Sends an AI request to supported LLMs and returns an answer
Sends an AI request to supported LLMs and returns an answer
USAGE
$ box ai:ask --prompt <value> --items <value>... [-t <value>] [--as-user <value>] [--no-color] [--json |
--csv] [-s | --save-to-file-path <value>] [--fields <value>] [--bulk-file-path <value>] [-h] [-v] [-y] [-q]
FLAGS
-h, --help Show CLI help
-q, --quiet Suppress any non-error output to stderr
-s, --save Save report to default reports folder on disk
-t, --token=<value> Provide a token to perform this call
-v, --verbose Show verbose output, which can be helpful for debugging
-y, --yes Automatically respond yes to all confirmation prompts
--as-user=<value> Provide an ID for a user
--bulk-file-path=<value> File path to bulk .csv or .json objects
--csv Output formatted CSV
--fields=<value> Comma separated list of fields to show
--items=<value>... (required) The items for the AI request
--json Output formatted JSON
--no-color Turn off colors for logging
--prompt=<value> (required) The prompt for the AI request
--save-to-file-path=<value> Override default file path to save report
DESCRIPTION
Sends an AI request to supported LLMs and returns an answer
EXAMPLES
$ box ai:ask --items=id=12345,type=file --prompt "What is the status of this document?"
See code: src/commands/ai/ask.js
Sends an AI request to supported Large Language Models (LLMs) and extracts metadata in form of key-value pairs
USAGE
$ box ai:extract --prompt <value> --items <value>... [-t <value>] [--as-user <value>] [--no-color] [--json |
--csv] [-s | --save-to-file-path <value>] [--fields <value>] [--bulk-file-path <value>] [-h] [-v] [-y] [-q]
[--ai-agent <value>]
FLAGS
-h, --help Show CLI help
-q, --quiet Suppress any non-error output to stderr
-s, --save Save report to default reports folder on disk
-t, --token=<value> Provide a token to perform this call
-v, --verbose Show verbose output, which can be helpful for debugging
-y, --yes Automatically respond yes to all confirmation prompts
--ai-agent=<value> The AI agent to be used for the extraction, provided as a JSON string. Example:
{"type": "ai_agent_extract", "basic_text": {"model": "azure__openai__gpt_4o_mini",
"prompt_template": "Answer the question based on {content}"}}
--as-user=<value> Provide an ID for a user
--bulk-file-path=<value> File path to bulk .csv or .json objects
--csv Output formatted CSV
--fields=<value> Comma separated list of fields to show
--items=<value>... (required) The items that LLM will process.
--json Output formatted JSON
--no-color Turn off colors for logging
--prompt=<value> (required) The prompt provided to a Large Language Model (LLM) in the request.
--save-to-file-path=<value> Override default file path to save report
DESCRIPTION
Sends an AI request to supported Large Language Models (LLMs) and extracts metadata in form of key-value pairs
EXAMPLES
$ box ai:extract --items=id=12345,type=file --prompt "firstName, lastName, location, yearOfBirth, company"
$ box ai:extract --prompt "firstName, lastName, location, yearOfBirth, company" --items "id=12345,type=file" --ai-agent '{"type":"ai_agent_extract","basicText":{"llmEndpointParams":{"type":"openai_params","frequencyPenalty": 1.5,"presencePenalty": 1.5,"stop": "<|im_end|>","temperature": 0,"topP": 1},"model": "azure__openai__gpt_4o_mini","numTokensForCompletion": 8400,"promptTemplate": "It is, consider these travel options and answer the.","systemMessage": "You are a helpful travel assistant specialized in budget travel"}}}'
See code: src/commands/ai/extract.js
Sends an AI request to supported Large Language Models (LLMs) and returns extracted metadata as a set of key-value pairs. For this request, you either need a metadata template or a list of fields you want to extract. Input is either a metadata template or a list of fields to ensure the structure.
