Services

Archi supports several services — containerized applications that interact with the AI pipelines. Services are enabled at deploy time with the --services flag.

archi create [...] --services chatbot,uploader,grafana

List all available services with:

archi list-services

Chat Interface

The primary user-facing service. Provides a web-based chat application for interacting with Archi's AI agents.

Default port: 7861

Key Features

  • Streaming responses with tool-call visualization
  • Agent selector dropdown for switching between agents
  • Built-in Data Viewer at /data
  • Settings panel for model/provider selection
  • BYOK support
  • Conversation history

Configuration

services:
  chat_app:
    agent_class: CMSCompOpsAgent
    agents_dir: examples/agents
    default_provider: local
    default_model: llama3.2
    trained_on: "Course documentation"
    hostname: "example.mit.edu"
    port: 7861
    external_port: 7861

Running

archi create [...] --services chatbot

Document Upload

Document upload is exposed in the chat UI and backed by the Data Manager service. Documents can be uploaded via the web interface or by copying files directly into the data directory.

See Data Sources — Adding Documents Manually for setup instructions.

Data Manager

A background service that handles data ingestion, vectorstore management, and scheduled re-scraping. It is automatically started with most deployments.

Default port: 7871

Features

  • Orchestrates all data collectors (links, git, JIRA, Redmine)
  • Manages the vectorstore (chunking, embedding, indexing)
  • Provides a scheduling system for periodic re-ingestion
  • Exposes API endpoints for ingestion status and schedule management

API Endpoints

Endpoint Method Description
/api/ingestion/status GET Current ingestion progress
/api/reload-schedules POST Trigger schedule reload from database
/api/schedules GET Current schedule status

Configuration

services:
  data_manager:
    port: 7871
    external_port: 7871

Piazza Interface

Reads posts from a Piazza forum and posts draft responses to a specified Slack channel.

Setup

  1. Go to Slack Apps and sign in to your workspace.
  2. Click Create New App → From scratch. Name the app and select the workspace.
  3. Go to Incoming Webhooks under Features and toggle it on.
  4. Click Add New Webhook and select the target channel.
  5. Copy the Webhook URL to your secrets file.

Configuration

Get the Piazza network ID from the class homepage URL (e.g., https://piazza.com/class/m0g3v0ahsqm2lg → m0g3v0ahsqm2lg).

services:
  piazza:
    agent_class: QAPipeline
    provider: local
    model: llama3.2
    network_id: <your Piazza network ID>
  chat_app:
    trained_on: "Your class materials"

Secrets

PIAZZA_EMAIL=...
PIAZZA_PASSWORD=...
SLACK_WEBHOOK=...

Running

archi create [...] --services chatbot,piazza

Redmine / Mailbox Interface

Reads new tickets in a Redmine project, drafts a response as a comment, and sends it as an email when the ticket is marked "Resolved" by an admin.

Configuration

services:
  redmine_mailbox:
    url: https://redmine.example.com
    project: my-project
    redmine_update_time: 10
    mailbox_update_time: 10
    answer_tag: "-- Archi -- Resolving email was sent"

Secrets

IMAP_USER=...
IMAP_PW=...
REDMINE_USER=...
REDMINE_PW=...
SENDER_SERVER=...
SENDER_PORT=587
SENDER_REPLYTO=...
SENDER_USER=...
SENDER_PW=...

Running

archi create [...] --services chatbot,redmine-mailer

Mattermost Interface

Reads posts from a Mattermost forum and posts draft responses to a specified channel.

Configuration

services:
  mattermost:
    update_time: 60

Secrets

MATTERMOST_WEBHOOK=...
MATTERMOST_PAK=...
MATTERMOST_CHANNEL_ID_READ=...
MATTERMOST_CHANNEL_ID_WRITE=...

Running

archi create [...] --services chatbot,mattermost

Grafana Monitoring

Monitor system performance and LLM usage with a pre-configured Grafana dashboard.

Default port: 3000

Note: If redeploying with an existing name (without removing volumes), the PostgreSQL Grafana user may not have been created. Deploy a fresh instance to avoid issues.

Configuration

services:
  grafana:
    external_port: 3000

Secrets

PG_PASSWORD=<your_database_password>
GRAFANA_PG_PASSWORD=<grafana_db_password>

Running

archi create [...] --services chatbot,grafana

After deployment, access Grafana at your-hostname:3000. The default login is admin/admin — you'll be prompted to change the password on first login. Navigate to Menu → Dashboards → Archi → Archi Usage for the main dashboard.

Tip: For the "Recent Conversation Messages" panel, click the three dots → Edit → find "Override 4" → enable Cell value inspect to expand long text entries. Click Apply to save.

Grader Interface

An automated grading service for handwritten assignments with a web interface.

Note: This service is experimental and not yet fully generalized.

Requirements

The following files are needed:

  • users.csv: Two columns — MIT email and Unique code
  • solution_with_rubric_*.txt: One file per problem, named with the problem number. Begins with the problem name and a line of dashes.
  • admin_password.txt: Admin code for resetting student attempts (passed as a secret).

Configuration

services:
  grader_app:
    provider: local
    model: llama3.2
    prompts:
      grading:
        final_grade_prompt: final_grade.prompt
      image_processing:
        image_processing_prompt: image_processing.prompt
    num_problems: 1
    local_rubric_dir: ~/grading/my_rubrics
    local_users_csv_dir: ~/grading/logins
  chat_app:
    trained_on: "rubrics, class info, etc."

Secrets

ADMIN_PASSWORD=your_password

Running

archi create [...] --services grader