---
title: "Getting Started with clinChat"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Getting Started with clinChat}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup}
library(clinChat)
```

## Overview

`clinChat` lets you explore clinical datasets through natural language while keeping the data local. It registers tools that the language model can call to summarize, count, and group your data.

## Example 1: Basic Clinical Trial Data

```{r, eval = FALSE}
# Simulated clinical trial data
set.seed(42)
trial_data <- data.frame(
  subject_id = paste0("S", sprintf("%03d", 1:80)),
  age = sample(18:85, 80, replace = TRUE),
  sex = sample(c("Male", "Female"), 80, replace = TRUE),
  arm = sample(c("Drug A", "Drug B", "Placebo"), 80, replace = TRUE),
  response = sample(c("Responder", "Non-responder"), 80, replace = TRUE, prob = c(0.55, 0.45)),
  baseline_score = round(rnorm(80, 50, 12), 1)
)

# Create a chat object
chat <- create_clinical_chat(
  data = trial_data,
  system_prompt = "You are a clinical data analyst. Use the available tools to answer accurately."
)

# Example questions you can ask (requires API key)
# chat$chat("How many subjects are in each treatment arm?")
# chat$chat("What is the mean age by sex?")
# chat$chat("Summarize the dataset")
```

## Example 2: Lab Results Dataset

```{r, eval = FALSE}
lab_data <- data.frame(
  patient_id = 1:60,
  age = sample(25:75, 60, replace = TRUE),
  diagnosis = sample(c("Type 2 Diabetes", "Hypertension", "Healthy"), 60, replace = TRUE),
  hba1c = round(rnorm(60, 6.8, 1.4), 1),
  systolic_bp = round(rnorm(60, 132, 16))
)

chat2 <- create_clinical_chat(lab_data)

# chat2$chat("How many patients have Type 2 Diabetes and what is their average HbA1c?")
# chat2$chat("Describe the columns in this dataset")
```

## Launching the Interactive App

```{r, eval = FALSE}
# Launch a Shiny chat interface
run_clinchat_app(
  data = trial_data,
  title = "Clinical Trial Explorer",
  provider = "openai/gpt-4o-mini"   # or your preferred provider
)
```

## Using the Tools Directly

```{r, eval = FALSE}
# Inspect the tools that will be registered
tools <- clinical_tools(trial_data)
length(tools)   # should be 4
```

## Privacy

All data queries happen locally via tools. Only the results of those tools (summaries, counts, aggregates) are passed to the language model. Raw patient-level rows are never sent by default.