📖 EmbedChain-Streamlit-Docker App Template

https://youtu.be/yJAWB13FhYQ

Disclaimer

• Thank you to amjadraza for his original work. This repository try to update some dependencies and add more examples.
• Thank you to vicenteherrera for his contributions to the repository, poetry, makefile, and docker improvements.
• @alonso_isidoro

🔧 Features

• Basic Skeleton App configured with openai API
• A ChatBot using embedchain and Streamlit
• Docker Support with Optimisation Cache etc
• Deployment on Streamlit Public Cloud
• Updated to latest embedchain version, actual is 0.1.102
• Added hugginface_demo

This repo contains an main.py file which has a template for a chatbot implementation.

Example Input Data:

Select the number of Data Sources from slider and enter the details.

Source	URL
youtube	https://www.youtube.com/watch?v=3qHkcs3kG44
pdf_file	https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf
web	https://nav.al/feedback
qna_pair	"Who is Naval Ravikant?", "Naval Ravikant is an Indian-American entrepreneur and investor."

Question: What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?

Adding your chain

To add your chain, you need to change the load_chain function in main.py. Depending on the type of your chain, you may also need to change the inputs/outputs that occur later on.

💻 Running Locally

1. Clone the repository📂

git clone https://github.com/amjadraza/embedchain-streamlit-app.git

2. Install dependencies with Poetry and activate virtual environment🔨

poetry install
poetry shell

3. Run the Streamlit server🚀

  streamlit run demo_app/main.py 

3.1 Run some scripts:

  python -m hugginface_demo

3.2 (optional) Run make:

    make all

possible errors

It is possible that this exception happens:

chromadb.errors.InvalidDimensionException: Embedding dimension 768 does not match collection dimensionality 1536. 
This is commonly a side-effect when an embedding function, different from the one used to add the embeddings, 
is used to retrieve an embedding from the database.

Actually i dont know how to maintain both chromedb instances, so i have to some cleaning after and before running 
hugginface script.

Run App using Docker

This project includes Dockerfile to run the app in Docker container. In order to optimise the Docker Image size and building time with cache techniques, I have follow tricks in below Article https://medium.com/@albertazzir/blazing-fast-python-docker-builds-with-poetry-a78a66f5aed0

Build the docker container

docker build . -t embedchain-streamlit-app:latest

To generate Image with DOCKER_BUILDKIT, follow below command

DOCKER_BUILDKIT=1 docker build --target=runtime . -t embedchain-streamlit-app:latest

1. Run the docker container directly

docker run -d --name embedchain-streamlit-app -p 8501:8501 embedchain-streamlit-app:latest

2. Run the docker container using docker-compose (Recommended)

docker-compose up

Deploy App on Streamlit Public Cloud

This app can be deployed on Streamlit Public Cloud using GitHub. Below is the Link to Publicly deployed App

https://embedchain.streamlit.app/

Report Feedbacks

As embedchain-streamlit-app:latest is a template project with minimal example. Report issues if you face any.

DISCLAIMER

This is a template App, when using with openai_api key, you will be charged a nominal fee depending on number of prompts etc.

Rename .env.example to .env, add your environment keys, ask for permission in order to access mistral, create your container with your credentials and run the app.

https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2