2023-08-22 18:09:35 +00:00
# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "aiohttp"
version = "3.8.5"
description = "Async http client/server framework (asyncio)"
optional = false
python-versions = ">=3.6"
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[ 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"
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optional = false
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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 = "blis"
version = "0.7.10"
description = "The Blis BLAS-like linear algebra library, as a self-contained C-extension."
optional = true
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[ package . dependencies ]
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]
[ [ package ] ]
name = "catalogue"
version = "2.0.9"
description = "Super lightweight function registries for your library"
optional = true
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2023-07-21 17:36:28 +00:00
[ [ package ] ]
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2023-08-22 18:09:35 +00:00
version = "2023.7.22"
2023-07-21 17:36:28 +00:00
description = "Python package for providing Mozilla's CA Bundle."
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2023-08-22 18:09:35 +00:00
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2023-07-21 17:36:28 +00:00
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[ package . dependencies ]
pycparser = "*"
[ [ package ] ]
name = "charset-normalizer"
version = "3.2.0"
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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name = "click"
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version = "8.1.7"
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description = "Composable command line interface toolkit"
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2023-07-21 17:36:28 +00:00
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[ package . dependencies ]
colorama = { version = "*" , markers = "platform_system == \"Windows\"" }
[ [ package ] ]
name = "colorama"
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description = "Cross-platform colored terminal text."
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version = "0.1.4"
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[ package . dependencies ]
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traitlets = ">=4"
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[ package . extras ]
lint = [ "black (>=22.6.0)" , "mdformat (>0.7)" , "mdformat-gfm (>=0.3.5)" , "ruff (>=0.0.156)" ]
test = [ "pytest" ]
typing = [ "mypy (>=0.990)" ]
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"
version = "0.1.1"
description = "The sweetest config system for Python"
optional = true
python-versions = ">=3.6"
files = [
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srsly = ">=2.4.0,<3.0.0"
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name = "cymem"
version = "2.0.7"
description = "Manage calls to calloc/free through Cython"
optional = true
python-versions = "*"
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[ [ package ] ]
name = "dataclasses-json"
version = "0.5.9"
description = "Easily serialize dataclasses to and from JSON"
optional = false
python-versions = ">=3.6"
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marshmallow-enum = ">=1.5.1,<2.0.0"
typing-inspect = ">=0.4.0"
[ package . extras ]
dev = [ "flake8" , "hypothesis" , "ipython" , "mypy (>=0.710)" , "portray" , "pytest (>=7.2.0)" , "setuptools" , "simplejson" , "twine" , "types-dataclasses" , "wheel" ]
[ [ package ] ]
name = "debugpy"
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version = "1.6.7.post1"
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description = "An implementation of the Debug Adapter Protocol for Python"
optional = false
python-versions = ">=3.7"
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description = "Decorators for Humans"
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2023-07-21 17:36:28 +00:00
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[ package . extras ]
test = [ "pytest (>=6)" ]
[ [ package ] ]
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version = "1.2.0"
description = "Get the currently executing AST node of a frame, and other information"
optional = false
python-versions = "*"
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[ package . extras ]
tests = [ "asttokens" , "littleutils" , "pytest" , "rich" ]
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"
version = "19.3.1"
description = "Faker is a Python package that generates fake data for you."
optional = true
python-versions = ">=3.8"
files = [
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[ package . dependencies ]
python-dateutil = ">=2.4"
typing-extensions = { version = ">=3.10.0.1" , markers = "python_version <= \"3.8\"" }
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[ [ package ] ]
name = "fastjsonschema"
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version = "2.18.0"
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description = "Fastest Python implementation of JSON schema"
optional = false
python-versions = "*"
files = [
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]
[ 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"
version = "3.12.3"
description = "A platform independent file lock."
