%global _empty_manifest_terminate_build 0
Name:		python-dff
Version:	0.4.1
Release:	1
Summary:	The Dialog Flow Framework (DFF) allows you to write conversational services.
License:	Apache Software License
URL:		https://github.com/deeppavlov/dialog_flow_framework
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/67/fa/cea1867b0eee11054d44eaa2dd733061f92af82699c1934e04d8eaa3d731/dff-0.4.1.tar.gz
BuildArch:	noarch

Requires:	python3-pydantic
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Requires:	python3-sphinx-favicon
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Requires:	python3-sqlalchemy[asyncio]
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Requires:	python3-sqlalchemy[asyncio]
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Requires:	python3-motor
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Requires:	python3-sqlalchemy[asyncio]
Requires:	python3-cryptography
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Requires:	python3-pytest
Requires:	python3-pytest-asyncio
Requires:	python3-pytest-cov
Requires:	python3-ydb
Requires:	python3-uvicorn
Requires:	python3-idna
Requires:	python3-anyio
Requires:	python3-requests
Requires:	python3-websockets
Requires:	python3-typing-extensions
Requires:	python3-nest-asyncio
Requires:	python3-h11
Requires:	python3-aiofiles
Requires:	python3-flake8
Requires:	python3-pydantic
Requires:	python3-aiosqlite
Requires:	python3-black
Requires:	python3-starlette
Requires:	python3-six
Requires:	python3-flask[async]
Requires:	python3-psutil
Requires:	python3-aioredis
Requires:	python3-asyncpg
Requires:	python3-isort
Requires:	python3-asyncmy
Requires:	python3-click
Requires:	python3-fastapi
Requires:	python3-sniffio
Requires:	python3-ydb
Requires:	python3-six

%description

# Dialog Flow Framework

The Dialog Flow Framework (DFF) allows you to write conversational services.
The service is written by defining a special dialog graph that describes the behavior of the dialog service.
The dialog graph contains the dialog script. DFF offers a specialized language (DSL) for quickly writing dialog graphs.
You can use it in services such as writing skills for Amazon Alexa, etc., chatbots for social networks, website call centers, etc.

[![Documentation Status](https://github.com/deeppavlov/dialog_flow_framework/workflows/build_and_publish_docs/badge.svg)](https://deeppavlov.github.io/dialog_flow_framework)
[![Codestyle](https://github.com/deeppavlov/dialog_flow_framework/workflows/codestyle/badge.svg)](https://github.com/deeppavlov/dialog_flow_framework/actions/workflows/codestyle.yml)
[![Tests](https://github.com/deeppavlov/dialog_flow_framework/workflows/test_coverage/badge.svg)](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)
![Python 3.7, 3.8, 3.9](https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9-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)

# Quick Start
## Installation

DFF can be installed via pip:

```bash
pip install dff
```

The above command will set the minimum dependencies to start working with DFF.
The installation process allows the user to choose from different packages based on their dependencies, which are:
```bash
pip install dff[core]  # minimal dependencies (by default)
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[full]  # full dependencies including all options above
pip install dff[tests]  # dependencies for running tests
pip install dff[test_full]  # full dependencies for running all tests (all options above)
pip install dff[tutorials]  # dependencies for running tutorials (all options above)
pip install dff[devel]  # dependencies for development
pip install dff[doc]  # dependencies for documentation
pip install dff[devel_full]  # full dependencies for development (all options above)
```

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]
```

## Basic example

```python
from dff.script import GLOBAL, TRANSITIONS, RESPONSE, Context, Message
from dff.pipeline import Pipeline
import dff.script.conditions.std_conditions as cnd
from typing import Tuple

# create a dialog script
script = {
GLOBAL: {
TRANSITIONS: {
("flow", "node_hi"): cnd.exact_match(Message(text="Hi")),
("flow", "node_ok"): cnd.true()
}
},
"flow": {
"node_hi": {RESPONSE: Message(text="Hi!!!")},
"node_ok": {RESPONSE: Message(text="Okey")},
},
}

