.9.2"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "The Jupyter Notebook format"
optional = false
python-versions = ">=3.8"
files = [
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
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fastjsonschema = "*"
jsonschema = ">=2.6"
jupyter-core = "*"
traitlets = ">=5.1"
[package.extras]
docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
test = ["pep440", "pre-commit", "pytest", "testpath"]
[[package]]
name = "nest-asyncio"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "1.5.8"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Patch asyncio to allow nested event loops"
optional = false
python-versions = ">=3.5"
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
update experimental poetry lock
2023-09-11 16:20:19 +00:00
[[package]]
name = "networkx"
version = "3.1"
description = "Python package for creating and manipulating graphs and networks"
optional = true
python-versions = ">=3.8"
files = [
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developer = ["mypy (>=1.1)", "pre-commit (>=3.2)"]
doc = ["nb2plots (>=0.6)", "numpydoc (>=1.5)", "pillow (>=9.4)", "pydata-sphinx-theme (>=0.13)", "sphinx (>=6.1)", "sphinx-gallery (>=0.12)", "texext (>=0.6.7)"]
extra = ["lxml (>=4.6)", "pydot (>=1.4.2)", "pygraphviz (>=1.10)", "sympy (>=1.10)"]
test = ["codecov (>=2.1)", "pytest (>=7.2)", "pytest-cov (>=4.0)"]
[[package]]
name = "nltk"
version = "3.8.1"
description = "Natural Language Toolkit"
optional = true
python-versions = ">=3.7"
files = [
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[package.dependencies]
click = "*"
joblib = "*"
regex = ">=2021.8.3"
tqdm = "*"
[package.extras]
all = ["matplotlib", "numpy", "pyparsing", "python-crfsuite", "requests", "scikit-learn", "scipy", "twython"]
corenlp = ["requests"]
machine-learning = ["numpy", "python-crfsuite", "scikit-learn", "scipy"]
plot = ["matplotlib"]
tgrep = ["pyparsing"]
twitter = ["twython"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "notebook"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "7.0.6"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Jupyter Notebook - A web-based notebook environment for interactive computing"
optional = false
python-versions = ">=3.8"
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
{file = "notebook-7.0.6-py3-none-any.whl", hash = "sha256:0fe8f67102fea3744fedf652e4c15339390902ca70c5a31c4f547fa23da697cc"},
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.dependencies]
jupyter-server = ">=2.4.0,<3"
jupyterlab = ">=4.0.2,<5"
jupyterlab-server = ">=2.22.1,<3"
notebook-shim = ">=0.2,<0.3"
tornado = ">=6.2.0"
[package.extras]
dev = ["hatch", "pre-commit"]
docs = ["myst-parser", "nbsphinx", "pydata-sphinx-theme", "sphinx (>=1.3.6)", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
test = ["importlib-resources (>=5.0)", "ipykernel", "jupyter-server[test] (>=2.4.0,<3)", "jupyterlab-server[test] (>=2.22.1,<3)", "nbval", "pytest (>=7.0)", "pytest-console-scripts", "pytest-timeout", "pytest-tornasync", "requests"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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test = ["pytest", "pytest-console-scripts", "pytest-jupyter", "pytest-tornasync"]
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[[package]]
name = "overrides"
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
version = "7.4.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "A decorator to automatically detect mismatch when overriding a method."
optional = false
python-versions = ">=3.6"
files = [
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[[package]]
name = "packaging"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "23.2"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Core utilities for Python packages"
optional = false
python-versions = ">=3.7"
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
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name = "pandocfilters"
version = "1.5.0"
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name = "parso"
version = "0.8.3"
description = "A Python Parser"
optional = false
python-versions = ">=3.6"
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[package.extras]
qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
testing = ["docopt", "pytest (<6.0.0)"]
[[package]]
name = "pexpect"
version = "4.8.0"
description = "Pexpect allows easy control of interactive console applications."
optional = false
python-versions = "*"
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ptyprocess = ">=0.5"
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[[package]]
name = "phonenumbers"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "8.13.24"
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
description = "Python version of Google's common library for parsing, formatting, storing and validating international phone numbers."
optional = true
python-versions = "*"
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
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2023-08-30 17:39:44 +00:00
]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "pickleshare"
version = "0.7.5"
description = "Tiny 'shelve'-like database with concurrency support"
optional = false
python-versions = "*"
files = [
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update experimental poetry lock
2023-09-11 16:20:19 +00:00
[[package]]
name = "pillow"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "10.1.0"
update experimental poetry lock
2023-09-11 16:20:19 +00:00
description = "Python Imaging Library (Fork)"
optional = true
python-versions = ">=3.8"
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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update experimental poetry lock
2023-09-11 16:20:19 +00:00
]
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docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-removed-in", "sphinxext-opengraph"]
tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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name = "platformdirs"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "3.11.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
optional = false
python-versions = ">=3.7"
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.1)", "sphinx-autodoc-typehints (>=1.24)"]
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "pluggy"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "1.3.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "plugin and hook calling mechanisms for python"
optional = false
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
python-versions = ">=3.8"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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dev = ["pre-commit", "tox"]
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Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[[package]]
name = "preshed"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "3.0.9"
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
description = "Cython hash table that trusts the keys are pre-hashed"
optional = true
python-versions = ">=3.6"
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Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
]
[package.dependencies]
cymem = ">=2.0.2,<2.1.0"
murmurhash = ">=0.28.0,<1.1.0"
[[package]]
name = "presidio-analyzer"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "2.2.350"
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
description = "Presidio analyzer package"
optional = true
python-versions = "*"
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
{file = "presidio_analyzer-2.2.350-py3-none-any.whl", hash = "sha256:27989af82e6c78ecb4b6f85f587c5d954e126234f3aa091fd2bd1c1d3c637489"},
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
]
[package.dependencies]
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
phonenumbers = ">=8.12,<9.0.0"
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
pyyaml = "*"
regex = "*"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
spacy = ">=3.4.4,<4.0.0"
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
tldextract = "*"
[package.extras]
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
stanza = ["spacy-stanza", "stanza"]
transformers = ["spacy-huggingface-pipelines"]
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[[package]]
name = "presidio-anonymizer"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "2.2.350"
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
description = "Persidio Anonymizer package - replaces analyzed text with desired values."
