Nebula Siwi: A Dialog System With Graph Database Backed Knowledge Graph

Wey Gu included in category Nebula Graph

2021-09-18 2023-02-09 1096 words 6 minutes

Dialog System With Graph Database Backed Knowledge Graph. 基于图数据库的智能问答助手

Contents

a PoC of Dialog System With Graph Database Backed Knowledge Graph.

Related GitHub Repo: https://github.com/wey-gu/nebula-siwi/

I created the Katacoda Interactive Env for this project 👉🏻 https://siwei.io/cources/

Now you can play with the data on Nebula Playground: https://nebula-graph.io/demo/

Siwi the voice assistant

Siwi (/ˈsɪwi/) is a PoC of Dialog System With Graph Database Backed Knowledge Graph.

For now, it’s a demo for task-driven(not general purpose) dialog bots with KG(Knowledge Graph) leveraging Nebula Graph with the minimal/sample dataset from Nebula Graph Manual/ NG中文手册.

Tips: Now you can play with the graph online without installing yourself!

Nebula Playground | Nebula Playground - China Mainland

Supported queries:

relation:

What is the relationship between Yao Ming and Lakers?
How does Yao Ming and Lakers connected?

serving:

Which team had Yao Ming served?

friendship:

Whom does Tim Duncan follow?
Who are Yao Ming’s friends?

1 Deploy and Try

TBD (leveraging docker and nebula-up)

2 How does it work?

This is one of the most naive pipeline for a specific domain/ single purpose chat bot built on a Knowledge Graph.

2.1 Backend

The Backend(Siwi API) is a Flask based API server:

Flask API server takes questions in HTTP POST, and calls the bot API.
In bot API part there are classfier(Symentic Parsing, Intent Matching, Slot Filling), and question actors(Call corresponding actions to query Knowledge Graph with intents and slots).
Knowledge Graph is built on an Open-Source Graph Database: Nebula Graph

2.2 Frontend

The Frontend is a VueJS Single Page Applicaiton(SPA):

I reused a Vue Bot UI to showcase a chat window in this human-agent interaction, typing is supported.
In addtion, leverating Chrome’s Web Speech API, a button to listen to human voice is introduced

2.3 A Query Flow

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┌────────────────┬──────────────────────────────────────┐
│                │                                      │
│                │  Speech                              │
│     ┌──────────▼──────────┐                           │
│     │            Frontend │   Siwi, /ˈsɪwi/           │
│     │ Web_Speech_API      │   A PoC of                │
│     │                     │   Dialog System           │
│     │ Vue.JS              │   With Graph Database     │
│     │                     │   Backed Knowledge Graph  │
│     └──────────┬──────────┘                           │
│                │  Sentence                            │
│                │                                      │
│   ┌────────────┼──────────────────────────────┐       │
│   │            │                              │       │
│   │            │              Backend         │       │
│   │ ┌──────────▼──────────┐                   │       │
│   │ │ Web API, Flask      │   ./app/          │       │
│   │ └──────────┬──────────┘                   │       │
│   │            │  Sentence    ./bot/          │       │
│   │ ┌──────────▼──────────┐                   │       │
│   │ │                     │                   │       │
│   │ │ Intent matching,    │   ./bot/classifier│       │
│   │ │ Symentic Processing │                   │       │
│   │ │                     │                   │       │
│   │ └──────────┬──────────┘                   │       │
│   │            │  Intent, Entities            │       │
│   │ ┌──────────▼──────────┐                   │       │
│   │ │                     │                   │       │
│   │ │ Intent Actor        │   ./bot/actions   │       │
│   │ │                     │                   │       │
│   └─┴──────────┬──────────┴───────────────────┘       │
│                │  Graph Query                         │
│     ┌──────────▼──────────┐                           │
│     │                     │                           │
│     │ Graph Database      │    Nebula Graph           │
│     │                     │                           │
│     └─────────────────────┘                           │
│                                                       │
│                                                       │
│                                                       │
└───────────────────────────────────────────────────────┘

