NLWeb Deployment

Before writing code

Fetch live docs:

1. Fetch https://github.com/nlweb-ai/NLWeb/blob/main/docs/setup-azure.md for Azure App Service deployment.
2. Fetch https://github.com/nlweb-ai/NLWeb/blob/main/docs/setup-snowflake.md for Snowflake Container Services.
3. Fetch https://github.com/nlweb-ai/NLWeb/blob/main/docs/setup-cloudflare-autorag.md for Cloudflare Worker + AutoRAG.
4. Fetch https://developers.cloudflare.com/ai-search/how-to/nlweb/ for Cloudflare's hosted NLWeb documentation.
5. Inspect deploy_azure_webapp.sh, setup.sh, startup_aiohttp.sh in the live repo for current commands.
6. Web-search the latest release notes for breaking deployment changes.

Conceptual Architecture

Deployment Targets Supported

Production Hardening Checklist

Before exposing /ask or /mcp to the internet:

1. Set mode: production in config_webserver.yaml — disables query-string config overrides.
2. Lock down the sites: allowlist in config_nlweb.yaml — only the sites you want public.
3. Disable who_endpoint_enabled if you don't want federated traffic going to nlwm.azurewebsites.net.
4. Turn off unused retrieval backends in config_retrieval.yaml (nlweb_west, shopify_mcp unless needed).
5. Configure OAuth if you need auth (see nlweb-auth-multitenancy).
6. Set TLS at the edge (App Service, CF, ALB, etc.).
7. Set rate limits — NLWeb itself has limited built-in protection; do it at the edge.
8. Configure CORS if a browser client calls /ask directly.
9. Persist conversations to a real storage provider (config_storage.yaml), not in-memory.
10. Configure observability — logs, /mcp/health checks, latency metrics.

Env Vars vs YAML Config

Secrets always in env vars — never in config_*.yaml. The convention NLWeb uses:

# config_llm.yaml
providers:
  azure_openai:
    api_key_env: AZURE_OPENAI_API_KEY     # references env var, doesn't store value
    endpoint_env: AZURE_OPENAI_ENDPOINT

.env is typical for dev; in cloud deployments use the platform's secret manager (Azure Key Vault, Snowflake secrets, CF Workers KV / Secrets, etc.) and inject as env vars.

The Two Server Processes

A full production NLWeb deployment may have:

1. Main aiohttp server (port 8000) — /ask, /mcp, /who, /sites, /config, /api/oauth/*
2. AppSDK adapter (port 8100) — only if you're integrating with ChatGPT Apps SDK. Optional.

Plus optionally the Node.js MCP server in openai-apps-sdk-integration/ if you want the React widget for ChatGPT.

Reverse-Proxy Concerns

NLWeb streams SSE. Make sure your reverse proxy:

• Disables response buffering for /ask paths (X-Accel-Buffering: no is sent, but nginx still needs proxy_buffering off).
• Sets long timeouts (60-300s) for /ask streams.
• Forwards real client IP (X-Forwarded-For) for rate limiting.
• Terminates TLS — NLWeb assumes plain HTTP behind a TLS-terminating proxy.

Data Reload as a CI Job

Most deployments reload site data on a schedule:

# .github/workflows/nlweb-reload.yml (sketch)
on:
  schedule:
    - cron: '0 3 * * *'   # daily 03:00 UTC
jobs:
  reload:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - run: pip install -r requirements.txt
      - run: python -m data_loading.db_load https://example.com/feed.xml my-site
        env:
          AZURE_SEARCH_API_KEY: ${{ secrets.AZURE_SEARCH_API_KEY }}
          AZURE_OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY }}

Run reload as a separate process — don't bake it into the server's startup.

Scaling

NLWeb is stateless per-request (state is in conversation storage + the vector backend). Scale horizontally:

• Multiple app instances behind a load balancer
• Shared vector backend (cloud-hosted, not Qdrant local file)
• Shared conversation storage (Qdrant remote / Azure Search / Elasticsearch)
• Sticky sessions NOT required for /ask (each request is self-contained)

LLM and embedding API quota is usually the binding constraint, not CPU.

Implementation Guidance

Azure App Service Deployment

Walk through deploy_azure_webapp.sh — it provisions:

• App Service Plan + Web App (Linux, Python 3.11+)
• Azure AI Search service
• Azure OpenAI deployment
• App settings (env vars) wired to the search/openai instances

Customize the resource names, set WEBSITES_PORT=8000 (or whichever the script uses), deploy via git push or az webapp deploy. Verify mode: production in the deployed config_webserver.yaml.

Snowflake Container Services

NLWeb runs as a containerized service inside Snowflake compute, queries Cortex Search (data is already in Snowflake tables). Use the setup-snowflake.md doc — it covers the SPCS service spec, image build, and Cortex Search setup.

Cloudflare Worker + AutoRAG

Cloudflare maintains a hosted variant. Two options:

• Self-host on CF Workers following docs/setup-cloudflare-autorag.md — covers the worker template and AutoRAG wiring.
• Use CF's managed deployment per https://developers.cloudflare.com/ai-search/how-to/nlweb/.

Docker

If a Dockerfile ships in the repo, use it. Otherwise:

FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
EXPOSE 8000
CMD ["python", "AskAgent/python/app-aiohttp.py"]

Mount config/ and .env as bind mounts or use env vars + ConfigMaps. Persist data/db/ (Qdrant local) on a volume if not using a remote vector store.

Health Checks

• Liveness: GET /mcp/health (or /sites as a fallback)
• Readiness: GET /sites — fails fast if config is broken

Logs and Observability

NLWeb logs to stdout via Python logging. Wire to your platform's log aggregator (Azure Monitor, CloudWatch, etc.). Key metrics:

• /ask latency (p50, p95, p99) — SSE makes this tricky; measure TTFB and total
• LLM API errors / 429s
• Retrieval backend latencies (per-backend)
• Conversation storage write latency

Production Failure Modes

• App boots but /ask 500s: usually an env var missing — check the log for the failing provider.
• Streaming requests time out at the proxy: increase proxy read timeout; turn off proxy buffering.
• Cold-start latency: first request after deploy takes 30-60s as models load. Pre-warm with a synthetic health check.
• Bills are huge: too many LLM calls per query — tune tool_selection_enabled, model tiers, and who_endpoint_enabled.
• Embedding rate limits during data reload: throttle --batch-size, use a separate embedding deployment, or run reloads off-peak.

Always re-fetch the per-target setup doc and deploy_*.sh scripts before deploying — these are the most release-sensitive parts of the codebase.