2023-10-17 01:13:31 +00:00
# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
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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2023-08-22 18:09:35 +00:00
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2023-08-22 18:09:35 +00:00
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name = "arrow"
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description = "Better dates & times for Python"
optional = false
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2023-10-17 01:13:31 +00:00
types-python-dateutil = ">=2.8.10"
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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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description = "Timeout context manager for asyncio programs"
optional = false
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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)" ]
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docs = [ "furo" , "myst-parser" , "sphinx" , "sphinx-notfound-page" , "sphinxcontrib-towncrier" , "towncrier" , "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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lxml = [ "lxml" ]
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version = "6.1.0"
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description = "An easy safelist-based HTML-sanitizing tool."
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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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Resolve: VectorSearch enabled SQLChain? (#10177)
Squashed from #7454 with updated features
We have separated the `SQLDatabseChain` from `VectorSQLDatabseChain` and
put everything into `experimental/`.
Below is the original PR message from #7454.
-------
We have been working on features to fill up the gap among SQL, vector
search and LLM applications. Some inspiring works like self-query
retrievers for VectorStores (for example
[Weaviate](https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/weaviate_self_query.html)
and
[others](https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/self_query.html))
really turn those vector search databases into a powerful knowledge
base! 🚀🚀
We are thinking if we can merge all in one, like SQL and vector search
and LLMChains, making this SQL vector database memory as the only source
of your data. Here are some benefits we can think of for now, maybe you
have more 👀:
With ALL data you have: since you store all your pasta in the database,
you don't need to worry about the foreign keys or links between names
from other data source.
Flexible data structure: Even if you have changed your schema, for
example added a table, the LLM will know how to JOIN those tables and
use those as filters.
SQL compatibility: We found that vector databases that supports SQL in
the marketplace have similar interfaces, which means you can change your
backend with no pain, just change the name of the distance function in
your DB solution and you are ready to go!
### Issue resolved:
- [Feature Proposal: VectorSearch enabled
SQLChain?](https://github.com/hwchase17/langchain/issues/5122)
### Change made in this PR:
- An improved schema handling that ignore `types.NullType` columns
- A SQL output Parser interface in `SQLDatabaseChain` to enable Vector
SQL capability and further more
- A Retriever based on `SQLDatabaseChain` to retrieve data from the
database for RetrievalQAChains and many others
- Allow `SQLDatabaseChain` to retrieve data in python native format
- Includes PR #6737
- Vector SQL Output Parser for `SQLDatabaseChain` and
`SQLDatabaseChainRetriever`
- Prompts that can implement text to VectorSQL
- Corresponding unit-tests and notebook
### Twitter handle:
- @MyScaleDB
### Tag Maintainer:
Prompts / General: @hwchase17, @baskaryan
DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
### Dependencies:
No dependency added
2023-09-07 00:08:12 +00:00
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extended-testing = [ "jinja2 (>=3,<4)" ]
2023-11-28 21:26:15 +00:00
[ package . source ]
type = "directory"
url = "../core"
Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[ [ package ] ]
name = "langcodes"
version = "3.3.0"
description = "Tools for labeling human languages with IETF language tags"
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data = [ "language-data (>=1.1,<2.0)" ]
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[ [ package ] ]
name = "langsmith"
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version = "0.0.77"
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description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
optional = false
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2024-01-06 17:46:00 +00:00
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2023-07-21 17:36:28 +00:00
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[ [ package ] ]
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[ package . extras ]
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[ [ package ] ]
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[ [ package ] ]
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[ [ package ] ]
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[ [ package ] ]
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]
Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[ [ package ] ]
name = "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
]
2023-07-21 17:36:28 +00:00
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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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2023-07-21 17:36:28 +00:00
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2023-08-22 18:09:35 +00:00
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2023-07-21 17:36:28 +00:00
description = "A decorator to automatically detect mismatch when overriding a method."
