E2E-AI-Chatbot 🤖

Pipeline | Installation | User Interface | Model | Database | Search | Contact

Overview

End-to-end AI Chatbot is a project that aims to build a chatbot that can answer any question in any domain. The project is built on top of the workflow chatbot Q&A with Gradio and GPT4All model. The project is currently in the development stage.

• Gradio is a Python library that allows you to quickly create customizable UI components around your machine learning models, deep learning models, and other functions. Mix and match components to support any combination of inputs and outputs. Built-in support for NLP, images, plotting, and more.
• GPT4All is an ecosystem to train and deploy powerful and customized large language models that run locally on consumer grade CPUs. Note that your CPU needs to support AVX or AVX2 instructions. The goal is simple - be the best instruction tuned assistant-style language model that any person or enterprise can freely use, distribute and build on. A GPT4All model is a 3GB - 8GB file that you can download and plug into the GPT4All open-source ecosystem software. Nomic AI supports and maintains this software ecosystem to enforce quality and security alongside spearheading the effort to allow any person or enterprise to easily train and deploy their own on-edge large language models.

Tech Stack

#	Name	Description
1	Python	Programming language used to build the project.
2	FastAPI	Web framework backend
3	Gradio	Backend UI low code
4	Docker	Package application
5	GPT4All	Model GPT offline
6	Redis	Cache chat history
7	MongoDB	Database save document
8	Mongo Express	Database UI
9	Logstash	Data migration
10	Elasticsearch	Search engine
11	Kibana	Search monitoring
12	Nginx	HTTP and reverse proxy server
13	Kubernetes	Container orchestration
14	AWS/Azure	Cloud
15	Pre-commit	Linting

Pipeline

Current:

Next stage:

• FastAPI & Gradio backend
• Dockerize packages
• Add UI ingest upload file
• Add login page
• Add pre-commit
• Add docs
• Nginx for http and https
• K8s
• CI/CD cloud (AWS/Azure)

Installation Requirements

• Minimum CPU 8GiB RAM
• Uncomment line 8 packages = [{include = "**"}] to use all internal packages (Passing Flake8)
• Install packages and download GPT4All model by

1. Run locally

With poetry:

chmod u+x ./setup.sh
bash ./setup.sh

With pip:

pip install poetry
poetry shell
poetry install

Build MongoDB, Mongo Express, Logstash, Elasticsearch, Kibana and Redis

docker compose -f docker-compose-service.yml up
poetry run python app.py --host 0.0.0.0 --port 8071

2. Run docker

docker compose up

User Interface App

make run

Run on: http://localhost:8071

Login:

Account: admin
Password: admin

Chatbot:

New version:

Ingest PDF:

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Model

1. GPT4ALL: Current best commercially licensable model based on GPT-J and trained by Nomic AI on the latest curated GPT4All dataset.

Database

1. MongoDB Run on: http://localhost:27017

poetry run python src/ingest_database.py --mongodb-host "mongodb://localhost:27017/" --data-path "static/pdf/"

Mongo Compass (Windows)

Mongo Express

Run on: http://localhost:8081

Data Migration

Run on: http://localhost:9600

Search

1. Elasticsearch & Kibana

poetry run python src/ingest_search.py --mongodb-host "mongodb://localhost:27017/" --es-host "http://localhost:9200/" --index_name "document"

Elasticsearch run on: http://localhost:9200

Kibana run on: http://localhost:5601

Cache Chat History

For generating question takes a long time 97(s)/question, we use Redis to cache chat history.

Quickly generate question 0.5(s)/question

Contact

• KhoiVN - @linkedin-khoivn8071 - nguyenkhoi8071@gmail.com
• Project Link: Github-E2E-AI-Chatbot
• Website: khoivn.space

Impressive

• From Langchain Framework: https://github.com/hwchase17/langchain
• From GPT4All: https://github.com/nomic-ai/gpt4all

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