optional = true
python-versions = ">=3.8"
files = [
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[ package . dependencies ]
typing-extensions = { version = ">=4.7.1" , markers = "python_version < \"3.11\"" }
[ package . extras ]
docs = [ "furo (>=2023.7.26)" , "sphinx (>=7.1.2)" , "sphinx-autodoc-typehints (>=1.24)" ]
testing = [ "covdefaults (>=2.3)" , "coverage (>=7.3)" , "diff-cover (>=7.7)" , "pytest (>=7.4)" , "pytest-cov (>=4.1)" , "pytest-mock (>=3.11.1)" , "pytest-timeout (>=2.1)" ]
2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "fqdn"
version = "1.5.1"
description = "Validates fully-qualified domain names against RFC 1123, so that they are acceptable to modern bowsers"
optional = false
python-versions = ">=2.7, !=3.0, !=3.1, !=3.2, !=3.3, !=3.4, <4"
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[ [ package ] ]
name = "frozenlist"
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python-versions = ">=3.8"
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[ [ package ] ]
name = "greenlet"
version = "2.0.2"
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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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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 = "murmurhash"
version = "1.0.9"
description = "Cython bindings for MurmurHash"
optional = true
python-versions = ">=3.6"
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2023-07-21 17:36:28 +00:00
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name = "mypy"
version = "0.991"
description = "Optional static typing for Python"
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typing-extensions = ">=3.10"
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reports = [ "lxml" ]
[ [ package ] ]
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[ [ package ] ]
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2023-08-22 18:09:35 +00:00
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2023-07-21 17:36:28 +00:00
nbformat = ">=5.1"
traitlets = ">=5.4"
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dev = [ "pre-commit" ]
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[ [ package ] ]
name = "nbconvert"
2023-08-22 18:09:35 +00:00
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2023-07-21 17:36:28 +00:00
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beautifulsoup4 = "*"
bleach = "!=5.0.0"
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jinja2 = ">=3.0"
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pandocfilters = ">=1.4.1"
pygments = ">=2.4.1"
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traitlets = ">=5.1"
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name = "pathspec"
2023-08-22 18:09:35 +00:00
version = "0.11.2"
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description = "Utility library for gitignore style pattern matching of file paths."
optional = false
python-versions = ">=3.7"
files = [
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{ file = "pathspec-0.11.2-py3-none-any.whl" , hash = "sha256:1d6ed233af05e679efb96b1851550ea95bbb64b7c490b0f5aa52996c11e92a20" } ,
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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 = "pathy"
version = "0.10.2"
description = "pathlib.Path subclasses for local and cloud bucket storage"
optional = true
python-versions = ">= 3.6"
files = [
{ file = "pathy-0.10.2-py3-none-any.whl" , hash = "sha256:681bc98dbff28e7de3e50efa8246910f727e8ac254c4318c47ce341f7c1ce21d" } ,
{ file = "pathy-0.10.2.tar.gz" , hash = "sha256:79c572ab7fed84dc46837346edae58565992d0477a789cd4691a41d8eab9917d" } ,
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[ package . dependencies ]
smart-open = ">=5.2.1,<7.0.0"
typer = ">=0.3.0,<1.0.0"
[ package . extras ]
all = [ "azure-storage-blob" , "boto3" , "google-cloud-storage (>=1.26.0,<2.0.0)" , "mock" , "pytest" , "pytest-coverage" , "typer-cli" ]
azure = [ "azure-storage-blob" ]
gcs = [ "google-cloud-storage (>=1.26.0,<2.0.0)" ]
s3 = [ "boto3" ]
test = [ "mock" , "pytest" , "pytest-coverage" , "typer-cli" ]
2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "pexpect"
version = "4.8.0"
description = "Pexpect allows easy control of interactive console applications."
optional = false
python-versions = "*"
files = [
{ file = "pexpect-4.8.0-py2.py3-none-any.whl" , hash = "sha256:0b48a55dcb3c05f3329815901ea4fc1537514d6ba867a152b581d69ae3710937" } ,
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]
[ package . dependencies ]
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"
version = "8.13.19"
description = "Python version of Google's common library for parsing, formatting, storing and validating international phone numbers."
