https://github.com/digdir/digdir-ai-assistants
Generative AI assistants
https://github.com/digdir/digdir-ai-assistants
generative-ai
Last synced: 9 months ago
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Generative AI assistants
- Host: GitHub
- URL: https://github.com/digdir/digdir-ai-assistants
- Owner: digdir
- License: mit
- Created: 2024-02-14T14:55:15.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-10-06T06:46:01.000Z (9 months ago)
- Last Synced: 2025-10-06T08:45:02.849Z (9 months ago)
- Topics: generative-ai
- Language: TypeScript
- Homepage:
- Size: 38 MB
- Stars: 2
- Watchers: 14
- Forks: 0
- Open Issues: 32
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# digdir-assistants
A mono-repo for generative AI assistants, based on RAG architecture.
Currently deployed as a Slack application, we also plan to integrate with web applications, in particular Altinn Studio.
# Agent flow
This diagram shows the main functional blocks and how data flows between them:
## See it in action
[#altinn-assistant](https://altinndevops.slack.com/archives/C06JQLHSZME/p1707478070231209) on Altinn Devops Slack
## Local development quickstart
1. Install dependencies:
`$ yarn install`
2. Build all packages and apps
`$ yarn build`
3. Run slack-app and admin ui
`$ yarn run:slack-app`
Note: in order for your local bot endpoint to receive traffic from Slack, you need to configure a proxy service such as `ngrok`, and configure a Slack app to use the URL allocated by ngrok.
4. Configure a new Slack app in your test workspace
> It is recommended to create a few Slack workspace specifically for development and testing of Slack apps
## Deploy to Azure Container app
1. Create a resouce group
2. Create a Container App Environment
3. Create a container repository
4. Create a Container App
> For step by step instructions see: [Create a Container App](/documentation/create-a-container-app.md)
## Manual job execution
1. Update crawler index
2. Update search phrase index
`$ cd cli`
`$ yarn run:generateSearchPhrases `
Example collection name: TEST_altinn-studio-docs-search-phrases`
# System architecture
The Altinn Assistants architecture consists of the following technology components:
1. Front-end (Slack app, Admin web app)
2. Database-as-a-service (Supabase)
3. Serverless function runtime (Supabase Functions)
4. GPU-accelerated (Python app running in a GPU-enabled VM)
5. Text search index (Typesense Cloud)
Sequence diagram showing execution of a typical end user query:
```mermaid
sequenceDiagram
participant slack.com
participant slack-app
participant llm-api
participant supabase-db
participant supabase-functions
participant semantic-search
participant gpu-services
slack.com->>slack-app: new text message
slack-app->>supabase-db: GET /config (cached)
supabase-db->>slack-app: workspace + channel config
slack-app->>llm-api: stage 1: analyze user query, translate if necessary
llm-api->>slack-app: query analysis, translation
slack-app->>llm-api: stage 2: query relaxation
llm-api->>slack-app: generated search phrases
slack-app->>semantic-search: hybrid search (cosine similarity + keyword)
semantic-search->>slack-app: sorted doc list
slack-app->>gpu-services: POST /colbert/rerank w/user query
gpu-services->>slack-app: Re-ranked doc list
slack-app->>llm-api: Prompt + retrieved context
llm-api->>slack-app: Helpful response (streamed)
slack-app->>slack.com: Helpful response (streamed response)
slack-app->>slack.com: Finalized response w/metadata
slack-app->>supabase-functions: Log query, response and metadata
supabase-functions->>supabase-db: upsert team, channel,
user and message data
slack-app->>llm-api: Translate to user's language
llm-api->>slack-app: (streamed response)
slack-app->>slack.com: (streamed response)
slack-app->>slack.com: Finalized response w/metadata
slack-app->>supabase-db: Log query, response and metadata
```