# init pipeline
pipeline = Pipeline.from_script(script, start_label=("flow", "node_hi"))


# handler requests
def turn_handler(in_request: Message, pipeline: Pipeline) -> Tuple[Message, Context]:
# Pass the next request of user into pipeline and it returns updated context with actor response
ctx = pipeline(in_request, 0)
# Get last actor response from the context
out_response = ctx.last_response
# The next condition branching needs for testing
return out_response, ctx


while True:
in_request = input("type your answer: ")
out_response, ctx = turn_handler(Message(text=in_request), pipeline)
print(out_response.text)
```

When you run this code, you get similar output:
```
type your answer: hi
Okey
type your answer: Hi
Hi!!!
type your answer: ok
Okey
type your answer: ok
Okey
```

To get more advanced examples, take a look at
[tutorials](https://github.com/deeppavlov/dialog_flow_framework/tree/dev/tutorials) on GitHub.

# Context Storages
## Description

Context Storages allow you to save and retrieve user dialogue states
(in the form of a `Context` object) using various database backends.

The following backends are currently supported:
* [JSON](https://www.json.org/json-en.html)
* [pickle](https://docs.python.org/3/library/pickle.html)
* [shelve](https://docs.python.org/3/library/shelve.html)
* [SQLite](https://www.sqlite.org/index.html)
* [PostgreSQL](https://www.postgresql.org/)
* [MySQL](https://www.mysql.com/)
* [MongoDB](https://www.mongodb.com/)
* [Redis](https://redis.io/)
* [Yandex DataBase](https://ydb.tech/)

Aside from this, we offer some interfaces for saving data to your local file system.
These are not meant to be used in production, but can be helpful for prototyping your application.

## Basic example

```python
from dff.script import Context
from dff.pipeline import Pipeline
from dff.context_storages import SQLContextStorage
from .script import some_df_script

db = SQLContextStorage("postgresql+asyncpg://user:password@host:port/dbname")

pipeline = Pipeline.from_script(some_df_script, start_label=("root", "start"), fallback_label=("root", "fallback"))


def handle_request(request):
user_id = request.args["user_id"]
new_context = pipeline(request, user_id)
return new_context.last_response

```

To get more advanced examples, take a look at
[tutorials](https://github.com/deeppavlov/dialog_flow_framework/tree/dev/tutorials/context_storages) on GitHub.

# Contributing to the Dialog Flow Framework

Please refer to [CONTRIBUTING.md](https://github.com/deeppavlov/dialog_flow_framework/blob/dev/CONTRIBUTING.md).


%package -n python3-dff
Summary:	The Dialog Flow Framework (DFF) allows you to write conversational services.
Provides:	python-dff
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-dff

# Dialog Flow Framework

The Dialog Flow Framework (DFF) allows you to write conversational services.
The service is written by defining a special dialog graph that describes the behavior of the dialog service.
The dialog graph contains the dialog script. DFF offers a specialized language (DSL) for quickly writing dialog graphs.
You can use it in services such as writing skills for Amazon Alexa, etc., chatbots for social networks, website call centers, etc.

[![Documentation Status](https://github.com/deeppavlov/dialog_flow_framework/workflows/build_and_publish_docs/badge.svg)](https://deeppavlov.github.io/dialog_flow_framework)
[![Codestyle](https://github.com/deeppavlov/dialog_flow_framework/workflows/codestyle/badge.svg)](https://github.com/deeppavlov/dialog_flow_framework/actions/workflows/codestyle.yml)
[![Tests](https://github.com/deeppavlov/dialog_flow_framework/workflows/test_coverage/badge.svg)](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)
![Python 3.7, 3.8, 3.9](https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9-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)

# Quick Start
## Installation

DFF can be installed via pip:

```bash
pip install dff
```

The above command will set the minimum dependencies to start working with DFF.
The installation process allows the user to choose from different packages based on their dependencies, which are:
```bash
pip install dff[core]  # minimal dependencies (by default)
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[full]  # full dependencies including all options above
pip install dff[tests]  # dependencies for running tests
pip install dff[test_full]  # full dependencies for running all tests (all options above)
pip install dff[tutorials]  # dependencies for running tutorials (all options above)
pip install dff[devel]  # dependencies for development
pip install dff[doc]  # dependencies for documentation
pip install dff[devel_full]  # full dependencies for development (all options above)
```

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]
```