optional = true
python-versions = ">=3.5"
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
{file = "presidio_anonymizer-2.2.350-py3-none-any.whl", hash = "sha256:62dfde94a098747c5fa54f3447dcca51d8eede7f8c6cff7c8805ca1a517bb6ea"},
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
]
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pycryptodome = ">=3.10.1"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "prometheus-client"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "0.18.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Python client for the Prometheus monitoring system."
optional = false
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
python-versions = ">=3.8"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
{file = "prometheus_client-0.18.0-py3-none-any.whl", hash = "sha256:8de3ae2755f890826f4b6479e5571d4f74ac17a81345fe69a6778fdb92579184"},
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
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twisted = ["twisted"]
[[package]]
name = "prompt-toolkit"
version = "3.0.39"
description = "Library for building powerful interactive command lines in Python"
optional = false
python-versions = ">=3.7.0"
files = [
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[package.dependencies]
wcwidth = "*"
[[package]]
name = "psutil"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "5.9.6"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Cross-platform lib for process and system monitoring in Python."
optional = false
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
files = [
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{file = "psutil-5.9.6-cp37-abi3-win_amd64.whl", hash = "sha256:6e5fb8dc711a514da83098bc5234264e551ad980cec5f85dabf4d38ed6f15e9a"},
{file = "psutil-5.9.6-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:daecbcbd29b289aac14ece28eca6a3e60aa361754cf6da3dfb20d4d32b6c7f57"},
{file = "psutil-5.9.6.tar.gz", hash = "sha256:e4b92ddcd7dd4cdd3f900180ea1e104932c7bce234fb88976e2a3b296441225a"},
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
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test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"]
[[package]]
name = "ptyprocess"
version = "0.7.0"
description = "Run a subprocess in a pseudo terminal"
optional = false
python-versions = "*"
files = [
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name = "pure-eval"
version = "0.2.2"
description = "Safely evaluate AST nodes without side effects"
optional = false
python-versions = "*"
files = [
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tests = ["pytest"]
[[package]]
name = "pycparser"
version = "2.21"
description = "C parser in Python"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
files = [
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{file = "pycparser-2.21.tar.gz", hash = "sha256:e644fdec12f7872f86c58ff790da456218b10f863970249516d60a5eaca77206"},
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Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[[package]]
name = "pycryptodome"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "3.19.0"
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
description = "Cryptographic library for Python"
optional = true
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "pydantic"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "2.4.2"
description = "Data validation using Python type hints"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.dependencies]
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
annotated-types = ">=0.4.0"
pydantic-core = "2.10.1"
typing-extensions = ">=4.6.1"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[package.extras]
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
email = ["email-validator (>=2.0.0)"]
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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Harrison/move experimental (#8084)
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Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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2023-11-07 23:15:09 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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2023-10-17 01:13:31 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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Harrison/move experimental (#8084)
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2023-10-17 01:13:31 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
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cffi = {version = "*", markers = "implementation_name == \"pypy\""}
[[package]]
name = "qtconsole"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "5.5.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Jupyter Qt console"
optional = false
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
python-versions = ">= 3.8"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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ipykernel = ">=4.1"
jupyter-client = ">=4.1"
jupyter-core = "*"
packaging = "*"
pygments = "*"
pyzmq = ">=17.1"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
qtpy = ">=2.4.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
traitlets = "<5.2.1 || >5.2.1,<5.2.2 || >5.2.2"
[package.extras]
doc = ["Sphinx (>=1.3)"]
test = ["flaky", "pytest", "pytest-qt"]
[[package]]
name = "qtpy"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "2.4.1"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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optional = false
python-versions = ">=3.7"
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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python-versions = ">=3.8"
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`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[[package]]
name = "regex"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "2023.10.3"
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
description = "Alternative regular expression module, to replace re."
optional = true
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
python-versions = ">=3.7"
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
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Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "requests"
version = "2.31.0"
description = "Python HTTP for Humans."
optional = false
python-versions = ">=3.7"
files = [
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[package.dependencies]
certifi = ">=2017.4.17"
charset-normalizer = ">=2,<4"
idna = ">=2.5,<4"
urllib3 = ">=1.21.1,<3"
[package.extras]
socks = ["PySocks (>=1.5.6,!=1.5.7)"]
use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[[package]]
name = "requests-file"
version = "1.5.1"
description = "File transport adapter for Requests"
optional = true
python-versions = "*"
files = [
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]
[package.dependencies]
requests = ">=1.0.0"
six = "*"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "rfc3339-validator"
version = "0.1.4"
description = "A pure python RFC3339 validator"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
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2023-11-07 23:15:09 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[[package]]
name = "ruff"
Bump all libraries to the latest `ruff` version. (#13350) This version of `ruff` is the one we'll be using to lint the docs and cookbooks (#12677), so I'm making it used everywhere else too.
2023-11-14 21:00:21 +00:00
version = "0.1.5"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
description = "An extremely fast Python linter and code formatter, written in Rust."
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
optional = false
python-versions = ">=3.7"
files = [
Bump all libraries to the latest `ruff` version. (#13350) This version of `ruff` is the one we'll be using to lint the docs and cookbooks (#12677), so I'm making it used everywhere else too.
