DeepPavlov Dream

DeepPavlov Dream is a platform for creating multi-skill generative AI assistants.

To learn more about the platform and how to build AI assistants with it, please visit Dream. If you want to learn more about DeepPavlov Agent that powers Dream visit DeepPavlov Agent documentation.

Star History

Distributions

We've already included six distributions: four of them are based on lightweight Deepy socialbot, one is a full-sized Dream chatbot (based on Alexa Prize Challenge version) in English and a Dream chatbot in Russian.

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, ./.env_ru). 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.

Dream Mini

Mini version of DeepPavlov Dream Socialbot. This is a generative-based socialbot that uses English DialoGPT model to generate most of the responses. It also contains intent catcher and responder components to cover special user requests. Link to the distribution.

Dream Russian

Russian version of DeepPavlov Dream Socialbot. This is a generative-based socialbot that uses Russian DialoGPT by DeepPavlov to generate most of the responses. It also contains intent catcher and responder components to cover special user requests. Link to the distribution.

Prompted Dream Distributions

Mini version of DeepPavlov Dream Socialbot with the use of prompt-based generative models. This is a generative-based socialbot that uses large language models to generate most of the responses. You can upload your own prompts (json files) to common/prompts, add prompt names to PROMPTS_TO_CONSIDER (comma-separated), and the provided information will be used in LLM-powered reply generation as a prompt. Link to the distribution.

Quick Start

System Requirements

• Operating System: Ubuntu 18.04+, Windows 10+ (через WSL & WSL2), MacOS Big Sur;
• Version of docker from 20 and above;
• Version of docker-compose v1.29.2;
• Operative Memory from 2 Gb (using proxy), from 4 Gb (LLM-based prompted distributions) and from 20 Gb (old scripted distributions).

Clone the repo

git clone https://github.com/deeppavlov/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/dev.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

Prompted Dream

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

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

Let's chat

DeepPavlov Agent provides several options for interaction: a command line interface, an HTTP API, and a Telegram bot

CLI

In a separate terminal tab run:

docker-compose exec agent python -m deeppavlov_agent.run agent.channel=cmd agent.pipeline_config=assistant_dists/dream/pipeline_conf.json

Enter your username and have a chat with Dream!

HTTP API

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

A basic chat interface will be available at http://localhost:4242/chat.

Telegram Bot

Currently, Telegram bot is deployed instead of HTTP API. Edit agent command definition inside docker-compose.override.yml config:

agent:
  command: sh -c 'bin/wait && python -m deeppavlov_agent.run agent.channel=telegram agent.telegram_token=<TELEGRAM_BOT_TOKEN> agent.pipeline_config=assistant_dists/dream/pipeline_conf.json'

NOTE: treat your Telegram token as a secret and do not commit it to public repositories!

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=proxy.deeppavlov.ai:8004
    - SERVICE_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/dev.yml -f assistant_dists/dream/proxy.yml up --build

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

Components English Version

Dream Architecture is presented in the following image:

Name	Requirements	Description
Rule Based Selector		Algorithm that selects list of skills to generate candidate responses to the current context based on topics, entities, emotions, toxicity, dialogue acts and dialogue history
Response Selector	50 MB RAM	Algorithm that selects a final responses among the given list of candidate responses

