Replace dff with chatsky (#368)

.github/workflows/update_dashboard.yml

@@ -5,7 +5,7 @@ on:
     branches:
       - 'master'
     paths:
-      - 'dff/utils/docker/**'
+      - 'chatsky/utils/docker/**'
   workflow_dispatch:
 
 concurrency:
@@ -41,7 +41,7 @@ jobs:
         - name: Build and upload image
           uses: docker/build-push-action@v5
           with:
-            context: dff/utils/docker
-            file: dff/utils/docker/dockerfile_stats
+            context: chatsky/utils/docker
+            file: chatsky/utils/docker/dockerfile_stats
             tags: ${{ steps.meta.outputs.tags }}
             labels: ${{ steps.meta.outputs.labels }}

CONTRIBUTING.md

@@ -1,10 +1,10 @@
 ## Introduction
 We will be glad to receive your pull requests (PRs) and issues for adding new features if you are missing something.
-We always look forward to your contributions to the Dialog Flow Framework (DFF). 
+We always look forward to your contributions to Chatsky. 
 
 ## Rules for submitting a PR
 
-All PRs are reviewed by DFF developers team.
+All PRs are reviewed by Chatsky developers team.
 In order to make the job of reviewing easier and increase the chance that your PR will be accepted,
 please add a short description with information about why this PR is needed and what changes will be made.
 Please use the following rules to write the names of branches and commit messages.
@@ -83,11 +83,11 @@ to the local documentation and verified during build.
 - `%pip install {args}`
     This directive generates dependency installation cell, adds a comment and sets up "quiet" flag.
 
-    It should be used in tutorials, like this: `# %pip install dff[...]`.
+    It should be used in tutorials, like this: `# %pip install chatsky[...]`.
 - `%doclink({args})`
     This directive generates a documentation link. It supports 2 or three arguments and the generated link will look like: `ARG1/ARG2#ARG3`.
 
-    The first argument can be either `api` for DFF codebase, `tutorial` for tutorials or `guide` for user guides.
+    The first argument can be either `api` for Chatsky codebase, `tutorial` for tutorials or `guide` for user guides.
 - `%mddoclink({args})`
     This directive is a shortcut for `%doclink` that generates a markdown format link instead.
 
@@ -133,7 +133,7 @@ poetry run poe format
 Tests are configured via [`.env_file`](.env_file).
 
 ### Docker
-DFF uses docker images for two purposes:
+Chatsky uses docker images for two purposes:
 1. Database images for integration testing.
 2. Images for statistics collection.
 

README.md

@@ -1,35 +1,35 @@
-# Dialog Flow Framework
+# Chatsky
 
-[![Documentation Status](https://github.com/deeppavlov/dialog_flow_framework/workflows/build_and_publish_docs/badge.svg?branch=dev)](https://deeppavlov.github.io/dialog_flow_framework)
-[![Codestyle](https://github.com/deeppavlov/dialog_flow_framework/workflows/codestyle/badge.svg?branch=dev)](https://github.com/deeppavlov/dialog_flow_framework/actions/workflows/codestyle.yml)
-[![Tests](https://github.com/deeppavlov/dialog_flow_framework/workflows/test_coverage/badge.svg?branch=dev)](https://github.com/deeppavlov/dialog_flow_framework/actions/workflows/test_coverage.yml)
-[![License Apache 2.0](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/deeppavlov/dialog_flow_framework/blob/master/LICENSE)
+[![Documentation Status](https://github.com/deeppavlov/chatsky/workflows/build_and_publish_docs/badge.svg?branch=dev)](https://deeppavlov.github.io/chatsky)
+[![Codestyle](https://github.com/deeppavlov/chatsky/workflows/codestyle/badge.svg?branch=dev)](https://github.com/deeppavlov/chatsky/actions/workflows/codestyle.yml)
+[![Tests](https://github.com/deeppavlov/chatsky/workflows/test_coverage/badge.svg?branch=dev)](https://github.com/deeppavlov/chatsky/actions/workflows/test_coverage.yml)
+[![License Apache 2.0](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/deeppavlov/chatsky/blob/master/LICENSE)
 ![Python 3.8, 3.9, 3.10, 3.11](https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11-green.svg)
-[![PyPI](https://img.shields.io/pypi/v/dff)](https://pypi.org/project/dff/)
-[![Downloads](https://pepy.tech/badge/dff)](https://pepy.tech/project/dff)
+[![PyPI](https://img.shields.io/pypi/v/chatsky)](https://pypi.org/project/chatsky/)
+[![Downloads](https://pepy.tech/badge/chatsky)](https://pepy.tech/project/chatsky)
 