optional = true
python-versions = "*"
files = [
{ file = "phonenumbers-8.13.19-py2.py3-none-any.whl" , hash = "sha256:ba542f20f6dc83be8f127f240f9b5b7e7c1dec42aceff1879400d4dc0c781d81" } ,
{ file = "phonenumbers-8.13.19.tar.gz" , hash = "sha256:38180247697240ccedd74dec4bfbdbc22bb108b9c5f991f270ca3e41395e6f96" } ,
]
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 = [
{ file = "pickleshare-0.7.5-py2.py3-none-any.whl" , hash = "sha256:9649af414d74d4df115d5d718f82acb59c9d418196b7b4290ed47a12ce62df56" } ,
{ file = "pickleshare-0.7.5.tar.gz" , hash = "sha256:87683d47965c1da65cdacaf31c8441d12b8044cdec9aca500cd78fc2c683afca" } ,
]
[ [ package ] ]
name = "pkgutil-resolve-name"
version = "1.3.10"
description = "Resolve a name to an object."
optional = false
python-versions = ">=3.6"
files = [
{ file = "pkgutil_resolve_name-1.3.10-py3-none-any.whl" , hash = "sha256:ca27cc078d25c5ad71a9de0a7a330146c4e014c2462d9af19c6b828280649c5e" } ,
{ file = "pkgutil_resolve_name-1.3.10.tar.gz" , hash = "sha256:357d6c9e6a755653cfd78893817c0853af365dd51ec97f3d358a819373bbd174" } ,
]
[ [ package ] ]
name = "platformdirs"
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version = "3.10.0"
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description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
optional = false
python-versions = ">=3.7"
files = [
2023-08-22 18:09:35 +00:00
{ file = "platformdirs-3.10.0-py3-none-any.whl" , hash = "sha256:d7c24979f292f916dc9cbf8648319032f551ea8c49a4c9bf2fb556a02070ec1d" } ,
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2023-07-21 17:36:28 +00:00
]
[ package . extras ]
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)" ]
test = [ "appdirs (==1.4.4)" , "covdefaults (>=2.3)" , "pytest (>=7.4)" , "pytest-cov (>=4.1)" , "pytest-mock (>=3.11.1)" ]
2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "pluggy"
version = "1.2.0"
description = "plugin and hook calling mechanisms for python"
optional = false
python-versions = ">=3.7"
files = [
{ file = "pluggy-1.2.0-py3-none-any.whl" , hash = "sha256:c2fd55a7d7a3863cba1a013e4e2414658b1d07b6bc57b3919e0c63c9abb99849" } ,
{ file = "pluggy-1.2.0.tar.gz" , hash = "sha256:d12f0c4b579b15f5e054301bb226ee85eeeba08ffec228092f8defbaa3a4c4b3" } ,
]
[ package . extras ]
dev = [ "pre-commit" , "tox" ]
testing = [ "pytest" , "pytest-benchmark" ]
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"
version = "3.0.8"
description = "Cython hash table that trusts the keys are pre-hashed"
optional = true
python-versions = ">=3.6"
files = [
{ file = "preshed-3.0.8-cp310-cp310-macosx_10_9_x86_64.whl" , hash = "sha256:ea4b6df8ef7af38e864235256793bc3056e9699d991afcf6256fa298858582fc" } ,
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[ package . dependencies ]
cymem = ">=2.0.2,<2.1.0"
murmurhash = ">=0.28.0,<1.1.0"
[ [ package ] ]
name = "presidio-analyzer"
version = "2.2.33"
description = "Presidio analyzer package"
optional = true
python-versions = "*"
files = [
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[ package . dependencies ]
phonenumbers = ">=8.12"
pyyaml = "*"
regex = "*"
spacy = ">=3.4.4"
tldextract = "*"
[ package . extras ]
transformers = [ "torch" , "transformers" ]
[ [ package ] ]
name = "presidio-anonymizer"
version = "2.2.33"
description = "Persidio Anonymizer package - replaces analyzed text with desired values."
optional = true
python-versions = ">=3.5"
files = [
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[ package . dependencies ]
pycryptodome = ">=3.10.1"
2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "prometheus-client"
version = "0.17.1"
description = "Python client for the Prometheus monitoring system."
optional = false
python-versions = ">=3.6"
files = [
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[ package . extras ]
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"
version = "5.9.5"
description = "Cross-platform lib for process and system monitoring in Python."
