2024-02-19 21:12:22 +00:00
# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "aiohttp"
2023-10-17 01:13:31 +00:00
version = "3.8.6"
2023-07-21 17:36:28 +00:00
description = "Async http client/server framework (asyncio)"
optional = false
python-versions = ">=3.6"
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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
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2023-10-17 01:13:31 +00:00
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2023-07-21 17:36:28 +00:00
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optional = false
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name = "arrow"
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description = "Better dates & times for Python"
optional = false
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[ [ package ] ]
name = "asttokens"
2023-11-07 23:15:09 +00:00
version = "2.4.1"
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description = "Annotate AST trees with source code positions"
optional = false
python-versions = "*"
files = [
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2023-10-17 01:13:31 +00:00
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[ package . extras ]
2023-11-07 23:15:09 +00:00
astroid = [ "astroid (>=1,<2)" , "astroid (>=2,<4)" ]
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[ [ package ] ]
name = "async-lru"
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version = "2.0.4"
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description = "Simple LRU cache for asyncio"
optional = false
python-versions = ">=3.8"
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typing-extensions = { version = ">=4.0.0" , markers = "python_version < \"3.11\"" }
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name = "async-timeout"
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description = "Timeout context manager for asyncio programs"
optional = false
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python-versions = ">=3.7"
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name = "attrs"
version = "23.1.0"
description = "Classes Without Boilerplate"
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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" ]
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name = "babel"
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description = "Internationalization utilities"
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2023-10-17 01:13:31 +00:00
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description = "Specifications for callback functions passed in to an API"
optional = false
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html5lib = [ "html5lib" ]
lxml = [ "lxml" ]
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name = "bleach"
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version = "6.1.0"
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description = "An easy safelist-based HTML-sanitizing tool."
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webencodings = "*"
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2023-10-17 01:13:31 +00:00
css = [ "tinycss2 (>=1.1.0,<1.3)" ]
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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"
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 = [
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"
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 = [
2023-10-17 01:13:31 +00:00
{ file = "catalogue-2.0.10-py3-none-any.whl" , hash = "sha256:58c2de0020aa90f4a2da7dfad161bf7b3b054c86a5f09fcedc0b2b740c109a9f" } ,
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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
]
2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "certifi"
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."
optional = false
python-versions = ">=3.6"
files = [
2023-08-22 18:09:35 +00:00
{ file = "certifi-2023.7.22-py3-none-any.whl" , hash = "sha256:92d6037539857d8206b8f6ae472e8b77db8058fec5937a1ef3f54304089edbb9" } ,
{ file = "certifi-2023.7.22.tar.gz" , hash = "sha256:539cc1d13202e33ca466e88b2807e29f4c13049d6d87031a3c110744495cb082" } ,
2023-07-21 17:36:28 +00:00
]
[ [ package ] ]
name = "cffi"
2023-10-17 01:13:31 +00:00
version = "1.16.0"
2023-07-21 17:36:28 +00:00
description = "Foreign Function Interface for Python calling C code."
optional = false
2023-10-17 01:13:31 +00:00
python-versions = ">=3.8"
2023-07-21 17:36:28 +00:00
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2023-10-17 01:13:31 +00:00
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2023-07-21 17:36:28 +00:00
]
[ package . dependencies ]
pycparser = "*"
[ [ package ] ]
name = "charset-normalizer"
2023-11-07 23:15:09 +00:00
version = "3.3.2"
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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2023-07-21 17:36:28 +00:00
]
[ [ package ] ]
name = "click"
2023-08-22 18:09:35 +00:00
version = "8.1.7"
2023-07-21 17:36:28 +00:00
description = "Composable command line interface toolkit"
2023-10-31 14:53:12 +00:00
optional = true
2023-07-21 17:36:28 +00:00
python-versions = ">=3.7"
files = [
2023-08-22 18:09:35 +00:00
{ file = "click-8.1.7-py3-none-any.whl" , hash = "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28" } ,
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2023-07-21 17:36:28 +00:00
]
[ package . dependencies ]
colorama = { version = "*" , markers = "platform_system == \"Windows\"" }
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 = [
{ file = "cloudpathlib-0.16.0-py3-none-any.whl" , hash = "sha256:f46267556bf91f03db52b5df7a152548596a15aabca1c8731ef32b0b25a1a6a3" } ,
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]
[ 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" ]
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"
2023-11-07 23:15:09 +00:00
version = "0.2.0"
2023-07-21 17:36:28 +00:00
description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc."
optional = false
2023-11-07 23:15:09 +00:00
python-versions = ">=3.8"
2023-07-21 17:36:28 +00:00
files = [
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2023-07-21 17:36:28 +00:00
]
[ package . dependencies ]
2023-08-22 18:09:35 +00:00
traitlets = ">=4"
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"
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 = [
2023-10-17 01:13:31 +00:00
{ file = "confection-0.1.3-py3-none-any.whl" , hash = "sha256:58b125c9bc6786f32e37fe4d98bc3a03e5f509a4b9de02541b99c559f2026092" } ,
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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 ]
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"
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 = [
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
]
2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "dataclasses-json"
2023-10-17 01:13:31 +00:00
version = "0.6.1"
description = "Easily serialize dataclasses to and from JSON."
