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Luria Voice includes optional integration with Ollama, a tool for running large language models locally. When Ollama is running on your machine, the cingulate R package can use it to generate first-draft narrative text for each cognitive domain based on the score data you have already loaded. This can reduce the time spent on initial write-ups, but all AI-generated text must be reviewed and edited by a licensed clinician before inclusion in any report.
AI-generated narratives are drafts only. They may contain errors, hallucinations, or clinically inappropriate language. A qualified neuropsychologist must review, revise, and take full professional responsibility for all narrative content before it appears in a finalized report.

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How the integration works

When cingulate is loaded in an R session, it checks whether Ollama is reachable at localhost:11434. If it is, narrative generation functions become available. If Ollama is not running, all other cingulate functions (data loading, tables, plots) continue to work normally — the AI features are entirely opt-in. There is no API key or cloud service involved. All model inference happens on your local machine, which means patient data never leaves your environment.

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Step 1: Install Ollama

Download and install Ollama from ollama.com. Follow the installer for your operating system. After installation, start the Ollama service: 
ollama serve
Ollama will listen on localhost:11434 by default.

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Step 2: Pull a language model

Pull a model suited for clinical text generation. llama3 is a good general-purpose starting point; larger models produce higher-quality output at the cost of more RAM and slower generation. 
ollama pull llama3
bash ollama pull llama3
You only need to pull a model once. It is stored locally and reused on subsequent runs.

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Step 3: Enable narrative generation in your report

In the setup chunk at the top of template.qmd, load cingulate. No additional configuration is needed — the package detects Ollama automatically. 
library(cingulate)
# Ollama is detected automatically at localhost:11434
# If Ollama is not running, all non-AI features still work normally

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Step 4: Generate a domain narrative

Inside a domain partial (for example, _attention.qmd), call generate_narrative() after loading score data. The function returns a character string of draft narrative text. 
#| label: attention-narrative
#| echo: false
#| message: false
library(cingulate)

attn_data <- load_domain_data("attention")

# Generate a draft narrative paragraph for this domain
draft_text <- generate_narrative(
  domain_data = attn_data,
  domain      = "attention",
  model       = "llama3"
)

# Write the draft to the document
cat(draft_text)
Pipe the output of generate_narrative() through cat() so Quarto renders it as prose rather than a quoted R string. Using message: false suppresses progress output from Ollama during rendering.

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Customizing the prompt

You can pass a custom system prompt to generate_narrative() to adjust the model’s tone, length, or clinical framing: 
draft_text <- generate_narrative(
  domain_data   = attn_data,
  domain        = "attention",
  model         = "llama3",
  system_prompt = "You are a licensed neuropsychologist writing a formal
                   evaluation report for referral to a specialist. Use
                   precise clinical language. Limit the response to
                   three sentences."
)

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Checking Ollama connection status

If you are unsure whether Ollama is running, you can check the connection from R before attempting narrative generation: 
library(cingulate)

if (ollama_is_available()) {
  message("Ollama is running. Narrative generation is available.")
} else {
  message("Ollama not detected. Skipping narrative generation.")
}
Use this pattern in domain partials if you want the report to render correctly regardless of whether Ollama is running: 
#| label: attention-narrative
#| echo: false
library(cingulate)

attn_data <- load_domain_data("attention")

if (ollama_is_available()) {
  draft_text <- generate_narrative(attn_data, domain = "attention")
  cat(draft_text)
}

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Workflow recommendation

Using AI narratives most effectively in practice:
  1. Run quarto render once with Ollama active to generate all domain narrative drafts.
  2. Read each draft carefully and mark areas requiring correction or expansion.
  3. Edit the narratives directly in the .qmd partial files, replacing or supplementing the AI output with your clinical judgment.
  4. Re-render the final report with your edited prose in place.
You do not need to call generate_narrative() on every render. Once you have edited the draft text and placed it directly in the .qmd file as plain prose, you can remove or disable the narrative generation code chunk entirely.
Last modified on May 20, 2026