We have 5 main repositories for all the codes in CheckMate. In the architecture diagram below, these are at the top, in light grey.
- User and Checkers Product Repository (this one)
- Handles WhatsApp bot, Telegram bot, and Telegram WebApp interactions for both users and checkers
- ML Repository
- Handles the OCR of image messages and the auto-categorisation of messages
- Agents Repository
- Handles the GenAI agent stuff
- Referrals Repository
- Handles referral links on the ref.checkmate.sg subdomain
- Website Repository
- Basically the website
Project is deployed on Google Cloud. Cloud Architecture comprises
- Firebase Functions (Orange below)
- Firestore (Green Below)
- Cloud Run (Blue Below)
- Cloud run is used for services that take too long to cold start
- Cloud Pub/Sub (Pink Below)
These SaaS are also being used in the product.
- OpenAI
- Used for GenAI features
- Typesense
- Used as vector DB and search engine
Finally, there are 3 main user interfaces, these are colored in dark grey.
- WhatsApp number/bot for users
- Telegram webapp for checkers
- Web endpoint for referrals
See below diagram for how the elements of the cloud architecture work together:
flowchart TB
A[User's WhatsApp]:::touchpoint
B[Referral Endpoint]:::touchpoint
C[Telegram Web App]:::touchpoint
classDef function fill:#FFD700
classDef database fill:#90EE90
classDef queue fill:#FFB6C1
classDef container fill:#ADD8E6
classDef touchpoint fill:#D3D3D3
subgraph Firebase Functions
direction TB
subgraph webhookHandlers
direction TB
webhookHandler:::function
end
subgraph eventHandlers
direction TB
onUserPublish:::function
onInstanceCreate:::function
onInstanceDelete:::function
onInstanceUpdate:::function
onMessageUpdate:::function
onMessageWrite:::function
onCheckerUpdate:::function
onVoteRequestUpdate:::function
end
subgraph apiHandlers
direction TB
apiHandler:::function
internalApiHandler:::function
telegramAuthHandler:::function
end
subgraph batchJobs
direction TB
checkSessionExpiring:::function
scheduledDeactivation:::function
sendInterimPrompt:::function
resetLeaderboard:::function
end
referralHandler:::function
subgraph agents
direction TB
agent-openai-asst-7fuliefohzfueuejhpyy7ntl:::function
end
end
subgraph PubSub
direction TB
userEvents:::queue
agentQueue:::queue
end
subgraph Firestore
direction TB
users[(Users)]:::database
checkers[(Checkers)]:::database
messages[(Messages)]:::database
instances[(Instances)]:::database
voteRequests[(Vote Requests)]:::database
blasts[(Blasts)]:::database
referrals[(Referrals)]:::database
end
subgraph Cloud Run
mlService:::container
seleniumChrome:::container
end
subgraph SaaS
OpenAI
Typesense
end
subgraph Github Repos
Repo1[Users and Checkers Repo]
Repo2[ML Repo]
Repo3[Agents Repo]
Repo4[Referrals Repo]
end
A --fires--> webhookHandler
webhookHandler --publishes--> userEvents
userEvents --triggers--> onUserPublish
onUserPublish --creates--> messages
onUserPublish --creates/updates--> users
onUserPublish --creates--> instances
onUserPublish --calls--> mlService
onUserPublish --calls--> OpenAI
mlService --calls--> OpenAI
onInstanceCreate --creates--> voteRequests
messages --triggers--> onMessageWrite
messages --triggers--> onMessageUpdate
instances --triggers--> onInstanceCreate
onInstanceCreate --publishes--> agentQueue
onInstanceCreate --syncs--> Typesense
onInstanceUpdate --syncs--> Typesense
onInstanceDelete --syncs--> Typesense
onMessageWrite --syncs---> Typesense
instances --triggers--> onInstanceUpdate
instances --triggers--> onInstanceDelete
checkers --triggers--> onCheckerUpdate
voteRequests --triggers--> onVoteRequestUpdate
onVoteRequestUpdate --updates--> checkers
onVoteRequestUpdate --updates--> messages
C --calls--> apiHandler
apiHandler --updates --> voteRequests
referralHandler --creates --> referrals
agents--calls --> seleniumChrome
agents--calls --> internalApiHandler
internalApiHandler --updates --> voteRequests
C --authenticates--> telegramAuthHandler
agentQueue --triggers--> agents
Repo1 --deploys--> eventHandlers
Repo1 --deploys--> apiHandlers
Repo1 --deploys--> batchJobs
Repo1 --deploys--> webhookHandlers
Repo2 --deploys --> mlService
Repo3 --deploys --> agents
Repo4 --deploys --> referralHandler
B --triggers--> referralHandler
%%Dummy Link For Aesthetic Purposes%%
apiHandler ~~~ internalApiHandler
internalApiHandler ~~~ telegramAuthHandler
checkSessionExpiring~~~scheduledDeactivation
scheduledDeactivation~~~sendInterimPrompt
sendInterimPrompt~~~resetLeaderboard
agent-openai-asst-7fuliefohzfueuejhpyy7ntl ~~~ apiHandler
userEvents ~~~ agentQueue
userEvents ~~~ agentQueue
onMessageWrite ~~~ Repo1
The primary data store is Firestore, which is a noSQL db. The entity-relationship diagram below shows the logical relation between various data entities in our application, but since this is a noSQL db, how each entity and relationship is implemented differs.