2023-07-21 17:36:28 +00:00
optional = false
2023-10-17 01:13:31 +00:00
python-versions = ">=3.7,<4.0"
2023-07-21 17:36:28 +00:00
files = [
2023-10-17 01:13:31 +00:00
{ file = "dataclasses_json-0.6.1-py3-none-any.whl" , hash = "sha256:1bd8418a61fe3d588bb0079214d7fb71d44937da40742b787256fd53b26b6c80" } ,
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2023-07-21 17:36:28 +00:00
]
[ package . dependencies ]
2023-10-17 01:13:31 +00:00
marshmallow = ">=3.18.0,<4.0.0"
typing-inspect = ">=0.4.0,<1"
2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "debugpy"
2023-10-17 01:13:31 +00:00
version = "1.8.0"
2023-07-21 17:36:28 +00:00
description = "An implementation of the Debug Adapter Protocol for Python"
optional = false
2023-10-17 01:13:31 +00:00
python-versions = ">=3.8"
2023-07-21 17:36:28 +00:00
files = [
2023-10-17 01:13:31 +00:00
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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 = [
{ file = "decorator-5.1.1-py3-none-any.whl" , hash = "sha256:b8c3f85900b9dc423225913c5aace94729fe1fa9763b38939a95226f02d37186" } ,
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]
[ [ 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"
2023-08-22 18:09:35 +00:00
version = "1.1.3"
2023-07-21 17:36:28 +00:00
description = "Backport of PEP 654 (exception groups)"
optional = false
python-versions = ">=3.7"
files = [
2023-08-22 18:09:35 +00:00
{ file = "exceptiongroup-1.1.3-py3-none-any.whl" , hash = "sha256:343280667a4585d195ca1cf9cef84a4e178c4b6cf2274caef9859782b567d5e3" } ,
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2023-07-21 17:36:28 +00:00
]
[ package . extras ]
test = [ "pytest (>=6)" ]
[ [ package ] ]
name = "executing"
2023-11-07 23:15:09 +00:00
version = "2.0.1"
2023-07-21 17:36:28 +00:00
description = "Get the currently executing AST node of a frame, and other information"
optional = false
2023-11-07 23:15:09 +00:00
python-versions = ">=3.5"
2023-07-21 17:36:28 +00:00
files = [
2023-11-07 23:15:09 +00:00
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2023-07-21 17:36:28 +00:00
]
[ package . extras ]
2023-10-17 01:13:31 +00:00
tests = [ "asttokens (>=2.1.0)" , "coverage" , "coverage-enable-subprocess" , "ipython" , "littleutils" , "pytest" , "rich" ]
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"
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 = [
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\"" }
2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "fastjsonschema"
2023-10-17 01:13:31 +00:00
version = "2.18.1"
2023-07-21 17:36:28 +00:00
description = "Fastest Python implementation of JSON schema"
optional = false
python-versions = "*"
files = [
2023-10-17 01:13:31 +00:00
{ file = "fastjsonschema-2.18.1-py3-none-any.whl" , hash = "sha256:aec6a19e9f66e9810ab371cc913ad5f4e9e479b63a7072a2cd060a9369e329a8" } ,
{ file = "fastjsonschema-2.18.1.tar.gz" , hash = "sha256:06dc8680d937628e993fa0cd278f196d20449a1adc087640710846b324d422ea" } ,
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"
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 = [
2023-11-07 23:15:09 +00:00
{ file = "filelock-3.13.1-py3-none-any.whl" , hash = "sha256:57dbda9b35157b05fb3e58ee91448612eb674172fab98ee235ccb0b5bee19a1c" } ,
{ file = "filelock-3.13.1.tar.gz" , hash = "sha256:521f5f56c50f8426f5e03ad3b281b490a87ef15bc6c526f168290f0c7148d44e" } ,
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 ]
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
2023-07-21 17:36:28 +00:00
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2023-09-11 16:20:19 +00:00
[ [ package ] ]
name = "fsspec"
2023-11-07 23:15:09 +00:00
version = "2023.10.0"
2023-09-11 16:20:19 +00:00
description = "File-system specification"
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2023-11-07 23:15:09 +00:00
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[ package . extras ]
abfs = [ "adlfs" ]
adl = [ "adlfs" ]
arrow = [ "pyarrow (>=1)" ]
dask = [ "dask" , "distributed" ]
devel = [ "pytest" , "pytest-cov" ]
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github = [ "requests" ]
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smb = [ "smbprotocol" ]
ssh = [ "paramiko" ]
tqdm = [ "tqdm" ]
2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "greenlet"
2023-11-07 23:15:09 +00:00
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2023-07-21 17:36:28 +00:00
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2023-10-17 01:13:31 +00:00
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name = "langchain-text-splitters"
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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 ] ]
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2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "langsmith"
2024-02-16 06:17:59 +00:00
version = "0.1.1"
2023-07-21 17:36:28 +00:00
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
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2023-08-22 18:09:35 +00:00
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requests = ">=2,<3"
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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"
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 = [
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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2023-07-21 17:36:28 +00:00
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[ [ package ] ]
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"
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 = [
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
]
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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]