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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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2023-07-21 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
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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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tests = [ "check-manifest" , "coverage" , "defusedxml" , "markdown2" , "olefile" , "packaging" , "pyroma" , "pytest" , "pytest-cov" , "pytest-timeout" ]
2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "pkgutil-resolve-name"
version = "1.3.10"
description = "Resolve a name to an object."
optional = false
python-versions = ">=3.6"
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2023-07-21 17:36:28 +00:00
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Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[ [ 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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{ file = "preshed-3.0.9-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl" , hash = "sha256:ef1e5173809d85edd420fc79563b286b88b4049746b797845ba672cf9435c0e7" } ,
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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"
2023-11-07 23:15:09 +00:00
version = "2.2.350"
Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
description = "Presidio analyzer package"
optional = true
python-versions = "*"
files = [
2023-11-07 23:15:09 +00:00
{ file = "presidio_analyzer-2.2.350-py3-none-any.whl" , hash = "sha256:27989af82e6c78ecb4b6f85f587c5d954e126234f3aa091fd2bd1c1d3c637489" } ,
Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
]
[ package . dependencies ]
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 ]
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"
2023-11-07 23:15:09 +00:00
version = "2.2.350"
Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
description = "Persidio Anonymizer package - replaces analyzed text with desired values."
optional = true
python-versions = ">=3.5"
files = [
2023-11-07 23:15:09 +00:00
{ file = "presidio_anonymizer-2.2.350-py3-none-any.whl" , hash = "sha256:62dfde94a098747c5fa54f3447dcca51d8eede7f8c6cff7c8805ca1a517bb6ea" } ,
Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
]
[ 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
{ file = "prometheus_client-0.18.0-py3-none-any.whl" , hash = "sha256:8de3ae2755f890826f4b6479e5571d4f74ac17a81345fe69a6778fdb92579184" } ,
{ file = "prometheus_client-0.18.0.tar.gz" , hash = "sha256:35f7a8c22139e2bb7ca5a698e92d38145bc8dc74c1c0bf56f25cca886a764e17" } ,
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" } ,
{ file = "prompt_toolkit-3.0.39.tar.gz" , hash = "sha256:04505ade687dc26dc4284b1ad19a83be2f2afe83e7a828ace0c72f3a1df72aac" } ,
]
[ package . dependencies ]
wcwidth = "*"
[ [ package ] ]
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
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
files = [
{ file = "psutil-5.9.6-cp27-cp27m-macosx_10_9_x86_64.whl" , hash = "sha256:fb8a697f11b0f5994550555fcfe3e69799e5b060c8ecf9e2f75c69302cc35c0d" } ,
{ file = "psutil-5.9.6-cp27-cp27m-manylinux2010_i686.whl" , hash = "sha256:91ecd2d9c00db9817a4b4192107cf6954addb5d9d67a969a4f436dbc9200f88c" } ,
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2023-07-21 17:36:28 +00:00
]
[ package . extras ]
test = [ "enum34" , "ipaddress" , "mock" , "pywin32" , "wmi" ]
[ [ package ] ]
name = "ptyprocess"
version = "0.7.0"
description = "Run a subprocess in a pseudo terminal"
optional = false
python-versions = "*"
files = [
{ file = "ptyprocess-0.7.0-py2.py3-none-any.whl" , hash = "sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35" } ,
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]
[ [ package ] ]
name = "pure-eval"
version = "0.2.2"
description = "Safely evaluate AST nodes without side effects"
optional = false
python-versions = "*"
files = [
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tests = [ "pytest" ]
[ [ package ] ]
name = "pycparser"
version = "2.21"
description = "C parser in Python"
optional = false
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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
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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 = "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
optional = false
python-versions = ">=3.7"
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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
annotated-types = ">=0.4.0"
pydantic-core = "2.10.1"
typing-extensions = ">=4.6.1"
2023-07-21 17:36:28 +00:00
[ package . extras ]
2023-10-17 01:13:31 +00:00
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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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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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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]
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" } ,
{ file = "spacy-3.7.2-cp310-cp310-macosx_11_0_arm64.whl" , hash = "sha256:f132c05368781be5d3be3d706afce7e7a9a0c9edc0dbb7c616162c37bc386561" } ,
{ file = "spacy-3.7.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl" , hash = "sha256:2e3767b2cabbe337d62779ae4fdc4d57a39755c17dfc499de3ad2bae622caa43" } ,
{ file = "spacy-3.7.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl" , hash = "sha256:7a748ade269bdbea9baaa49ec00882404e7e921163cdc14f5612320d0a957dfd" } ,
{ file = "spacy-3.7.2-cp310-cp310-win_amd64.whl" , hash = "sha256:66467128e494bfa4dc9c3996e4cbb26bac4741bca4cdd8dd83a6e71182148945" } ,
