add initial pii detection microservice (#153)

* add initial framework for pii detection

Signed-off-by: Chendi Xue <chendi.xue@intel.com>

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* add e2e test to tests

Signed-off-by: Chendi Xue <chendi.xue@intel.com>

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* update README per comments

Signed-off-by: Chendi Xue <chendi.xue@intel.com>

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* remove model specification in README

Signed-off-by: Chendi Xue <chendi.xue@intel.com>

* Remove big_model and update README

Signed-off-by: Chendi Xue <chendi.xue@intel.com>

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* enable debug mode in test bash

Signed-off-by: Chendi Xue <chendi.xue@intel.com>

* rename test file

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* mv pandas import into test

Signed-off-by: Chendi Xue <chendi.xue@intel.com>

* add new requirement for prometheus and except for user didn't provide
hg_token

Signed-off-by: Chendi Xue <chendi.xue@intel.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* mv pandas import to function

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* remove ip_addr hardcode

Signed-off-by: Chendi Xue <chendi.xue@intel.com>

---------

Signed-off-by: Chendi Xue <chendi.xue@intel.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: chen, suyue <suyue.chen@intel.com>

comps/guardrails/pii_detection/.gitignore

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+**/*pdf
+**/*csv
+**/*log
+**/*pyc

comps/guardrails/pii_detection/README.md

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+# PII Detection Microservice
+
+PII Detection a method to detect Personal Identifiable Information in text. This microservice provides users a unified API to either upload your files or send a list of text, and return with a list following original sequence of labels marking if it contains PII or not.
+
+# 🚀1. Start Microservice with Python（Option 1）
+
+## 1.1 Install Requirements
+
+```bash
+pip install -r requirements.txt
+```
+
+## 1.2 Start PII Detection Microservice with Python Script
+
+Start pii detection microservice with below command.
+
+```bash
+python pii_detection.py
+```
+
+# 🚀2. Start Microservice with Docker (Option 2)
+
+## 2.1 Prepare PII detection model
+
+export HUGGINGFACEHUB_API_TOKEN=${HP_TOKEN}
+
+## 2.1.1 use LLM endpoint (will add later)
+
+intro placeholder
+
+## 2.2 Build Docker Image
+
+```bash
+cd ../../../ # back to GenAIComps/ folder
+docker build -t opea/guardrails-pii-detection:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/guardrails/pii_detection/docker/Dockerfile .
+```
+
+## 2.3 Run Docker with CLI
+
+```bash
+docker run -d --rm --runtime=runc --name="guardrails-pii-detection-endpoint" -p 6357:6357 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN} -e HF_TOKEN=${HUGGINGFACEHUB_API_TOKEN} opea/guardrails-pii-detection:latest
+```
+
+> debug mode
+
+```bash
+docker run --rm --runtime=runc --name="guardrails-pii-detection-endpoint" -p 6357:6357 -v ./comps/guardrails/pii_detection/:/home/user/comps/guardrails/pii_detection/ --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN} -e HF_TOKEN=${HUGGINGFACEHUB_API_TOKEN} opea/guardrails-pii-detection:latest
+```
+
+# 🚀3. Get Status of Microservice
+
+```bash
+docker container logs -f guardrails-pii-detection-endpoint
+```
+
+# 🚀4. Consume Microservice
+
+Once microservice starts, user can use below script to invoke the microservice for pii detection.
+
+```python
+import requests
+import json
+
+proxies = {"http": ""}
+url = "http://localhost:6357/v1/dataprep"
+urls = [
+ "https://towardsdatascience.com/no-gpu-no-party-fine-tune-bert-for-sentiment-analysis-with-vertex-ai-custom-jobs-d8fc410e908b?source=rss----7f60cf5620c9---4"
+]
+payload = {"link_list": json.dumps(urls)}
+
+try:
+ resp = requests.post(url=url, data=payload, proxies=proxies)
+ print(resp.text)
+ resp.raise_for_status() # Raise an exception for unsuccessful HTTP status codes
+ print("Request successful!")
+except requests.exceptions.RequestException as e:
+ print("An error occurred:", e)
+```

comps/guardrails/pii_detection/__init__.py

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+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0

comps/guardrails/pii_detection/config.py

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+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+import pathlib
+
+# Embedding model
+
+EMBED_MODEL = os.getenv("EMBED_MODEL", "BAAI/bge-base-en-v1.5")
+
+# Redis Connection Information
+REDIS_HOST = os.getenv("REDIS_HOST", "localhost")
+REDIS_PORT = int(os.getenv("REDIS_PORT", 6379))
+
+
+def get_boolean_env_var(var_name, default_value=False):
+ """Retrieve the boolean value of an environment variable.
+
+ Args:
+ var_name (str): The name of the environment variable to retrieve.
+ default_value (bool): The default value to return if the variable
+ is not found.
+
+ Returns:
+ bool: The value of the environment variable, interpreted as a boolean.
+ """
+ true_values = {"true", "1", "t", "y", "yes"}
+ false_values = {"false", "0", "f", "n", "no"}
+
+ # Retrieve the environment variable's value
+ value = os.getenv(var_name, "").lower()
+
+ # Decide the boolean value based on the content of the string
+ if value in true_values:
+ return True
+ elif value in false_values:
+ return False
+ else:
+ return default_value
+
+
+LLM_URL = os.getenv("LLM_ENDPOINT_URL", None)
+
+current_file_path = pathlib.Path(__file__).parent.resolve()
+comps_path = os.path.join(current_file_path, "../../../")