move workplace-search to chatbot-rag-app (elastic#116)

Author: joemcelroy

example-apps/chatbot-rag-app/.gitignore

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+frontend/build
+frontend/node_modules
+api/__pycache__
+api/.env
+.venv
+venv
+.DS_Store

example-apps/chatbot-rag-app/Dockerfile

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+# app/Dockerfile
+
+FROM node:16-alpine as build-step
+WORKDIR /app
+ENV PATH /node_modules/.bin:$PATH
+COPY frontend ./frontend
+RUN rm -rf /app/frontend/node_modules
+RUN cd frontend && yarn install
+RUN cd frontend && REACT_APP_API_HOST=/api yarn build
+
+FROM python:3.9-slim
+
+WORKDIR /app
+RUN mkdir -p ./frontend/build
+COPY --from=build-step ./app/frontend/build ./frontend/build 
+RUN mkdir ./api
+
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    software-properties-common \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+
+
+COPY api ./api
+COPY requirements.txt ./requirements.txt
+RUN pip3 install -r ./requirements.txt
+ENV FLASK_ENV production
+
+EXPOSE 4000
+WORKDIR /app/api
+CMD [ "python3", "-m" , "flask", "run", "--host=0.0.0.0", "--port=4000" ]

example-apps/chatbot-rag-app/README.md

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+# 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.
+
+![Screenshot of the sample app](./app-demo.gif)
+
+## 1. Download the Project
+
+Download the project from Github and extract the `chatbot-rag-app` folder.
+
+```bash
+curl https://codeload.github.com/elastic/elasticsearch-labs/tar.gz/main | \
+tar -xz --strip=2 elasticsearch-labs-main/example-apps/chatbot-rag-app
+```
+
+## 2. Installing and connecting to Elasticsearch
+
+### Install Elasticsearch
+
+There are a number of ways to install Elasticsearch. Cloud is best for most use-cases. Visit the [Install Elasticsearch](https://www.elastic.co/search-labs/tutorials/install-elasticsearch) for more information.
+
+### Connect to Elasticsearch
+
+This app requires the following environment variables to be set to connect to Elasticsearch
+
+```sh
+export ELASTIC_CLOUD_ID=...
+export ELASTIC_USERNAME=...
+export ELASTIC_PASSWORD=...
+```
+
+### Change the 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, you can set the following environment variables:
+
+```sh
+ES_INDEX=workplace-app-docs
+ES_INDEX_CHAT_HISTORY=workplace-app-docs-chat-history
+```
+
+## 3. Connecting to LLM
+
+We support three LLM providers: Azure, OpenAI and Bedrock.
+
+To use one of them, you need to set the `LLM_TYPE` environment variable:
+
+```sh
+export LLM_TYPE=azure
+```
+
+### OpenAI
+
+To use OpenAI LLM, you will need to provide the OpenAI key via `OPENAI_API_KEY` environment variable:
+
+```sh
+export LLM_TYPE=openai
+export OPENAI_API_KEY=...
+```
+
+You can get your OpenAI key from the [OpenAI dashboard](https://platform.openai.com/account/api-keys).
+
+### Azure OpenAI
+
+If you are using Azure LLM, you will need to set the following environment variables:
+
+```sh
+export LLM_TYPE=azure
+export OPENAI_VERSION=... # e.g. 2023-05-15
+export OPENAI_BASE_URL=...
+export OPENAI_API_KEY=...
+export OPENAI_ENGINE=... # deployment name in Azure
+```
+
+### Bedrock LLM
+
+To use Bedrock LLM you need to set the following environment variables in order to AWS.
+
+```sh
+export LLM_TYPE=bedrock
+export AWS_ACCESS_KEY=...
+export AWS_SECRET_KEY=...
+export AWS_REGION=... # e.g. us-east-1
+export AWS_MODEL_ID=... # Default is anthropic.claude-v2
+```
+
+#### AWS Config
+
+Optionally, you can connect to AWS via the config file in `~/.aws/config` described here:
+https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html#configuring-credentials
+
+```
+[default]
+aws_access_key_id=...
+aws_secret_access_key=...
+region=...
+```
+
+### Vertex AI
+
+To use Vertex AI you need to set the following environment variables. More infos [here](https://python.langchain.com/docs/integrations/llms/google_vertex_ai_palm).
+
+```sh
+    export LLM_TYPE=vertex
