- 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
- Basic Skeleton App configured with
openaiAPI - 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.
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?
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.
- Clone the repository📂
git clone https://github.com/amjadraza/embedchain-streamlit-app.git- Install dependencies with Poetry and activate virtual environment🔨
poetry install
poetry shell- Run the Streamlit server🚀
streamlit run demo_app/main.py 3.1 Run some scripts:
python -m hugginface_demo3.2 (optional) Run make:
make allIt 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.
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
- Run the docker container directly
docker run -d --name embedchain-streamlit-app -p 8501:8501 embedchain-streamlit-app:latest
- Run the docker container using docker-compose (Recommended)
docker-compose up
This app can be deployed on Streamlit Public Cloud using GitHub. Below is the Link to Publicly deployed App
https://embedchain.streamlit.app/
As embedchain-streamlit-app:latest is a template project with minimal example. Report issues if you face any.
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.