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Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
]
[ package . dependencies ]
catalogue = ">=2.0.6,<2.1.0"
cymem = ">=2.0.2,<2.1.0"
jinja2 = "*"
langcodes = ">=3.2.0,<4.0.0"
murmurhash = ">=0.28.0,<1.1.0"
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" } ,
2023-11-27 17:51:12 +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" } ,
2023-11-27 17:51:12 +00:00
{ file = "SQLAlchemy-2.0.23-cp310-cp310-musllinux_1_1_aarch64.whl" , hash = "sha256:0666031df46b9badba9bed00092a1ffa3aa063a5e68fa244acd9f08070e936d3" } ,
2023-11-07 23:15:09 +00:00
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]
[ package . dependencies ]
2023-10-17 01:13:31 +00:00
greenlet = { version = "!=0.4.17" , markers = "platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\"" }
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)" ]
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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)" ]
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mssql = [ "pyodbc" ]
mssql-pymssql = [ "pymssql" ]
mssql-pyodbc = [ "pyodbc" ]
mypy = [ "mypy (>=0.910)" ]
mysql = [ "mysqlclient (>=1.4.0)" ]
mysql-connector = [ "mysql-connector-python" ]
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oracle = [ "cx-oracle (>=8)" ]
2023-07-21 17:36:28 +00:00
oracle-oracledb = [ "oracledb (>=1.0.1)" ]
postgresql = [ "psycopg2 (>=2.7)" ]
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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 ]
catalogue = ">=2.0.3,<2.1.0"
2023-07-21 17:36:28 +00:00
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name = "stack-data"
2023-10-17 01:13:31 +00:00
version = "0.6.3"
2023-07-21 17:36:28 +00:00
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python-versions = "*"
files = [
2023-10-17 01:13:31 +00:00
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2023-07-21 17:36:28 +00:00
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executing = ">=1.2.0"
pure-eval = "*"
[ package . extras ]
tests = [ "cython" , "littleutils" , "pygments" , "pytest" , "typeguard" ]
2023-09-11 16:20:19 +00:00
[ [ package ] ]
name = "sympy"
version = "1.12"
description = "Computer algebra system (CAS) in Python"
optional = true
python-versions = ">=3.8"
files = [
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mpmath = ">=0.19"
2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "tenacity"
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version = "8.2.3"
2023-07-21 17:36:28 +00:00
description = "Retry code until it succeeds"
optional = false
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python-versions = ">=3.7"
2023-07-21 17:36:28 +00:00
files = [
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[ package . extras ]
doc = [ "reno" , "sphinx" , "tornado (>=4.5)" ]
[ [ package ] ]
name = "terminado"
version = "0.17.1"
description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library."
optional = false
python-versions = ">=3.7"
files = [
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ptyprocess = { version = "*" , markers = "os_name != \"nt\"" }
pywinpty = { version = ">=1.1.0" , markers = "os_name == \"nt\"" }
tornado = ">=6.1.0"
[ package . extras ]
docs = [ "myst-parser" , "pydata-sphinx-theme" , "sphinx" ]
test = [ "pre-commit" , "pytest (>=7.0)" , "pytest-timeout" ]
Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[ [ package ] ]
name = "thinc"
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"
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 ]
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)" ]
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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 = [
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[ [ package ] ]
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version = "1.2.1"
description = "A tiny CSS parser"
optional = false
python-versions = ">=3.7"
files = [
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[ package . dependencies ]
webencodings = ">=0.4"
[ package . extras ]
doc = [ "sphinx" , "sphinx_rtd_theme" ]
test = [ "flake8" , "isort" , "pytest" ]
Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[ [ package ] ]
name = "tldextract"
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" } ,
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Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
]
[ package . dependencies ]
filelock = ">=3.0.8"
idna = "*"
requests = ">=2.1.0"
requests-file = ">=1.4"
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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2023-07-21 17:36:28 +00:00
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Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
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2023-09-11 16:20:19 +00:00
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2023-09-11 16:20:19 +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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urllib3 = ">=2"
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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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[ 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 ]
2023-12-07 22:24:58 +00:00
extended-testing = [ "faker" , "jinja2" , "presidio-analyzer" , "presidio-anonymizer" , "sentence-transformers" , "vowpal-wabbit-next" ]
Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
2023-07-21 17:36:28 +00:00
[ metadata ]
lock-version = "2.0"
python-versions = ">=3.8.1,<4.0"
2024-01-06 17:46:00 +00:00
content-hash = "dcd7f05c52dc68c59d781da135baed65a0c684cb2d4f94ad4e9393e005279ee0"
