question_answering_system

QA System Based on Milvus & Towhee

This project combines Milvus and Towhee to build a question and answer system. This aims to provide a solution to achieve semantic similarity matching with Milvus combined with AI models.

More example about LLM(ChatGPT etc.) for chatbot, you can refer to the Enhanced QA.

Data description

The dataset for this system is a CSV format file which needs to contain a column of questions and a column of answers. And there is a sample data in the data directory.

How to deploy the system

1. Start Milvus and MySQL

The system will use Milvus to store and search the feature vector data, and Mysql is used to store the correspondence between the ids returned by Milvus and the questions data set, then you need to start Milvus and Mysql first.

• Start Milvus v2.2.10

First, you are supposed to refer to the Install Milvus v2.2.10 for how to run Milvus docker.

$ wget https://github.com/milvus-io/milvus/releases/download/v2.2.10/milvus-standalone-docker-compose.yml -O docker-compose.yml
$ sudo docker-compose up -d

• Start MySQL

$ docker run -p 3306:3306 -e MYSQL_ROOT_PASSWORD=123456 -d mysql:5.7

2. Start Server

The next step is to start the system server. It provides HTTP backend services, and there are two ways to start: running with Docker or source code.

2.1 Run server with Docker

• Set parameters

  Please modify the parameters according to your own environment. Here listing some parameters that need to be set, for more information please refer to config.py.

  Parameter	Description	example
  MILVUS_HOST	The IP address of Milvus, you can get it by ifconfig.	127.0.0.1
  MILVUS_PORT	The port of Milvus.	19530
  MYSQL_HOST	The IP address of MySQL.	127.0.0.1
  MYSQL_PORT	The port of MySQL	3306

  Please use your IP address to instead of '127.0.0.1'.

  $ export MILVUS_HOST='<your-ip>'
  $ export MILVUS_PORT='19530'
  $ export MYSQL_HOST='<your-ip>'

• Run Docker

  This image qa-chatbot-server:v1 is based Milvus2.0-rc3, if you want to use docker to start the Q&A server with Milvus2.0-rc5, please use the Dockerfile to build a new qa-chatbot image.

  $ docker run -d \
  -p 8000:8000 \
  -e "MILVUS_HOST=${MILVUS_HOST}" \
  -e "MILVUS_PORT=${MILVUS_PORT}" \
  -e "MYSQL_HOST=${MYSQL_HOST}" \
  milvusbootcamp/qa-chatbot-server:2.2.10

2.2 Run source code

• Install the Python packages

  $ git clone https://github.com/milvus-io/bootcamp.git
  $ cd bootcamp/solutions/nlp/question_answering_system/server
  $ pip install -r requirements.txt

• Set configuration

  $ vim src/config.py

  Please modify the parameters according to your own environment. Here listing some parameters that need to be set, for more information please refer to config.py.

  Parameter	Description	Default setting
  MILVUS_HOST	The IP address of Milvus, you can get it by ifconfig.	127.0.0.1
  MILVUS_PORT	Port of Milvus.	19530
  VECTOR_DIMENSION	Dimension of the vectors.	384
  MYSQL_HOST	The IP address of Mysql.	127.0.0.1
  MYSQL_PORT	Port of Milvus.	3306
  DEFAULT_TABLE	The milvus and mysql default collection name.	qa_search

• Run the code

  Then start the server with Fastapi.

$ python src/main.py

2.3 API docs

After starting the service, Please visit 127.0.0.1:8000/docs in your browser to view all the APIs.

/qa/load_data

This API is used to import Q&A datasets into the system.

/qa/search

This API is used to get similar questions in the system.

/qa/answer

This API is used to get the answer to a given question in the system.

/qa/count

This API is used to get the number of the questions in the system.

/qa/drop

This API is used to delete a specified collection.

3. Start Client

• Start the front-end

  # Please modify API_URL to the IP address and port of the server.
  $ export API_URL='http://127.0.0.1:8000'
  $ docker run -d -p 80:80 \
  -e API_URL=${API_URL} \
  milvusbootcamp/qa-chatbot-client:2.2.10

• How to use

  Enter 127.0.0.1:80 in the browser to open the interface for reverse image search.

  WEBCLIENT_IP specifies the IP address that runs qa-chatbot-client docker.

  i. Load data: Click the upload button, and then select a csv Q&A data file from the local to import it into the Q&A chatbot system. For the data format, you can refer to example_data in the data directory of this project.

  ii. Retrieve similar questions: Enter a question in the dialog, and then you'll get five questions most similar to the question in the Q&A library.

  iii. Obtain answer: Click any of the similar questions obtained in the previous step, and you'll get the answer.