2023-09-11 16:20:19 +00:00
[ [ package ] ]
name = "pillow"
2023-10-17 01:13:31 +00:00
version = "10.1.0"
2023-09-11 16:20:19 +00:00
description = "Python Imaging Library (Fork)"
optional = true
python-versions = ">=3.8"
files = [
2023-10-17 01:13:31 +00:00
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]
[ package . extras ]
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2023-07-21 17:36:28 +00:00
[ [ package ] ]
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2023-10-17 01:13:31 +00:00
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2023-07-21 17:36:28 +00:00
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2023-10-17 01:13:31 +00:00
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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"
2023-10-17 01:13:31 +00:00
version = "1.3.0"
2023-07-21 17:36:28 +00:00
description = "plugin and hook calling mechanisms for python"
optional = false
2023-10-17 01:13:31 +00:00
python-versions = ">=3.8"
2023-07-21 17:36:28 +00:00
files = [
2023-10-17 01:13:31 +00:00
{ file = "pluggy-1.3.0-py3-none-any.whl" , hash = "sha256:d89c696a773f8bd377d18e5ecda92b7a3793cbe66c87060a6fb58c7b6e1061f7" } ,
{ file = "pluggy-1.3.0.tar.gz" , hash = "sha256:cf61ae8f126ac6f7c451172cf30e3e43d3ca77615509771b3a984a0730651e12" } ,
2023-07-21 17:36:28 +00:00
]
[ 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"
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"
files = [
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"
experimental[patch]: missing resolution strategy in anonymization (#16653)
- **Description:** Presidio-based anonymizers are not working because
`_remove_conflicts_and_get_text_manipulation_data` was being called
without a conflict resolution strategy. This PR fixes this issue. In
addition, it removes some mutable default arguments (antipattern).
To reproduce the issue, just run the very first cell of this
[notebook](https://python.langchain.com/docs/guides/privacy/2/) from
langchain's documentation.
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is
whichever of langchain, community, core, experimental, etc. is being
modified.
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes if applicable,
- **Dependencies:** any dependencies required for this change,
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` from the root
of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run
tests, lint, etc: https://python.langchain.com/docs/contributing/
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
2024-01-29 17:56:16 +00:00
version = "2.2.352"
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 = [
experimental[patch]: missing resolution strategy in anonymization (#16653)
- **Description:** Presidio-based anonymizers are not working because
`_remove_conflicts_and_get_text_manipulation_data` was being called
without a conflict resolution strategy. This PR fixes this issue. In
addition, it removes some mutable default arguments (antipattern).
To reproduce the issue, just run the very first cell of this
[notebook](https://python.langchain.com/docs/guides/privacy/2/) from
langchain's documentation.
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is
whichever of langchain, community, core, experimental, etc. is being
modified.
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes if applicable,
- **Dependencies:** any dependencies required for this change,
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` from the root
of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run
tests, lint, etc: https://python.langchain.com/docs/contributing/
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
2024-01-29 17:56:16 +00:00
{ file = "presidio_analyzer-2.2.352-py3-none-any.whl" , hash = "sha256:d35f1129bf3190d2cc5400e9cad20d63f9fcc7839c024492a9d1a808ca7c1e1f" } ,
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 ]
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 = "*"
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 ]
experimental[patch]: missing resolution strategy in anonymization (#16653)
- **Description:** Presidio-based anonymizers are not working because
`_remove_conflicts_and_get_text_manipulation_data` was being called
without a conflict resolution strategy. This PR fixes this issue. In
addition, it removes some mutable default arguments (antipattern).
To reproduce the issue, just run the very first cell of this
[notebook](https://python.langchain.com/docs/guides/privacy/2/) from
langchain's documentation.
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is
whichever of langchain, community, core, experimental, etc. is being
modified.
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes if applicable,
- **Dependencies:** any dependencies required for this change,
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` from the root
of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run
tests, lint, etc: https://python.langchain.com/docs/contributing/
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
2024-01-29 17:56:16 +00:00
azure-ai-language = [ "azure-ai-textanalytics" , "azure-core" ]
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"
experimental[patch]: missing resolution strategy in anonymization (#16653)
- **Description:** Presidio-based anonymizers are not working because
`_remove_conflicts_and_get_text_manipulation_data` was being called
without a conflict resolution strategy. This PR fixes this issue. In
addition, it removes some mutable default arguments (antipattern).
To reproduce the issue, just run the very first cell of this
[notebook](https://python.langchain.com/docs/guides/privacy/2/) from
langchain's documentation.
