2023-08-22 18:09:35 +00:00
# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "aiohttp"
version = "3.8.5"
description = "Async http client/server framework (asyncio)"
optional = false
python-versions = ">=3.6"
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[ package . dependencies ]
aiosignal = ">=1.1.2"
async-timeout = ">=4.0.0a3,<5.0"
attrs = ">=17.3.0"
charset-normalizer = ">=2.0,<4.0"
frozenlist = ">=1.1.1"
multidict = ">=4.5,<7.0"
yarl = ">=1.0,<2.0"
[ package . extras ]
speedups = [ "Brotli" , "aiodns" , "cchardet" ]
[ [ package ] ]
name = "aiosignal"
version = "1.3.1"
description = "aiosignal: a list of registered asynchronous callbacks"
optional = false
python-versions = ">=3.7"
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optional = false
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Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[ [ package ] ]
name = "blis"
version = "0.7.10"
description = "The Blis BLAS-like linear algebra library, as a self-contained C-extension."
optional = true
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[ package . dependencies ]
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]
[ [ package ] ]
name = "catalogue"
version = "2.0.9"
description = "Super lightweight function registries for your library"
optional = true
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2023-07-21 17:36:28 +00:00
[ [ package ] ]
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2023-08-22 18:09:35 +00:00
version = "2023.7.22"
2023-07-21 17:36:28 +00:00
description = "Python package for providing Mozilla's CA Bundle."
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2023-08-22 18:09:35 +00:00
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2023-07-21 17:36:28 +00:00
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[ package . dependencies ]
pycparser = "*"
[ [ package ] ]
name = "charset-normalizer"
version = "3.2.0"
description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
optional = false
python-versions = ">=3.7.0"
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name = "click"
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version = "8.1.7"
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description = "Composable command line interface toolkit"
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2023-07-21 17:36:28 +00:00
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[ package . dependencies ]
colorama = { version = "*" , markers = "platform_system == \"Windows\"" }
[ [ package ] ]
name = "colorama"
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description = "Cross-platform colored terminal text."
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version = "0.1.4"
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[ package . dependencies ]
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traitlets = ">=4"
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[ package . extras ]
lint = [ "black (>=22.6.0)" , "mdformat (>0.7)" , "mdformat-gfm (>=0.3.5)" , "ruff (>=0.0.156)" ]
test = [ "pytest" ]
typing = [ "mypy (>=0.990)" ]
Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[ [ package ] ]
name = "confection"
version = "0.1.1"
description = "The sweetest config system for Python"
optional = true
python-versions = ">=3.6"
files = [
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srsly = ">=2.4.0,<3.0.0"
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name = "cymem"
version = "2.0.7"
description = "Manage calls to calloc/free through Cython"
optional = true
python-versions = "*"
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[ [ package ] ]
name = "dataclasses-json"
version = "0.5.9"
description = "Easily serialize dataclasses to and from JSON"
optional = false
python-versions = ">=3.6"
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marshmallow-enum = ">=1.5.1,<2.0.0"
typing-inspect = ">=0.4.0"
[ package . extras ]
dev = [ "flake8" , "hypothesis" , "ipython" , "mypy (>=0.710)" , "portray" , "pytest (>=7.2.0)" , "setuptools" , "simplejson" , "twine" , "types-dataclasses" , "wheel" ]
[ [ package ] ]
name = "debugpy"
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version = "1.6.7.post1"
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description = "An implementation of the Debug Adapter Protocol for Python"
optional = false
python-versions = ">=3.7"
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description = "Decorators for Humans"
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2023-07-21 17:36:28 +00:00
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[ package . extras ]
test = [ "pytest (>=6)" ]
[ [ package ] ]
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version = "1.2.0"
description = "Get the currently executing AST node of a frame, and other information"
optional = false
python-versions = "*"
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[ package . extras ]
tests = [ "asttokens" , "littleutils" , "pytest" , "rich" ]
Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[ [ package ] ]
name = "faker"
version = "19.3.1"
description = "Faker is a Python package that generates fake data for you."
optional = true
python-versions = ">=3.8"
files = [
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[ package . dependencies ]
python-dateutil = ">=2.4"
typing-extensions = { version = ">=3.10.0.1" , markers = "python_version <= \"3.8\"" }
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[ [ package ] ]
name = "fastjsonschema"
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version = "2.18.0"
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description = "Fastest Python implementation of JSON schema"
optional = false
python-versions = "*"
files = [
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]
[ package . extras ]
devel = [ "colorama" , "json-spec" , "jsonschema" , "pylint" , "pytest" , "pytest-benchmark" , "pytest-cache" , "validictory" ]
Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[ [ package ] ]
name = "filelock"
version = "3.12.3"
description = "A platform independent file lock."
