Generate an example object from a JSON schema
Source:R/helper_other_r_json_schema.R
r_json_schema_to_example.RdThis function generates an example JSON object from a JSON schema. This is used when enforcing a JSON schema through text-based handling (requiring an example to be added to the prompt text).
See also
Other json:
answer_as_json()
Examples
base_prompt <- "How can I solve 8x + 7 = -23?"
# This example will show how to enforce JSON format in the response,
# with and without a schema, using the `answer_as_json()` prompt wrap.
# If you use type = 'auto', the function will automatically detect the
# best way to enforce JSON based on the LLM provider you are using.
# `answer_as_json()` supports two ways of supplying a schema for structured output:
# - 1) an 'ellmer' definition (e.g., `ellmer::type_object()`;
# see https://ellmer.tidyverse.org/articles/structured-data.html)
# - 2) a R list object representing a JSON schema
# `answer_as_json()` will convert the schema type which you supply to any
# LLM provider; so, whether you use an ellmer LLM provider or another type,
# you can supply either a R list object or an ellmer definition, and don't
# have to worry about compatibility
# Supplying a schema as an ellmer definition is likely the easiest
# Below, we will show:
# - 1) enforcing JSON with a schema; 'ellmer' definition
# - 2) enforcing JSON with a schema; R list object
# - 3) enforcing JSON without a schema
#### Enforcing JSON with a schema (ellmer definition): ####
# Make an ellmer definition of structured output
# For instance, a persona:
ellmer_schema <- ellmer::type_object(
name = ellmer::type_string(),
age = ellmer::type_integer(),
hobbies = ellmer::type_array(ellmer::type_string())
)
if (FALSE) { # \dontrun{
# Example Ellmer LLM provider
ellmer_openai <- llm_provider_ellmer(ellmer::chat_openai(
model = "gpt-4.1-mini"
))
# Example regular LLM provider
tidyprompt_openai <- llm_provider_openai()$set_parameters(
list(model = "gpt-4.1-mini")
)
# You can supply the ellmer definition to both types of LLM provider
# to generate an R list object adhering to the schema
result_ellmer_x_ellmer <- "Create a persona" |>
answer_as_json(ellmer_schema) |>
send_prompt(ellmer_openai)
result_tidyrpompt_x_ellmer <- "Create a persona" |>
answer_as_json(ellmer_schema) |>
send_prompt(tidyprompt_openai)
} # }
#### Enforcing JSON with a schema (R list object definition): ####
# Make a list representing a JSON schema,
# which the LLM response must adhere to:
json_schema <- list(
name = "steps_to_solve", # Required for OpenAI API
description = NULL, # Optional for OpenAI API
schema = list(
type = "object",
properties = list(
steps = list(
type = "array",
items = list(
type = "object",
properties = list(
explanation = list(type = "string"),
output = list(type = "string")
),
required = c("explanation", "output"),
additionalProperties = FALSE
)
),
final_answer = list(type = "string")
),
required = c("steps", "final_answer"),
additionalProperties = FALSE
)
# 'strict' parameter is set as argument 'answer_as_json()'
)
# Note: when you are not using an OpenAI API, you can also pass just the
# internal 'schema' list object to 'answer_as_json()' instead of the full
# 'json_schema' list object
# Generate example R object based on schema:
r_json_schema_to_example(json_schema)
#> $steps
#> $steps[[1]]
#> $steps[[1]]$explanation
#> [1] "..."
#>
#> $steps[[1]]$output
#> [1] "..."
#>
#>
#>
#> $final_answer
#> [1] "..."
#>
if (FALSE) { # \dontrun{
## Text-based with schema (works for any provider/model):
# - Adds request to prompt for a JSON object
# - Adds schema to prompt
# - Extracts JSON from textual response (feedback for retry if no JSON received)
# - Validates JSON against schema with 'jsonvalidate' package (feedback for retry if invalid)
# - Parses JSON to R object
json_4 <- base_prompt |>
answer_as_json(schema = json_schema) |>
send_prompt(llm_provider_ollama())
# --- Sending request to LLM provider (llama3.1:8b): ---
# How can I solve 8x + 7 = -23?
#
# Your must format your response as a JSON object.
#
# Your JSON object should match this example JSON object:
# {
# "steps": [
# {
# "explanation": "...",
# "output": "..."
# }
# ],
# "final_answer": "..."
# }
# --- Receiving response from LLM provider: ---
# Here is the solution to the equation:
#
# ```
# {
# "steps": [
# {
# "explanation": "First, we want to isolate the term with 'x' by
# subtracting 7 from both sides of the equation.",
# "output": "8x + 7 - 7 = -23 - 7"
# },
# {
# "explanation": "This simplifies to: 8x = -30",
# "output": "8x = -30"
# },
# {
# "explanation": "Next, we want to get rid of the coefficient '8' by
# dividing both sides of the equation by 8.",
# "output": "(8x) / 8 = (-30) / 8"
# },
# {
# "explanation": "This simplifies to: x = -3.75",
# "output": "x = -3.75"
# }
# ],
# "final_answer": "-3.75"
# }
# ```
## Ollama with schema:
# - Sets 'format' parameter to 'json', enforcing JSON
# - Adds request to prompt for a JSON object, as is recommended by the docs
# - Adds schema to prompt
# - Validates JSON against schema with 'jsonvalidate' package (feedback for retry if invalid)
json_5 <- base_prompt |>
answer_as_json(json_schema, type = "auto") |>
send_prompt(llm_provider_ollama())