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is
whichever of langchain, community, core, experimental, etc. is being
modified.
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes if applicable,
- **Dependencies:** any dependencies required for this change,
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` from the root
of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run
tests, lint, etc: https://python.langchain.com/docs/contributing/
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
2024-01-29 17:56:16 +00:00
version = "2.2.352"
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 = [
experimental[patch]: missing resolution strategy in anonymization (#16653)
- **Description:** Presidio-based anonymizers are not working because
`_remove_conflicts_and_get_text_manipulation_data` was being called
without a conflict resolution strategy. This PR fixes this issue. In
addition, it removes some mutable default arguments (antipattern).
To reproduce the issue, just run the very first cell of this
[notebook](https://python.langchain.com/docs/guides/privacy/2/) from
langchain's documentation.
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is
whichever of langchain, community, core, experimental, etc. is being
modified.
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes if applicable,
- **Dependencies:** any dependencies required for this change,
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` from the root
of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run
tests, lint, etc: https://python.langchain.com/docs/contributing/
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
2024-01-29 17:56:16 +00:00
{ file = "presidio_anonymizer-2.2.352-py3-none-any.whl" , hash = "sha256:e09df86d4aeb2dfd2428e3c27a6f17fa5655de813ac164d7c2bbf3dd2d905f72" } ,
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 ]
pycryptodome = ">=3.10.1"
2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "prometheus-client"
2023-11-07 23:15:09 +00:00
version = "0.18.0"
2023-07-21 17:36:28 +00:00
description = "Python client for the Prometheus monitoring system."
optional = false
2023-11-07 23:15:09 +00:00
python-versions = ">=3.8"
2023-07-21 17:36:28 +00:00
files = [
2023-11-07 23:15:09 +00:00
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2023-07-21 17:36:28 +00:00
]
[ 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 = [
{ file = "prompt_toolkit-3.0.39-py3-none-any.whl" , hash = "sha256:9dffbe1d8acf91e3de75f3b544e4842382fc06c6babe903ac9acb74dc6e08d88" } ,
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wcwidth = "*"
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name = "psutil"
2023-10-17 01:13:31 +00:00
version = "5.9.6"
2023-07-21 17:36:28 +00:00
description = "Cross-platform lib for process and system monitoring in Python."
optional = false
2023-10-17 01:13:31 +00:00
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]
[ package . extras ]
test = [ "enum34" , "ipaddress" , "mock" , "pywin32" , "wmi" ]
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name = "ptyprocess"
version = "0.7.0"
description = "Run a subprocess in a pseudo terminal"
optional = false
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files = [
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tests = [ "pytest" ]
[ [ package ] ]
name = "pycparser"
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description = "C parser in Python"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
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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"
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.*"
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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
]
2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "pydantic"
2023-10-17 01:13:31 +00:00
version = "2.4.2"
description = "Data validation using Python type hints"
2023-07-21 17:36:28 +00:00
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python-versions = ">=3.7"
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[ package . dependencies ]
2023-10-17 01:13:31 +00:00
annotated-types = ">=0.4.0"
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typing-extensions = ">=4.6.1"
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[ package . extras ]
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email = [ "email-validator (>=2.0.0)" ]
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EXPERIMENTAL Generic LLM wrapper to support chat model interface with configurable chat prompt format (#8295)
## Update 2023-09-08
This PR now supports further models in addition to Lllama-2 chat models.
See [this comment](#issuecomment-1668988543) for further details. The
title of this PR has been updated accordingly.
## Original PR description
This PR adds a generic `Llama2Chat` model, a wrapper for LLMs able to
serve Llama-2 chat models (like `LlamaCPP`,
`HuggingFaceTextGenInference`, ...). It implements `BaseChatModel`,
converts a list of chat messages into the [required Llama-2 chat prompt
format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2) and
forwards the formatted prompt as `str` to the wrapped `LLM`. Usage
example:
```python
# uses a locally hosted Llama2 chat model
llm = HuggingFaceTextGenInference(
inference_server_url="http://127.0.0.1:8080/",
max_new_tokens=512,
top_k=50,
temperature=0.1,
repetition_penalty=1.03,
)
# Wrap llm to support Llama2 chat prompt format.
# Resulting model is a chat model
model = Llama2Chat(llm=llm)
messages = [
SystemMessage(content="You are a helpful assistant."),
MessagesPlaceholder(variable_name="chat_history"),
HumanMessagePromptTemplate.from_template("{text}"),
]
prompt = ChatPromptTemplate.from_messages(messages)
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
chain = LLMChain(llm=model, prompt=prompt, memory=memory)
# use chat model in a conversation
# ...
```
Also part of this PR are tests and a demo notebook.