-The Dialog Flow Framework (DFF) allows you to develop conversational services.
-DFF offers a specialized domain-specific language (DSL) for quickly writing dialogs in pure Python. The service is created by defining a special dialog graph that determines the behavior of the dialog agent. The latter is then leveraged in the DFF pipeline.
+Chatsky allows you to develop conversational services.
+Chatsky offers a specialized domain-specific language (DSL) for quickly writing dialogs in pure Python. The service is created by defining a special dialog graph that determines the behavior of the dialog agent. The latter is then leveraged in the Chatsky pipeline.
 You can use the framework in various services such as social networks, call centers, websites, personal assistants, etc.
 
-DFF, a versatile Python-based conversational service framework, can be deployed across a spectrum of platforms,
+Chatsky, a versatile Python-based conversational service framework, can be deployed across a spectrum of platforms,
 ensuring flexibility for both novice and seasoned developers:
 
-- Cloud platforms like AWS, Azure, and GCP offer scalable environments for DFF,
+- Cloud platforms like AWS, Azure, and GCP offer scalable environments for Chatsky,
   with options such as AWS Lambda and Azure Functions providing serverless execution.
-- For containerized deployment, Docker and Kubernetes streamline the orchestration of DFF applications.
+- For containerized deployment, Docker and Kubernetes streamline the orchestration of Chatsky applications.
 - Furthermore, the framework's adaptability extends to IoT ecosystems,
   making it suitable for integration with edge devices in scenarios like smart homes or industrial automation.
 
 Whether deployed on cloud platforms, containerized environments, or directly on IoT devices,
-DFF's accessibility and customization options make it a robust choice for developing conversational services
+Chatsky's accessibility and customization options make it a robust choice for developing conversational services
 in the evolving landscape of Python applications and IoT connectivity.
 
-## Why choose DFF
+## Why choose Chatsky
 
 * Written in pure Python, the framework is easily accessible for both beginners and experienced developers.
-* For the same reason, all the abstractions used in DFF can be easily customized and extended using regular language synthax.
-* DFF offers easy and straightforward tools for state management which is as easy as setting values of a Python dictionary.
+* For the same reason, all the abstractions used in Chatsky can be easily customized and extended using regular language synthax.
+* Chatsky offers easy and straightforward tools for state management which is as easy as setting values of a Python dictionary.
 * The framework is being actively maintained and thoroughly tested. The team is open to suggestions and quickly reacts to bug reports.
 
 # Quick Start
@@ -44,44 +44,44 @@ in the evolving landscape of Python applications and IoT connectivity.
 
 ## Installation
 
-DFF can be installed via pip:
+Chatsky can be installed via pip:
 
 ```bash
-pip install dff
+pip install chatsky
 ```
 
-The above command will set the minimum dependencies to start working with DFF. 
+The above command will set the minimum dependencies to start working with Chatsky. 
 The installation process allows the user to choose from different packages based on their dependencies, which are:
 ```bash
-pip install dff[json]  # dependencies for using JSON
-pip install dff[pickle] # dependencies for using Pickle
-pip install dff[redis]  # dependencies for using Redis
-pip install dff[mongodb]  # dependencies for using MongoDB
-pip install dff[mysql]  # dependencies for using MySQL
-pip install dff[postgresql]  # dependencies for using PostgreSQL
-pip install dff[sqlite]  # dependencies for using SQLite
-pip install dff[ydb]  # dependencies for using Yandex Database
-pip install dff[telegram]  # dependencies for using Telegram
-pip install dff[benchmark]  # dependencies for benchmarking
+pip install chatsky[json]  # dependencies for using JSON
+pip install chatsky[pickle] # dependencies for using Pickle
+pip install chatsky[redis]  # dependencies for using Redis
+pip install chatsky[mongodb]  # dependencies for using MongoDB
+pip install chatsky[mysql]  # dependencies for using MySQL
+pip install chatsky[postgresql]  # dependencies for using PostgreSQL
+pip install chatsky[sqlite]  # dependencies for using SQLite
+pip install chatsky[ydb]  # dependencies for using Yandex Database
+pip install chatsky[telegram]  # dependencies for using Telegram
+pip install chatsky[benchmark]  # dependencies for benchmarking
 ```
 