Annotators

Name	Requirements	Description
ASR	40 MB RAM	calculates overall ASR confidence for a given utterance and grades it as either very low, low, medium, or high (for Amazon markup)
Badlisted Words	150 MB RAM	detects words and phrases from the badlist
Combined Classification	1.5 GB RAM, 3.5 GB GPU	BERT-based model including topic classification, dialog acts classification, sentiment, toxicity, emotion, factoid classification
Combined Classification lightweight	1.6 GB RAM	The same model as Combined Classification, but takes 42% less time thanks to the lighter backbone
COMeT Atomic	2 GB RAM, 1.1 GB GPU	Commonsense prediction models COMeT Atomic
COMeT ConceptNet	2 GB RAM, 1.1 GB GPU	Commonsense prediction models COMeT ConceptNet
Convers Evaluator Annotator	1 GB RAM, 4.5 GB GPU	is trained on the Alexa Prize data from the previous competitions and predicts whether the candidate response is interesting, comprehensible, on-topic, engaging, or erroneous
Emotion Classification	2.5 GB RAM	emotion classification annotator
Entity Detection	1.5 GB RAM, 3.2 GB GPU	extracts entities and their types from utterances
Entity Linking	2.5 GB RAM, 1.3 GB GPU	finds Wikidata entity ids for the entities detected with Entity Detection
Entity Storer	220 MB RAM	a rule-based component, which stores entities from the user's and socialbot's utterances if opinion expression is detected with patterns or MIDAS Classifier and saves them along with the detected attitude to dialogue state
Fact Random	50 MB RAM	returns random facts for the given entity (for entities from user utterance)
Fact Retrieval	7.4 GB RAM, 1.2 GB GPU	extracts facts from Wikipedia and wikiHow
Intent Catcher	1.7 GB RAM, 2.4 GB GPU	classifies user utterances into a number of predefined intents which are trained on a set of phrases and regexps
KBQA	2 GB RAM, 1.4 GB GPU	answers user's factoid questions based on Wikidata KB
MIDAS Classification	1.1 GB RAM, 4.5 GB GPU	BERT-based model trained on a semantic classes subset of MIDAS dataset
MIDAS Predictor	30 MB RAM	BERT-based model trained on a semantic classes subset of MIDAS dataset
NER	2.2 GB RAM, 5 GB GPU	extracts person names, names of locations, organizations from uncased text
News API Annotator	80 MB RAM	extracts the latest news about entities or topics using the GNews API. DeepPavlov Dream deployments utilize our own API key.
Personality Catcher	30 MB RAM	the skill is to change the system's personality description via chatting interface, it works as a system command, the response is system-like message
Prompt Selector	50 MB RAM	Annotator utilizing Sentence Ranker to rank prompts and selecting N_SENTENCES_TO_RETURN most relevant prompts (based on questions provided in prompts)
Property Extraction	6.3 GiB RAM	extracts user attributes from utterances
Rake Keywords	40 MB RAM	extracts keywords from utterances with the help of RAKE algorithm
Relative Persona Extractor	50 MB RAM	Annotator utilizing Sentence Ranker to rank persona sentences and selecting N_SENTENCES_TO_RETURN the most relevant sentences
Sentrewrite	200 MB RAM	rewrites user's utterances by replacing pronouns with specific names that provide more useful information to downstream components
Sentseg	1 GB RAM	allows us to handle long and complex user's utterances by splitting them into sentences and recovering punctuation
Spacy Nounphrases	180 MB RAM	extracts nounphrases using Spacy and filters out generic ones
Speech Function Classifier	1.1 GB RAM, 4.5 GB GPU	a hierarchical algorithm based on several linear models and a rule-based approach for the prediction of speech functions described by Eggins and Slade
Speech Function Predictor	1.1 GB RAM, 4.5 GB GPU	yields probabilities of speech functions that can follow a speech function predicted by Speech Function Classifier
Spelling Preprocessing	50 MB RAM	pattern-based component to rewrite different colloquial expressions to a more formal style of conversation
Topic Recommendation	40 MB RAM	offers a topic for further conversation using the information about the discussed topics and user's preferences. Current version is based on Reddit personalities (see Dream Report for Alexa Prize 4).
Toxic Classification	3.5 GB RAM, 3 GB GPU	Toxic classification model from Transformers specified as PRETRAINED_MODEL_NAME_OR_PATH
User Persona Extractor	40 MB RAM	determines which age category the user belongs to based on some key words
Wiki Parser	100 MB RAM	extracts Wikidata triplets for the entities detected with Entity Linking
Wiki Facts	1.7 GB RAM	model that extracts related facts from Wikipedia and WikiHow pages