- Tag maintainer: @hwchase17
- Twitter handle: `@mrt1nz`
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
2023-11-18 00:32:13 +00:00
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[ package . dependencies ]
cffi = { version = "*" , markers = "implementation_name == \"pypy\"" }
[ [ package ] ]
name = "qtconsole"
2023-11-07 23:15:09 +00:00
version = "5.5.0"
2023-07-21 17:36:28 +00:00
description = "Jupyter Qt console"
optional = false
2023-11-07 23:15:09 +00:00
python-versions = ">= 3.8"
2023-07-21 17:36:28 +00:00
files = [
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[ package . dependencies ]
ipykernel = ">=4.1"
jupyter-client = ">=4.1"
jupyter-core = "*"
packaging = "*"
pygments = "*"
pyzmq = ">=17.1"
2023-10-17 01:13:31 +00:00
qtpy = ">=2.4.0"
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"
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version = "2.4.1"
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description = "Provides an abstraction layer on top of the various Qt bindings (PyQt5/6 and PySide2/6)."
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packaging = "*"
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[ [ package ] ]
name = "referencing"
2023-08-22 18:09:35 +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"
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
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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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
]
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 = "*"
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.*"
files = [
{ file = "rfc3339_validator-0.1.4-py2.py3-none-any.whl" , hash = "sha256:24f6ec1eda14ef823da9e36ec7113124b39c04d50a4d3d3a3c2859577e7791fa" } ,
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]
[ package . dependencies ]
six = "*"
[ [ package ] ]
name = "rfc3986-validator"
version = "0.1.1"
description = "Pure python rfc3986 validator"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
files = [
{ file = "rfc3986_validator-0.1.1-py2.py3-none-any.whl" , hash = "sha256:2f235c432ef459970b4306369336b9d5dbdda31b510ca1e327636e01f528bfa9" } ,
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]
[ [ package ] ]
name = "rpds-py"
2023-11-07 23:15:09 +00:00
version = "0.12.0"
2023-07-21 17:36:28 +00:00
description = "Python bindings to Rust's persistent data structures (rpds)"
optional = false
python-versions = ">=3.8"
files = [
2023-11-07 23:15:09 +00:00
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]
[ package . extras ]
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all = [ "safetensors[jax]" , "safetensors[numpy]" , "safetensors[paddlepaddle]" , "safetensors[pinned-tf]" , "safetensors[quality]" , "safetensors[testing]" , "safetensors[torch]" ]
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[ [ package ] ]
name = "scikit-learn"
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version = "1.3.2"
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description = "A set of python modules for machine learning and data mining"
optional = true
python-versions = ">=3.8"
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{ file = "sentencepiece-0.1.99.tar.gz" , hash = "sha256:189c48f5cb2949288f97ccdb97f0473098d9c3dcf5a3d99d4eabe719ec27297f" } ,
]
2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "setuptools"
version = "67.8.0"
description = "Easily download, build, install, upgrade, and uninstall Python packages"
optional = false
python-versions = ">=3.7"
files = [
{ file = "setuptools-67.8.0-py3-none-any.whl" , hash = "sha256:5df61bf30bb10c6f756eb19e7c9f3b473051f48db77fddbe06ff2ca307df9a6f" } ,
{ file = "setuptools-67.8.0.tar.gz" , hash = "sha256:62642358adc77ffa87233bc4d2354c4b2682d214048f500964dbe760ccedf102" } ,
]
[ 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 = [
{ file = "six-1.16.0-py2.py3-none-any.whl" , hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254" } ,
{ file = "six-1.16.0.tar.gz" , hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926" } ,
]
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"
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...)"
optional = true
python-versions = ">=3.6,<4.0"
files = [
2023-10-17 01:13:31 +00:00
{ file = "smart_open-6.4.0-py3-none-any.whl" , hash = "sha256:8d3ef7e6997e8e42dd55c74166ed21e6ac70664caa32dd940b26d54a8f6b4142" } ,
{ file = "smart_open-6.4.0.tar.gz" , hash = "sha256:be3c92c246fbe80ebce8fbacb180494a481a77fcdcb7c1aadb2ea5b9c2bee8b9" } ,
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" ]
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" } ,
{ file = "sniffio-1.3.0.tar.gz" , hash = "sha256:e60305c5e5d314f5389259b7f22aaa33d8f7dee49763119234af3755c55b9101" } ,
]
[ [ package ] ]
name = "soupsieve"
2023-10-17 01:13:31 +00:00
version = "2.5"
2023-07-21 17:36:28 +00:00
description = "A modern CSS selector implementation for Beautiful Soup."