# --- Sending request to LLM provider (llama3.1:8b): ---
# How can I solve 8x + 7 = -23?
#
# Your must format your response as a JSON object.
#
# Your JSON object should match this example JSON object:
# {
# "steps": [
# {
# "explanation": "...",
# "output": "..."
# }
# ],
# "final_answer": "..."
# }
# --- Receiving response from LLM provider: ---
# {
# "steps": [
# {
# "explanation": "First, subtract 7 from both sides of the equation to
# isolate the term with x.",
# "output": "8x = -23 - 7"
# },
# {
# "explanation": "Simplify the right-hand side of the equation.",
# "output": "8x = -30"
# },
# {
# "explanation": "Next, divide both sides of the equation by 8 to solve for x.",
# "output": "x = -30 / 8"
# },
# {
# "explanation": "Simplify the right-hand side of the equation.",
# "output": "x = -3.75"
# }
# ],
# "final_answer": "-3.75"
# }
## OpenAI with schema:
# - Sets 'response_format' parameter to 'json_object', enforcing JSON
# - Adds json_schema to the API request, API enforces JSON adhering schema
# - Parses JSON to R object
json_6 <- base_prompt |>
answer_as_json(json_schema, type = "auto") |>
send_prompt(llm_provider_openai())
# --- Sending request to LLM provider (gpt-4o-mini): ---
# How can I solve 8x + 7 = -23?
# --- Receiving response from LLM provider: ---
# {"steps":[
# {"explanation":"Start with the original equation.",
# "output":"8x + 7 = -23"},
# {"explanation":"Subtract 7 from both sides to isolate the term with x.",
# "output":"8x + 7 - 7 = -23 - 7"},
# {"explanation":"Simplify the left side and the right side of the equation.",
# "output":"8x = -30"},
# {"explanation":"Now, divide both sides by 8 to solve for x.",
# "output":"x = -30 / 8"},
# {"explanation":"Simplify the fraction by dividing both the numerator and the
# denominator by 2.",
# "output":"x = -15 / 4"}
# ], "final_answer":"x = -15/4"}
# You can also use the R list object schema definition with an
# ellmer LLM provider; `answer_as_json()` will do the conversion for you
json_7 <- base_prompt |>
answer_as_json(json_schema) |>
send_prompt(ellmer_openai)
} # }
#### Enforcing JSON without a schema: ####
if (FALSE) { # \dontrun{
## Text-based (works for any provider/model):
# Adds request to prompt for a JSON object
# Extracts JSON from textual response (feedback for retry if no JSON received)
# Parses JSON to R object
json_1 <- base_prompt |>
answer_as_json() |>
send_prompt(llm_provider_ollama())
# --- Sending request to LLM provider (llama3.1:8b): ---
# How can I solve 8x + 7 = -23?
#
# Your must format your response as a JSON object.
# --- Receiving response from LLM provider: ---
# Here is the solution to the equation formatted as a JSON object:
#
# ```
# {
# "equation": "8x + 7 = -23",
# "steps": [
# {
# "step": "Subtract 7 from both sides of the equation",
# "expression": "-23 - 7"
# },
# {
# "step": "Simplify the expression on the left side",
# "result": "-30"
# },
# {
# "step": "Divide both sides by -8 to solve for x",
# "expression": "-30 / -8"
# },
# {
# "step": "Simplify the expression on the right side",
# "result": "3.75"
# }
# ],
# "solution": {
# "x": 3.75
# }
# }
# ```
## Ollama:
# - Sets 'format' parameter to 'json', enforcing JSON
# - Adds request to prompt for a JSON object, as is recommended by the docs
# - Parses JSON to R object
json_2 <- base_prompt |>
answer_as_json(type = "auto") |>
send_prompt(llm_provider_ollama())
# --- Sending request to LLM provider (llama3.1:8b): ---
# How can I solve 8x + 7 = -23?
#
# Your must format your response as a JSON object.
# --- Receiving response from LLM provider: ---
# {"steps": [
# "Subtract 7 from both sides to get 8x = -30",
# "Simplify the right side of the equation to get 8x = -30",
# "Divide both sides by 8 to solve for x, resulting in x = -30/8",
# "Simplify the fraction to find the value of x"
# ],
# "value_of_x": "-3.75"}
## OpenAI-type API without schema:
# - Sets 'response_format' parameter to 'json_object', enforcing JSON
# - Adds request to prompt for a JSON object, as is required by the API
# - Parses JSON to R object
json_3 <- base_prompt |>
answer_as_json(type = "auto") |>
send_prompt(llm_provider_openai())
# --- Sending request to LLM provider (gpt-4o-mini): ---
# How can I solve 8x + 7 = -23?
#
# Your must format your response as a JSON object.
# --- Receiving response from LLM provider: ---
# {
# "solution_steps": [
# {
# "step": 1,
# "operation": "Subtract 7 from both sides",
# "equation": "8x + 7 - 7 = -23 - 7",
# "result": "8x = -30"
# },
# {
# "step": 2,
# "operation": "Divide both sides by 8",
# "equation": "8x / 8 = -30 / 8",
# "result": "x = -3.75"
# }
# ],
# "solution": {
# "x": -3.75
# }
# }
} # }