+    export VERTEX_PROJECT_ID=<gcp-project-id>
+    export VERTEX_REGION=<gcp-region> # Default is us-central1
+    export GOOGLE_APPLICATION_CREDENTIALS=<path-json-service-account>
+```
+
+## 3. Ingest Data
+
+You can index the sample data from the provided .json files in the `data` folder:
+
+```sh
+python data/index-data.py
+```
+
+by default, this will index the data into the `workplace-app-docs` index. You can change this by setting the `ES_INDEX` environment variable.
+
+### Indexing your own data
+
+`index-data.py` is a simple script that uses Langchain to index data into Elasticsearch, using the `JSONLoader` and `CharacterTextSplitter` to split the large documents into passages. Modify this script to index your own data.
+
+Langchain offers many different ways to index data, if you cant just load it via JSONLoader. See the [Langchain documentation](https://python.langchain.com/docs/modules/data_connection/document_loaders)
+
+Remember to keep the `ES_INDEX` environment variable set to the index you want to index into and to query from.
+
+## Running the App
+
+Once you have indexed data into the Elasticsearch index, there are two ways to run the app: via Docker or locally. Docker is advised for testing & production use. Locally is advised for development.
+
+### Through Docker
+
+Build the Docker image and run it with the following environment variables.
+
+```sh
+docker build -f Dockerfile -t chatbot-rag-app .
+```
+
+Then run it with the following environment variables. In the example below, we are using OpenAI LLM.
+
+If you're using one of the other LLMs, you will need to set the appropriate environment variables via `-e` flag.
+
+```sh
+docker run -p 4000:4000 \
+  -e "ELASTIC_CLOUD_ID=<cloud_id>" \
+  -e "ELASTIC_USERNAME=elastic" \
+  -e "ELASTIC_PASSWORD=<password>" \
+  -e "LLM_TYPE=openai" \
+  -e "OPENAI_API_KEY=<openai_key>" \
+  -d chatbot-rag-app
+```
+
+### Locally (for development)
+
+With the environment variables set, you can run the following commands to start the server and frontend.
+
+#### Pre-requisites
+
+- Python 3.8+
+- Node 14+
+
+#### Install the dependencies
+
+For Python we recommend using a virtual environment.
+
+_ℹ️ Here's a good [primer](https://realpython.com/python-virtual-environments-a-primer) on virtual environments from Real Python._
+
+```sh
+# Create a virtual environment
+python -m venv .venv
+
+# Activate the virtual environment
+source .venv/bin/activate
+```
+
+```sh
+# Install Python dependencies
+pip install -r requirements.txt
+
+# Install Node dependencies
+cd frontend && yarn
+```
+
+#### Run API and frontend
+
+```sh
+# Launch API app
+python api/app.py
+
+# In a separate terminal launch frontend app
+cd frontend && yarn start
+```
+
+You can now access the frontend at http://localhost:3000. Changes are automatically reloaded.

example-apps/chatbot-rag-app/api/app.py

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+from flask import Flask, jsonify, request, Response
+from flask_cors import CORS
+from queue import Queue
+from uuid import uuid4
+from chat import chat, ask_question, parse_stream_message
+import threading
+
+app = Flask(__name__, static_folder="../frontend/build", static_url_path="/")
+CORS(app)
+
+
+@app.route("/")
+def api_index():
+    return app.send_static_file("index.html")
+
+
+@app.route("/api/chat", methods=["POST"])
+def api_chat():
+    request_json = request.get_json()
+    question = request_json.get("question")
+    if question is None:
+        return jsonify({"msg": "Missing question from request JSON"}), 400
+
+    stream_queue = Queue()
+    session_id = request.args.get("session_id", str(uuid4()))
+
+    print("Chat session ID: ", session_id)
+
+    threading.Thread(
+        target=ask_question, args=(question, stream_queue, session_id)
+    ).start()
+
+    return Response(
+        parse_stream_message(session_id, stream_queue), mimetype="text/event-stream"
+    )
+
+
+if __name__ == "__main__":
+    app.run(port=3001, debug=True)