optional = false
2023-10-17 01:13:31 +00:00
python-versions = ">=3.8"
2023-07-21 17:36:28 +00:00
files = [
2023-10-17 01:13:31 +00:00
{ file = "soupsieve-2.5-py3-none-any.whl" , hash = "sha256:eaa337ff55a1579b6549dc679565eac1e3d000563bcb1c8ab0d0fefbc0c2cdc7" } ,
{ file = "soupsieve-2.5.tar.gz" , hash = "sha256:5663d5a7b3bfaeee0bc4372e7fc48f9cff4940b3eec54a6451cc5299f1097690" } ,
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"
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
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 = [
2023-10-17 01:13:31 +00:00
{ file = "spacy-3.7.2-cp310-cp310-macosx_10_9_x86_64.whl" , hash = "sha256:b4e285366d36c85f784d606a2d966912a18f4d24d47330c1c6acbdd9f19ee373" } ,
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{ file = "spacy-3.7.2-cp310-cp310-win_amd64.whl" , hash = "sha256:66467128e494bfa4dc9c3996e4cbb26bac4741bca4cdd8dd83a6e71182148945" } ,
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{ file = "spacy-3.7.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl" , hash = "sha256:9a334667625153f7aaf188c20af7e82c886e41a88483a056accba5a7d51095c6" } ,
{ file = "spacy-3.7.2-cp311-cp311-win_amd64.whl" , hash = "sha256:43e6147d3583b62a2d3af0cd913ac025068196d587345751e198391ff0b8c1e9" } ,
{ file = "spacy-3.7.2-cp312-cp312-macosx_10_9_x86_64.whl" , hash = "sha256:2558df8c11905a0f77a2a3639a12ef8a522d171bcd88eaec039bedf6c60d7e01" } ,
{ file = "spacy-3.7.2-cp312-cp312-macosx_11_0_arm64.whl" , hash = "sha256:df1b9c4bbadc89bad10dba226d52c113e231ea6ad35c8a916ab138b31f69fa24" } ,
{ file = "spacy-3.7.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl" , hash = "sha256:bbbe055d2170ac7505a9f580bbdcd2146d0701bdbd6cea2333e18b0db655b97a" } ,
{ file = "spacy-3.7.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl" , hash = "sha256:d35129b16ae2ca4212bf22a5c88b67b1e019e434fc48b69d3b95f80bc9e14e42" } ,
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{ file = "spacy-3.7.2-cp37-cp37m-macosx_10_9_x86_64.whl" , hash = "sha256:b12ab9c4923ffd38da84baf09464982da44e8275d680fb3c5da2051d7dd7bd2d" } ,
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{ file = "spacy-3.7.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl" , hash = "sha256:bcaad95e3e7d0ea8f381f3e2d9e80b7f346ecb6566de9bd55361736fa563fc22" } ,
{ file = "spacy-3.7.2-cp37-cp37m-win_amd64.whl" , hash = "sha256:5d9b12284871ca5daa7774604a964486957567a86f1af898da0260e94b815e0d" } ,
{ file = "spacy-3.7.2-cp38-cp38-macosx_10_9_x86_64.whl" , hash = "sha256:2bd89770f61d5980e788ef382297322cceb7dcc4b848d68cb1da8af7d80d6eb6" } ,
{ file = "spacy-3.7.2-cp38-cp38-macosx_11_0_arm64.whl" , hash = "sha256:d42f9151a2f01b34227ed31c8db8b7c67889ebcc637eae390faec8093ea1fb12" } ,
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{ file = "spacy-3.7.2-cp38-cp38-win_amd64.whl" , hash = "sha256:9c2f3f04b4b894a6c42ee93cec2f2b158f246f344927e65d9d19b72c5a6493ea" } ,
{ file = "spacy-3.7.2-cp39-cp39-macosx_10_9_x86_64.whl" , hash = "sha256:b22e0e8dac76740d55556fa13ebb9e1c829779ea0b7ec7a9e04f32efc66f74b9" } ,
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{ file = "spacy-3.7.2-cp39-cp39-win_amd64.whl" , hash = "sha256:e8a7291e7e1cfcb6041b26f96d0a66b603725c1beff4e0391c3d9226fae16e04" } ,
{ file = "spacy-3.7.2.tar.gz" , hash = "sha256:cedf4927bf0d3fec773a6ce48d5d2c91bdb02fed3c7d5ec07bdb873f1126f1a0" } ,
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"
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"
preshed = ">=3.0.2,<3.1.0"
pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<3.0.0"
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"
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"
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
[ package . extras ]
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)" ]
cuda100 = [ "cupy-cuda100 (>=5.0.0b4,<13.0.0)" ]
cuda101 = [ "cupy-cuda101 (>=5.0.0b4,<13.0.0)" ]
cuda102 = [ "cupy-cuda102 (>=5.0.0b4,<13.0.0)" ]
cuda110 = [ "cupy-cuda110 (>=5.0.0b4,<13.0.0)" ]
cuda111 = [ "cupy-cuda111 (>=5.0.0b4,<13.0.0)" ]
cuda112 = [ "cupy-cuda112 (>=5.0.0b4,<13.0.0)" ]
cuda113 = [ "cupy-cuda113 (>=5.0.0b4,<13.0.0)" ]
cuda114 = [ "cupy-cuda114 (>=5.0.0b4,<13.0.0)" ]
cuda115 = [ "cupy-cuda115 (>=5.0.0b4,<13.0.0)" ]
cuda116 = [ "cupy-cuda116 (>=5.0.0b4,<13.0.0)" ]
cuda117 = [ "cupy-cuda117 (>=5.0.0b4,<13.0.0)" ]
cuda11x = [ "cupy-cuda11x (>=11.0.0,<13.0.0)" ]
cuda12x = [ "cupy-cuda12x (>=11.5.0,<13.0.0)" ]
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)" ]
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" } ,
{ file = "spacy_legacy-3.0.12-py2.py3-none-any.whl" , hash = "sha256:476e3bd0d05f8c339ed60f40986c07387c0a71479245d6d0f4298dbd52cda55f" } ,
]
[ [ package ] ]
name = "spacy-loggers"
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 = [
2023-10-17 01:13:31 +00:00
{ file = "spacy-loggers-1.0.5.tar.gz" , hash = "sha256:d60b0bdbf915a60e516cc2e653baeff946f0cfc461b452d11a4d5458c6fe5f24" } ,