Services

Name	Requirements	Description
DialoGPT	1.2 GB RAM, 2.1 GB GPU	generative service based on Transformers generative model, the model is set in docker compose argument PRETRAINED_MODEL_NAME_OR_PATH (for example, microsoft/DialoGPT-small with 0.2-0.5 sec on GPU)
DialoGPT Persona-based	1.2 GB RAM, 2.1 GB GPU	generative service based on Transformers generative model, the model was pre-trained on the PersonaChat dataset to generate a response conditioned on a several sentences of the socialbot's persona
Image Captioning	4 GB RAM, 5.4 GB GPU	creates text representation of a received image
Infilling	1 GB RAM, 1.2 GB GPU	(turned off but the code is available) generative service based on Infilling model, for the given utterance returns utterance where _ from original text is replaced with generated tokens
Knowledge Grounding	2 GB RAM, 2.1 GB GPU	generative service based on BlenderBot architecture providing a response to the context taking into account an additional text paragraph
Masked LM	1.1 GB RAM, 1 GB GPU	(turned off but the code is available)
Seq2seq Persona-based	1.5 GB RAM, 1.5 GB GPU	generative service based on Transformers seq2seq model, the model was pre-trained on the PersonaChat dataset to generate a response conditioned on a several sentences of the socialbot's persona
Sentence Ranker	1.2 GB RAM, 2.1 GB GPU	ranking model given as PRETRAINED_MODEL_NAME_OR_PATH which for a pair os sentences returns a float score of correspondence
StoryGPT	2.6 GB RAM, 2.15 GB GPU	generative service based on fine-tuned GPT-2, for the given set of keywords returns a short story using the keywords
GPT-3.5	100 MB RAM	generative service based on OpenAI API service, the model is set in docker compose argument PRETRAINED_MODEL_NAME_OR_PATH (in particular, in this service, text-davinci-003 is used.
ChatGPT	100 MB RAM	generative service based on OpenAI API service, the model is set in docker compose argument PRETRAINED_MODEL_NAME_OR_PATH (in particular, in this service, gpt-3.5-turbo is used.
Prompt StoryGPT	3 GB RAM, 4 GB GPU	generative service based on fine-tuned GPT-2, for the given topic represented by one noun returns short story on a given topic
GPT-J 6B	1.5 GB RAM, 24.2 GB GPU	generative service based on Transformers generative model, the model is set in docker compose argument PRETRAINED_MODEL_NAME_OR_PATH (in particular, in this service, GPT-J model is used.
BLOOMZ 7B	2.5 GB RAM, 29 GB GPU	generative service based on Transformers generative model, the model is set in docker compose argument PRETRAINED_MODEL_NAME_OR_PATH (in particular, in this service, BLOOMZ-7b1 model is used.
GPT-JT 6B	2.5 GB RAM, 25.1 GB GPU	generative service based on Transformers generative model, the model is set in docker compose argument PRETRAINED_MODEL_NAME_OR_PATH (in particular, in this service, GPT-JT model is used.