- message: Top-level collection, called
messages. Basically a group of instances with the same/similar contents. - instance: Subcollection under each Message, called
instances. An instance refers to a particular WhatsApp message sent in by a user. - voteRequest: Subcollection under each Messaage, called
voteRequests. A voteRequest represents a request to the checkers to vote on a message. - checker: Top-level collection, called
checkers - user: Top-level collection, called
users - blast: Top-level collection, called
blasts - userBlast: A Subcollection under each
blast, where relationship to user is implement as the document ID, which is the user's ID (a.k.a WhatsApp number). Basically tracks a blast received by a user. - customReply: Map (a.k.a object) under each message, called
customReply. Relationship to checker implement byDocumentReferencefieldlastUpdatedBy. Basically tracks details of a custom reply, which overrides the generic category-based response if it is present. - closestMatch: Map (a.k.a object) under each instance called
closestMatch.DocumentReferencefieldsinstanceRefandparentRefpoint to the closest matching instance, and its parent message respectively. - leaderboardStats: Map (a.k.a object) under each checker, called
leaderBoardStats. Exists to facilitate tracking and calculation of the leaderboard. - programData: Map (a.k.a object) under each checker, called
programData. Details related to the volunteer program.
erDiagram
message {
string machineCategory
boolean isMachineCategorised "whether this message was autocategorised"
string originalText "text as sent by user"
string text "pii-stripped text. For text, shows the latest iteration of the instance"
string caption "Latest caption for image, if applicable"
timestamp firstTimestamp "Timestamp of first instance"
timestamp lastTimestamp "Timestamp of latest instance"
timestamp lastRefreshedTimestamp "Timestamp where details were last updated"
boolean isPollStarted "Whether or not a voting poll has been started for this message"
boolean isAssessed "Should message be considered assessed and ready for reply"
timestamp assessedTimestamp "Timestamp of when message was assessed"
timestamp assessmentExpiry "When assessment should expire, if any, currently not used"
boolean assessmentExpired "Whether assessment has expired, currently not used"
boolean isScam
boolean isIllicit
boolean isSpam
boolean isLegitimate
boolean isUnsure
boolean isInfo
boolean isSatire
boolean isHarmful "whether sum of scam + illicit + info if truth score < 2.5 > threshold"
boolean isHarmless "whether sum of legitimate + spam + info if truth score >4 > threshold"
boolean isIrrelevant
number truthScore "mean vote score for info votes"
string primaryCategory "either scam, illicit, irrelevant, spam, legitimate, misleading, untrue, accurate, unsure, or pass"
number instanceCount "number of instances"
string rationalisation "genAI created rationalisation of why the message might have been categorised as such"
map tags "a map of possible tags, currently limited to the keys 'generated' or 'incorrect'. Value will be true if the tag exists"
number numberPointScale "either 5 or 6, depending on how many points the truth score scale had for this message"
}
customReply {
string type "text or image"
string text
string caption
timestamp lastUpdatedTimestamp
}
instance {
string source
string id "message id needed to reply, specific to the source it came from"
timestamp timestamp "when instance was created"
string type "text/image"
string text "text if text message or ocr-extracted text if image message"
string textHash "md5 hash of text or ocr-extracted text"
string caption "caption of image, if applicable"
string captionHash "md5 hash of caption"
string sender "sender name or number, extracted by OCR, if applicable"
string imageType "either convo, email, letter, others"
string ocrVersion "'1' for paddleOCR or '2' for genAI-vertex"
string from "Sender ID or phone number"
string subject "letter or email subject"
string hash "Image hash, for closestMatchimage only"
string mediaId "Media ID from whatsApp, for image only"
string mimeType "For image only"
string storageUrl "Cloud storage URL of image, if applicable"