2023-11-14 21:00:21 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
update experimental poetry lock
2023-09-11 16:20:19 +00:00
[[package]]
name = "safetensors"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "0.4.0"
description = ""
update experimental poetry lock
2023-09-11 16:20:19 +00:00
optional = true
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
python-versions = ">=3.7"
update experimental poetry lock
2023-09-11 16:20:19 +00:00
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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update experimental poetry lock
2023-09-11 16:20:19 +00:00
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Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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dev = ["safetensors[all]"]
jax = ["flax (>=0.6.3)", "jax (>=0.3.25)", "jaxlib (>=0.3.25)", "safetensors[numpy]"]
update experimental poetry lock
2023-09-11 16:20:19 +00:00
numpy = ["numpy (>=1.21.6)"]
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
paddlepaddle = ["paddlepaddle (>=2.4.1)", "safetensors[numpy]"]
pinned-tf = ["safetensors[numpy]", "tensorflow (==2.11.0)"]
update experimental poetry lock
2023-09-11 16:20:19 +00:00
quality = ["black (==22.3)", "click (==8.0.4)", "flake8 (>=3.8.3)", "isort (>=5.5.4)"]
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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update experimental poetry lock
2023-09-11 16:20:19 +00:00
[[package]]
name = "scikit-learn"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "1.3.2"
update experimental poetry lock
2023-09-11 16:20:19 +00:00
description = "A set of python modules for machine learning and data mining"
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python-versions = ">=3.8"
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Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
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update experimental poetry lock
2023-09-11 16:20:19 +00:00
]
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merge
2023-10-06 01:09:35 +00:00
numpy = ">=1.17.3,<2.0"
update experimental poetry lock
2023-09-11 16:20:19 +00:00
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[package.dependencies]
numpy = ">=1.18.5,<1.26.0"
[package.extras]
dev = ["flake8", "mypy", "pycodestyle", "typing_extensions"]
doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-panels (>=0.5.2)", "sphinx-tabs"]
test = ["asv", "gmpy2", "mpmath", "pytest", "pytest-cov", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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objc = ["pyobjc-framework-Cocoa"]
win32 = ["pywin32"]
update experimental poetry lock
2023-09-11 16:20:19 +00:00
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python-versions = ">=3.6.0"
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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name = "setuptools"
version = "67.8.0"
description = "Easily download, build, install, upgrade, and uninstall Python packages"
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version = "1.16.0"
description = "Python 2 and 3 compatibility utilities"
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Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[[package]]
name = "smart-open"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "6.4.0"
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
description = "Utils for streaming large files (S3, HDFS, GCS, Azure Blob Storage, gzip, bz2...)"
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Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
]
[package.extras]
all = ["azure-common", "azure-core", "azure-storage-blob", "boto3", "google-cloud-storage (>=2.6.0)", "paramiko", "requests"]
azure = ["azure-common", "azure-core", "azure-storage-blob"]
gcs = ["google-cloud-storage (>=2.6.0)"]
http = ["requests"]
s3 = ["boto3"]
ssh = ["paramiko"]
test = ["azure-common", "azure-core", "azure-storage-blob", "boto3", "google-cloud-storage (>=2.6.0)", "moto[server]", "paramiko", "pytest", "pytest-rerunfailures", "requests", "responses"]
webhdfs = ["requests"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "sniffio"
version = "1.3.0"
description = "Sniff out which async library your code is running under"
optional = false
python-versions = ">=3.7"
files = [
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[[package]]
name = "soupsieve"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "2.5"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "A modern CSS selector implementation for Beautiful Soup."
optional = false
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
python-versions = ">=3.8"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[[package]]
name = "spacy"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "3.7.2"
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
description = "Industrial-strength Natural Language Processing (NLP) in Python"
optional = true
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
python-versions = ">=3.7"
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
]
[package.dependencies]
catalogue = ">=2.0.6,<2.1.0"
cymem = ">=2.0.2,<2.1.0"
jinja2 = "*"
langcodes = ">=3.2.0,<4.0.0"
murmurhash = ">=0.28.0,<1.1.0"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
numpy = [
{version = ">=1.15.0", markers = "python_version < \"3.9\""},
{version = ">=1.19.0", markers = "python_version >= \"3.9\""},
]
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
packaging = ">=20.0"
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requests = ">=2.13.0,<3.0.0"
setuptools = "*"
smart-open = ">=5.2.1,<7.0.0"
spacy-legacy = ">=3.0.11,<3.1.0"
spacy-loggers = ">=1.0.0,<2.0.0"
srsly = ">=2.4.3,<3.0.0"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
thinc = ">=8.1.8,<8.3.0"
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
tqdm = ">=4.38.0,<5.0.0"
typer = ">=0.3.0,<0.10.0"
wasabi = ">=0.9.1,<1.2.0"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
weasel = ">=0.1.0,<0.4.0"
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
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cuda110 = ["cupy-cuda110 (>=5.0.0b4,<13.0.0)"]
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cuda80 = ["cupy-cuda80 (>=5.0.0b4,<13.0.0)"]
cuda90 = ["cupy-cuda90 (>=5.0.0b4,<13.0.0)"]
cuda91 = ["cupy-cuda91 (>=5.0.0b4,<13.0.0)"]
cuda92 = ["cupy-cuda92 (>=5.0.0b4,<13.0.0)"]
ja = ["sudachidict-core (>=20211220)", "sudachipy (>=0.5.2,!=0.6.1)"]
ko = ["natto-py (>=0.9.0)"]
lookups = ["spacy-lookups-data (>=1.0.3,<1.1.0)"]
th = ["pythainlp (>=2.0)"]
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
transformers = ["spacy-transformers (>=1.1.2,<1.4.0)"]
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[[package]]
name = "spacy-legacy"
version = "3.0.12"
description = "Legacy registered functions for spaCy backwards compatibility"
optional = true
python-versions = ">=3.6"
files = [
{file = "spacy-legacy-3.0.12.tar.gz", hash = "sha256:b37d6e0c9b6e1d7ca1cf5bc7152ab64a4c4671f59c85adaf7a3fcb870357a774"},
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]
[[package]]
name = "spacy-loggers"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "1.0.5"
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
description = "Logging utilities for SpaCy"
optional = true
python-versions = ">=3.6"
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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{file = "spacy_loggers-1.0.5-py3-none-any.whl", hash = "sha256:196284c9c446cc0cdb944005384270d775fdeaf4f494d8e269466cfa497ef645"},
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "sqlalchemy"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "2.0.23"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Database Abstraction Library"
optional = false
python-versions = ">=3.7"
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
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Bump all libraries to the latest `ruff` version. (#13350) This version of `ruff` is the one we'll be using to lint the docs and cookbooks (#12677), so I'm making it used everywhere else too.
