DeepPavlov Dream

DeepPavlov Dream is a platform for creating multi-skill chatbots.

To get architecture documentation, please refer to DeepPavlov Agent readthedocs documentation.

Distributions

We've already included five distributions: four of them are based on lightweight Deepy socialbot and one is a full-sized Dream chatbot.

Deepy Base

Base version of Lunar assistant. Deepy Base contains Spelling Preprocessing annotator, template-based Harvesters Maintenance Skill, and AIML-based open-domain Program-y Skill based on Dialog Flow Framework.

Deepy Advanced

Advanced version of Lunar assistant. Deepy Advanced contains Spelling Preprocessing, Sentence Segmentation, Entity Linking and Intent Catcher annotators, Harvesters Maintenance GoBot Skill for goal-oriented responses, and AIML-based open-domain Program-y Skill based on Dialog Flow Framework.

Deepy FAQ

FAQ version of Lunar assistant. Deepy FAQ contains Spelling Preprocessing annotator, template-based Frequently Asked Questions Skill, and AIML-based open-domain Program-y Skill based on Dialog Flow Framework.

Deepy GoBot

Goal-oriented version of Lunar assistant. Deepy GoBot Base contains Spelling Preprocessing annotator, Harvesters Maintenance GoBot Skill for goal-oriented responses, and AIML-based open-domain Program-y Skill based on Dialog Flow Framework.

Dream

Full version of DeepPavlov Dream Socialbot. This is almost the same version of the DREAM socialbot as at the end of Alexa Prize Challenge 4. Some API services are replaced with trainable models. Some services (e.g., News Annotator, Game Skill, Weather Skill) require private keys for underlying APIs, most of them can be obtained for free. If you want to use these services in local deployments, add your keys to the environmental variables (e.g., ./.env). This version of Dream Socialbot consumes a lot of resources because of its modular architecture and original goals (participation in Alexa Prize Challenge). We provide a demo of Dream Socialbot on our website.

Quick Start

Clone the repo

git clone https://github.com/deepmipt/dream.git

Install docker and docker-compose

If you get a "Permission denied" error running docker-compose, make sure to configure your docker user correctly.

Run one of the Dream distributions

Deepy Base

docker-compose -f docker-compose.yml -f assistant_dists/deepy_base/docker-compose.override.yml up --build

Deepy Advanced

docker-compose -f docker-compose.yml -f assistant_dists/deepy_adv/docker-compose.override.yml up --build

Deepy FAQ

docker-compose -f docker-compose.yml -f assistant_dists/deepy_faq/docker-compose.override.yml up --build

Deepy GoBot

docker-compose -f docker-compose.yml -f assistant_dists/deepy_gobot_base/docker-compose.override.yml up --build

Dream (via proxy)

The easiest way to try out Dream is to deploy it via proxy. All the requests will be redirected to DeepPavlov API, so you don't have to use any local resources. See proxy usage for details.

docker-compose -f docker-compose.yml -f assistant_dists/dream/docker-compose.override.yml -f assistant_dists/dream/proxy.yml up --build

Dream (locally)

Please note, that DeepPavlov Dream components require a lot of resources. Refer to the components section to see estimated requirements.

docker-compose -f docker-compose.yml -f assistant_dists/dream/docker-compose.override.yml -f assistant_dists/dream/dev.yml up --build

We've also included a config with GPU allocations for multi-GPU environments.

AGENT_PORT=4242 docker-compose -f docker-compose.yml -f assistant_dists/dream/docker-compose.override.yml -f assistant_dists/dream/dev.yml -f assistant_dists/dream/test.yml up

When you need to restart particular docker container without re-building (make sure mapping in assistant_dists/dream/dev.yml is correct):

AGENT_PORT=4242 docker-compose -f docker-compose.yml -f assistant_dists/dream/docker-compose.override.yml -f assistant_dists/dream/dev.yml restart container-name

Let's chat

In a separate terminal tab run:

docker-compose exec agent python -m deeppavlov_agent.run -pl assistant_dists/dream/pipeline_conf.json

Enter your username and have a chat with Dream!

Let's talk via HTTP API

Once you've started the bot, DeepPavlov's Agent API will run on `http://localhost:4242'. You can learn about its API from the DeepPavlov Agent Docs.

Configuration and proxy usage

Dream uses several docker-compose configuration files:

./docker-compose.yml is the core config which includes containers for DeepPavlov Agent and mongo database;

./assistant_dists/*/docker-compose.override.yml lists all components for the distribution;

./assistant_dists/dream/dev.yml includes volume bindings for easier Dream debugging;

./assistant_dists/dream/proxy.yml is a list of proxied containers.

If your deployment resources are limited, you can replace containers with their proxied copies hosted by DeepPavlov. To do this, override those container definitions inside proxy.yml, e.g.:

convers-evaluator-annotator:
  command: ["nginx", "-g", "daemon off;"]
  build:
    context: dp/proxy/
    dockerfile: Dockerfile
  environment:
    - PROXY_PASS=dream.deeppavlov.ai:8004
    - PORT=8004

and include this config in your deployment command:

docker-compose -f docker-compose.yml -f assistant_dists/dream/docker-compose.override.yml -f assistant_dists/dream/proxy.yml up --build

By default, proxy.yml contains all available proxy definitions.

Components

Dream Architecture is presented in the following image:

Annotators

Skills

Papers

Alexa Prize 3

Kuratov Y. et al. DREAM technical report for the Alexa Prize 2019 //Alexa Prize Proceedings. – 2020.

Alexa Prize 4

Baymurzina D. et al. DREAM Technical Report for the Alexa Prize 4 //Alexa Prize Proceedings. – 2021.

License

DeepPavlov Dream is licensed under Apache 2.0.

Program-y (see dream/skills/dff_program_y_skill, dream/skills/dff_program_y_wide_skill, dream/skills/dff_program_y_dangerous_skill) is licensed under Apache 2.0. Eliza (see dream/skills/eliza) is licensed under MIT License.

Report creating

For making certification xlsx - file with bot responses, you can use xlsx_responder.py script by executing

docker-compose -f docker-compose.yml -f dev.yml exec -T -u $(id -u) agent python3 \
        utils/xlsx_responder.py --url http://0.0.0.0:4242 \
        --input 'tests/dream/test_questions.xlsx' \
        --output 'tests/dream/output/test_questions_output.xlsx'\
      --cache tests/dream/output/test_questions_output_$(date --iso-8601=seconds).json

Make sure all services are deployed. --input - xlsx file with certification questions, --output - xlsx file with bot responses, --cache - json, that contains a detailed markup and is used for a cache.