boolean isForwarded "Not used for now"
boolean isFrequentlyForwarded "Not used for now"
boolean isReplied "System has replied to the citizen with a final assessment"
boolean isInterimPromptSent "Have we sent an interim prompt"
boolean isInterimReplySent "Have we sent an interim reply"
boolean isMeaningfulInterimReplySent "track is a non-unsure meaningful interim reply was sent"
boolean isRationalisationSent "Has the rationalisation been sent"
boolean isRationalisationUseful "Is the rationalisation feature useful"
boolean isReplyForced "track if final reply is forced after 23 hrs"
boolean isMatched "track if message was matched"
boolean isReplyImmediate "track if final reply is immediate"
string replyCategory "scam, illicit, untrue, misleading, accurate, spam, legitimate, irrelevant, irrelevant_auto, unsure"
timestamp replyTimestamp "time of reply to the sender"
string matchType "either exact, stripped, similarity or none"
array embedding "embedding"
boolean scamShieldConsent "whether users consented to sending the message to scamshield, for scam and illicit only"
boolean isSatisfactionSurveySent "whether satisfaction survey is sent"
number satisfactionScore "satisfaction score given by user on NPS survey"
}
closestMatch {
documentReference instanceRef "reference to the instance that was matched"
documentReference parentRef "reference to the parent message of the instance that was matched"
string text "text of the closest match"
string score "similarity score"
string algorithm "algorithm used to calculate similarity"
}
voteRequest {
string platformId "whatsapp number or telegram Id"
string platform "whatsapp/telegram/agent"
boolean hasAgreed "whether person has agreed to vote"
boolean triggerL2Vote "whether or not a vote should be triggered"
boolean triggerL2Others "whether or not L2 scam message should be triggered"
string sentMessageId "message id of the forwarded dubious message to checkers"
number truthScore "number between 1 and 5. Legacy implementations before release 2.10.0 have the field vote instead on a 0-5 scale, which truthScore replaces"
string category "scam, illicit, info, satire, legitimate, spam, irrelevant, unsure, pass, or null for not yet voted"
string reasoning "reasoning for the vote, for GenAI to post"
timestamp createdTimestamp "time when vote request is sent to user"
timestamp acceptedTimestamp "time when user first viewed the message"
timestamp votedTimestamp "time when user has voted"
boolean isCorrect "whether the vote aligns with majority. Null if majority is unsure or if category is pass"
number score "score that this vote contributes to the leaderboard"
number duration "number of minutes taken since start of vote to vote"
map tags "a map of possible tags, currently limited to the keys 'generated' or 'incorrect'. Value will be true if the tag exists"
number numberPointScale "either 5 or 6, depending on how many points the truth score scale had for this message"
boolean isAutoPassed "whether the message was auto-passed"
}
checker {
string name
string type "human or ai"
boolean isActive "whether checker is active and receiving votes from the system"
boolean isOnboardingComplete "whether onboarding is complete"
boolean isAdmin "whether checker is an admin"
string singpassOpenId "Singpass Open ID, not in use"
string telegramId "telegram ID"
string whatsappId "whatsapp phone number"
string tier "tier of checker, either beginner, intermediate, or expert"
number level "Not used for now, for future gamification"
number experience "Not used for now, for future gamification"
number numVoted "Number of messages voted on"
number numReferred "Number of new users referred"
number numReported "Number of non-trivial instances sent in sent in"
number numCorrectVotes "Number of votes that align with majority, for messages that didn't end with unsure"
number numNonUnsureVotes "Number of votes on messages that didn't end as unsure, for computing accuracy"
number numVerifiedLinks "Not used for now"
number voteWeight "Everyone equal for now"
string preferredPlatform "whatsapp/telegram"
string getNameMessageId "ID of the message sent to prompt factChecker for their name. Used to track reply on whatsapp."