{ 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
]
2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "sqlalchemy"
2023-11-07 23:15:09 +00:00
version = "2.0.23"
2023-07-21 17:36:28 +00:00
description = "Database Abstraction Library"
optional = false
python-versions = ">=3.7"
files = [
2023-11-07 23:15:09 +00:00
{ file = "SQLAlchemy-2.0.23-cp310-cp310-macosx_10_9_x86_64.whl" , hash = "sha256:638c2c0b6b4661a4fd264f6fb804eccd392745c5887f9317feb64bb7cb03b3ea" } ,
{ file = "SQLAlchemy-2.0.23-cp310-cp310-macosx_11_0_arm64.whl" , hash = "sha256:e3b5036aa326dc2df50cba3c958e29b291a80f604b1afa4c8ce73e78e1c9f01d" } ,
experimental[patch]: missing resolution strategy in anonymization (#16653)
- **Description:** Presidio-based anonymizers are not working because
`_remove_conflicts_and_get_text_manipulation_data` was being called
without a conflict resolution strategy. This PR fixes this issue. In
addition, it removes some mutable default arguments (antipattern).
To reproduce the issue, just run the very first cell of this
[notebook](https://python.langchain.com/docs/guides/privacy/2/) from
langchain's documentation.
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is
whichever of langchain, community, core, experimental, etc. is being
modified.
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes if applicable,
- **Dependencies:** any dependencies required for this change,
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` from the root
of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run
tests, lint, etc: https://python.langchain.com/docs/contributing/
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
2024-01-29 17:56:16 +00:00
{ file = "SQLAlchemy-2.0.23-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl" , hash = "sha256:787af80107fb691934a01889ca8f82a44adedbf5ef3d6ad7d0f0b9ac557e0c34" } ,
2023-11-07 23:15:09 +00:00
{ file = "SQLAlchemy-2.0.23-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl" , hash = "sha256:c14eba45983d2f48f7546bb32b47937ee2cafae353646295f0e99f35b14286ab" } ,
experimental[patch]: missing resolution strategy in anonymization (#16653)
- **Description:** Presidio-based anonymizers are not working because
`_remove_conflicts_and_get_text_manipulation_data` was being called
without a conflict resolution strategy. This PR fixes this issue. In
addition, it removes some mutable default arguments (antipattern).
To reproduce the issue, just run the very first cell of this
[notebook](https://python.langchain.com/docs/guides/privacy/2/) from
langchain's documentation.
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is
whichever of langchain, community, core, experimental, etc. is being
modified.
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes if applicable,
- **Dependencies:** any dependencies required for this change,
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` from the root
of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run
tests, lint, etc: https://python.langchain.com/docs/contributing/
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
2024-01-29 17:56:16 +00:00
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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" } ,
experimental[patch]: missing resolution strategy in anonymization (#16653)
- **Description:** Presidio-based anonymizers are not working because
`_remove_conflicts_and_get_text_manipulation_data` was being called
without a conflict resolution strategy. This PR fixes this issue. In
addition, it removes some mutable default arguments (antipattern).
To reproduce the issue, just run the very first cell of this
[notebook](https://python.langchain.com/docs/guides/privacy/2/) from
langchain's documentation.
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is
whichever of langchain, community, core, experimental, etc. is being
modified.
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes if applicable,
- **Dependencies:** any dependencies required for this change,
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` from the root
of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run
tests, lint, etc: https://python.langchain.com/docs/contributing/
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
2024-01-29 17:56:16 +00:00
{ file = "SQLAlchemy-2.0.23-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl" , hash = "sha256:9585b646ffb048c0250acc7dad92536591ffe35dba624bb8fd9b471e25212a35" } ,
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" } ,
experimental[patch]: missing resolution strategy in anonymization (#16653)
- **Description:** Presidio-based anonymizers are not working because
`_remove_conflicts_and_get_text_manipulation_data` was being called
without a conflict resolution strategy. This PR fixes this issue. In
addition, it removes some mutable default arguments (antipattern).