2.4 Source Code Tree

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.
├── README.md
├── src
│   ├── siwi                        # Siwi-API Backend
│   │   ├── app                     # Web Server, take HTTP requests and calls Bot API
│   │   └── bot                     # Bot API
│   │       ├── actions             # Take Intent, Slots, Query Knowledge Graph here
│   │       ├── bot                 # Entrypoint of the Bot API
│   │       ├── classifier          # Symentic Parsing, Intent Matching, Slot Filling
│   │       └── test                # Example Data Source as equivalent/mocked module
│   └── siwi_frontend               # Browser End
│       ├── README.md
│       ├── package.json
│       └── src
│           ├── App.vue             # Listening to user and pass Questions to Siwi-API
│           └── main.js
└── wsgi.py

3 Manually Run Components

3.1 Backend

Install and run.

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# Install siwi backend
python3 -m build

# Configure Nebula Graph Endpoint
export NG_ENDPOINTS=127.0.0.1:9669

# Run Backend API server
gunicorn --bind :5000 wsgi --workers 1 --threads 1 --timeout 60

For OpenFunction/ KNative

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docker build -t weygu/siwi-api .
docker run --rm --name siwi-api \
     --env=PORT=5000 \
     --env=NG_ENDPOINTS=127.0.0.1:9669 \
     --net=host \
     weygu/siwi-api

Try it out Web API:

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$ curl --header "Content-Type: application/json" \
       --request POST \
       --data '{"question": "What is the relationship between Yao Ming and Lakers?"}' \
       http://192.168.8.128:5000/query | jq

{
  "answer": "There are at least 23 relations between Yao Ming and Lakers, one relation path is: Yao Ming follows Shaquille O'Neal serves Lakers."
}

Call Bot Python API:

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from nebula2.gclient.net import ConnectionPool
from nebula2.Config import Config

# define a config
config = Config()
config.max_connection_pool_size = 10
# init connection pool
connection_pool = ConnectionPool()
# if the given servers are ok, return true, else return false
ok = connection_pool.init([('127.0.0.1', 9669)], config)

# import siwi bot
from siwi.bot import bot

# instantiate a bot
b = bot.SiwiBot(connection_pool)

# make the question query
b.query("Which team had Jonathon Simmons served?")

Then a response will be like this:

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In [4]: b.query("Which team had Jonathon Simmons serv
   ...: ed?")

[DEBUG] ServeAction intent: {'entities': {'Jonathon Simmons': 'player'}, 'intents': ('serve',)}

[DEBUG] query for RelationshipAction:
	USE basketballplayer;
  MATCH p=(v)-[e:serve*1]->(v1) WHERE id(v) == "player112"
  RETURN p LIMIT 100;

[2021-07-02 02:59:36,392]:Get connection to ('127.0.0.1', 9669)

Out[4]: 'Jonathon Simmons had served 3 teams. Spurs from 2015 to 2015; 76ers from 2019 to 2019; Magic from 2017 to 2017; '

3.2 Frontend

Referring to siwi_frontend

4 Further work

Use NBA-API to fallback undefined pattern questions
Wrap and manage sessions instead of get and release session per request, this is somehow costly actually.
Use NLP methods to implement proper Symentic Parsing, Intent Matching, Slot Filling
Build Graph to help with Intent Matching, especially for a general purpose bot
Use larger Dataset i.e. from wyattowalsh/basketball

5 Thanks to Upstream Projects ❤️

5.1 Backend

I learnt a lot from the KGQA on MedicalKG created by Huanyong Liu
Flask
pyahocorasick created by Wojciech Muła
PyYaml

5.2 Frontend

VueJS for frontend framework
Vue Bot UI, as a lovely bot UI in vue
Vue Web Speech, for speech API vue wrapper
Axios for browser http client
Solarized for color scheme
Vitesome for landing page design

Image credit goes to https://unsplash.com/photos/0E_vhMVqL9g