 For example, if you are going to use one of the database backends,
 you can specify the corresponding requirements yourself. Multiple dependencies can be installed at once, e.g.
 ```bash
-pip install dff[postgresql,mysql]
+pip install chatsky[postgresql,mysql]
 ```
 
 ## Basic example
 
 The following code snippet builds a simplistic chat bot that replies with messages
 ``Hi!`` and ``OK`` depending on user input, which only takes a few lines of code.
 All the abstractions used in this example are thoroughly explained in the dedicated
-[user guide](https://deeppavlov.github.io/dialog_flow_framework/user_guides/basic_conceptions.html).
+[user guide](https://deeppavlov.github.io/chatsky/user_guides/basic_conceptions.html).
 
 ```python
-from dff.script import GLOBAL, TRANSITIONS, RESPONSE, Message
-from dff.pipeline import Pipeline
-import dff.script.conditions.std_conditions as cnd
+from chatsky.script import GLOBAL, TRANSITIONS, RESPONSE, Message
+from chatsky.pipeline import Pipeline
+import chatsky.script.conditions.std_conditions as cnd
 
 # create a dialog script
 script = {
@@ -129,19 +129,19 @@ Response:  OK
 ```
 
 More advanced examples are available as a part of documentation:
-[tutorials](https://deeppavlov.github.io/dialog_flow_framework/tutorials.html).
+[tutorials](https://deeppavlov.github.io/chatsky/tutorials.html).
 
 ## Further steps
 
-To further explore the API of the framework, you can make use of the [detailed documentation](https://deeppavlov.github.io/dialog_flow_framework/index.html). 
-Broken down into several sections to highlight all the aspects of development with DFF,
+To further explore the API of the framework, you can make use of the [detailed documentation](https://deeppavlov.github.io/chatsky/index.html). 
+Broken down into several sections to highlight all the aspects of development with Chatsky,
 the documentation for the library is constantly available online.
 
-# Contributing to the Dialog Flow Framework
+# Contributing to Chatsky
 
 We are open to accepting pull requests and bug reports.
-Please refer to [CONTRIBUTING.md](https://github.com/deeppavlov/dialog_flow_framework/blob/master/CONTRIBUTING.md).
+Please refer to [CONTRIBUTING.md](https://github.com/deeppavlov/chatsky/blob/master/CONTRIBUTING.md).
 
 # License
 
-DFF is distributed under the terms of the [Apache License 2.0](https://github.com/deeppavlov/dialog_flow_framework/blob/master/LICENSE).
+Chatsky is distributed under the terms of the [Apache License 2.0](https://github.com/deeppavlov/chatsky/blob/master/LICENSE).

dff/__init__.py → chatsky/__init__.py

@@ -10,7 +10,7 @@
 
 nest_asyncio.apply()
 
-from dff.pipeline import Pipeline
-from dff.script import Context, Script
+from chatsky.pipeline import Pipeline
+from chatsky.script import Context, Script
 
-import dff.__rebuild_pydantic_models__
+import chatsky.__rebuild_pydantic_models__

chatsky/__rebuild_pydantic_models__.py

@@ -0,0 +1,9 @@
+# flake8: noqa: F401
+
+from chatsky.pipeline import Pipeline
+from chatsky.pipeline.types import ExtraHandlerRuntimeInfo
+from chatsky.script import Context, Script
+
+Script.model_rebuild()
+Context.model_rebuild()
+ExtraHandlerRuntimeInfo.model_rebuild()

dff/config/README.md → chatsky/config/README.md

@@ -5,6 +5,6 @@
 This directory provides yaml files for Superset dashboard configuration.
 The files inside are not supposed to be edited manually for lest of compatibility breaks.
 Placeholders inside the files will be filled automatically when you use the 
-`dff.stats` CLI command to generate a configuration archive.
+`chatsky.stats` CLI command to generate a configuration archive.
 