Skills

Name	Requirements	Description
Alexa Handler	30 MB RAM	handler for several specific Alexa commands
Christmas Skill	30 MB RAM	supports FAQ, facts, and scripts for Christmas
Comet Dialog skill	300 MB RAM	uses COMeT ConceptNet model to express an opinion, to ask a question or give a comment about user's actions mentioned in the dialogue
Convert Reddit	1.2 GB RAM	uses a ConveRT encoder to build efficient representations for sentences
Dummy Skill	a part of agent container	a fallback skill with multiple non-toxic candidate responses
Dummy Skill Dialog	600 MB RAM	returns the next turn from the Topical Chat dataset if the response of the user to the Dummy Skill is similar to the corresponding response in the source data
Eliza	30 MB RAM	Chatbot (https://github.com/wadetb/eliza)
Emotion Skill	40 MB RAM	returns template responses to emotions detected by Emotion Classification from Combined Classification annotator
Factoid QA	170 MB RAM	answers factoid questions
Game Cooperative Skill	100 MB RAM	provides user with a conversation about computer games: the charts of the best games for the past year, past month, and last week
Harvesters Maintenance Skill	30 MB RAM	Harvesters maintenance skill
Harvesters Maintenance Gobot Skill	30 MB RAM	Harvesters maintenance Goal-oriented skill
Knowledge Grounding Skill	100 MB RAM	generates a response based on the dialogue history and provided knowledge related to the current conversation topic
Meta Script Skill	150 MB RAM	provides a multi-turn dialogue around human activities. The skill uses COMeT Atomic model to generate commonsensical descriptions and questions on several aspects
Misheard ASR	40 MB RAM	uses the ASR Processor annotations to give feedback to the user when ASR confidence is too low
News API Skill	60 MB RAM	presents the top-rated latest news about entities or topics using the GNews API
Oscar Skill	30 MB RAM	supports FAQ, facts, and scripts for Oscar
Personal Info Skill	40 MB RAM	queries and stores user's name, birthplace, and location
DFF Program Y Skill	800 MB RAM	[New DFF version] Chatbot Program Y (https://github.com/keiffster/program-y) adapted for Dream socialbot
DFF Program Y Dangerous Skill	100 MB RAM	[New DFF version] Chatbot Program Y (https://github.com/keiffster/program-y) adapted for Dream socialbot, containing responses to dangerous situations in a dialog
DFF Program Y Wide Skill	110 MB RAM	[New DFF version] Chatbot Program Y (https://github.com/keiffster/program-y) adapted for Dream socialbot, which includes only very general templates (with lower confidence)
Small Talk Skill	35 MB RAM	asks questions using the hand-written scripts for 25 topics, including but not limited to love, sports, work, pets, etc.
SuperBowl Skill	30 MB RAM	supports FAQ, facts, and scripts for SuperBowl
Text QA	1.8 GB RAM, 2.8 GB GPU	The service finds the answer of a factoid question in text.
Valentine's Day Skill	30 MB RAM	supports FAQ, facts, and scripts for Valentine's Day
Wikidata Dial Skill	100 MB RAM	generates an utterance using Wikidata triplets. Not turned on, needs improvement
DFF Animals Skill	200 MB RAM	is created using DFF and has three branches of conversation about animals: user's pets, pets of the socialbot, and wild animals
DFF Art Skill	100 MB RAM	DFF-based skill to discuss art
DFF Book Skill	400 MB RAM	[New DFF version] detects book titles and authors mentioned in the user's utterance with the help of Wiki parser and Entity linking and recommends books by leveraging information from the GoodReads database
DFF Bot Persona Skill	150 MB RAM	aims to discuss user favorites and 20 most popular things with short stories expressing the socialbot's opinion towards them
DFF Coronavirus Skill	110 MB RAM	[New DFF version] retrieves data about the number of coronavirus cases and deaths in different locations sourced from the John Hopkins University Center for System Science and Engineering
DFF Food Skill	150 MB RAM	constructed with DFF to encourage food-related conversation
DFF Friendship Skill	100 MB RAM	[New DFF version] DFF-based skill to greet the user in the beginning of the dialog, and forward the user to some scripted skill
DFF Funfact Skill	100 MB RAM	[New DFF version] Tells user fun facts
DFF Gaming Skill	80 MB RAM	provides a video games discussion. Gaming Skill is for more general talk about video games
DFF Gossip Skill	95 MB RAM	DFF-based skill to discuss other people with news about them
DFF Image Skill	100 MB RAM	[New DFF version] Scripted skill that based on the sent image captioning (from annotations) responses with specified responses in case of food, animals or people detected, and default responses otherwise
DFF Template Skill	50 MB RAM	[New DFF version] DFF-based skill that provides an example of DFF usage
DFF Template Prompted Skill	50 MB RAM	[New DFF version] DFF-based skill that provides answers generated by language model based on specified prompts and the dialog context. The model to be used is specified in GENERATIVE_SERVICE_URL. For example, you may use Transformer LM GPTJ service.
DFF Grounding Skill	90 MB RAM	[New DFF version] DFF-based skill to answer what is the topic of the conversation, to generate acknowledgement, to generate universal responses on some dialog acts by MIDAS
DFF Intent Responder	100 MB RAM	[New DFF version] provides template-based replies for some of the intents detected by Intent Catcher annotator
DFF Movie Skill	1.1 GB RAM	is implemented using DFF and takes care of the conversations related to movies
DFF Music Skill	70 MB RAM	DFF-based skill to discuss music
DFF Science Skill	90 MB RAM	DFF-based skill to discuss science
DFF Short Story Skill	90 MB RAM	[New DFF version] tells user short stories from 3 categories: (1) bedtime stories, such as fables and moral stories, (2) horror stories, and (3) funny ones
DFF Sport Skill	70 MB RAM	DFF-based skill to discuss sports
DFF Travel Skill	70 MB RAM	DFF-based skill to discuss travel
DFF Weather Skill	1.4 GB RAM	[New DFF version] uses the OpenWeatherMap service to get the forecast for the user's location
DFF Wiki Skill	150 MB RAM	used for making scenarios with the extraction of entities, slot filling, facts insertion, and acknowledgements

Prompted Skills

Name	Requirements	Description
AI FAQ Skill	150 MB RAM	[New DFF version] Everything you wanted to know about modern AI but was afraid to ask! This FAQ Assistant chats with you while explaining the simplest topics from today's technology world.
Fashion Stylist Skill	150 MB RAM	[New DFF version] Stay protected in every season with da Costa Industries Clothes Assistant! Experience the ultimate comfort and protection, no matter the weather. Stay warm in winter a...
Dream Persona Skill	150 MB RAM	[New DFF version] Prompt-based skill that utilizes given generative service to generate responses based on the given prompt
Marketing Skill	150 MB RAM	[New DFF version] Connect with your audience like never before with Marketing AI Assistant! Reach new heights of success by tapping into the power of empathy. Say goodbye..
Fairytale Skill	150 MB RAM	[New DFF version] This assistant will tell you or your children a short but engaging fairytale. Choose the characters and the topic and leave the rest to AI imagination.
Nutrition Skill	150 MB RAM	[New DFF version] Discover the secret to healthy eating with our AI assistant! Find nutritious food options for you and your loved ones with ease. Say goodbye to mealtime stress and hello to delici...
Life Coaching Skill	150 MB RAM	[New DFF version] Unlock your full potential with Rhodes & Co's patented AI assistant! Reach peak performance at work and at home. Get into top form effortlessly and inspire others with.

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.