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
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[ package . extras ]
test = [ "enum34" , "ipaddress" , "mock" , "pywin32" , "wmi" ]
[ [ package ] ]
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tests = [ "pytest" ]
[ [ package ] ]
name = "pycparser"
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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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2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "pydantic"
2023-08-22 18:09:35 +00:00
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2023-07-21 17:36:28 +00:00
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[ package . extras ]
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[ [ package ] ]
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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"
version = "2023.8.8"
description = "Alternative regular expression module, to replace re."
optional = true
python-versions = ">=3.6"
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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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2023-07-21 17:36:28 +00:00
[ [ package ] ]
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[ [ package ] ]
name = "ruff"
version = "0.0.249"
description = "An extremely fast Python linter, written in Rust."
optional = false
python-versions = ">=3.7"
files = [
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[ [ package ] ]
name = "send2trash"
version = "1.8.2"
description = "Send file to trash natively under Mac OS X, Windows and Linux"
optional = false
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
files = [
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]
[ package . extras ]
nativelib = [ "pyobjc-framework-Cocoa" , "pywin32" ]
objc = [ "pyobjc-framework-Cocoa" ]
win32 = [ "pywin32" ]
[ [ package ] ]
name = "setuptools"
version = "67.8.0"
description = "Easily download, build, install, upgrade, and uninstall Python packages"
optional = false
python-versions = ">=3.7"
files = [
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]
[ package . extras ]
docs = [ "furo" , "jaraco.packaging (>=9)" , "jaraco.tidelift (>=1.4)" , "pygments-github-lexers (==0.0.5)" , "rst.linker (>=1.9)" , "sphinx (>=3.5)" , "sphinx-favicon" , "sphinx-hoverxref (<2)" , "sphinx-inline-tabs" , "sphinx-lint" , "sphinx-notfound-page (==0.8.3)" , "sphinx-reredirects" , "sphinxcontrib-towncrier" ]
testing = [ "build[virtualenv]" , "filelock (>=3.4.0)" , "flake8-2020" , "ini2toml[lite] (>=0.9)" , "jaraco.envs (>=2.2)" , "jaraco.path (>=3.2.0)" , "pip (>=19.1)" , "pip-run (>=8.8)" , "pytest (>=6)" , "pytest-black (>=0.3.7)" , "pytest-checkdocs (>=2.4)" , "pytest-cov" , "pytest-enabler (>=1.3)" , "pytest-mypy (>=0.9.1)" , "pytest-perf" , "pytest-ruff" , "pytest-timeout" , "pytest-xdist" , "tomli-w (>=1.0.0)" , "virtualenv (>=13.0.0)" , "wheel" ]
testing-integration = [ "build[virtualenv]" , "filelock (>=3.4.0)" , "jaraco.envs (>=2.2)" , "jaraco.path (>=3.2.0)" , "pytest" , "pytest-enabler" , "pytest-xdist" , "tomli" , "virtualenv (>=13.0.0)" , "wheel" ]
[ [ package ] ]
name = "six"
version = "1.16.0"
description = "Python 2 and 3 compatibility utilities"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
files = [
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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"
version = "6.3.0"
description = "Utils for streaming large files (S3, HDFS, GCS, Azure Blob Storage, gzip, bz2...)"
optional = true
python-versions = ">=3.6,<4.0"
files = [
{ file = "smart_open-6.3.0-py3-none-any.whl" , hash = "sha256:b4c9ae193ad6d3e7add50944b86afa0d150bd821ab8ec21edb26d9a06b66f6a8" } ,
{ file = "smart_open-6.3.0.tar.gz" , hash = "sha256:d5238825fe9a9340645fac3d75b287c08fbb99fb2b422477de781c9f5f09e019" } ,
]
[ 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" ]
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 = [
{ file = "sniffio-1.3.0-py3-none-any.whl" , hash = "sha256:eecefdce1e5bbfb7ad2eeaabf7c1eeb404d7757c379bd1f7e5cce9d8bf425384" } ,
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]
[ [ package ] ]
name = "soupsieve"
version = "2.4.1"
description = "A modern CSS selector implementation for Beautiful Soup."