-Use `dff.stats -h` for more info.
+Use `chatsky.stats -h` for more info.

dff/config/superset_dashboard/dashboards/DFF_statistics_dashboard_1.yaml → chatsky/config/superset_dashboard/dashboards/chatsky_statistics_dashboard_1.yaml

@@ -1,7 +1,7 @@
-dashboard_title: DFF statistics dashboard
+dashboard_title: Chatsky statistics dashboard
 description: null
 css: ''
-slug: dff-stats
+slug: chatsky-stats
 uuid: 68bce374-99bc-4890-b8c2-cb172409b894
 position:
   CHART-91whs_IaiF:
@@ -297,7 +297,7 @@ position:
   HEADER_ID:
     id: HEADER_ID
     meta:
-      text: DFF statistics dashboard
+      text: Chatsky statistics dashboard
     type: HEADER
   MARKDOWN-8Q9BhcEwva:
     children: []
@@ -378,7 +378,7 @@ position:
 
 
         To make them available in the dashboard, you need to define a custom extractor
-        function for them (see the [Extractor functions](https://deeppavlov.github.io/dialog_flow_framework/tutorials/tutorials.stats.1_extractor_functions.html) ).
+        function for them (see the [Extractor functions](https://deeppavlov.github.io/chatsky/tutorials/tutorials.stats.1_extractor_functions.html) ).
         The output of that function will then be persisted to the `data` column of
         the logs table, while the name of the function will be available in the `data
         key` column. That makes it easy to filter the relevant log entries and use
@@ -621,7 +621,7 @@ position:
         ## Service users
 
 
-        This plot aggregates the count of unique users querying the DFF service at
+        This plot aggregates the count of unique users querying the Chatsky service at
         any given point in time. The time periods to aggregate over can be changed
         using the filter on the left.
 

dff/config/superset_dashboard/databases/dff_database.yaml → chatsky/config/superset_dashboard/databases/chatsky_database.yaml

@@ -1,4 +1,4 @@
-database_name: dff_database
+database_name: chatsky_database
 sqlalchemy_uri: clickhousedb+connect://username:XXXXXXXXXX@clickhouse:8123/test
 cache_timeout: null
 expose_in_sqllab: true

dff/config/superset_dashboard/datasets/dff_database/dff_final_nodes.yaml → chatsky/config/superset_dashboard/datasets/chatsky_database/chatsky_final_nodes.yaml

@@ -1,4 +1,4 @@
-table_name: dff_final_nodes
+table_name: chatsky_final_nodes
 main_dttm_col: null
 description: null
 default_endpoint: null

dff/config/superset_dashboard/datasets/dff_database/dff_node_stats.yaml → chatsky/config/superset_dashboard/datasets/chatsky_database/chatsky_node_stats.yaml

@@ -1,4 +1,4 @@
-table_name: dff_node_stats
+table_name: chatsky_node_stats
 main_dttm_col: null
 description: null
 default_endpoint: null

dff/config/superset_dashboard/datasets/dff_database/dff_stats.yaml → chatsky/config/superset_dashboard/datasets/chatsky_database/chatsky_stats.yaml

@@ -1,4 +1,4 @@
-table_name: dff_stats
+table_name: chatsky_stats
 main_dttm_col: null
 description: null
 default_endpoint: null

dff/context_storages/database.py → chatsky/context_storages/database.py

@@ -21,7 +21,7 @@
 
 class DBContextStorage(ABC):
     r"""
-    An abstract interface for `dff` DB context storages.
+    An abstract interface for `chatsky` DB context storages.
     It includes the most essential methods of the python `dict` class.
     Can not be instantiated.
 
