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!
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?
You can try with it from scratch here: https://siwei.io/learn/nebula-101-siwi-kgqa/
This is one of the most naive pipeline for a specific domain/ single purpose chat bot built on a Knowledge Graph.
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
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
┌────────────────┬──────────────────────────────────────┐
│ │ │
│ │ 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 │
│ │ │ │
│ └─────────────────────┘ │
│ │
│ │
│ │
└───────────────────────────────────────────────────────┘
.
├── 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.pyThe backend relis on the Nebula Graph, an Open Source Distributed Graph Database.
Install Nebula Graph in oneliner:
curl -fsSL nebula-up.siwei.io/install.sh | bashLoad the basketballplayer dataset.
~/.nebula-up/console.sh
nebula-console -addr graphd -port 9669 -user root -p nebula -e ":play basketballplayer"Install and run.
# 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 60For OpenFunction/ KNative
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-apiTry it out Web API:
$ curl -s --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:
from nebula3.gclient.net import ConnectionPool
from nebula3.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:
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; 'Referring to siwi_frontend
┌─────────────────────────────┐
│ kind: Ingress │ ┌───────────────────┐
│ path: / │ │ Pod │
│ -> siwi-frontend ────┼─────┤ siwi-frontend │
│ │ │ │
│ │ └───────────────────┘
│ │
│ path: /query │ ┌───────────────────────────────────┐
│ -> siwi-api ────┼─────┤ KNative Service │
│ KNative Serving │ │ serving-xxxx │
│ │ │ │
│ │ │ apiVersion: serving.knative.dev/v1│
│ │ │ kind: Service │
└─────────────────────────────┘ └─────────┬─────────────────────────┘
│
└────────────┐
│
┌───────────────────────────────────────────────────────┐ │
│apiVersion: core.openfunction.io/v1alpha1 │ │
│kind: Function │ │
│spec: │ │
│ version: "v1.0.0" │ │
│ image: "weygu/siwi-api:latest" │ │
│ imageCredentials: │ │
│ name: push-secret │ │
│ port: 8080 │ │
│ build: │ │
│ builder: openfunction/builder:v1 │ │
│ env: │ │
│ FUNC_NAME: "siwi_api" │ │
│ FUNC_TYPE: "http" │ │
│ FUNC_SRC: "main.py" │ │
│ srcRepo: │ │
│ url: "https://github.com/wey-gu/nebula-siwi.git" │ │
│ sourceSubPath: "src" │ │
│ serving: │ │
│ runtime: Knative ─────────────────────────────────┼──┘
│ params: │
│ NG_ENDPOINTS: "NEBULA_GRAPH_ENDPOINT" │
│ template: │ │
│ containers: │ │
│ - name: function │ │
│ imagePullPolicy: Always │ │
└───────────────────────────────────────┼───────────────┘
│
┌──────────┘
│
┌────────────────────────────┴───────────────────────────┐
│apiVersion:lapps.nebula-graph.io/v1alpha1 │
│kind: NebulaCluster │
│spec: │
│ graphd: │
│ config: │
│ system_memory_high_watermark_ratio: "1.0" │
│ image: vesoft/nebula-graphd │
│ replicas: 1 │
│... │
└────────────────────────────────────────────────────────┘
Assumed we have a k8s with OpenFunctions installed
Install a Nebula Graph with kubesphere-all-in-one nebula installer on KubeSphere:
curl -sL nebula-kind.siwei.io/install-ks-1.sh | bashGet Nebula Graph NodePort:
NEBULA_GRAPH_ENDPOINT=$(kubectl get svc nebula-graphd-svc-nodeport -o yaml -o jsonpath='{.spec.clusterIP}:{.spec.ports[0].port}')
echo $NEBULA_GRAPH_ENDPOINTLoad Dataset into the nebula cluster:
wget https://docs.nebula-graph.io/2.0/basketballplayer-2.X.ngql
~/.nebula-kind/bin/console -u root -p password --address=<nebula-graphd-svc-nodeport> --port=32669 -f basketballplayer-2.X.ngqlCreate the siwi-api powered by Openfunction:
cat siwi-api-function.yaml | sed "s/NEBULA_GRAPH_ENDPOINT/$NEBULA_GRAPH_ENDPOINT/g" | kubectl apply -f -Get the function nebula-siwi and the KNative Service:
kubectl get functions nebula-siwi
FUNCTION=$(kubectl get functions nebula-siwi -o go-template='{{.status.serving.resourceRef}}')
kubectl get ksvc -l openfunction.io/serving=$FUNCTION
KSVC=$(kubectl get ksvc -l openfunction.io/serving=$FUNCTION -o=jsonpath='{.items[0].metadata.name}')
kubectl get revision -l serving.knative.dev/service=$KSVC
REVISION=$(kubectl get revision -l serving.knative.dev/service=$KSVC -o=jsonpath='{.items[0].metadata.name}')
echo $REVISIONVerify the function worked fine:
curl -s --header "Content-Type: application/json" \
--request POST \
--data '{"question": "What is the relationship between Yao Ming and Lakers ?"}' \
$(kubectl get ksvc -l openfunction.io/serving=$FUNCTION -o=jsonpath='{.items[0].status.url}')/queryCreate the siwi-app resources on K8s:
cat siwi-app.yaml | sed "s/REVISION/$REVISION/g" | kubectl apply -f -Verify the function worked fine through the ingress:
Here nodeport with http port 31059 was used as ingress controller endpoint.
curl -s --header "Content-Type: application/json" \
--request POST \
--data '{"question": "how does Tim Duncan and Lakers connected?"}' \
demo-siwi.local:31059/queryVerify the frontend:
curl $(kubectl get svc -l app=siwi -o=jsonpath='{.items[0].spec.clusterIP}')Verify the frontend beind the ingress:
curl demo-siwi.local:31059Get all resources in siwi-app:
kubectl get service,pod,ingress,function -l app=siwiAnd it should be something like this:
[root@wey nebula-siwi]# kubectl get service,pod,ingress,function -l app=siwi
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
service/siwi-frontend-file ClusterIP 10.233.60.81 <none> 80/TCP 64m
NAME READY STATUS RESTARTS AGE
pod/siwi-frontend-file 1/1 Running 0 64m
NAME CLASS HOSTS ADDRESS PORTS AGE
ingress.networking.k8s.io/siwi-service <none> demo-siwi.local 80 59m
NAME BUILDSTATE SERVINGSTATE BUILDER SERVING AGE
function.core.openfunction.io/nebula-siwi Succeeded Running builder-sbfz6 serving-vvjvl 26h
[root@wey nebula-siwi]# kubectl get service,pod,ingress,function -l app=siwi
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
service/siwi-frontend-file ClusterIP 10.233.60.81 <none> 80/TCP 65m
NAME READY STATUS RESTARTS AGE
pod/siwi-frontend-file 1/1 Running 0 65m
NAME CLASS HOSTS ADDRESS PORTS AGE
ingress.networking.k8s.io/siwi-service <none> demo-siwi.local 80 59m
NAME BUILDSTATE SERVINGSTATE BUILDER SERVING AGE
function.core.openfunction.io/nebula-siwi Succeeded Running builder-sbfz6 serving-vvjvl 26hdocker build -t weygu/siwi-frontend . -f Dockerfile.froentend
docker push weygu/siwi-frontend- 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
- I learnt a lot from the KGQA on MedicalKG created by Huanyong Liu
- Flask
- pyahocorasick created by Wojciech Muła
- PyYaml
- 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