AI Chat¶

LLM-powered chat assistant scoped to each fractal. It queries your logs using BQL, discovers fields, and presents structured findings in a conversational interface.

AI Chat conversation

Setup¶

Chat requires a LiteLLM proxy container and an API key for at least one supported provider (OpenAI, Anthropic, etc). AI keys are not configured during initial setup; add them manually to your .env file after installation.

1. Add your API key to .env¶

Open the .env file in your install directory and set LITELLM_API_KEY to your provider key:

LITELLM_API_KEY=sk-ant-...

2. Configure a model (optional)¶

The default litellm-config.yaml uses Anthropic. To use a different provider, edit the file:

model_list:
  - model_name: bifract-chat
    litellm_params:
      model: openai/gpt-4o-mini
      api_key: os.environ/LITELLM_API_KEY

Change the model field to match your provider. The model name must stay bifract-chat.

3. Restart the stack¶

docker compose up -d

LiteLLM runs on the internal Docker network only and is not exposed to the host.

Features¶

Per-fractal conversations scoped to the selected fractal's log data
Tool use via run_query (BQL) and get_fields to explore logs
Streaming responses token-by-token via SSE
Time range control from a selector in the chat header
Multiple conversations with create, rename, and delete support
Search integration by clicking the magnifying glass on any query tool call

Tip

Importing an alert feed gives the assistant context on your detection rules, enabling it to write more relevant BQL queries for your environment.

Supported Providers¶

Any provider supported by LiteLLM works. Change the model field in litellm-config.yaml to match your provider (e.g. openai/gpt-4o-mini, anthropic/claude-haiku-4-5-20251001). Set LITELLM_API_KEY to the corresponding API key. Some providers may need drop_params: true in litellm_params.