@@ -224,5 +224,5 @@ def context_storage_factory(path: str, **kwargs) -> DBContextStorage:
     For more information, see the function doc:\n{context_storage_factory.__doc__}
     """
     _class, module = PROTOCOLS[prefix]["class"], PROTOCOLS[prefix]["module"]
-    target_class = getattr(importlib.import_module(f".{module}", package="dff.context_storages"), _class)
+    target_class = getattr(importlib.import_module(f".{module}", package="chatsky.context_storages"), _class)
     return target_class(path, **kwargs)

dff/context_storages/json.py → chatsky/context_storages/json.py

@@ -2,7 +2,7 @@
 JSON
 ----
 The JSON module provides a json-based version of the :py:class:`.DBContextStorage` class.
-This class is used to store and retrieve context data in a JSON. It allows the DFF to easily
+This class is used to store and retrieve context data in a JSON. It allows Chatsky to easily
 store and retrieve context data.
 """
 
@@ -20,7 +20,7 @@
 from pydantic import BaseModel, model_validator
 
 from .database import DBContextStorage, threadsafe_method
-from dff.script import Context
+from chatsky.script import Context
 
 
 class SerializableStorage(BaseModel, extra="allow"):

dff/context_storages/mongo.py → chatsky/context_storages/mongo.py

@@ -3,7 +3,7 @@
 -----
 The Mongo module provides a MongoDB-based version of the :py:class:`.DBContextStorage` class.
 This class is used to store and retrieve context data in a MongoDB.
-It allows the DFF to easily store and retrieve context data in a format that is highly scalable
+It allows Chatsky to easily store and retrieve context data in a format that is highly scalable
 and easy to work with.
 
 MongoDB is a widely-used, open-source NoSQL database that is known for its scalability and performance.
@@ -26,7 +26,7 @@
 
 import json
 
-from dff.script import Context
+from chatsky.script import Context
 
 from .database import DBContextStorage, threadsafe_method
 from .protocol import get_protocol_install_suggestion

dff/context_storages/pickle.py → chatsky/context_storages/pickle.py

@@ -3,7 +3,7 @@
 ------
 The Pickle module provides a pickle-based version of the :py:class:`.DBContextStorage` class.
 This class is used to store and retrieve context data in a pickle format.
-It allows the DFF to easily store and retrieve context data in a format that is efficient
+It allows Chatsky to easily store and retrieve context data in a format that is efficient
 for serialization and deserialization and can be easily used in python.
 
 Pickle is a python library that allows to serialize and deserialize python objects.
@@ -24,7 +24,7 @@
     pickle_available = False
 
 from .database import DBContextStorage, threadsafe_method
-from dff.script import Context
+from chatsky.script import Context
 
 
 class PickleContextStorage(DBContextStorage):

dff/context_storages/protocol.py → chatsky/context_storages/protocol.py

@@ -1,15 +1,15 @@
 """
 Protocol
 --------
-The Protocol module contains the base code for the different communication protocols used in the DFF.
-It defines the :py:data:`.PROTOCOLS` constant, which lists all the supported protocols in the DFF.
+The Protocol module contains the base code for the different communication protocols used in Chatsky.
+It defines the :py:data:`.PROTOCOLS` constant, which lists all the supported protocols in Chatsky.
 
 The module also includes a function :py:func:`.get_protocol_install_suggestion()` that is used to provide
 suggestions for installing the necessary dependencies for a specific protocol.
 This function takes the name of the desired protocol as an argument and returns
 a string containing the necessary installation commands for that protocol.
 
-The DFF supports a variety of communication protocols,
+Chatsky supports a variety of communication protocols,
 which allows it to communicate with different types of databases.
 """
 
@@ -31,5 +31,5 @@ def get_protocol_install_suggestion(protocol_name: str) -> str:
     protocol = PROTOCOLS.get(protocol_name, {})
     slug = protocol.get("slug")
     if slug:
-        return f"Try to run `pip install dff[{slug}]`"
+        return f"Try to run `pip install chatsky[{slug}]`"
     return ""