optional = true
python-versions = ">=3.8"
files = [
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[ package . dependencies ]
typing-extensions = { version = ">=4.7.1" , markers = "python_version < \"3.11\"" }
[ package . extras ]
docs = [ "furo (>=2023.7.26)" , "sphinx (>=7.1.2)" , "sphinx-autodoc-typehints (>=1.24)" ]
testing = [ "covdefaults (>=2.3)" , "coverage (>=7.3)" , "diff-cover (>=7.7)" , "pytest (>=7.4)" , "pytest-cov (>=4.1)" , "pytest-mock (>=3.11.1)" , "pytest-timeout (>=2.1)" ]
2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "fqdn"
version = "1.5.1"
description = "Validates fully-qualified domain names against RFC 1123, so that they are acceptable to modern bowsers"
optional = false
python-versions = ">=2.7, !=3.0, !=3.1, !=3.2, !=3.3, !=3.4, <4"
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[ [ package ] ]
name = "frozenlist"
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python-versions = ">=3.8"
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[ [ package ] ]
name = "greenlet"
version = "2.0.2"
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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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Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
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]
Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
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name = "murmurhash"
version = "1.0.9"
description = "Cython bindings for MurmurHash"
optional = true
python-versions = ">=3.6"
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2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "mypy"
version = "0.991"
description = "Optional static typing for Python"
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description = "A decorator to automatically detect mismatch when overriding a method."
optional = false
python-versions = ">=3.6"
files = [
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2023-07-21 17:36:28 +00:00
]
[ [ package ] ]
name = "packaging"
version = "23.1"
description = "Core utilities for Python packages"
optional = false
python-versions = ">=3.7"
files = [
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]
[ [ package ] ]
name = "pandocfilters"
version = "1.5.0"
description = "Utilities for writing pandoc filters in python"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
files = [
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[ [ package ] ]
name = "parso"
version = "0.8.3"
description = "A Python Parser"
optional = false
python-versions = ">=3.6"
files = [
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]
[ package . extras ]
qa = [ "flake8 (==3.8.3)" , "mypy (==0.782)" ]
testing = [ "docopt" , "pytest (<6.0.0)" ]
[ [ package ] ]
name = "pathspec"
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version = "0.11.2"
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description = "Utility library for gitignore style pattern matching of file paths."
optional = false
python-versions = ">=3.7"
files = [
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{ file = "pathspec-0.11.2-py3-none-any.whl" , hash = "sha256:1d6ed233af05e679efb96b1851550ea95bbb64b7c490b0f5aa52996c11e92a20" } ,
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2023-07-21 17:36:28 +00:00
]
Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[ [ package ] ]
name = "pathy"
version = "0.10.2"
description = "pathlib.Path subclasses for local and cloud bucket storage"
optional = true
python-versions = ">= 3.6"
files = [
{ file = "pathy-0.10.2-py3-none-any.whl" , hash = "sha256:681bc98dbff28e7de3e50efa8246910f727e8ac254c4318c47ce341f7c1ce21d" } ,
{ file = "pathy-0.10.2.tar.gz" , hash = "sha256:79c572ab7fed84dc46837346edae58565992d0477a789cd4691a41d8eab9917d" } ,
]
[ package . dependencies ]
smart-open = ">=5.2.1,<7.0.0"
typer = ">=0.3.0,<1.0.0"
[ package . extras ]
all = [ "azure-storage-blob" , "boto3" , "google-cloud-storage (>=1.26.0,<2.0.0)" , "mock" , "pytest" , "pytest-coverage" , "typer-cli" ]
azure = [ "azure-storage-blob" ]
gcs = [ "google-cloud-storage (>=1.26.0,<2.0.0)" ]
s3 = [ "boto3" ]
test = [ "mock" , "pytest" , "pytest-coverage" , "typer-cli" ]
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[ [ package ] ]
name = "pexpect"
version = "4.8.0"
description = "Pexpect allows easy control of interactive console applications."