2023-11-14 21:00:21 +00:00
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Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
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Bump all libraries to the latest `ruff` version. (#13350) This version of `ruff` is the one we'll be using to lint the docs and cookbooks (#12677), so I'm making it used everywhere else too.
2023-11-14 21:00:21 +00:00
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Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
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{file = "SQLAlchemy-2.0.23-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:616fe7bcff0a05098f64b4478b78ec2dfa03225c23734d83d6c169eb41a93e55"},
{file = "SQLAlchemy-2.0.23-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:0e680527245895aba86afbd5bef6c316831c02aa988d1aad83c47ffe92655e74"},
Bump all libraries to the latest `ruff` version. (#13350) This version of `ruff` is the one we'll be using to lint the docs and cookbooks (#12677), so I'm making it used everywhere else too.
2023-11-14 21:00:21 +00:00
{file = "SQLAlchemy-2.0.23-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9585b646ffb048c0250acc7dad92536591ffe35dba624bb8fd9b471e25212a35"},
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
{file = "SQLAlchemy-2.0.23-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4895a63e2c271ffc7a81ea424b94060f7b3b03b4ea0cd58ab5bb676ed02f4221"},
Bump all libraries to the latest `ruff` version. (#13350) This version of `ruff` is the one we'll be using to lint the docs and cookbooks (#12677), so I'm making it used everywhere else too.
2023-11-14 21:00:21 +00:00
{file = "SQLAlchemy-2.0.23-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:cc1d21576f958c42d9aec68eba5c1a7d715e5fc07825a629015fe8e3b0657fb0"},
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
{file = "SQLAlchemy-2.0.23-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:967c0b71156f793e6662dd839da54f884631755275ed71f1539c95bbada9aaab"},
{file = "SQLAlchemy-2.0.23-cp39-cp39-win32.whl", hash = "sha256:0a8c6aa506893e25a04233bc721c6b6cf844bafd7250535abb56cb6cc1368884"},
{file = "SQLAlchemy-2.0.23-cp39-cp39-win_amd64.whl", hash = "sha256:f3420d00d2cb42432c1d0e44540ae83185ccbbc67a6054dcc8ab5387add6620b"},
{file = "SQLAlchemy-2.0.23-py3-none-any.whl", hash = "sha256:31952bbc527d633b9479f5f81e8b9dfada00b91d6baba021a869095f1a97006d"},
{file = "SQLAlchemy-2.0.23.tar.gz", hash = "sha256:c1bda93cbbe4aa2aa0aa8655c5aeda505cd219ff3e8da91d1d329e143e4aff69"},
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
]
[package.dependencies]
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
greenlet = {version = "!=0.4.17", markers = "platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\""}
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
typing-extensions = ">=4.2.0"
[package.extras]
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
aiomysql = ["aiomysql (>=0.2.0)", "greenlet (!=0.4.17)"]
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
aioodbc = ["aioodbc", "greenlet (!=0.4.17)"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
aiosqlite = ["aiosqlite", "greenlet (!=0.4.17)", "typing-extensions (!=3.10.0.1)"]
asyncio = ["greenlet (!=0.4.17)"]
asyncmy = ["asyncmy (>=0.2.3,!=0.2.4,!=0.2.6)", "greenlet (!=0.4.17)"]
mariadb-connector = ["mariadb (>=1.0.1,!=1.1.2,!=1.1.5)"]
mssql = ["pyodbc"]
mssql-pymssql = ["pymssql"]
mssql-pyodbc = ["pyodbc"]
mypy = ["mypy (>=0.910)"]
mysql = ["mysqlclient (>=1.4.0)"]
mysql-connector = ["mysql-connector-python"]
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
oracle = ["cx-oracle (>=8)"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
oracle-oracledb = ["oracledb (>=1.0.1)"]
postgresql = ["psycopg2 (>=2.7)"]
postgresql-asyncpg = ["asyncpg", "greenlet (!=0.4.17)"]
postgresql-pg8000 = ["pg8000 (>=1.29.1)"]
postgresql-psycopg = ["psycopg (>=3.0.7)"]
postgresql-psycopg2binary = ["psycopg2-binary"]
postgresql-psycopg2cffi = ["psycopg2cffi"]
postgresql-psycopgbinary = ["psycopg[binary] (>=3.0.7)"]
pymysql = ["pymysql"]
sqlcipher = ["sqlcipher3-binary"]
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[[package]]
name = "srsly"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "2.4.8"
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
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Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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2023-07-21 17:36:28 +00:00
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update experimental poetry lock
2023-09-11 16:20:19 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
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2023-07-21 17:36:28 +00:00
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Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
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name = "thinc"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "8.2.1"
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
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Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
]
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blis = ">=0.7.8,<0.8.0"
catalogue = ">=2.0.4,<2.1.0"
confection = ">=0.0.1,<1.0.0"
cymem = ">=2.0.2,<2.1.0"
murmurhash = ">=1.0.2,<1.1.0"
numpy = [
{version = ">=1.15.0", markers = "python_version < \"3.9\""},
{version = ">=1.19.0", markers = "python_version >= \"3.9\""},
]
packaging = ">=20.0"
preshed = ">=3.0.2,<3.1.0"
pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<3.0.0"
setuptools = "*"
srsly = ">=2.4.0,<3.0.0"
wasabi = ">=0.8.1,<1.2.0"
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cuda = ["cupy (>=5.0.0b4)"]
cuda-autodetect = ["cupy-wheel (>=11.0.0)"]
cuda100 = ["cupy-cuda100 (>=5.0.0b4)"]
cuda101 = ["cupy-cuda101 (>=5.0.0b4)"]
cuda102 = ["cupy-cuda102 (>=5.0.0b4)"]
cuda110 = ["cupy-cuda110 (>=5.0.0b4)"]
cuda111 = ["cupy-cuda111 (>=5.0.0b4)"]
cuda112 = ["cupy-cuda112 (>=5.0.0b4)"]
cuda113 = ["cupy-cuda113 (>=5.0.0b4)"]
cuda114 = ["cupy-cuda114 (>=5.0.0b4)"]