timestamp lastVotedTimestamp "When the checker last voted"
}
leaderboardStats {
number numVoted "number of votes cast where the parent message category is not unsure"
number numCorrectVotes "number of votes that align with majority, for messages that didn't end with unsure"
number totalTimeTaken "total time taken to vote where the parent message category is not unsure"
number score "total score"
}
programData {
boolean isOnProgram "Indicates if the user is currently on the program"
timestamp programStart "Start time of the program, can be null if not started"
timestamp programEnd "End time of the program, can be null if not completed"
number numVotesTarget "Target number of messages voted on to complete the program"
number numReferralTarget "Target number of referrals made to complete the program"
number numReportTarget "Number of non-trivial messages sent in to complete the program"
number accuracyTarget "Target accuracy of non-unsure votes"
number numVotesAtProgramStart "Number of votes checker has made at program start"
number numReferralsAtProgramStart "Number of referrals checker has made at program start"
number numReportsAtProgramStart "Number of non-trivial message reports checker has made at program start"
number numCorrectVotesAtProgramStart "Number of non-unsure correct votes checker has made at program start"
number numNonUnsureVotesAtProgramStart "Number of non-unsure votes checker has made at program start"
number numVotesAtProgramEnd "Number of votes checker has made at program end, can be null"
number numReferralsAtProgramEnd "Number of referrals checker has made at program end, can be null"
number numReportsAtProgramEnd "Number of non-trivial message reports checker has made at program end, can be null"
number numCorrectVotesAtProgramEnd "Number of non-unsure correct votes checker has made at program end, can be null"
number numNonUnsureVotesAtProgramEnd "Number of non-unsure votes checker has made at program end, can be null"
}
user {
string whatsappId "Whatsapp id of user if user sent message from Whatsapp, needed for reply, null otherwise"
string telegramId "Telegram id of user if user sent message from Telegram, needed for reply, null otherwise"
string emailId "email address of user if user wrote in via email, needed for reply, null otherwise"
number instanceCount "number of instances sent in"
timestamp lastSent "the last time the user sent an instance"
timestamp firstMessageReceiptTime "the first time the user sent something into the bot"
string firstMessageType "either prepopulated, normal, or irrelevant"
timestamp satisfactionSurveyLastSent "last time satisfaction survey was sent, used to implement cooldown for sending the survey"
string initialJourney "map mapping out the journey in the first 24 hrs of usage, where key is the timestamp of the step taken"
string referralId "referral code"
number referralCount "number of other users referred"
string utm "map containing utm parameters, source, medium, content, campaign, term"
string language "en or cn, users preferred language"
boolean isSubscribedUpdates "whether to blast msgs to this user"
boolean isReferralMessageSent "whether the referral message has been sent to the user, this should happen when the first non-irrelevant message is replied to"
boolean isReminderMessageSent "whether the reminder message has been sent to the user, this should happen when the first message is replied to"
boolean isIgnored "whether the user should be ignored, i.e. blocked, in which case they get no responses"
}
blasts {
string type "image or text"
string text "text or caption if image"
string storageUrl "image storage url if applicable"
boolean isActive "the one that should be sent"
timestamp createdDate "when the blast was created"
timestamp blastDate "when the blast was sent out"
}
userBlast {
string feedbackCategory "positive, negative or neutral"
timestamp sentTimestamp "when blast was sent to user"
}
message ||--|{ instance: has
user ||--o{ instance: sends
message ||--o{ voteRequest: triggers
checker ||--o{ voteRequest: votes
instance ||--|| closestMatch: has
closestMatch ||--|| instance: is
checker ||--|| leaderboardStats: has
checker ||--|| programData: has
message ||--|| customReply: has
checker ||--|| customReply: composes
blasts ||--o{ userBlast: has
user ||--o{ userBlast: provides
The below diagram displays the core flow when users interact with the WhatsApp bot. Each subgraph corresponds to a particular function in functions/src/definitions/, and some nested subgraphs, if there, refer to subfunctions declared within the cloud functions. Some of the functions are also referenced in the above architecture diagram.
