kagiPro FastGPT Endpoint Guide • kagiPro

FastGPT Endpoint: Practical Prompt Workflows

FastGPT is the endpoint to use when you need direct prompt-response interactions with optional web grounding.

This guide focuses on how to structure prompts for repeatable runs, scale to batches, and handle transient failures without dropping the full job.

Create one connection for all runs

library(kagiPro)

conn <- kagi_connection(
  api_key = function() keyring::key_get("API_kagi")
)

Build a single prompt query

q_fast <- query_fastgpt(
  query = "What is Python 3.11?",
  cache = TRUE,
  web_search = TRUE
)

q_fast

Like other query builders in kagiPro, this returns a named list for consistency.

Execute and store the first response

out_fast <- "fastgpt_output"
dir.create(out_fast, recursive = TRUE, showWarnings = FALSE)

kagi_request(
  connection = conn,
  query = q_fast[[1]],
  output = out_fast,
  overwrite = TRUE
)

Persisting responses to JSON is useful for prompt comparison and reproducibility.

Scale prompts to a batch job

q_fast_many <- query_fastgpt(
  query = c(
    "What are ecosystem services?",
    "What is biodiversity offsetting?",
    "How does pollination support food systems?"
  ),
  cache = TRUE,
  web_search = TRUE
)

kagi_request(
  connection = conn,
  query = q_fast_many,
  output = "fastgpt_batch",
  overwrite = TRUE,
  workers = 2
)

This pattern is suitable for generating comparable outputs across a fixed prompt set.

Keep long runs resilient with graceful error handling

kagi_request(
  connection = conn,
  query = q_fast_many,
  output = "fastgpt_batch_safe",
  overwrite = TRUE,
  workers = 2,
  error_mode = "write_dummy"
)

When a request fails, dummy JSON is written and the job continues. For FastGPT, dummy records include data$output = null and data$tokens = 0.

Convert FastGPT output to parquet

kagi_request_parquet(
  input_json = "fastgpt_batch_safe",
  output = "fastgpt_batch_parquet",
  overwrite = TRUE
)

This gives a clean table-like format for reporting or joins with other run metadata.

Operational recommendations

Keep prompts concise and task-specific.
Version your prompt sets explicitly in scripts.
Use graceful mode for unattended batch processing.