## Basic example

```python
from dff.script import GLOBAL, TRANSITIONS, RESPONSE, Context, Message
from dff.pipeline import Pipeline
import dff.script.conditions.std_conditions as cnd
from typing import Tuple

# create a dialog script
script = {
GLOBAL: {
TRANSITIONS: {
("flow", "node_hi"): cnd.exact_match(Message(text="Hi")),
("flow", "node_ok"): cnd.true()
}
},
"flow": {
"node_hi": {RESPONSE: Message(text="Hi!!!")},
"node_ok": {RESPONSE: Message(text="Okey")},
},
}

# init pipeline
pipeline = Pipeline.from_script(script, start_label=("flow", "node_hi"))


# handler requests
def turn_handler(in_request: Message, pipeline: Pipeline) -> Tuple[Message, Context]:
# Pass the next request of user into pipeline and it returns updated context with actor response
ctx = pipeline(in_request, 0)
# Get last actor response from the context
out_response = ctx.last_response
# The next condition branching needs for testing
return out_response, ctx


while True:
in_request = input("type your answer: ")
out_response, ctx = turn_handler(Message(text=in_request), pipeline)
print(out_response.text)
```

When you run this code, you get similar output:
```
type your answer: hi
Okey
type your answer: Hi
Hi!!!
type your answer: ok
Okey
type your answer: ok
Okey
```

To get more advanced examples, take a look at
[tutorials](https://github.com/deeppavlov/dialog_flow_framework/tree/dev/tutorials) on GitHub.

# Context Storages
## Description

Context Storages allow you to save and retrieve user dialogue states
(in the form of a `Context` object) using various database backends.

The following backends are currently supported:
* [JSON](https://www.json.org/json-en.html)
* [pickle](https://docs.python.org/3/library/pickle.html)
* [shelve](https://docs.python.org/3/library/shelve.html)
* [SQLite](https://www.sqlite.org/index.html)
* [PostgreSQL](https://www.postgresql.org/)
* [MySQL](https://www.mysql.com/)
* [MongoDB](https://www.mongodb.com/)
* [Redis](https://redis.io/)
* [Yandex DataBase](https://ydb.tech/)

Aside from this, we offer some interfaces for saving data to your local file system.
These are not meant to be used in production, but can be helpful for prototyping your application.

## Basic example

```python
from dff.script import Context
from dff.pipeline import Pipeline
from dff.context_storages import SQLContextStorage
from .script import some_df_script

db = SQLContextStorage("postgresql+asyncpg://user:password@host:port/dbname")

pipeline = Pipeline.from_script(some_df_script, start_label=("root", "start"), fallback_label=("root", "fallback"))


def handle_request(request):
user_id = request.args["user_id"]
new_context = pipeline(request, user_id)
return new_context.last_response

```

To get more advanced examples, take a look at
[tutorials](https://github.com/deeppavlov/dialog_flow_framework/tree/dev/tutorials/context_storages) on GitHub.

# Contributing to the Dialog Flow Framework

Please refer to [CONTRIBUTING.md](https://github.com/deeppavlov/dialog_flow_framework/blob/dev/CONTRIBUTING.md).


%package help
Summary:	Development documents and examples for dff
Provides:	python3-dff-doc
%description help

# Dialog Flow Framework

The Dialog Flow Framework (DFF) allows you to write conversational services.
The service is written by defining a special dialog graph that describes the behavior of the dialog service.
The dialog graph contains the dialog script. DFF offers a specialized language (DSL) for quickly writing dialog graphs.
You can use it in services such as writing skills for Amazon Alexa, etc., chatbots for social networks, website call centers, etc.