cuda115 = ["cupy-cuda115 (>=5.0.0b4)"]
cuda116 = ["cupy-cuda116 (>=5.0.0b4)"]
cuda117 = ["cupy-cuda117 (>=5.0.0b4)"]
cuda11x = ["cupy-cuda11x (>=11.0.0)"]
cuda80 = ["cupy-cuda80 (>=5.0.0b4)"]
cuda90 = ["cupy-cuda90 (>=5.0.0b4)"]
cuda91 = ["cupy-cuda91 (>=5.0.0b4)"]
cuda92 = ["cupy-cuda92 (>=5.0.0b4)"]
datasets = ["ml-datasets (>=0.2.0,<0.3.0)"]
mxnet = ["mxnet (>=1.5.1,<1.6.0)"]
tensorflow = ["tensorflow (>=2.0.0,<2.6.0)"]
torch = ["torch (>=1.6.0)"]
update experimental poetry lock
2023-09-11 16:20:19 +00:00
[[package]]
name = "threadpoolctl"
version = "3.2.0"
description = "threadpoolctl"
optional = true
python-versions = ">=3.8"
files = [
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "tinycss2"
version = "1.2.1"
description = "A tiny CSS parser"
optional = false
python-versions = ">=3.7"
files = [
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[package.dependencies]
webencodings = ">=0.4"
[package.extras]
doc = ["sphinx", "sphinx_rtd_theme"]
test = ["flake8", "isort", "pytest"]
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[[package]]
name = "tldextract"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "5.1.0"
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
description = "Accurately separates a URL's subdomain, domain, and public suffix, using the Public Suffix List (PSL). By default, this includes the public ICANN TLDs and their exceptions. You can optionally support the Public Suffix List's private domains as well."
optional = true
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
python-versions = ">=3.8"
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
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Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
]
[package.dependencies]
filelock = ">=3.0.8"
idna = "*"
requests = ">=2.1.0"
requests-file = ">=1.4"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
[package.extras]
testing = ["black", "mypy", "pytest", "pytest-gitignore", "pytest-mock", "responses", "ruff", "tox", "types-filelock", "types-requests"]
update experimental poetry lock
2023-09-11 16:20:19 +00:00
[[package]]
name = "tokenizers"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "0.14.1"
merge
2023-10-06 01:09:35 +00:00
description = ""
update experimental poetry lock
2023-09-11 16:20:19 +00:00
optional = true
merge
2023-10-06 01:09:35 +00:00
python-versions = ">=3.7"
update experimental poetry lock
2023-09-11 16:20:19 +00:00
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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update experimental poetry lock
2023-09-11 16:20:19 +00:00
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merge
2023-10-06 01:09:35 +00:00
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update experimental poetry lock
2023-09-11 16:20:19 +00:00
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merge
2023-10-06 01:09:35 +00:00
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update experimental poetry lock
2023-09-11 16:20:19 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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update experimental poetry lock
2023-09-11 16:20:19 +00:00
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merge
2023-10-06 01:09:35 +00:00
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update experimental poetry lock
2023-09-11 16:20:19 +00:00
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update experimental poetry lock
2023-09-11 16:20:19 +00:00
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merge
2023-10-06 01:09:35 +00:00
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update experimental poetry lock
2023-09-11 16:20:19 +00:00
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merge
2023-10-06 01:09:35 +00:00
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update experimental poetry lock
2023-09-11 16:20:19 +00:00
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merge
2023-10-06 01:09:35 +00:00
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update experimental poetry lock
2023-09-11 16:20:19 +00:00
]
[package.dependencies]
numpy = "*"
pillow = ">=5.3.0,<8.3.dev0 || >=8.4.dev0"
requests = "*"
merge
2023-10-06 01:09:35 +00:00
torch = "2.1.0"
update experimental poetry lock
2023-09-11 16:20:19 +00:00
[package.extras]
scipy = ["scipy"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "tornado"
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
version = "6.3.3"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
optional = false
python-versions = ">= 3.8"
files = [
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[[package]]
name = "tqdm"
version = "4.66.1"
description = "Fast, Extensible Progress Meter"
optional = true
python-versions = ">=3.7"
files = [
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colorama = {version = "*", markers = "platform_system == \"Windows\""}
[package.extras]
dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
notebook = ["ipywidgets (>=6)"]
slack = ["slack-sdk"]
telegram = ["requests"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "traitlets"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "5.13.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Traitlets Python configuration system"
optional = false
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
python-versions = ">=3.8"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
{file = "traitlets-5.13.0-py3-none-any.whl", hash = "sha256:baf991e61542da48fe8aef8b779a9ea0aa38d8a54166ee250d5af5ecf4486619"},
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.extras]
docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
test = ["argcomplete (>=3.0.3)", "mypy (>=1.6.0)", "pre-commit", "pytest (>=7.0,<7.5)", "pytest-mock", "pytest-mypy-testing"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
update experimental poetry lock
2023-09-11 16:20:19 +00:00
[[package]]
name = "transformers"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "4.35.0"
update experimental poetry lock
2023-09-11 16:20:19 +00:00
description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
optional = true