Dotted lines refer to asynchronous processes that take longer than the order of seconds. Most of the interfaces between subgraphs/functions are asynchronous in the software engineering sense but they happen near-instantaneously.
flowchart TB
classDef human fill:#FFA500
classDef mlservice fill:#f9f
classDef agentservice fill:#ffbbff
classDef dbUpdate fill:#bbffbb
A[User interacts with WhatsApp bot]:::human
B[WhatsApp fires webhook to our endpoint]
subgraph Legend
Legend1[Human Interface]:::human
Legend2[Calls ML Service]:::mlservice
Legend3[Calls Agent Service]:::agentservice
Legend4[Updates Database]:::dbUpdate
end
subgraph webhookHandlers/handler.ts
C1[Authenticate/Check if correct WhatsApp]
C2[Put into user or checker queue, depending on WABA phone number ID]
direction TB
end
C3{User/Checker Queue?}
C4[Deprecated]
subgraph eventHandlers/userHandlers.ts
D1{Is it a message <br> to be checked <br> or some other <br> interaction}
D2[Handle interaction]
D3{Text or Image}
subgraph func newTextInstanceHandler
D3a1[Autocategorise and check trivial]:::mlservice
D3a2[Embed and check similarity against database]
D3a3{Matched?}
D3a4[Add Message Object]:::dbUpdate
D3a5[Add Instance Object to Message]:::dbUpdate
D3a1 --> D3a2
D3a2 --> D3a3
D3a3 --No--> D3a4
D3a4 --> D3a5
D3a3 --Yes--> D3a5
end
subgraph func eventHandlers/newImageInstanceHandler
D3b1[Download Image]
D3b2[Check Image Hash against database]
D3b3{Matched?}
D3b4[Perform OCR to get text and get category]:::mlservice
D3b5[Embed text and check similarity against database]
D3b6{Matched?}
D3b7[Add Message Object]:::dbUpdate
D3b8[Add Instance Object To Message]:::dbUpdate
D3b1 --> D3b2
D3b2 --> D3b3
D3b3 --No--> D3b4
D3b4 --> D3b5
D3b5 --> D3b6
D3b6 --No--> D3b7
D3b6 --Yes--> D3b8
D3b7 --> D3b8
D3b3 --Yes--> D3b8
end
D1 --Other Interaction--> D2
D1 --Message to be Checked--> D3
D3 --Image--> D3b1
D3 --Text-->D3a1
end
subgraph eventHandlers/onInstanceCreate.ts
direction TB
E1[Issue reply to user based on current message state - assessed or not]:::human
E2{Poll previously <br> started?}
E3[Trigger FactChecking Agents by putting into agents queue]
E4[Add VoteRequest Object and send Telegram/WhatsApp notification for each active checker]
E1 --> E2
E2 --No--> E3
E3 --> E4
end
F[Checker votes on Telegram App]:::human
F1[Post to api/handlers/patchVoteRequest]
G[Agent votes]:::agentservice
subgraph eventHandlers/onVoteRequestUpdate.ts
direction TB
H1[Count votes for every category]
H2[Check against thresholds to determine if message has a predominant category]
H3[Check against thresholds to determine if a message is considered properly assessed]
H4[Update Message Object]:::dbUpdate
H1 --> H2
H2 --> H3
H3 --> H4
end
subgraph eventHandlers/onMessageUpdate.ts
direction TB
I1{isAssessed<br>changed?}
I2[Trigger GenAI rationalisation of message depending on category]
I3[Issue reply to user based on current message state]:::human
I1 --Yes--> I2
I2 --> I3
end
A --> B
B --> C1
C1 --> C2
C2 --> C3
C3 --user--> eventHandlers/userHandlers.ts
C3 --checker--> C4
D3a5 --> eventHandlers/onInstanceCreate.ts
D3b8 --> eventHandlers/onInstanceCreate.ts
E4 -.-> F
E3 --> G
F --> F1
F1 --> eventHandlers/onVoteRequestUpdate.ts
G --> eventHandlers/onVoteRequestUpdate.ts
H4 --> I1
Beyond the above main flow, there are many sub flows executing in the application
- Update checker score and leaderboard
- Reset leaderboard (batch job monthly)
- Remind checkers who have votes outstanding for more than 72 hrs (batch job daily)
- Send interim replies to qualifying messages (batch job every 20 mins)
- Trigger agents
- Onboard as checkers
We aim to document all these flows eventually, but for now do look at the code to figure these out.
We currently have 3 environments, prod, uat, and local. The /integration-tests folder contains a 4th mocked environment, SIT, which is run in the CI pipeline. Generally, you'd do most of your feature development on the local dev environment, and in a feature branch. Once the feature branch is ready, make a PR to the develop branch. Successful merge into the develop branch will trigger a UAT deployment. Successful merge from develop into main will in turn trigger a prod deployment. All these happen through Github Actions.
npmgitnode >= 18 && <= 20
git clone https://github.com/CheckMateSG/checkMate.gitcd checkMate- Make sure you've signed up for better.sg as a volunteer, and have your betterSG email at hand
npm install -g firebase-tools- you may have to install/upgrade your java
npm run postinstall- run
firebase login --no-localhostthen login with your betterSG email - Contact @sarge1989 to set you up with a cloudflare tunnel, and provide your WhatsApp number so the routing can be done to your setup. Ngrok will not work for this step, hence the need for this.