To reproduce the issue, just run the very first cell of this
[notebook](https://python.langchain.com/docs/guides/privacy/2/) from
langchain's documentation.
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is
whichever of langchain, community, core, experimental, etc. is being
modified.
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes if applicable,
- **Dependencies:** any dependencies required for this change,
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` from the root
of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run
tests, lint, etc: https://python.langchain.com/docs/contributing/
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
2024-01-29 17:56:16 +00:00
{ file = "SQLAlchemy-2.0.23-cp39-cp39-musllinux_1_1_aarch64.whl" , hash = "sha256:cc1d21576f958c42d9aec68eba5c1a7d715e5fc07825a629015fe8e3b0657fb0" } ,
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" } ,
2023-08-22 18:09:35 +00:00
]
[ package . dependencies ]
2024-02-19 21:12:22 +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\"" }
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)" ]
2023-11-07 23:15:09 +00:00
aioodbc = [ "aioodbc" , "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" ]
2023-11-07 23:15:09 +00:00
oracle = [ "cx-oracle (>=8)" ]
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"
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
description = "Modern high-performance serialization utilities for Python"
optional = true
python-versions = ">=3.6"
files = [
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 ]
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2023-07-21 17:36:28 +00:00
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2023-07-21 17:36:28 +00:00
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description = "Retry code until it succeeds"
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description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library."
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docs = [ "myst-parser" , "pydata-sphinx-theme" , "sphinx" ]
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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 = "thinc"
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
description = "A refreshing functional take on deep learning, compatible with your favorite libraries"
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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 ]
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)" ]
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)" ]
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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)" ]
2023-09-11 16:20:19 +00:00
[ [ package ] ]
name = "threadpoolctl"
version = "3.2.0"
description = "threadpoolctl"
optional = true
python-versions = ">=3.8"
files = [
{ file = "threadpoolctl-3.2.0-py3-none-any.whl" , hash = "sha256:2b7818516e423bdaebb97c723f86a7c6b0a83d3f3b0970328d66f4d9104dc032" } ,
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]
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 = [
{ file = "tinycss2-1.2.1-py3-none-any.whl" , hash = "sha256:2b80a96d41e7c3914b8cda8bc7f705a4d9c49275616e886103dd839dfc847847" } ,
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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"
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
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 = [
2023-11-07 23:15:09 +00:00
{ file = "tldextract-5.1.0-py3-none-any.whl" , hash = "sha256:c8eecb15f556b43db6eebd21667640fb6fba9bc9539b48707432014913a78d13" } ,
{ file = "tldextract-5.1.0.tar.gz" , hash = "sha256:366acfb099c7eb5dc83545c391d73da6e3afe4eaec652417c3cf13b002a160e1" } ,
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"
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" ]
2023-09-11 16:20:19 +00:00
[ [ package ] ]
name = "tokenizers"
2023-10-17 01:13:31 +00:00
version = "0.14.1"
2023-10-06 01:09:35 +00:00
description = ""
2023-09-11 16:20:19 +00:00
optional = true
2023-10-06 01:09:35 +00:00
python-versions = ">=3.7"
2023-09-11 16:20:19 +00:00
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2023-10-17 01:13:31 +00:00
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[ package . dependencies ]
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dev = [ "tokenizers[testing]" ]
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filelock = "*"
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fsspec = "*"
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jinja2 = "*"
networkx = "*"
sympy = "*"
typing-extensions = "*"
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opt-einsum = [ "opt-einsum (>=3.3)" ]
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name = "torchvision"
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version = "0.16.0"
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description = "image and video datasets and models for torch deep learning"
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python-versions = ">=3.8"
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requests = "*"
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torch = "2.1.0"
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[ package . extras ]
scipy = [ "scipy" ]
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[ [ package ] ]
name = "tornado"
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version = "6.3.3"
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description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
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python-versions = ">= 3.8"
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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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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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[ [ package ] ]
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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
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2023-11-07 23:15:09 +00:00
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[ package . dependencies ]
idna = ">=2.0"
multidict = ">=4.0"
[ [ package ] ]
name = "zipp"
2023-10-17 01:13:31 +00:00
version = "3.17.0"
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 = [
2023-10-17 01:13:31 +00:00
{ file = "zipp-3.17.0-py3-none-any.whl" , hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31" } ,
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2023-07-21 17:36:28 +00:00
]
[ package . extras ]
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" ]
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 ]
2024-02-13 05:58:35 +00:00
extended-testing = [ "faker" , "jinja2" , "pandas" , "presidio-analyzer" , "presidio-anonymizer" , "sentence-transformers" , "tabulate" , "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
2023-07-21 17:36:28 +00:00
[ metadata ]
lock-version = "2.0"
python-versions = ">=3.8.1,<4.0"
2024-02-29 17:13:21 +00:00
content-hash = "bde47b4a6eed5f419e34857a3bcbf5efece9ed41d4f8e68ed37ef853e4208ab7"