python-versions = ">=3.8.0"
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
{file = "transformers-4.35.0-py3-none-any.whl", hash = "sha256:45aa9370d7d9ba1c43e6bfa04d7f8b61238497d4b646e573fd95e597fe4040ff"},
{file = "transformers-4.35.0.tar.gz", hash = "sha256:e4b41763f651282fc979348d3aa148244387ddc9165f4b18455798c770ae23b9"},
update experimental poetry lock
2023-09-11 16:20:19 +00:00
]
[package.dependencies]
filelock = "*"
merge
2023-10-06 01:09:35 +00:00
huggingface-hub = ">=0.16.4,<1.0"
update experimental poetry lock
2023-09-11 16:20:19 +00:00
numpy = ">=1.17"
packaging = ">=20.0"
pyyaml = ">=5.1"
regex = "!=2019.12.17"
requests = "*"
safetensors = ">=0.3.1"
merge
2023-10-06 01:09:35 +00:00
tokenizers = ">=0.14,<0.15"
update experimental poetry lock
2023-09-11 16:20:19 +00:00
tqdm = ">=4.27"
[package.extras]
accelerate = ["accelerate (>=0.20.3)"]
agents = ["Pillow (<10.0.0)", "accelerate (>=0.20.3)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch (>=1.10,!=1.12.0)"]
merge
2023-10-06 01:09:35 +00:00
all = ["Pillow (<10.0.0)", "accelerate (>=0.20.3)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune]", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx", "timm", "tokenizers (>=0.14,<0.15)", "torch (>=1.10,!=1.12.0)", "torchaudio", "torchvision"]
update experimental poetry lock
2023-09-11 16:20:19 +00:00
audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
codecarbon = ["codecarbon (==1.2.0)"]
deepspeed = ["accelerate (>=0.20.3)", "deepspeed (>=0.9.3)"]
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.20.3)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "optuna", "parameterized", "protobuf", "psutil", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
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dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (<10.0.0)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.14,<0.15)", "urllib3 (<2.0.0)"]
dev-torch = ["GitPython (<3.1.19)", "Pillow (<10.0.0)", "accelerate (>=0.20.3)", "beautifulsoup4", "black (>=23.1,<24.0)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "ray[tune]", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm", "tokenizers (>=0.14,<0.15)", "torch (>=1.10,!=1.12.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
merge
2023-10-06 01:09:35 +00:00
docs = ["Pillow (<10.0.0)", "accelerate (>=0.20.3)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "hf-doc-builder", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune]", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx", "timm", "tokenizers (>=0.14,<0.15)", "torch (>=1.10,!=1.12.0)", "torchaudio", "torchvision"]
update experimental poetry lock
2023-09-11 16:20:19 +00:00
docs-specific = ["hf-doc-builder"]
flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)"]
flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
ftfy = ["ftfy"]
integrations = ["optuna", "ray[tune]", "sigopt"]
ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
modelcreation = ["cookiecutter (==1.7.3)"]
natten = ["natten (>=0.14.6)"]
onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
optuna = ["optuna"]
quality = ["GitPython (<3.1.19)", "black (>=23.1,<24.0)", "datasets (!=2.5.0)", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "ruff (>=0.0.241,<=0.0.259)", "urllib3 (<2.0.0)"]
ray = ["ray[tune]"]
retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
sagemaker = ["sagemaker (>=2.31.0)"]
sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
serving = ["fastapi", "pydantic (<2)", "starlette", "uvicorn"]
sigopt = ["sigopt"]
sklearn = ["scikit-learn"]
speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
testing = ["GitPython (<3.1.19)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "tensorboard", "timeout-decorator"]
update experimental poetry lock
2023-09-11 16:20:19 +00:00
tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx"]
tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx"]
tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
timm = ["timm"]
merge
2023-10-06 01:09:35 +00:00
tokenizers = ["tokenizers (>=0.14,<0.15)"]
update experimental poetry lock
2023-09-11 16:20:19 +00:00
torch = ["accelerate (>=0.20.3)", "torch (>=1.10,!=1.12.0)"]
torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
torch-vision = ["Pillow (<10.0.0)", "torchvision"]
merge
2023-10-06 01:09:35 +00:00
torchhub = ["filelock", "huggingface-hub (>=0.16.4,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.14,<0.15)", "torch (>=1.10,!=1.12.0)", "tqdm (>=4.27)"]
update experimental poetry lock
2023-09-11 16:20:19 +00:00
video = ["av (==9.2.0)", "decord (==0.6.0)"]
vision = ["Pillow (<10.0.0)"]
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[[package]]
name = "typer"
version = "0.9.0"
description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
optional = true
python-versions = ">=3.6"
files = [
{file = "typer-0.9.0-py3-none-any.whl", hash = "sha256:5d96d986a21493606a358cae4461bd8cdf83cbf33a5aa950ae629ca3b51467ee"},
{file = "typer-0.9.0.tar.gz", hash = "sha256:50922fd79aea2f4751a8e0408ff10d2662bd0c8bbfa84755a699f3bada2978b2"},
]
[package.dependencies]
click = ">=7.1.1,<9.0.0"
typing-extensions = ">=3.7.4.3"
[package.extras]
all = ["colorama (>=0.4.3,<0.5.0)", "rich (>=10.11.0,<14.0.0)", "shellingham (>=1.3.0,<2.0.0)"]
dev = ["autoflake (>=1.3.1,<2.0.0)", "flake8 (>=3.8.3,<4.0.0)", "pre-commit (>=2.17.0,<3.0.0)"]
doc = ["cairosvg (>=2.5.2,<3.0.0)", "mdx-include (>=1.4.1,<2.0.0)", "mkdocs (>=1.1.2,<2.0.0)", "mkdocs-material (>=8.1.4,<9.0.0)", "pillow (>=9.3.0,<10.0.0)"]
test = ["black (>=22.3.0,<23.0.0)", "coverage (>=6.2,<7.0)", "isort (>=5.0.6,<6.0.0)", "mypy (==0.910)", "pytest (>=4.4.0,<8.0.0)", "pytest-cov (>=2.10.0,<5.0.0)", "pytest-sugar (>=0.9.4,<0.10.0)", "pytest-xdist (>=1.32.0,<4.0.0)", "rich (>=10.11.0,<14.0.0)", "shellingham (>=1.3.0,<2.0.0)"]
