AskLit

AskLit is a "no-code" factory for publishing high-quality AI chatbots grounded in your own documents. It is designed to be deployed to Streamlit Community Cloud in minutes by anyone, regardless of technical expertise.

New to GitHub, Streamlit, or knowledge-base authoring? Follow the illustrated beginner deployment guide. It explains how to write a prompt, prepare PDF/DOCX source files, use the scaffolder, keep secrets out of GitHub, and deploy the finished app. You can also see the working Tulane demonstration repository and live app.

For a more robust deployment, you can deploy it to fly.io or another inexpensive hosting option.

🚀 Quick Start: The Scaffolder

The easiest way to get started is using the AskLit Project Scaffolder. This tool allows you to build your entire app—including your knowledge base and custom branding—right in your browser, then export it directly to GitHub.

1. Requirements

Before you start, make sure you have:

• GitHub Account: To host your code.
• Streamlit Community Cloud Account: To host your app (Connect it to your GitHub).
• LLM Provider Account: An API key from OpenAI, Anthropic, Google, or Groq.
  • Note: If you want to use a custom or local endpoint, AskLit supports any OpenAI-compatible API.

2. Using the Scaffolder

1. Launch the Scaffolder: Open the "Project Scaffolder" tab in the sidebar of this app.
2. Identity & Branding:
   • Set your app title and welcome message.
   • Prompt & Knowledge Base Pairings: Define one or more prompts and the knowledge base each one should search.
   • Custom Branding: Upload your own logo and favicon.
   • (Insert Screenshot: Scaffolder Step 1)
3. AI Configuration: Choose your model provider (e.g., OpenAI) and the model name (e.g., gpt-4o).
4. Knowledge Upload: Drag and drop your PDFs, Word docs, or Text files. The tool will chunk and embed them locally so you can see your knowledge base built in real-time.
   • (Insert Screenshot: Scaffolder Step 3)
5. Export & Deploy:
   • Connect GitHub: Click the button to authorize AskLit to create a repo for you.
   • Secrets Generator: Copy the pre-formatted TOML block from the "Deployment Secrets" box. Use the built-in Password Hasher if you need to set a password.
   • Push: Click "Create Repo & Push". This creates a private repository on your account with all your settings and documents pre-indexed.

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🛠️ Configuration & Secrets

Once your repository is created, you need to tell Streamlit your API keys.

1. Streamlit Secrets Manager

In your Streamlit Cloud dashboard, go to Settings > Secrets and paste your configuration.

Basic Example:

OPENAI_API_KEY = "sk-..."
ADMIN_ROUTE = "manage" # Your secret admin URL parameter
ADMIN_PASSWORD_HASH = "..." # Generate this using the Hash Tool
"app.disable_admin" = "false" # Set to true to completely hide admin pages

2. Custom OpenAI Endpoints

If you are using a provider like OpenRouter, Together.ai, or a local LM Studio instance, you can set a custom base URL:

OPENAI_API_KEY = "your-provider-key"
OPENAI_BASE_URL = "https://openrouter.ai/api/v1"
"model.name" = "anthropic/claude-3-opus" # Use the provider's specific model string

3. Limited Azure educator credentials

For workshops or shared trials, do not distribute an Azure AI Services account key. Put Azure API Management in front of the Foundry endpoint and give the cohort a quota-limited APIM subscription key. AskLit has a dedicated azure_apim provider that sends this credential in the gateway subscription header.

See Protecting AskLit with Azure API Management for the gateway policy, Azure setup, credential rotation, and verification steps.

4. Branding Overrides

You can override any branding element in secrets without redeploying:

"branding.logo_url" = "https://example.com/logo.png"
"branding.footer_text" = "Custom Footer"
"branding.hide_asklit_badge" = "false"

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🏗️ Advanced: Local Development

Multiple Prompt / Knowledge Base Pairings

AskLit discovers every .yml, .yaml, and .md file under prompts/. If more than one prompt file exists, the chat sidebar shows a radio button for each prompt. YAML prompt files can connect a prompt to a knowledge base and, optionally, to a specific list of filenames:

label: Housing
knowledgebase:
  name: housing
  files:
    - eviction_guide.pdf
    - repairs.md

prompt: |
  You answer housing questions using the connected knowledge base.

conversation starters:
  - What should I know before court?

Leaving files empty connects the prompt to every indexed document in that knowledge base. Admins can edit prompt text, knowledge base names, and connected file lists after deployment.

If you are a developer and want to run or modify AskLit locally:

1. Setup Environment:

   pip install -r requirements.txt
   cp .streamlit/secrets.toml.example .streamlit/secrets.toml

2. Run:

   streamlit run app.py

3. Admin Access: Visit http://localhost:8501/?manage (matching your ADMIN_ROUTE).

📚 Repository Layout

app.py                         Navigation entrypoint
scaffold.py                    The "Project Scaffolder" wizard
chat_ui.py                     The public chat interface
admin/                         Management tools (Settings, Knowledge Base, Logs)
asklit/                        Core logic (RAG, LLM, Ingestion)
config/defaults.toml           Default settings (overridden by DB and Secrets)
data/                          Your pre-indexed knowledge base (SQLite + Chroma)

⚖️ License & Attribution

AskLit is a project of the Suffolk University Law School LIT Lab.

Made with AskLit