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 is built on pydantic-ai — a Python agent framework that supports multiple LLM providers out of the box.
• 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-5.2
OPENAI_API_KEY=your_api_key

# Optional - adjust LLM temperature (default: 0.3)
BASEROW_ENTERPRISE_ASSISTANT_LLM_TEMPERATURE=0.3

About temperature:

• Controls randomness in the main assistant’s LLM responses.
• Default: 0.3 (focused, consistent responses)
• Higher values (depending on the model) = more creative/varied responses.
• Lower values (e.g., 0-0.1) = more analytical responses. Note that even with temperature of 0.0, the results will not be fully deterministic.

3) Provider presets

Choose one provider block and set its variables. pydantic-ai uses the standard environment variables for each provider (e.g. OPENAI_API_KEY, GROQ_API_KEY).

OpenAI / OpenAI-compatible

BASEROW_ENTERPRISE_ASSISTANT_LLM_MODEL=openai:gpt-5.2
OPENAI_API_KEY=your_api_key
# Optional: point to an alternative OpenAI-compatible endpoint
OPENAI_BASE_URL=https://eu.api.openai.com/v1
# or
OPENAI_BASE_URL=https://<your-resource-name>.openai.azure.com

Anthropic

BASEROW_ENTERPRISE_ASSISTANT_LLM_MODEL=anthropic:claude-sonnet-4-20250514
ANTHROPIC_API_KEY=your_api_key

AWS Bedrock

pydantic-ai supports two authentication methods for Bedrock. Use whichever matches your setup.

Option A — Standard AWS credentials (boto3)

BASEROW_ENTERPRISE_ASSISTANT_LLM_MODEL=bedrock:openai.gpt-oss-120b-1:0
AWS_ACCESS_KEY_ID=your_access_key
AWS_SECRET_ACCESS_KEY=your_secret_key
AWS_DEFAULT_REGION=eu-central-1

Any boto3-compatible credential method works: env vars, IAM roles, instance profiles, ~/.aws/credentials, etc.

Option B — Bedrock bearer token

BASEROW_ENTERPRISE_ASSISTANT_LLM_MODEL=bedrock:openai.gpt-oss-120b-1:0
AWS_BEARER_TOKEN_BEDROCK=your_bearer_token
AWS_DEFAULT_REGION=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
# Point to your Ollama instance (defaults to http://localhost:11434/v1)
OLLAMA_BASE_URL=http://localhost:11434/v1

pydantic-ai auto-detects the provider from the model prefix and routes requests 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.

6) Supported models

OpenAI, Anthropic, AWS Bedrock, Groq, Gemini/Vertex AI and any OpenAI-compatible endpoint (Azure, DeepSeek, Fireworks, LiteLLM, Perplexity, Together AI, etc.).

7) Framework change: UDSPy to pydantic-ai

The assistant previously used UDSPy as its agent framework. It now uses pydantic-ai. Most environment variables are unchanged or bridged for backward compatibility.

What stays the same

Variable	Notes
BASEROW_ENTERPRISE_ASSISTANT_LLM_MODEL	Works exactly as before. Both provider/model and provider:model formats are accepted.
BASEROW_ENTERPRISE_ASSISTANT_LLM_TEMPERATURE	Still supported. Overrides the orchestrator temperature when set.
OPENAI_API_KEY	Unchanged.
GROQ_API_KEY	Unchanged.
AWS_BEARER_TOKEN_BEDROCK	Still works — pydantic-ai supports Bedrock bearer token auth natively.

Bridged for backward compatibility (no action needed)

Old variable	Equivalent	Notes
UDSPY_LM_MODEL	BASEROW_ENTERPRISE_ASSISTANT_LLM_MODEL	If set and the new var is absent, the old value is used automatically.
UDSPY_LM_API_KEY	OPENAI_API_KEY / GROQ_API_KEY / etc.	Propagated to all provider key variables as a fallback.
UDSPY_LM_OPENAI_COMPATIBLE_BASE_URL	OPENAI_BASE_URL	Still works; bridged automatically.
AWS_REGION_NAME	AWS_DEFAULT_REGION	Still works; bridged automatically.

New variables

Variable	Notes
OPENAI_BASE_URL	Preferred replacement for UDSPY_LM_OPENAI_COMPATIBLE_BASE_URL.
AWS_DEFAULT_REGION	Preferred replacement for AWS_REGION_NAME.
OLLAMA_BASE_URL	Replaces UDSPY_LM_OPENAI_COMPATIBLE_BASE_URL for Ollama. Defaults to http://localhost:11434/v1.
ANTHROPIC_API_KEY	New provider — Anthropic models are now supported.