dff 0.1a7

[](https://github.com/deepmipt/dff/blob/master/LICENSE)

Dialog Flow Framework

The Dialog Flow Framework (DFF) is a dialogue systems development environment that supports both rapid prototyping and long-term team development workflow for dialogue systems. A simple structure allows easily building and visualizing a dialogue graph.

Links

Github

Quick Start

Installation

pip install dff

Basic example

from dff.core.keywords import GLOBAL, TRANSITIONS, RESPONSE
from dff.core import Context, Actor
import dff.conditions as cnd
from typing import Union

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

# init actor
actor = Actor(plot, start_label=("flow", "node_hi"))


# handler requests
def turn_handler(in_request: str, ctx: Union[Context, dict], actor: Actor):
# Context.cast - gets an object type of [Context, str, dict] returns an object type of Context
ctx = Context.cast(ctx)
# Add in current context a next request of user
ctx.add_request(in_request)
# pass the context into actor and it returns updated context with actor response
ctx = actor(ctx)
# get last actor response from the context
out_response = ctx.last_response
# the next condition branching needs for testing
return out_response, ctx


ctx = {}
while True:
in_request = input("type your answer: ")
out_response, ctx = turn_handler(in_request, ctx, actor)
print(out_response)

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 examples on GitHub.

Extentions

Contributing to the Dialog Flow Framework

Please refer to CONTRIBUTING.md.