- Contact @sarge1989 to obtain .secret.local and .env.local files, which for now will be sent via password-encrypted zip. Place these two files in the
/functionsdirectory - @sarge1989 will also give you the link to the whatsapp non-production number
- Create your own Telegram bot via botfather
- Replace
TELEGRAM_CHECKER_BOT_TOKENin.secret.localwith the bot token. Note, it isTELEGRAM_CHECKER_BOT_TOKENand notTELEGRAM_BOT_TOKENorTELEGRAM_WEBHOOK_TOKEN - Go to botfather, type /mybots, select to the bot you created, go to > Bot Settings" > "Menu Button". Then add the cloudflare tunnel URL provided by @sarge1989 in step 6 above that routes to your localhost:5000.
- Open the bot you just created and press /start
- Replace
CHECKER_APP_HOSTin.env.localwith the same cloudflare tunnel URL - In .env.local, replace
CHECKER1_TELEGRAM_IDandCHECKER1_PHONE_NUMBERwith your own Telegram ID and WhatsApp Phone number respectively. Note that Whatsapp Phone number should include the country code e.g. 6591111111. Telegram ID can be obtained via this telegram bot
- Execute the steps in the below section Each Time Developing
- Go to the chat with the WhatsApp User bot non-prod number and send in
/mockdb - Ensure that the Firestore Emulator has been populated with some data
- Send "hi" to the WhatsApp User bot non-prod number. This should trigger the first usage onboarding
- Send a message such as
Best Fixed Deposit Rates yield 3.75% if you deposit via Syfe (to get institutional fixed deposit rates) (as of June 2024)into the bot. You'll notice something onUserPublish might take a while, but this should trigger the asynchronous checking flow- You can expect to see this on your console:
> {"severity":"INFO","message":"Unable to get Google identity token in lower environments"} > {"severity":"INFO","message":"Unable to get Google identity token in lower environments"} > {"severity":"WARNING","message":"Path doesn't exist in database"} i functions: Beginning execution of "asia-southeast1-onMessageWriteV2" > {"severity":"INFO","message":"Transaction success for messageId wamid.HBgKNjU5M452y262ya53452U0QjZBQkYyNEM5NwA=!"}```
- You can expect to see this on your console:
- You should see a notification in your Telegram Bot. Go through the voting process.
- Once done, you should get a reply on the user whatsapp bot.
- With that, you've basically gone through the end-to-end flow for one message (albeit with only 1 voter in the pool)
- Open 3 shells from in root directory
- [Shell 1]
make firebase - [Shell 2]
make functions[hot reload for functions] - [Shell 3]
make checkers-app[hot reload for webapp] - If your database is empty, start by sending in
/mockdbto the WhatsApp non-prod number, which will populate some baseline data in the local emulated firestore database. - Firestore Emulator can be visited at http://127.0.0.1:4000/firestore
- Can start on development. There should be live refreshing on the changes you make
- When shutting down the emulator, proceed in this order.
- Control-C in Shell 3. Enter "Y" if prompted. Otherwise, pre-existing data may not be saved to local.
- Close the windows running the Java applications. Otherwise, the port will be used up.
In the event the Makefile doesn't work,
- Open 3 shells from in root directory
- [Shell 1]
cd functions - [Shell 1]
npm run build:watch[hot reload for functions] - [Shell 2]
cd checkers-app[hot reload for webapp] - [Shell 2]
npm run build:watch - [Shell 3]
npm run serve - Can start on development
- Firebase Console - https://console.firebase.google.com/, login with your bettersg email. Go here to manage the product resources
- Adding subcollections - https://stackoverflow.com/questions/47514419/how-to-add-subcollection-to-a-document-in-firebase-cloud-firestore
- Getting started with firestore and firebase https://firebase.google.com/docs/functions/get-started
- WhatsApp send message API documentation - https://developers.facebook.com/docs/whatsapp/cloud-api/guides/send-messages
- WhatsApp webhook object documentation - https://developers.facebook.com/docs/whatsapp/cloud-api/webhooks/components
- Telegram Bot API documentation - https://core.telegram.org/bots/api