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Docs Agent CLI reference

This page provides a list of the Docs Agent command lines and their usages and examples.

The Docs Agent CLI helps developers to manage the Docs Agent project and interact with language models. It can handle various tasks such as processing documents, populating vector databases, launching the chatbot, running benchmark test, sending prompts to language models, and more.

Important: All agent commands need to run in the poetry shell environment.

Processing documents

Chunk Markdown files into small text chunks

The command below splits Markdown files (and other source files) into small chunks of plain text files:

agent chunk

Populate a vector database using text chunks

The command below populates a vector database using plain text files (created by running the agent chunk command):

agent populate

Populate a vector database and delete stale text chunks

The command below deletes stale entries in the existing vector database before populating it with the new text chunks:

agent populate --enable_delete_chunks

Show the Docs Agent configuration

The command below prints all the fields and values in the current config.yaml file:

agent show-config

Clean up the Docs Agent development environment

The command below deletes development databases specified in the config.yaml file:

agent cleanup-dev

Write logs to a CSV file

The command below writes the summaries of all captured debugging information (in the logs/debugs directory) to a .csv file:

agent write-logs-to-csv

Launching the chatbot web app

Launch the Docs Agent web app

The command below launches Docs Agent's Flask-based chatbot web application:

agent chatbot

Launch the Docs Agent web app using a different port

The command below launches the Docs Agent web app to run on port 5005:

agent chatbot --port 5005

Launch the Docs Agent web app as a widget

The command below launches the Docs Agent web app to use a widget-friendly template:

agent chatbot --app_mode widget

Launch the Docs Agent web app in full mode

The command below launches the Docs Agent web app to use a special template that uses three Gemini models (AQA, Gemini 1.5, and Gemini 1.0):

agent chatbot --app_mode full

Launch the Docs Agent web app with a log view enabled

The command below launches the Docs Agent web app while enabling a log view page (which is accessible at <APP_URL>/logs):

agent chatbot --enable_show_logs

Running benchmark test

Run the Docs Agent benchmark test

The command below runs benchmark test using the questions and answers listed in the benchmarks.yaml file:

agent benchmark

Interacting with language models

Ask a question

The command below reads a question from the arguments, asks the Gemini model, and prints its response:

agent tellme <QUESTION>

Replace QUESTION with a question written in plain English, for example:

agent tellme does flutter support material design 3?

Note: This agent tellme command is used to set up the gemini command in the Set up Docs Agent CLI guide.

Ask a question to a specific product

The command below enables you to ask a question to a specific product in your Docs Agent setup:

agent tellme <QUESTION> --product <PRODUCT>

The example below asks the question to the Flutter product in your Docs Agent setup:

agent tellme which modules are available? --product=Flutter

You may also specify multiple products, for example:

agent tellme which modules are available? --product=Flutter --product=Angular --product=Android

Ask for advice

The command below reads a request and a filename from the arguments, asks the Gemini model, and prints its response:

agent helpme <REQUEST> --file <PATH_TO_FILE>

Replace REQUEST with a prompt and PATH_TO_FILE with a file's absolute or relative path, for example:

agent helpme write comments for this C++ file? --file ../my-project/test.cc

Ask for advice using RAG

The command below uses a local or online vector database (specified in the config.yaml file) to retrieve relevant context for the request:

agent helpme <REQUEST> --file <PATH_TO_FILE> --rag

Ask for advice in a session

The command below starts a new session (--new), which tracks responses, before running the agent helpme command:

agent helpme <REQUEST> --file <PATH_TO_FILE> --new

For example:

agent helpme write a draft of all features found in this README file? --file ./README.md --new

After starting a session, use the --cont flag to include the previous responses as context to the request:

agent helpme <REQUEST> --cont

For example:

agent helpme write a concept doc that delves into more details of these features? --cont

Print the context in the current session

The command below prints the questions, files, and responses that are being used as context in the current session:

agent show-session

Ask the model to perform the request to each file in a directory

The command below applies the request to each file found in the specified directory:

agent helpme <REQUEST> --perfile <PATH_TO_DIRECTORY>

For example:

agent helpme explain what this file does? --perfile ~/my-project --new

Ask the model to include all files in a directory as context

The command below includes all files found in the specified directory as context to the request:

agent helpme <REQUEST> --allfiles <PATH_TO_DIRECTORY>

For example:

agent helpme write a concept doc covering all features in this project? --allfiles ~/my-project --new

Ask the model to print the output in JSON

The command below prints the output from the model in JSON format:

agent helpme <REQUEST> --response_type json

For example:

agent helpme how do I cook pasta? --response_type json

Ask the model to run a pre-defined chain of prompts

The command below runs a task (a sequence of prompts) defined in a .yaml file stored in the tasks directory:

agent runtask --task <TASK>

For example:

agent runtask --task DraftReleaseNotes

View the list of available Docs Agent tasks

To see the list of all tasks available in your project, run agent runtask without any arguments:

agent runtask

Ask the model to run a task using custom input

If a task script has a <INPUT> placeholder, you can provide a custom input string to the task:

agent runtask --task <TASK> --custom_input <INPUT_STRING>

For example:

agent runtask --task IndexPageGenerator --custom_input ~/my_example/docs/development/

Ask the model to print the output in plain text

By default, the agent runtask command uses Python's Rich console to format its output. You can disable it by using the --plaintext flag:

agent runtask --task <TASK> --plaintext

For example:

agent runtask --task DraftReleaseNotes --plaintext

Managing online corpora

List all existing online corpora

The command below prints the list of all existing online corpora created using the Semantic Retrieval API:

agent list-corpora

Share an online corpora with a user

The command below enables user01@gmail.com to read text chunks stored in corpora/example01:

agent share-corpus --name corpora/example01 --user user01@gmail.com --role READER

The command below enables user01@gmail.com to read and write to corpora/example01:

agent share-corpus --name corpora/example01 --user user01@gmail.com --role WRITER

Share an online corpora with everyone

The command below enables EVERYONE to read text chunks stored in corpora/example01:

agent open-corpus --name corpora/example01

Remove a user permission from an online corpora

The command below remove an existing user permission set in corpora/example01:

agent remove-corpus-permission --name corpora/example01/permissions/123456789123456789

Delete an online corpora

The command below deletes an online corpus:

agent delete-corpus --name corpora/example01