optional = false
python-versions = "*"
files = [
{ file = "pexpect-4.8.0-py2.py3-none-any.whl" , hash = "sha256:0b48a55dcb3c05f3329815901ea4fc1537514d6ba867a152b581d69ae3710937" } ,
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]
[ package . dependencies ]
ptyprocess = ">=0.5"
Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[ [ package ] ]
name = "phonenumbers"
version = "8.13.19"
description = "Python version of Google's common library for parsing, formatting, storing and validating international phone numbers."
optional = true
python-versions = "*"
files = [
{ file = "phonenumbers-8.13.19-py2.py3-none-any.whl" , hash = "sha256:ba542f20f6dc83be8f127f240f9b5b7e7c1dec42aceff1879400d4dc0c781d81" } ,
{ file = "phonenumbers-8.13.19.tar.gz" , hash = "sha256:38180247697240ccedd74dec4bfbdbc22bb108b9c5f991f270ca3e41395e6f96" } ,
]
2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "pickleshare"
version = "0.7.5"
description = "Tiny 'shelve'-like database with concurrency support"
optional = false
python-versions = "*"
files = [
{ file = "pickleshare-0.7.5-py2.py3-none-any.whl" , hash = "sha256:9649af414d74d4df115d5d718f82acb59c9d418196b7b4290ed47a12ce62df56" } ,
{ file = "pickleshare-0.7.5.tar.gz" , hash = "sha256:87683d47965c1da65cdacaf31c8441d12b8044cdec9aca500cd78fc2c683afca" } ,
]
[ [ package ] ]
name = "pkgutil-resolve-name"
version = "1.3.10"
description = "Resolve a name to an object."
optional = false
python-versions = ">=3.6"
files = [
{ file = "pkgutil_resolve_name-1.3.10-py3-none-any.whl" , hash = "sha256:ca27cc078d25c5ad71a9de0a7a330146c4e014c2462d9af19c6b828280649c5e" } ,
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]
[ [ package ] ]
name = "platformdirs"
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version = "3.10.0"
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description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
optional = false
python-versions = ">=3.7"
files = [
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{ file = "platformdirs-3.10.0-py3-none-any.whl" , hash = "sha256:d7c24979f292f916dc9cbf8648319032f551ea8c49a4c9bf2fb556a02070ec1d" } ,
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2023-07-21 17:36:28 +00:00
]
[ package . extras ]
2023-08-22 18:09:35 +00:00
docs = [ "furo (>=2023.7.26)" , "proselint (>=0.13)" , "sphinx (>=7.1.1)" , "sphinx-autodoc-typehints (>=1.24)" ]
test = [ "appdirs (==1.4.4)" , "covdefaults (>=2.3)" , "pytest (>=7.4)" , "pytest-cov (>=4.1)" , "pytest-mock (>=3.11.1)" ]
2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "pluggy"
version = "1.2.0"
description = "plugin and hook calling mechanisms for python"
optional = false
python-versions = ">=3.7"
files = [
{ file = "pluggy-1.2.0-py3-none-any.whl" , hash = "sha256:c2fd55a7d7a3863cba1a013e4e2414658b1d07b6bc57b3919e0c63c9abb99849" } ,
{ file = "pluggy-1.2.0.tar.gz" , hash = "sha256:d12f0c4b579b15f5e054301bb226ee85eeeba08ffec228092f8defbaa3a4c4b3" } ,
]
[ package . extras ]
dev = [ "pre-commit" , "tox" ]
testing = [ "pytest" , "pytest-benchmark" ]
Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[ [ package ] ]
name = "preshed"
version = "3.0.8"
description = "Cython hash table that trusts the keys are pre-hashed"
optional = true
python-versions = ">=3.6"
files = [
{ file = "preshed-3.0.8-cp310-cp310-macosx_10_9_x86_64.whl" , hash = "sha256:ea4b6df8ef7af38e864235256793bc3056e9699d991afcf6256fa298858582fc" } ,
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[ package . dependencies ]
cymem = ">=2.0.2,<2.1.0"
murmurhash = ">=0.28.0,<1.1.0"
[ [ package ] ]
name = "presidio-analyzer"
version = "2.2.33"
description = "Presidio analyzer package"
optional = true
python-versions = "*"
files = [
{ file = "presidio_analyzer-2.2.33-py3-none-any.whl" , hash = "sha256:1e0d4237f9ac28953e910900b42852927dbf8935de7bf023aebddc752a5bf9ea" } ,
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[ package . dependencies ]
phonenumbers = ">=8.12"
pyyaml = "*"
regex = "*"
spacy = ">=3.4.4"
tldextract = "*"
[ package . extras ]
transformers = [ "torch" , "transformers" ]
[ [ package ] ]
name = "presidio-anonymizer"
version = "2.2.33"
description = "Persidio Anonymizer package - replaces analyzed text with desired values."
