Baserow AI-Assistant: Quick DevOps Setup

This guide shows how to enable the AI-assistant in Baserow, configure the required environment variables, and (optionally) turn on knowledge-base lookups via an embeddings server.

1) Core concepts

  • The assistant runs via UDSPy — see https://github.com/baserow/udspy/
  • UDSPy speaks to any OpenAI-compatible API.
  • You must set BASEROW_ENTERPRISE_ASSISTANT_LLM_MODEL with the provider and model of your choosing.
  • The assistant has been mostly tested with the gpt-oss-120b family. Other models can work as well.

2) Minimal enablement

Set the model you want, restart Baserow, and let migrations run.

Important: When running Baserow with Docker Compose or multiple services, BASEROW_ENTERPRISE_ASSISTANT_LLM_MODEL must be set in all services (both backend and frontend) for the assistant to work properly.

# Required
BASEROW_ENTERPRISE_ASSISTANT_LLM_MODEL=openai/gpt-4o
OPENAI_API_KEY=your_api_key

# Optional - adjust LLM temperature (default: 0)
BASEROW_ENTERPRISE_ASSISTANT_LLM_TEMPERATURE=0

About temperature:

  • Controls randomness in LLM responses (0.0 to 2.0)
  • Default: 0 (deterministic, consistent responses - recommended for production)
  • Higher values (e.g., 0.7-1.0) = more creative/varied responses
  • Lower values (e.g., 0-0.3) = more focused/consistent responses

3) Provider presets

Choose one provider block and set its variables.

OpenAI / OpenAI-compatible

BASEROW_ENTERPRISE_ASSISTANT_LLM_MODEL=openai/gpt-4o
OPENAI_API_KEY=your_api_key
# Optional alternative endpoints (OpenAI EU or Azure OpenAI, etc.)
UDSPY_LM_OPENAI_COMPATIBLE_BASE_URL=https://eu.api.openai.com/v1
# or
UDSPY_LM_OPENAI_COMPATIBLE_BASE_URL=https://<your-resource-name>.openai.azure.com
# or any OpenAI compatible endpoint

AWS Bedrock

BASEROW_ENTERPRISE_ASSISTANT_LLM_MODEL=bedrock/openai.gpt-oss-120b-1:0
AWS_BEARER_TOKEN_BEDROCK=your_bedrock_token
AWS_REGION_NAME=eu-central-1

Groq

BASEROW_ENTERPRISE_ASSISTANT_LLM_MODEL=groq/openai/gpt-oss-120b
GROQ_API_KEY=your_api_key

Ollama

BASEROW_ENTERPRISE_ASSISTANT_LLM_MODEL=ollama/gpt-oss:120b
OLLAMA_API_KEY=your_api_key
# Optionally and alternative endpoint
UDSPY_LM_OPENAI_COMPATIBLE_BASE_URL=http://localhost:11434/v1

Under the hood, UDSPy auto-detects provider from the model prefix and builds an OpenAI-compatible client accordingly.

4) Knowledge-base lookup

If your deployment method doesn’t auto-provision embeddings, run the Baserow embeddings service and point Baserow at it.

For developers using Docker Compose: See embeddings-server.md for setup instructions.

Run the embeddings container

docker run -d --name baserow-embeddings -p 80:80 baserow/embeddings:latest

Point Baserow to it

BASEROW_EMBEDDINGS_API_URL=http://your-embedder-service
# e.g., http://localhost if you mapped -p 80:80 locally
# Then restart Baserow and allow migrations to run.

After restart and migrations, knowledge-base lookup will be available.

5) Troubleshooting

The assistant doesn’t appear or doesn’t work

If the assistant is not visible in the sidebar or doesn’t work, verify that:

  1. BASEROW_ENTERPRISE_ASSISTANT_LLM_MODEL is set correctly in both the backend and frontend services
  2. The required API key for your chosen provider is set (e.g., OPENAI_API_KEY, GROQ_API_KEY, etc.)

Verifying environment variables in development

To check if the variables are set correctly in development, from the host run:

# Check backend
just dcd run --rm backend bash -c env | grep LLM_MODEL
just dcd run --rm backend bash -c env | grep API_KEY

# Check frontend
just dcd run --rm web-frontend bash -c env | grep LLM_MODEL

Both commands must return the same value for BASEROW_ENTERPRISE_ASSISTANT_LLM_MODEL. If either is missing or they differ, update your environment configuration and restart the services.

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