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Improve LLM doc chat user exp (#9)
* cleanup notebook and dependencies * updates to readme and structure * pin openai SDK version
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‎.env.template

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# General OpenAI Env Vars
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OPENAI_TEXT_MODEL=gpt-35-turbo
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OPENAI_DEPLOYMENT_TYPE=Text
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OPENAI_EMBEDDING_MODEL=text-embedding-ada-002
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OPENAI_EMBEDDINGS_ENGINE_QUERY=text-embedding-ada-002
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OPENAI_API_KEY=<your key here>
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# use OPENAI_API_VERSION only with Azure OpenAI
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#OPENAI_API_VERSION=2023-05-15
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OPENAI_TEMPERATURE=0.7
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OPENAI_MAX_TOKENS=50
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CHUNK_SIZE=500
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CHUNK_OVERLAP=0.2
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# OpenAI Direct Env Vars
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OPENAI_API_KEY=<your key here>
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OPENAI_API_BASE=https://api.openai.com/v1/
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# Azure OpenAI Env Vars
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#OPENAI_API_VERSION=2023-05-15 # use OPENAI_API_VERSION only with Azure OpenAI
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AZURE_EMBED_MODEL_DEPLOYMENT_NAME=<your deployment name here>
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AZURE_TEXT_MODEL_DEPLOYMENT_NAME=<your deployment name here>
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AZURE_OPENAI_API_BASE=https://<your deployment name>.openai.azure.com/
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OPENAI_API_BASE=https://api.openai.com/v1/
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REDIS_ADDRESS=redis
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REDIS_PORT=6379
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REDIS_PASSWORD=
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# General Env Vars
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CHUNK_SIZE=500
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CHUNK_OVERLAP=0.2
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# Redis Env Vars
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REDIS_HOST=redis
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REDIS_PORT=6379
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REDIS_PASSWORD=

‎.gitignore

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.env
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.env-azure
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.env-azure-local
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.env-openai
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.env-openai
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.local
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.cache
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.jupyter
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.ipython
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.npm

‎LICENSE

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MIT License
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Copyright (c) 2023 Redis Ventures
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Copyright (c) 2023 Redis Inc
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal

‎README.md

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https://user-images.githubusercontent.com/13009163/237002719-26e3118d-77ee-4ded-96f5-6ba801cae66c.mov
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## Setting up the Environment
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## Getting Started
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The tutorial can run in multiple ways. The first step is to configure the ``.env`` file in this repository. This file contains the following variables:
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The LLM Document Chat tutorial is **intended to run on a dockerized Jupyter lab environment** to ensure ideal experience and minimal Python environment hickups. At a minimum, you will need to install [Docker Desktop](https://www.docker.com/products/docker-desktop/) (*which comes with Docker Compose*) to run this example.
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The project maintains a `.env.template` with the following variables pre-defined:
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```bash
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OPENAI_TEXT_MODEL=text-davinci-003
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OPENAI_DEPLOYMENT_TYPE=Text
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# General OpenAI Env Vars
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OPENAI_TEXT_MODEL=gpt-35-turbo
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OPENAI_EMBEDDING_MODEL=text-embedding-ada-002
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OPENAI_EMBEDDINGS_ENGINE_QUERY=text-embedding-ada-002
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OPENAI_API_KEY=<your key here>
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OPENAI_TEMPERATURE=0.7
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OPENAI_MAX_TOKENS=50
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CHUNK_SIZE=500
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CHUNK_OVERLAP=100
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# OpenAI Direct Env Vars
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OPENAI_API_KEY=<your key here>
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OPENAI_API_BASE=https://api.openai.com/v1/
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# Azure OpenAI Env Vars
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#OPENAI_API_VERSION=2023-05-15 # use OPENAI_API_VERSION only with Azure OpenAI
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AZURE_EMBED_MODEL_DEPLOYMENT_NAME=<your deployment name here>
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AZURE_TEXT_MODEL_DEPLOYMENT_NAME=<your deployment name here>
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AZURE_OPENAI_API_BASE=https://<your deployment name>.openai.azure.com/
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OPENAI_API_BASE=https://api.openai.com/v1/
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REDIS_ADDRESS=localhost
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# General Env Vars
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CHUNK_SIZE=500
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CHUNK_OVERLAP=0.2
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# Redis Env Vars
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REDIS_HOST=redis
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REDIS_PORT=6379
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REDIS_PASSWORD=
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```
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### To Use Azure OpenAI
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Make a copy of this file to create `.env` as follows:
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```bash
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$ cp .env.template .env
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```
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Update portions of the env file based on your choices below:
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1. **[Choose your OpenAI provider](#choose-your-openai-provider)**
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2. **[Choose your Redis provider](#choose-your-redis-provider)**
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### Choose your OpenAI provider
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You can choose between **[Azure OpenAI Service](#to-use-azure-openai)** (fully hosted and managed by Azure) and **[OpenAI](#to-use-openai)** direct.
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#### To Use Azure OpenAI (Recommended)
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To use Azure OpenAI, you will need to follow these instructions
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1. Create an Azure OpenAI resource
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2. Create model deployments for the text and embedding models
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4. Configure the ``.env`` file adding the specific values for your deployments
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1. Create an Azure OpenAI resource.
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2. Create model deployments for the text and embedding models.
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4. Configure the ``.env`` file adding the specific values for your deployments.
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```bash
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AZURE_EMBED_MODEL_DEPLOYMENT_NAME=<your deployment name here>
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AZURE_TEXT_MODEL_DEPLOYMENT_NAME=<your deployment name here>
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AZURE_OPENAI_API_BASE=https://<your deployment name>.openai.azure.com/
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```
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### To Use OpenAI
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#### To Use OpenAI
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To use OpenAI, you will need to follow these instructions
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1. Create an OpenAI account
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2. Create an OpenAI API key
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3. Configure the ``.env`` file adding the specific values for your deployments
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1. Create an OpenAI account.
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2. Create an OpenAI API key.
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3. Configure the ``.env`` file adding the specific values for your deployments.
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```bash
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OPENAI_API_KEY=<your key here>
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```
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### Azure Redis Enterprise, Redis Enterprise Cloud, and Redis Stack
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### Choose your Redis provider
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The tutorial will require the usage of the Redis Search & Query features, including support for Vector Similarity Search. There are three options for running Redis:
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The tutorial will require the usage of the Redis-stack container because of the use of RediSearch
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and RedisJSON. There are three options for Redis
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1. Redis Enterprise Cloud
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1. [Redis Enterprise Cloud - Free Instance](https://redis.com/try-free)
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2. Azure Redis Enterprise (ACRE)
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3. Redis Stack (docker)
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4. Redis Stack (local)
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3. Redis Stack (local docker)
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For each, the following options are required
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For each, the following options are **required** and must be updated in your local `.env` file:
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```bash
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REDIS_PASSWORD=<your password here>
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REDIS_ADDRESS=<your redis address here>
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REDIS_HOST=<your redis host address here>
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REDIS_PORT=<your redis port here>
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```
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There are some ``docker-compose.yml`` files in the ``docker`` directory that will help spin up
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redis-stack locally and redisinsight in the case where a remote Redis is being used (like ACRE).
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### Run
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## Run
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To run the script, follow these steps:
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To open the jupyter environment through docker, follow these steps:
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1. Clone this repository to your local machine.
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2. copy the ``.env.template`` to ``.env`` and configure the values as outlined above.
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3. If using a local Python environment, just run the notebook
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3. Otherwise, select a docker-compose file to run and execute ``docker-compose up`` to start the environment.
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2. Copy the ``.env.template`` to ``.env`` and configure the values as outlined above.
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3. Run with Docker Compose:
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**For Cloud or Azure Redis Enterprise**
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```bash
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docker compose -f docker/cloud/docker-compose.yml up
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```
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**For Local (Docker) Redis Stack**
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```bash
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docker compose -f docker/local/docker-compose.yml up
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```
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4. Open the Jupyter lab session in your browser at `http://127.0.0.1:8888/lab?token={YOUR GENERATED TOKEN}`. *Check the terminal logs for the token string.*

‎docker/azure/docker-compose.yml renamed to ‎docker/cloud/docker-compose.yml

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container_name: jupyter
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volumes:
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- ../../:/home/jovyan/
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env_file:
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- ../../.env
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ports:
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- 8888:8888
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depends_on:

‎docker/local/docker-compose.yml

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container_name: jupyter
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volumes:
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- ../../:/home/jovyan/
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env_file:
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- ../../.env
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ports:
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- 8888:8888
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depends_on:

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