Python Dialogflow CX Scripting API (SCRAPI)
A high level scripting API for bot builders, developers, and maintainers.
Table of Contents
-
Introduction
-
Getting Started
- Usage
-
Library Composition
- Contributing
- License
- Contact
- Acknowledgements
Introduction
The Python Dialogflow CX Scripting API (DFCX SCRAPI) is a high level API that extends the official Google Python Client for Dialogflow CX. SCRAPI makes using DFCX easier, more friendly, and more pythonic for bot builders, developers, and maintainers.
SCRAPI --> Python Dialogflow CX
as
Keras --> Tensorflow
What Can I Do With DFCX SCRAPI?
With DFCX SCRAPI you can perform many bot building and maintenance actions at scale including, but not limited to:
- Create, Update, Delete, Get, and List for all CX resources types (i.e. Intents, Entity Types, Pages, Flows, etc.)
- Convert commonly accessed CX Resources to Pandas Dataframes
- Have fully automated conversations with a CX agent (powerful for regression testing!)
- Extract Validation information
- Extract Change History information
- Search across all Flows/Pages/Routes to find a specific parameter or utterance using Search Util functions
- Quickly move CX resources between agents using Copy Util functions!
- Build the fundamental protobuf objects that CX uses for each resource type using Builder methods
- ...and much, much more!
Built With
Authentication
Authentication can vary depending on how and where you are interacting with SCRAPI.
Google Colab
If you're using SCRAPI with a Google Colab notebook, you can add the following to the top of your notebook for easy authentication:
project_id = '<YOUR_GCP_PROJECT_ID>'
!gcloud auth application-default login --no-launch-browser
!gcloud auth application-default set-quota-project $project_id
After running the above, Colab will pick up your credentials from the environment and pass them to SCRAPI directly. No need to use Service Account keys!
You can then use SCRAPI simply like this:
from dfcx_scrapi.core.intents import Intents
agent_id = '<YOUR_AGENT_ID>'
i = Intents()
intents_map = i.get_intents_map(agent_id)
Cloud Functions / Cloud Run
If you're using SCRAPI with Cloud Functions or Cloud Run, SCRAPI can pick up on the default environment creds used by these services without any additional configuration!
- Add
dfcx-scrapi
to your requirements.txt
file - Ensure the Cloud Function / Cloud Run service account has the appropriate Dialogflow IAM Role
Once you are setup with the above, your function code can be used easily like this:
from dfcx_scrapi.core.intents import Intents
agent_id = '<YOUR_AGENT_ID>'
i = Intents()
intents_map = i.get_intents_map(agent_id)
Local Python Environment
Similar to Cloud Functions / Cloud Run, SCRAPI can pick up on your local authentication creds if you are using the gcloud CLI.
- Install gcloud CLI.
- Run
gcloud init
. - Run
gcloud auth login
- Run
gcloud auth list
to ensure your principal account is active.
This will authenticate your principal GCP account with the gcloud CLI, and SCRAPI can pick up the creds from here.
Exceptions and Misc.
There are some classes in SCRAPI which still rely on Service Account Keys, notably the DataframeFunctions class due to how it authenticates with Google Sheets.
In order to use these functions, you will need a Service Account that has appropriate access to your GCP project.
For more information and to view the official documentation for service accounts go to Creating and Managing GCP Service Accounts.
Once you've obtained a Service Account Key with appropriate permissions, you can use it as follows:
from dfcx_scrapi.core.intents import Intents
from dfcx_scrapi.tools.dataframe_functions import DataframeFunctions
agent_id = '<YOUR_AGENT_ID>'
creds_path = '<PATH_TO_YOUR_SERVICE_ACCOUNT_JSON_FILE>'
i = Intents(creds_path=creds_path)
dffx = DataframeFunctions(creds_path=creds_path)
df = i.bulk_intent_to_df(agent_id)
dffx.dataframe_to_sheets('GOOGLE_SHEET_NAME', 'TAB_NAME', df)
Getting Started
Environment Setup
Set up Google Cloud Platform credentials and install dependencies.
gcloud auth login
gcloud auth application-default login
gcloud config set project <project name>
python3 -m venv venv
source ./venv/bin/activate
pip install -r requirements.txt
Usage
To run a simple bit of code you can do the following:
- Import a Class from
dfcx_scrapi.core
- Assign your Service Account to a local variable
from dfcx_scrapi.core.intents import Intents
creds_path = '<PATH_TO_YOUR_SERVICE_ACCOUNT_JSON_FILE>'
agent_path = '<FULL_DFCX_AGENT_ID_PATH>'
i = Intents(creds_path, agent_id=agent_path)
df = i.bulk_intent_to_df()
Library Composition
Here is a brief overview of the SCRAPI library's structure and the motivation behind that structure.
Core
The Core folder is synonymous with the core Resource types in the DFCX Agents (agents, intents, flows, etc.)
- This folder contains the high level building blocks of SCRAPI
- These classes and methods can be used to build higher level methods or custom tools and applications
Tools
The Tools folder contains various customized toolkits that allow you to do more complex bot management tasks, such as
- Manipulate Agent Resource types into various DataFrame structures
- Copy Agent Resources between Agents and GCP Projects on a resource by resource level
- Move data to and from DFCX and other GCP Services like BigQuery, Sheets, etc.
- Create customized search queries inside of your agent resources
Builders
The Builders folder contains simple methods for constructing the underlying protos in Dialogflow CX
- Proto objects are the fundamental building blocks of Dialogflow CX
- Builder classes allow the user to construct Dialogflow CX resource offline without any API calls
- Once the resource components are constructed, they can then be pushed to a live Dialogflow CX agent via API
Contributing
We welcome any contributions or feature requests you would like to submit!
- Fork the Project
- Create your Feature Branch (git checkout -b feature/AmazingFeature)
- Commit your Changes (git commit -m 'Add some AmazingFeature')
- Push to the Branch (git push origin feature/AmazingFeature)
- Open a Pull Request
License
Distributed under the Apache 2.0 License. See LICENSE for more information.
Contact
Patrick Marlow - pmarlow@google.com - @kmaphoenix
Milad Tabrizi - miladt@google.com - @MRyderOC
Project Link: https://github.com/GoogleCloudPlatform/dfcx-scrapi
Acknowledgements
Dialogflow CX Python Client Library
Hugging Face - Pegasus Paraphrase