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chatbot-rag-app

README.md

Elastic Chatbot RAG App

This is a sample app that combines Elasticsearch, Langchain and a number of different LLMs to create a chatbot experience with ELSER with your own private data.

Requires at least 8.11.0 of Elasticsearch.

Screenshot of the sample app

Download the Project

Download the project from Github and extract the chatbot-rag-app folder.

curl https://codeload.github.com/elastic/elasticsearch-labs/tar.gz/main | \
tar -xz --strip=2 elasticsearch-labs-main/example-apps/chatbot-rag-app

Make your .env file

Copy env.example to .env and fill in values noted inside.

Installing and connecting to Elasticsearch

There are a number of ways to install Elasticsearch. Cloud is best for most use-cases. Visit the Install Elasticsearch for more information.

Once you decided your approach, edit your .env file accordingly.

Elasticsearch index and chat_history index

By default, the app will use the workplace-app-docs index and the chat history index will be workplace-app-docs-chat-history. If you want to change these, edit ES_INDEX and ES_INDEX_CHAT_HISTORY entries in your .env file.

Connecting to LLM

We support several LLM providers, but only one is used at runtime, and selected by the LLM_TYPE entry in your .env file. Edit that file to choose an LLM, and configure its templated connection settings:

Running the App

There are two ways to run the app: via Docker or locally. Docker is advised for ease while locally is advised if you are making changes to the application.

Run with docker

Docker compose is the easiest way, as you get one-step to:

Double-check you have a .env file with all your variables set first!

docker compose up --build --force-recreate

Note: First time creating the index can fail on timeout. Wait a few minutes and retry.

Run locally

If you want to run this example with Python and Node.js, you need to do a few things listed in the Dockerfile. The below uses the same production mode as used in Docker to avoid problems in debug mode.

Double-check you have a .env file with all your variables set first!

Build the frontend

The web assets are in the frontend directory, and built with yarn.

# Install and use a recent node, if you don't have one.
nvm install --lts
nvm use --lts
# Build the frontend web assets
(cd frontend; yarn install; REACT_APP_API_HOST=/api yarn build)

Configure your python environment

Before we can run the app, we need a working Python environment with the correct packages installed:

python3 -m venv .venv
source .venv/bin/activate
# install dev requirements for pip-compile and dotenv
pip install pip-tools "python-dotenv[cli]"
pip-compile
pip install -r requirements.txt

Run the ingest command

First, ingest the data into elasticsearch:

$ dotenv run -- flask create-index
".elser_model_2" model not available, downloading it now
Model downloaded, starting deployment
Loading data from ./data/data.json
Loaded 15 documents
Split 15 documents into 26 chunks
Creating Elasticsearch sparse vector store in http://localhost:9200

Note: First time creating the index can fail on timeout. Wait a few minutes and retry.

Run the app

Now, run the app, which listens on http://localhost:4000

$ dotenv run -- flask run
 * Serving Flask app 'api/app.py'
 * Debug mode: off

Customizing the app

Indexing your own data

The ingesting logic is stored in data/index_data.py. This is a simple script that uses Langchain to index data into Elasticsearch, using RecursiveCharacterTextSplitter to split the large JSON documents into passages. Modify this script to index your own data.

See Langchain documentation for more ways to load documents.