[![Documentation Status](https://github.com/deeppavlov/dialog_flow_framework/workflows/build_and_publish_docs/badge.svg)](https://deeppavlov.github.io/dialog_flow_framework)
[![Codestyle](https://github.com/deeppavlov/dialog_flow_framework/workflows/codestyle/badge.svg)](https://github.com/deeppavlov/dialog_flow_framework/actions/workflows/codestyle.yml)
[![Tests](https://github.com/deeppavlov/dialog_flow_framework/workflows/test_coverage/badge.svg)](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)
![Python 3.7, 3.8, 3.9](https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9-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)

# Quick Start
## Installation

DFF can be installed via pip:

```bash
pip install dff
```

The above command will set the minimum dependencies to start working with DFF.
The installation process allows the user to choose from different packages based on their dependencies, which are:
```bash
pip install dff[core]  # minimal dependencies (by default)
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[full]  # full dependencies including all options above
pip install dff[tests]  # dependencies for running tests
pip install dff[test_full]  # full dependencies for running all tests (all options above)
pip install dff[tutorials]  # dependencies for running tutorials (all options above)
pip install dff[devel]  # dependencies for development
pip install dff[doc]  # dependencies for documentation
pip install dff[devel_full]  # full dependencies for development (all options above)
```

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]
```

## Basic example

```python
from dff.script import GLOBAL, TRANSITIONS, RESPONSE, Context, Message
from dff.pipeline import Pipeline
import dff.script.conditions.std_conditions as cnd
from typing import Tuple

# create a dialog script
script = {
GLOBAL: {
TRANSITIONS: {
("flow", "node_hi"): cnd.exact_match(Message(text="Hi")),
("flow", "node_ok"): cnd.true()
}
},
"flow": {
"node_hi": {RESPONSE: Message(text="Hi!!!")},
"node_ok": {RESPONSE: Message(text="Okey")},
},
}

# init pipeline
pipeline = Pipeline.from_script(script, start_label=("flow", "node_hi"))


# handler requests
def turn_handler(in_request: Message, pipeline: Pipeline) -> Tuple[Message, Context]:
# Pass the next request of user into pipeline and it returns updated context with actor response
ctx = pipeline(in_request, 0)
# Get last actor response from the context
out_response = ctx.last_response
# The next condition branching needs for testing
return out_response, ctx


while True:
in_request = input("type your answer: ")
out_response, ctx = turn_handler(Message(text=in_request), pipeline)
print(out_response.text)
```

When you run this code, you get similar output:
```
type your answer: hi
Okey
type your answer: Hi
Hi!!!
type your answer: ok
Okey
type your answer: ok
Okey
```

To get more advanced examples, take a look at
[tutorials](https://github.com/deeppavlov/dialog_flow_framework/tree/dev/tutorials) on GitHub.

# Context Storages
## Description

Context Storages allow you to save and retrieve user dialogue states
(in the form of a `Context` object) using various database backends.

The following backends are currently supported:
* [JSON](https://www.json.org/json-en.html)
* [pickle](https://docs.python.org/3/library/pickle.html)
* [shelve](https://docs.python.org/3/library/shelve.html)
* [SQLite](https://www.sqlite.org/index.html)
* [PostgreSQL](https://www.postgresql.org/)
* [MySQL](https://www.mysql.com/)
* [MongoDB](https://www.mongodb.com/)
* [Redis](https://redis.io/)
* [Yandex DataBase](https://ydb.tech/)

Aside from this, we offer some interfaces for saving data to your local file system.
These are not meant to be used in production, but can be helpful for prototyping your application.

## Basic example

```python
from dff.script import Context
from dff.pipeline import Pipeline
from dff.context_storages import SQLContextStorage
from .script import some_df_script

db = SQLContextStorage("postgresql+asyncpg://user:password@host:port/dbname")

pipeline = Pipeline.from_script(some_df_script, start_label=("root", "start"), fallback_label=("root", "fallback"))


def handle_request(request):
user_id = request.args["user_id"]
new_context = pipeline(request, user_id)
return new_context.last_response

```

To get more advanced examples, take a look at
[tutorials](https://github.com/deeppavlov/dialog_flow_framework/tree/dev/tutorials/context_storages) on GitHub.

# Contributing to the Dialog Flow Framework

Please refer to [CONTRIBUTING.md](https://github.com/deeppavlov/dialog_flow_framework/blob/dev/CONTRIBUTING.md).


%prep
%autosetup -n dff-0.4.1

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-dff -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.1-1
- Package Spec generated