optional = false
python-versions = ">=3.7"
files = [
{ file = "soupsieve-2.4.1-py3-none-any.whl" , hash = "sha256:1c1bfee6819544a3447586c889157365a27e10d88cde3ad3da0cf0ddf646feb8" } ,
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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 = "spacy"
version = "3.6.1"
description = "Industrial-strength Natural Language Processing (NLP) in Python"
optional = true
python-versions = ">=3.6"
files = [
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cymem = ">=2.0.2,<2.1.0"
jinja2 = "*"
langcodes = ">=3.2.0,<4.0.0"
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numpy = ">=1.15.0"
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requests = ">=2.13.0,<3.0.0"
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spacy-loggers = ">=1.0.0,<2.0.0"
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tqdm = ">=4.38.0,<5.0.0"
typer = ">=0.3.0,<0.10.0"
wasabi = ">=0.9.1,<1.2.0"
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apple = [ "thinc-apple-ops (>=0.1.0.dev0,<1.0.0)" ]
cuda = [ "cupy (>=5.0.0b4,<13.0.0)" ]
cuda-autodetect = [ "cupy-wheel (>=11.0.0,<13.0.0)" ]
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[ [ package ] ]
name = "spacy-legacy"
version = "3.0.12"
description = "Legacy registered functions for spaCy backwards compatibility"
optional = true
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[ [ package ] ]
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2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "sqlalchemy"
2023-08-22 18:09:35 +00:00
version = "2.0.20"
2023-07-21 17:36:28 +00:00
description = "Database Abstraction Library"
optional = false
python-versions = ">=3.7"
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2023-08-22 18:09:35 +00:00
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[ package . dependencies ]
greenlet = { version = "!=0.4.17" , markers = "platform_machine == \"win32\" or platform_machine == \"WIN32\" or platform_machine == \"AMD64\" or platform_machine == \"amd64\" or platform_machine == \"x86_64\" or platform_machine == \"ppc64le\" or platform_machine == \"aarch64\"" }
2023-07-21 17:36:28 +00:00
typing-extensions = ">=4.2.0"
[ package . extras ]
2023-08-22 18:09:35 +00:00
aiomysql = [ "aiomysql (>=0.2.0)" , "greenlet (!=0.4.17)" ]
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" ]
oracle = [ "cx-oracle (>=7)" ]
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"
version = "2.4.7"
description = "Modern high-performance serialization utilities for Python"
optional = true
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2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "stack-data"
version = "0.6.2"
description = "Extract data from python stack frames and tracebacks for informative displays"
optional = false
python-versions = "*"
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tests = [ "cython" , "littleutils" , "pygments" , "pytest" , "typeguard" ]
[ [ package ] ]
name = "tenacity"
2023-08-22 18:09:35 +00:00
version = "8.2.3"
2023-07-21 17:36:28 +00:00
description = "Retry code until it succeeds"
optional = false
2023-08-22 18:09:35 +00:00
python-versions = ">=3.7"
2023-07-21 17:36:28 +00:00
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doc = [ "reno" , "sphinx" , "tornado (>=4.5)" ]
[ [ package ] ]
name = "terminado"
version = "0.17.1"
description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library."