USAGE
$ box ai:extract-structured --items <value>... [-t <value>] [--as-user <value>] [--no-color] [--json | --csv] [-s |
--save-to-file-path <value>] [--fields <value>...] [--bulk-file-path <value>] [-h] [-v] [-y] [-q]
[--metadata-template <value>] [--ai-agent <value>]
FLAGS
-h, --help Show CLI help
-q, --quiet Suppress any non-error output to stderr
-s, --save Save report to default reports folder on disk
-t, --token=<value> Provide a token to perform this call
-v, --verbose Show verbose output, which can be helpful for debugging
-y, --yes Automatically respond yes to all confirmation prompts
--ai-agent=<value> The AI agent to be used for the structured extraction, provided as a JSON string.
Example: {"type": "ai_agent_extract_structured", "basic_text": {"model":
"azure__openai__gpt_4o_mini", "prompt_template": "Answer the question based on
{content}"}}
--as-user=<value> Provide an ID for a user
--bulk-file-path=<value> File path to bulk .csv or .json objects
--csv Output formatted CSV
--fields=<value>... The fields to be extracted from the provided items.
--items=<value>... (required) The items that LLM will process.
--json Output formatted JSON
--metadata-template=<value> The metadata template containing the fields to extract.
--no-color Turn off colors for logging
--save-to-file-path=<value> Override default file path to save report
DESCRIPTION
Sends an AI request to supported Large Language Models (LLMs) and returns extracted metadata as a set of key-value
pairs. For this request, you either need a metadata template or a list of fields you want to extract. Input is either
a metadata template or a list of fields to ensure the structure.
EXAMPLES
$ box ai:extract-structured --items="id=12345,type=file" --fields "key=hobby,type=multiSelect,description=Person hobby,prompt=What is your hobby?,displayName=Hobby,options=Guitar;Books"
$ box ai:extract-structured --items="id=12345,type=file" --metadata-template="type=metadata_template,scope=enterprise,template_key=test" --ai-agent '{"type":"ai_agent_extract_structured","basicText":{"llmEndpointParams":{"type":"openai_params","frequencyPenalty": 1.5,"presencePenalty": 1.5,"stop": "<|im_end|>","temperature": 0,"topP": 1},"model": "azure__openai__gpt_4o_mini","numTokensForCompletion": 8400,"promptTemplate": "It is, consider these travel options and answer the.","systemMessage": "You are a helpful travel assistant specialized in budget travel"}}}'
See code: src/commands/ai/extract-structured.js
Sends an AI request to supported LLMs and returns an answer specifically focused on the creation of new text.
USAGE
$ box ai:text-gen --items <value>... --prompt <value> [-t <value>] [--as-user <value>] [--no-color] [--json |
--csv] [-s | --save-to-file-path <value>] [--fields <value>] [--bulk-file-path <value>] [-h] [-v] [-y] [-q]
[--dialogue-history <value>...]
FLAGS
-h, --help Show CLI help
-q, --quiet Suppress any non-error output to stderr
-s, --save Save report to default reports folder on disk
-t, --token=<value> Provide a token to perform this call
-v, --verbose Show verbose output, which can be helpful for debugging
-y, --yes Automatically respond yes to all confirmation prompts
--as-user=<value> Provide an ID for a user
--bulk-file-path=<value> File path to bulk .csv or .json objects
--csv Output formatted CSV
--dialogue-history=<value>... The history of prompts and answers previously passed to the LLM.
--fields=<value> Comma separated list of fields to show
--items=<value>... (required) The items to be processed by the LLM, often files. The array can include
exactly one element.
--json Output formatted JSON
--no-color Turn off colors for logging
--prompt=<value> (required) The prompt for the AI request
--save-to-file-path=<value> Override default file path to save report
DESCRIPTION
Sends an AI request to supported LLMs and returns an answer specifically focused on the creation of new text.
EXAMPLES
$ box ai:text-gen --dialogue-history=prompt="What is the status of this document?",answer="It is in review",created-at="2024-07-09T11:29:46.835Z" --items=id=12345,type=file --prompt="What is the status of this document?"
See code: src/commands/ai/text-gen.js