remove CVEs (#8092) This PR aims to move all code with CVEs into `langchain.experimental`. Note that we are NOT yet removing from the core `langchain` package - we will give people a week to migrate here. See MIGRATE.md for how to migrate Zero changes to functionality Vulnerabilities this addresses: PALChain: - https://security.snyk.io/vuln/SNYK-PYTHON-LANGCHAIN-5752409 - https://security.snyk.io/vuln/SNYK-PYTHON-LANGCHAIN-5759265 SQLDatabaseChain - https://security.snyk.io/vuln/SNYK-PYTHON-LANGCHAIN-5759268 `load_prompt` (Python files only) - https://security.snyk.io/vuln/SNYK-PYTHON-LANGCHAIN-5725807
2023-07-21 20:32:39 +00:00
[[package]]
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
name = "types-python-dateutil"
version = "2.8.19.14"
description = "Typing stubs for python-dateutil"
remove CVEs (#8092) This PR aims to move all code with CVEs into `langchain.experimental`. Note that we are NOT yet removing from the core `langchain` package - we will give people a week to migrate here. See MIGRATE.md for how to migrate Zero changes to functionality Vulnerabilities this addresses: PALChain: - https://security.snyk.io/vuln/SNYK-PYTHON-LANGCHAIN-5752409 - https://security.snyk.io/vuln/SNYK-PYTHON-LANGCHAIN-5759265 SQLDatabaseChain - https://security.snyk.io/vuln/SNYK-PYTHON-LANGCHAIN-5759268 `load_prompt` (Python files only) - https://security.snyk.io/vuln/SNYK-PYTHON-LANGCHAIN-5725807
2023-07-21 20:32:39 +00:00
optional = false
python-versions = "*"
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
{file = "types-python-dateutil-2.8.19.14.tar.gz", hash = "sha256:1f4f10ac98bb8b16ade9dbee3518d9ace017821d94b057a425b069f834737f4b"},
{file = "types_python_dateutil-2.8.19.14-py3-none-any.whl", hash = "sha256:f977b8de27787639986b4e28963263fd0e5158942b3ecef91b9335c130cb1ce9"},
remove CVEs (#8092) This PR aims to move all code with CVEs into `langchain.experimental`. Note that we are NOT yet removing from the core `langchain` package - we will give people a week to migrate here. See MIGRATE.md for how to migrate Zero changes to functionality Vulnerabilities this addresses: PALChain: - https://security.snyk.io/vuln/SNYK-PYTHON-LANGCHAIN-5752409 - https://security.snyk.io/vuln/SNYK-PYTHON-LANGCHAIN-5759265 SQLDatabaseChain - https://security.snyk.io/vuln/SNYK-PYTHON-LANGCHAIN-5759268 `load_prompt` (Python files only) - https://security.snyk.io/vuln/SNYK-PYTHON-LANGCHAIN-5725807
2023-07-21 20:32:39 +00:00
]
Diffbot Graph Transformer / Neo4j Graph document ingestion (#9979) Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-09-06 20:32:59 +00:00
[[package]]
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
name = "types-pyyaml"
version = "6.0.12.12"
description = "Typing stubs for PyYAML"
Diffbot Graph Transformer / Neo4j Graph document ingestion (#9979) Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-09-06 20:32:59 +00:00
optional = false
python-versions = "*"
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
{file = "types-PyYAML-6.0.12.12.tar.gz", hash = "sha256:334373d392fde0fdf95af5c3f1661885fa10c52167b14593eb856289e1855062"},
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Diffbot Graph Transformer / Neo4j Graph document ingestion (#9979) Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-09-06 20:32:59 +00:00
]
[[package]]
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
name = "types-requests"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "2.31.0.10"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
description = "Typing stubs for requests"
Diffbot Graph Transformer / Neo4j Graph document ingestion (#9979) Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-09-06 20:32:59 +00:00
optional = false
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
python-versions = ">=3.7"
Diffbot Graph Transformer / Neo4j Graph document ingestion (#9979) Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-09-06 20:32:59 +00:00
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
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Diffbot Graph Transformer / Neo4j Graph document ingestion (#9979) Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-09-06 20:32:59 +00:00
]
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
[package.dependencies]
urllib3 = ">=2"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "typing-extensions"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "4.8.0"
description = "Backported and Experimental Type Hints for Python 3.8+"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
optional = false
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
python-versions = ">=3.8"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
{file = "typing_extensions-4.8.0-py3-none-any.whl", hash = "sha256:8f92fc8806f9a6b641eaa5318da32b44d401efaac0f6678c9bc448ba3605faa0"},
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[[package]]
name = "typing-inspect"
version = "0.9.0"
description = "Runtime inspection utilities for typing module."
optional = false
python-versions = "*"
files = [
{file = "typing_inspect-0.9.0-py3-none-any.whl", hash = "sha256:9ee6fc59062311ef8547596ab6b955e1b8aa46242d854bfc78f4f6b0eff35f9f"},
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]
[package.dependencies]
mypy-extensions = ">=0.3.0"
typing-extensions = ">=3.7.4"
[[package]]
name = "uri-template"
version = "1.3.0"
description = "RFC 6570 URI Template Processor"
optional = false
python-versions = ">=3.7"
files = [
{file = "uri-template-1.3.0.tar.gz", hash = "sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7"},
{file = "uri_template-1.3.0-py3-none-any.whl", hash = "sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363"},
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[package.extras]
dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake8-commas", "flake8-comprehensions", "flake8-continuation", "flake8-datetimez", "flake8-docstrings", "flake8-import-order", "flake8-literal", "flake8-modern-annotations", "flake8-noqa", "flake8-pyproject", "flake8-requirements", "flake8-typechecking-import", "flake8-use-fstring", "mypy", "pep8-naming", "types-PyYAML"]
[[package]]
name = "urllib3"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "2.0.7"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "HTTP library with thread-safe connection pooling, file post, and more."