optional = true
python-versions = ">=3.5"
files = [
{ file = "presidio_anonymizer-2.2.33-py3-none-any.whl" , hash = "sha256:d1e7feff5ff2bc0eed13425356bce19e8e5ffda1f733d5d603b282ccfbe742d0" } ,
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[ package . dependencies ]
pycryptodome = ">=3.10.1"
2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "prometheus-client"
version = "0.17.1"
description = "Python client for the Prometheus monitoring system."
optional = false
python-versions = ">=3.6"
files = [
{ file = "prometheus_client-0.17.1-py3-none-any.whl" , hash = "sha256:e537f37160f6807b8202a6fc4764cdd19bac5480ddd3e0d463c3002b34462101" } ,
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]
[ package . extras ]
twisted = [ "twisted" ]
[ [ package ] ]
name = "prompt-toolkit"
version = "3.0.39"
description = "Library for building powerful interactive command lines in Python"
optional = false
python-versions = ">=3.7.0"
files = [
{ file = "prompt_toolkit-3.0.39-py3-none-any.whl" , hash = "sha256:9dffbe1d8acf91e3de75f3b544e4842382fc06c6babe903ac9acb74dc6e08d88" } ,
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Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
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2023-07-21 17:36:28 +00:00
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2023-08-22 18:09:35 +00:00
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2023-07-21 17:36:28 +00:00
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python-versions = ">=3.7"
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[ package . dependencies ]
packaging = "*"
[ package . extras ]
test = [ "pytest (>=6,!=7.0.0,!=7.0.1)" , "pytest-cov (>=3.0.0)" , "pytest-qt" ]
[ [ package ] ]
name = "referencing"
2023-08-22 18:09:35 +00:00
version = "0.30.2"
2023-07-21 17:36:28 +00:00
description = "JSON Referencing + Python"
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python-versions = ">=3.8"
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2023-08-22 18:09:35 +00:00
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2023-07-21 17:36:28 +00:00
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attrs = ">=22.2.0"
rpds-py = ">=0.7.0"
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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2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "requests"
version = "2.31.0"
description = "Python HTTP for Humans."
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certifi = ">=2017.4.17"
charset-normalizer = ">=2,<4"
idna = ">=2.5,<4"
urllib3 = ">=1.21.1,<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
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name = "requests-file"
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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"
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[ package . dependencies ]
six = "*"
[ [ package ] ]
name = "rfc3986-validator"
version = "0.1.1"
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optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
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[ [ package ] ]
name = "ruff"
version = "0.0.249"
description = "An extremely fast Python linter, written in Rust."
optional = false
python-versions = ">=3.7"
files = [
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[ [ package ] ]
name = "send2trash"
version = "1.8.2"
description = "Send file to trash natively under Mac OS X, Windows and Linux"
optional = false
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
files = [
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]
[ package . extras ]
nativelib = [ "pyobjc-framework-Cocoa" , "pywin32" ]
objc = [ "pyobjc-framework-Cocoa" ]
win32 = [ "pywin32" ]
[ [ package ] ]
name = "setuptools"
version = "67.8.0"
description = "Easily download, build, install, upgrade, and uninstall Python packages"
optional = false
python-versions = ">=3.7"
files = [
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]
[ package . extras ]
docs = [ "furo" , "jaraco.packaging (>=9)" , "jaraco.tidelift (>=1.4)" , "pygments-github-lexers (==0.0.5)" , "rst.linker (>=1.9)" , "sphinx (>=3.5)" , "sphinx-favicon" , "sphinx-hoverxref (<2)" , "sphinx-inline-tabs" , "sphinx-lint" , "sphinx-notfound-page (==0.8.3)" , "sphinx-reredirects" , "sphinxcontrib-towncrier" ]
testing = [ "build[virtualenv]" , "filelock (>=3.4.0)" , "flake8-2020" , "ini2toml[lite] (>=0.9)" , "jaraco.envs (>=2.2)" , "jaraco.path (>=3.2.0)" , "pip (>=19.1)" , "pip-run (>=8.8)" , "pytest (>=6)" , "pytest-black (>=0.3.7)" , "pytest-checkdocs (>=2.4)" , "pytest-cov" , "pytest-enabler (>=1.3)" , "pytest-mypy (>=0.9.1)" , "pytest-perf" , "pytest-ruff" , "pytest-timeout" , "pytest-xdist" , "tomli-w (>=1.0.0)" , "virtualenv (>=13.0.0)" , "wheel" ]
testing-integration = [ "build[virtualenv]" , "filelock (>=3.4.0)" , "jaraco.envs (>=2.2)" , "jaraco.path (>=3.2.0)" , "pytest" , "pytest-enabler" , "pytest-xdist" , "tomli" , "virtualenv (>=13.0.0)" , "wheel" ]
[ [ package ] ]
name = "six"
version = "1.16.0"
description = "Python 2 and 3 compatibility utilities"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
files = [
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]
Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[ [ package ] ]
name = "smart-open"
version = "6.3.0"
description = "Utils for streaming large files (S3, HDFS, GCS, Azure Blob Storage, gzip, bz2...)"