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docs = [ "myst-parser" , "pydata-sphinx-theme" , "sphinx" ]
test = [ "pre-commit" , "pytest (>=7.0)" , "pytest-timeout" ]
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 = "thinc"
version = "8.1.12"
description = "A refreshing functional take on deep learning, compatible with your favorite libraries"
optional = true
python-versions = ">=3.6"
files = [
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[ package . dependencies ]
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"
[ package . extras ]
cuda = [ "cupy (>=5.0.0b4)" ]
cuda-autodetect = [ "cupy-wheel (>=11.0.0)" ]
cuda100 = [ "cupy-cuda100 (>=5.0.0b4)" ]
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cuda80 = [ "cupy-cuda80 (>=5.0.0b4)" ]
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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)" ]
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"
version = "3.4.4"
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
python-versions = ">=3.7"
files = [
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idna = "*"
requests = ">=2.1.0"
requests-file = ">=1.4"
2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "tomli"
version = "2.0.1"
description = "A lil' TOML parser"
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name = "tornado"
2023-08-22 18:09:35 +00:00
version = "6.3.3"
2023-07-21 17:36:28 +00:00
description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
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2023-08-22 18:09:35 +00:00
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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 = [
{ file = "tqdm-4.66.1-py3-none-any.whl" , hash = "sha256:d302b3c5b53d47bce91fea46679d9c3c6508cf6332229aa1e7d8653723793386" } ,
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[ package . dependencies ]
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" ]
2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "traitlets"
version = "5.9.0"
description = "Traitlets Python configuration system"
optional = false
python-versions = ">=3.7"
files = [
{ file = "traitlets-5.9.0-py3-none-any.whl" , hash = "sha256:9e6ec080259b9a5940c797d58b613b5e31441c2257b87c2e795c5228ae80d2d8" } ,
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docs = [ "myst-parser" , "pydata-sphinx-theme" , "sphinx" ]
test = [ "argcomplete (>=2.0)" , "pre-commit" , "pytest" , "pytest-mock" ]
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 = [
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2023-07-21 20:32:39 +00:00
[ [ package ] ]
name = "types-pyyaml"
version = "6.0.12.11"
description = "Typing stubs for PyYAML"
optional = false
python-versions = "*"
files = [
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2023-07-21 17:36:28 +00:00
[ [ package ] ]
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files = [
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typing-extensions = ">=3.7.4"
[ [ package ] ]
name = "uri-template"
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description = "RFC 6570 URI Template Processor"
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files = [
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[ [ package ] ]
name = "urllib3"
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description = "HTTP library with thread-safe connection pooling, file post, and more."
optional = false
python-versions = ">=3.7"
files = [
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socks = [ "pysocks (>=1.5.6,!=1.5.7,<2.0)" ]
zstd = [ "zstandard (>=0.18.0)" ]
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 = "wasabi"
version = "1.1.2"
description = "A lightweight console printing and formatting toolkit"
optional = true
python-versions = ">=3.6"
files = [
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2023-07-21 17:36:28 +00:00
[ [ package ] ]
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2023-07-21 17:36:28 +00:00
description = "WebSocket client for Python with low level API options"
optional = false
2023-08-22 18:09:35 +00:00
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2023-07-21 17:36:28 +00:00
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2023-08-22 18:09:35 +00:00
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2023-07-21 17:36:28 +00:00
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2023-08-22 18:09:35 +00:00
docs = [ "Sphinx (>=6.0)" , "sphinx-rtd-theme (>=1.1.0)" ]
2023-07-21 17:36:28 +00:00
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]
[ package . dependencies ]
idna = ">=2.0"
multidict = ">=4.0"
[ [ package ] ]
name = "zipp"
version = "3.16.2"
description = "Backport of pathlib-compatible object wrapper for zip files"
optional = false
python-versions = ">=3.8"
files = [
{ file = "zipp-3.16.2-py3-none-any.whl" , hash = "sha256:679e51dd4403591b2d6838a48de3d283f3d188412a9782faadf845f298736ba0" } ,
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]
[ package . extras ]
docs = [ "furo" , "jaraco.packaging (>=9.3)" , "jaraco.tidelift (>=1.4)" , "rst.linker (>=1.9)" , "sphinx (>=3.5)" , "sphinx-lint" ]
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 ]
extended-testing = [ "faker" , "presidio-analyzer" , "presidio-anonymizer" ]
2023-07-21 17:36:28 +00:00
[ metadata ]
lock-version = "2.0"
python-versions = ">=3.8.1,<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
content-hash = "66ac482bd05eb74414210ac28fc1e8dae1a9928a4a1314e1326fada3551aa8ad"