optional = false
python-versions = ">=3.7"
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
{file = "urllib3-2.0.7-py3-none-any.whl", hash = "sha256:fdb6d215c776278489906c2f8916e6e7d4f5a9b602ccbcfdf7f016fc8da0596e"},
{file = "urllib3-2.0.7.tar.gz", hash = "sha256:c97dfde1f7bd43a71c8d2a58e369e9b2bf692d1334ea9f9cae55add7d0dd0f84"},
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.extras]
brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
secure = ["certifi", "cryptography (>=1.9)", "idna (>=2.0.0)", "pyopenssl (>=17.1.0)", "urllib3-secure-extra"]
socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
zstd = ["zstandard (>=0.18.0)"]
update experimental poetry lock
2023-09-11 16:20:19 +00:00
[[package]]
name = "vowpal-wabbit-next"
version = "0.6.0"
description = "Experimental python bindings for VowpalWabbit"
optional = true
python-versions = ">=3.7"
files = [
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{file = "vowpal_wabbit_next-0.6.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c8b7052ce7212fd1cae8ffd966e240c814f3c1df08fd612437d48f0f23e7694c"},
{file = "vowpal_wabbit_next-0.6.0-cp37-cp37m-win_amd64.whl", hash = "sha256:d24d9c380d0e9b41151337c7f9e2a33ec5bfd738fdee9f65c1a40e486234aca3"},
{file = "vowpal_wabbit_next-0.6.0-cp38-cp38-macosx_10_13_universal2.whl", hash = "sha256:0d77a8c55249ec9a7f404939ecc6948db0527e522e8a7ae149ec7cd29b3ade04"},
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]
[package.dependencies]
numpy = "*"
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[[package]]
name = "wasabi"
version = "1.1.2"
description = "A lightweight console printing and formatting toolkit"
optional = true
python-versions = ">=3.6"
files = [
{file = "wasabi-1.1.2-py3-none-any.whl", hash = "sha256:0a3f933c4bf0ed3f93071132c1b87549733256d6c8de6473c5f7ed2e171b5cf9"},
{file = "wasabi-1.1.2.tar.gz", hash = "sha256:1aaef3aceaa32edb9c91330d29d3936c0c39fdb965743549c173cb54b16c30b5"},
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[package.dependencies]
colorama = {version = ">=0.4.6", markers = "sys_platform == \"win32\" and python_version >= \"3.7\""}
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[[package]]
name = "wcwidth"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "0.2.9"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Measures the displayed width of unicode strings in a terminal"
optional = false
python-versions = "*"
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
{file = "wcwidth-0.2.9-py2.py3-none-any.whl", hash = "sha256:9a929bd8380f6cd9571a968a9c8f4353ca58d7cd812a4822bba831f8d685b223"},
{file = "wcwidth-0.2.9.tar.gz", hash = "sha256:a675d1a4a2d24ef67096a04b85b02deeecd8e226f57b5e3a72dbb9ed99d27da8"},
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
[[package]]
name = "weasel"
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
version = "0.3.4"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
description = "Weasel: A small and easy workflow system"
optional = true
python-versions = ">=3.6"
files = [
Update Deps in Experimental (#13029)
2023-11-07 23:15:09 +00:00
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Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
]
[package.dependencies]
cloudpathlib = ">=0.7.0,<0.17.0"
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smart-open = ">=5.2.1,<7.0.0"
srsly = ">=2.4.3,<3.0.0"
typer = ">=0.3.0,<0.10.0"
wasabi = ">=0.9.1,<1.2.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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[[package]]
name = "websocket-client"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "1.6.4"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "WebSocket client for Python with low level API options"
optional = false
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
python-versions = ">=3.8"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.extras]
`poetry lock` the experimental package. (#9478)
2023-08-22 18:09:35 +00:00
docs = ["Sphinx (>=6.0)", "sphinx-rtd-theme (>=1.1.0)"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
optional = ["python-socks", "wsaccel"]
test = ["websockets"]
[[package]]
name = "widgetsnbextension"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "4.0.9"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Jupyter interactive widgets for Jupyter Notebook"
optional = false
python-versions = ">=3.7"
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Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
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Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
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]
[package.dependencies]
idna = ">=2.0"
multidict = ">=4.0"
[[package]]
name = "zipp"
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
version = "3.17.0"
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
description = "Backport of pathlib-compatible object wrapper for zip files"
optional = false
python-versions = ">=3.8"
files = [
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
{file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"},
{file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"},
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
]
[package.extras]
Upgrade experimental package dependencies and use Poetry 1.6.1. (#11339) Part of upgrading our CI to use Poetry 1.6.1.
2023-10-17 01:13:31 +00:00
docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[extras]
update experimental poetry lock
2023-09-11 16:20:19 +00:00
extended-testing = ["faker", "presidio-analyzer", "presidio-anonymizer", "sentence-transformers", "vowpal-wabbit-next"]
Add data anonymizer (#9863) ### Description The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. Anonynization consists of two steps: 1. **Identification:** Identify all data fields that contain personally identifiable information (PII). 2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data. We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`. ### Future works - **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data. - **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object. ### Twitter handle @deepsense_ai / @MaksOpp --------- Co-authored-by: MaksOpp <maks.operlejn@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
Harrison/move experimental (#8084)
2023-07-21 17:36:28 +00:00
[metadata]
lock-version = "2.0"
python-versions = ">=3.8.1,<4.0"
Bump all libraries to the latest `ruff` version. (#13350) This version of `ruff` is the one we'll be using to lint the docs and cookbooks (#12677), so I'm making it used everywhere else too.
2023-11-14 21:00:21 +00:00
content-hash = "b834d2b8bcfb0c10549937841a9c6838ca8fde99d23e6c6deb8a6e3f4f4e43af"
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