optional = true
python-versions = ">=3.6,<4.0"
files = [
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{ file = "smart_open-6.3.0.tar.gz" , hash = "sha256:d5238825fe9a9340645fac3d75b287c08fbb99fb2b422477de781c9f5f09e019" } ,
]
[ package . extras ]
all = [ "azure-common" , "azure-core" , "azure-storage-blob" , "boto3" , "google-cloud-storage (>=2.6.0)" , "paramiko" , "requests" ]
azure = [ "azure-common" , "azure-core" , "azure-storage-blob" ]
gcs = [ "google-cloud-storage (>=2.6.0)" ]
http = [ "requests" ]
s3 = [ "boto3" ]
ssh = [ "paramiko" ]
test = [ "azure-common" , "azure-core" , "azure-storage-blob" , "boto3" , "google-cloud-storage (>=2.6.0)" , "moto[server]" , "paramiko" , "pytest" , "pytest-rerunfailures" , "requests" , "responses" ]
webhdfs = [ "requests" ]
2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "sniffio"
version = "1.3.0"
description = "Sniff out which async library your code is running under"
optional = false
python-versions = ">=3.7"
files = [
{ file = "sniffio-1.3.0-py3-none-any.whl" , hash = "sha256:eecefdce1e5bbfb7ad2eeaabf7c1eeb404d7757c379bd1f7e5cce9d8bf425384" } ,
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]
[ [ package ] ]
name = "soupsieve"
version = "2.4.1"
description = "A modern CSS selector implementation for Beautiful Soup."
optional = false
python-versions = ">=3.7"
files = [
{ file = "soupsieve-2.4.1-py3-none-any.whl" , hash = "sha256:1c1bfee6819544a3447586c889157365a27e10d88cde3ad3da0cf0ddf646feb8" } ,
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]
Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[ [ package ] ]
name = "spacy"
version = "3.6.1"
description = "Industrial-strength Natural Language Processing (NLP) in Python"
optional = true
python-versions = ">=3.6"
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requests = ">=2.13.0,<3.0.0"
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spacy-loggers = ">=1.0.0,<2.0.0"
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typer = ">=0.3.0,<0.10.0"
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[ package . extras ]
apple = [ "thinc-apple-ops (>=0.1.0.dev0,<1.0.0)" ]
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cuda-autodetect = [ "cupy-wheel (>=11.0.0,<13.0.0)" ]
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ray = [ "spacy-ray (>=0.1.0,<1.0.0)" ]
th = [ "pythainlp (>=2.0)" ]
transformers = [ "spacy-transformers (>=1.1.2,<1.3.0)" ]
[ [ package ] ]
name = "spacy-legacy"
version = "3.0.12"
description = "Legacy registered functions for spaCy backwards compatibility"
optional = true
python-versions = ">=3.6"
files = [
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[ [ package ] ]
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2023-07-21 17:36:28 +00:00
[ [ package ] ]
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2023-08-22 18:09:35 +00:00
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2023-07-21 17:36:28 +00:00
description = "Database Abstraction Library"
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[ package . dependencies ]
greenlet = { version = "!=0.4.17" , markers = "platform_machine == \"win32\" or platform_machine == \"WIN32\" or platform_machine == \"AMD64\" or platform_machine == \"amd64\" or platform_machine == \"x86_64\" or platform_machine == \"ppc64le\" or platform_machine == \"aarch64\"" }
2023-07-21 17:36:28 +00:00
typing-extensions = ">=4.2.0"
[ package . extras ]
2023-08-22 18:09:35 +00:00
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mssql-pyodbc = [ "pyodbc" ]
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mysql-connector = [ "mysql-connector-python" ]
oracle = [ "cx-oracle (>=7)" ]
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postgresql-psycopg2binary = [ "psycopg2-binary" ]
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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
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2023-07-21 17:36:28 +00:00
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[ [ package ] ]
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2023-08-22 18:09:35 +00:00
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2023-07-21 17:36:28 +00:00
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2023-07-21 17:36:28 +00:00
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[ [ package ] ]
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docs = [ "myst-parser" , "pydata-sphinx-theme" , "sphinx" ]
test = [ "pre-commit" , "pytest (>=7.0)" , "pytest-timeout" ]
Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[ [ package ] ]
name = "thinc"
version = "8.1.12"
description = "A refreshing functional take on deep learning, compatible with your favorite libraries"
optional = true
python-versions = ">=3.6"
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confection = ">=0.0.1,<1.0.0"
cymem = ">=2.0.2,<2.1.0"
murmurhash = ">=1.0.2,<1.1.0"
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setuptools = "*"
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cuda91 = [ "cupy-cuda91 (>=5.0.0b4)" ]
cuda92 = [ "cupy-cuda92 (>=5.0.0b4)" ]
datasets = [ "ml-datasets (>=0.2.0,<0.3.0)" ]
mxnet = [ "mxnet (>=1.5.1,<1.6.0)" ]
tensorflow = [ "tensorflow (>=2.0.0,<2.6.0)" ]
torch = [ "torch (>=1.6.0)" ]
2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "tinycss2"
version = "1.2.1"
description = "A tiny CSS parser"
optional = false
python-versions = ">=3.7"
files = [
{ file = "tinycss2-1.2.1-py3-none-any.whl" , hash = "sha256:2b80a96d41e7c3914b8cda8bc7f705a4d9c49275616e886103dd839dfc847847" } ,
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[ package . dependencies ]
webencodings = ">=0.4"
[ package . extras ]
doc = [ "sphinx" , "sphinx_rtd_theme" ]
test = [ "flake8" , "isort" , "pytest" ]
Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[ [ package ] ]
name = "tldextract"
version = "3.4.4"
description = "Accurately separates a URL's subdomain, domain, and public suffix, using the Public Suffix List (PSL). By default, this includes the public ICANN TLDs and their exceptions. You can optionally support the Public Suffix List's private domains as well."
optional = true
python-versions = ">=3.7"
files = [
{ file = "tldextract-3.4.4-py3-none-any.whl" , hash = "sha256:581e7dbefc90e7bb857bb6f768d25c811a3c5f0892ed56a9a2999ddb7b1b70c2" } ,
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]
[ package . dependencies ]
filelock = ">=3.0.8"
idna = "*"
requests = ">=2.1.0"
requests-file = ">=1.4"
2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "tomli"
version = "2.0.1"
description = "A lil' TOML parser"
optional = false
python-versions = ">=3.7"
files = [
{ file = "tomli-2.0.1-py3-none-any.whl" , hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc" } ,
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]
[ [ package ] ]
name = "tornado"
2023-08-22 18:09:35 +00:00
version = "6.3.3"
2023-07-21 17:36:28 +00:00
description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
optional = false
python-versions = ">= 3.8"
files = [
2023-08-22 18:09:35 +00:00
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2023-07-21 17:36:28 +00:00
]
Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[ [ package ] ]
name = "tqdm"
version = "4.66.1"
description = "Fast, Extensible Progress Meter"
optional = true
python-versions = ">=3.7"
files = [
{ file = "tqdm-4.66.1-py3-none-any.whl" , hash = "sha256:d302b3c5b53d47bce91fea46679d9c3c6508cf6332229aa1e7d8653723793386" } ,
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[ package . dependencies ]
colorama = { version = "*" , markers = "platform_system == \"Windows\"" }
[ package . extras ]
dev = [ "pytest (>=6)" , "pytest-cov" , "pytest-timeout" , "pytest-xdist" ]
notebook = [ "ipywidgets (>=6)" ]
slack = [ "slack-sdk" ]
telegram = [ "requests" ]
2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "traitlets"
version = "5.9.0"
description = "Traitlets Python configuration system"
optional = false
python-versions = ">=3.7"
files = [
{ file = "traitlets-5.9.0-py3-none-any.whl" , hash = "sha256:9e6ec080259b9a5940c797d58b613b5e31441c2257b87c2e795c5228ae80d2d8" } ,
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]
[ package . extras ]
docs = [ "myst-parser" , "pydata-sphinx-theme" , "sphinx" ]
test = [ "argcomplete (>=2.0)" , "pre-commit" , "pytest" , "pytest-mock" ]
Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[ [ package ] ]
name = "typer"
version = "0.9.0"
description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
optional = true
python-versions = ">=3.6"
files = [
{ file = "typer-0.9.0-py3-none-any.whl" , hash = "sha256:5d96d986a21493606a358cae4461bd8cdf83cbf33a5aa950ae629ca3b51467ee" } ,
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]
[ package . dependencies ]
click = ">=7.1.1,<9.0.0"
typing-extensions = ">=3.7.4.3"
[ package . extras ]
all = [ "colorama (>=0.4.3,<0.5.0)" , "rich (>=10.11.0,<14.0.0)" , "shellingham (>=1.3.0,<2.0.0)" ]
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doc = [ "cairosvg (>=2.5.2,<3.0.0)" , "mdx-include (>=1.4.1,<2.0.0)" , "mkdocs (>=1.1.2,<2.0.0)" , "mkdocs-material (>=8.1.4,<9.0.0)" , "pillow (>=9.3.0,<10.0.0)" ]
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2023-07-21 20:32:39 +00:00
[ [ package ] ]
name = "types-pyyaml"
version = "6.0.12.11"
description = "Typing stubs for PyYAML"
optional = false
python-versions = "*"
files = [
{ file = "types-PyYAML-6.0.12.11.tar.gz" , hash = "sha256:7d340b19ca28cddfdba438ee638cd4084bde213e501a3978738543e27094775b" } ,
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]
2023-09-06 20:32:59 +00:00
[ [ package ] ]
name = "types-requests"
version = "2.31.0.2"
description = "Typing stubs for requests"
optional = false
python-versions = "*"
files = [
{ file = "types-requests-2.31.0.2.tar.gz" , hash = "sha256:6aa3f7faf0ea52d728bb18c0a0d1522d9bfd8c72d26ff6f61bfc3d06a411cf40" } ,
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[ package . dependencies ]
types-urllib3 = "*"
[ [ package ] ]
name = "types-urllib3"
version = "1.26.25.14"
description = "Typing stubs for urllib3"
optional = false
python-versions = "*"
files = [
{ file = "types-urllib3-1.26.25.14.tar.gz" , hash = "sha256:229b7f577c951b8c1b92c1bc2b2fdb0b49847bd2af6d1cc2a2e3dd340f3bda8f" } ,
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2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "typing-extensions"
version = "4.7.1"
description = "Backported and Experimental Type Hints for Python 3.7+"
optional = false
python-versions = ">=3.7"
files = [
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[ [ package ] ]
name = "typing-inspect"
version = "0.9.0"
description = "Runtime inspection utilities for typing module."
optional = false
python-versions = "*"
files = [
{ file = "typing_inspect-0.9.0-py3-none-any.whl" , hash = "sha256:9ee6fc59062311ef8547596ab6b955e1b8aa46242d854bfc78f4f6b0eff35f9f" } ,
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[ package . dependencies ]
mypy-extensions = ">=0.3.0"
typing-extensions = ">=3.7.4"
[ [ package ] ]
name = "uri-template"
version = "1.3.0"
description = "RFC 6570 URI Template Processor"
optional = false
python-versions = ">=3.7"
files = [
{ file = "uri-template-1.3.0.tar.gz" , hash = "sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7" } ,
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[ package . extras ]
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[ [ package ] ]
name = "urllib3"
version = "2.0.4"
description = "HTTP library with thread-safe connection pooling, file post, and more."
optional = false
python-versions = ">=3.7"
files = [
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[ package . extras ]
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secure = [ "certifi" , "cryptography (>=1.9)" , "idna (>=2.0.0)" , "pyopenssl (>=17.1.0)" , "urllib3-secure-extra" ]
socks = [ "pysocks (>=1.5.6,!=1.5.7,<2.0)" ]
zstd = [ "zstandard (>=0.18.0)" ]
Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[ [ package ] ]
name = "wasabi"
version = "1.1.2"
description = "A lightweight console printing and formatting toolkit"
optional = true
python-versions = ">=3.6"
files = [
{ file = "wasabi-1.1.2-py3-none-any.whl" , hash = "sha256:0a3f933c4bf0ed3f93071132c1b87549733256d6c8de6473c5f7ed2e171b5cf9" } ,
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[ package . dependencies ]
colorama = { version = ">=0.4.6" , markers = "sys_platform == \"win32\" and python_version >= \"3.7\"" }
2023-07-21 17:36:28 +00:00
[ [ package ] ]
name = "wcwidth"
version = "0.2.6"
description = "Measures the displayed width of unicode strings in a terminal"
optional = false
python-versions = "*"
files = [
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[ [ package ] ]
name = "webcolors"
version = "1.13"
description = "A library for working with the color formats defined by HTML and CSS."
optional = false
python-versions = ">=3.7"
files = [
{ file = "webcolors-1.13-py3-none-any.whl" , hash = "sha256:29bc7e8752c0a1bd4a1f03c14d6e6a72e93d82193738fa860cbff59d0fcc11bf" } ,
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[ package . extras ]
docs = [ "furo" , "sphinx" , "sphinx-copybutton" , "sphinx-inline-tabs" , "sphinx-notfound-page" , "sphinxext-opengraph" ]
tests = [ "pytest" , "pytest-cov" ]
[ [ package ] ]
name = "webencodings"
version = "0.5.1"
description = "Character encoding aliases for legacy web content"
optional = false
python-versions = "*"
files = [
{ file = "webencodings-0.5.1-py2.py3-none-any.whl" , hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78" } ,
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]
[ [ package ] ]
name = "websocket-client"
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version = "1.6.2"
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description = "WebSocket client for Python with low level API options"
optional = false
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python-versions = ">=3.8"
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files = [
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]
[ package . extras ]
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docs = [ "Sphinx (>=6.0)" , "sphinx-rtd-theme (>=1.1.0)" ]
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optional = [ "python-socks" , "wsaccel" ]
test = [ "websockets" ]
[ [ package ] ]
name = "widgetsnbextension"
version = "4.0.8"
description = "Jupyter interactive widgets for Jupyter Notebook"
optional = false
python-versions = ">=3.7"
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]
[ package . dependencies ]
idna = ">=2.0"
multidict = ">=4.0"
[ [ package ] ]
name = "zipp"
version = "3.16.2"
description = "Backport of pathlib-compatible object wrapper for zip files"
optional = false
python-versions = ">=3.8"
files = [
{ file = "zipp-3.16.2-py3-none-any.whl" , hash = "sha256:679e51dd4403591b2d6838a48de3d283f3d188412a9782faadf845f298736ba0" } ,
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]
[ package . extras ]
docs = [ "furo" , "jaraco.packaging (>=9.3)" , "jaraco.tidelift (>=1.4)" , "rst.linker (>=1.9)" , "sphinx (>=3.5)" , "sphinx-lint" ]
testing = [ "big-O" , "jaraco.functools" , "jaraco.itertools" , "more-itertools" , "pytest (>=6)" , "pytest-black (>=0.3.7)" , "pytest-checkdocs (>=2.4)" , "pytest-cov" , "pytest-enabler (>=2.2)" , "pytest-ignore-flaky" , "pytest-mypy (>=0.9.1)" , "pytest-ruff" ]
Add data anonymizer (#9863)
### Description
The feature for anonymizing data has been implemented. In order to
protect private data, such as when querying external APIs (OpenAI), it
is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally
identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do
not reveal any personal information about the individual but can be used
for reference. We're not using regular encryption, because the language
model won't be able to understand the meaning or context of the
encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for
anonymization purposes because of the wide range of functionalities they
provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For
example, the workflow could look like this: `anonymize -> LLMChain ->
deanonymize`. By doing this, we will retain anonymity in requests to,
for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is
treated as a separate entity and separately anonymized. Therefore, two
occurrences of the name John Doe in the text will be changed to two
different names. It is therefore worth introducing support for full
instance detection, so that repeated occurrences are treated as a single
object.
### Twitter handle
@deepsense_ai / @MaksOpp
---------
Co-authored-by: MaksOpp <maks.operlejn@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-30 17:39:44 +00:00
[ extras ]
extended-testing = [ "faker" , "presidio-analyzer" , "presidio-anonymizer" ]
2023-07-21 17:36:28 +00:00
[ metadata ]
lock-version = "2.0"
python-versions = ">=3.8.1,<4.0"
2023-09-06 20:32:59 +00:00
content-hash = "443e88f690572715cf58671e4480a006574c7141a1258dff0a0818b954184901"
