## 1. Project Overview & Quickstart (docker/genai-stack) ## File: README.md # GenAI Stack The GenAI Stack will get you started building your own GenAI application in no time. The demo applications can serve as inspiration or as a starting point. Learn more about the details in the [introduction blog post](https://neo4j.com/blog/introducing-genai-stack-developers/). # Configure Create a `.env` file from the environment template file `env.example` Available variables: | Variable Name | Default value | Description | |------------------------|------------------------------------|-------------------------------------------------------------------------| | OLLAMA_BASE_URL | http://host.docker.internal:11434 | REQUIRED - URL to Ollama LLM API | | NEO4J_URI | neo4j://database:7687 | REQUIRED - URL to Neo4j database | | NEO4J_USERNAME | neo4j | REQUIRED - Username for Neo4j database | | NEO4J_PASSWORD | password | REQUIRED - Password for Neo4j database | | LLM | llama2 | REQUIRED - Can be any Ollama model tag, or gpt-4 or gpt-3.5 or claudev2 | | EMBEDDING_MODEL | sentence_transformer | REQUIRED - Can be sentence_transformer, openai, aws, ollama or google-genai-embedding-001| | AWS_ACCESS_KEY_ID | | REQUIRED - Only if LLM=claudev2 or embedding_model=aws | | AWS_SECRET_ACCESS_KEY | | REQUIRED - Only if LLM=claudev2 or embedding_model=aws | | AWS_DEFAULT_REGION | | REQUIRED - Only if LLM=claudev2 or embedding_model=aws | | OPENAI_API_KEY | | REQUIRED - Only if LLM=gpt-4 or LLM=gpt-3.5 or embedding_model=openai | | GOOGLE_API_KEY | | REQUIRED - Only required when using GoogleGenai LLM or embedding model google-genai-embedding-001| | LANGCHAIN_ENDPOINT | "https://api.smith.langchain.com" | OPTIONAL - URL to Langchain Smith API | | LANGCHAIN_TRACING_V2 | false | OPTIONAL - Enable Langchain tracing v2 | | LANGCHAIN_PROJECT | | OPTIONAL - Langchain project name | | LANGCHAIN_API_KEY | | OPTIONAL - Langchain API key | ## LLM Configuration MacOS and Linux users can use any LLM that's available via Ollama. Check the "tags" section under the model page you want to use on https://ollama.ai/library and write the tag for the value of the environment variable `LLM=` in the `.env` file. All platforms can use GPT-3.5-turbo and GPT-4 (bring your own API keys for OpenAI models). **MacOS** Install [Ollama](https://ollama.ai) on MacOS and start it before running `docker compose up` using `ollama serve` in a separate terminal. **Linux** No need to install Ollama manually, it will run in a container as part of the stack when running with the Linux profile: run `docker compose --profile linux up`. Make sure to set the `OLLAMA_BASE_URL=http://llm:11434` in the `.env` file when using Ollama docker container. To use the Linux-GPU profile: run `docker compose --profile linux-gpu up`. Also change `OLLAMA_BASE_URL=http://llm-gpu:11434` in the `.env` file. **Windows** Ollama now supports Windows. Install [Ollama](https://ollama.ai) on Windows and start it before running `docker compose up` using `ollama serve` in a separate terminal. Alternatively, Windows users can generate an OpenAI API key and configure the stack to use `gpt-3.5` or `gpt-4` in the `.env` file. # Develop > [!WARNING] > There is a performance issue that impacts python applications in the `4.24.x` releases of Docker Desktop. Please upgrade to the latest release before using this stack. **To start everything** ``` docker compose up ``` If changes to build scripts have been made, **rebuild**. ``` docker compose up --build ``` To enter **watch mode** (auto rebuild on file changes). First start everything, then in new terminal: ``` docker compose watch ``` **Shutdown** If health check fails or containers don't start up as expected, shutdown completely to start up again. ``` docker compose down ``` # Applications Here's what's in this repo: | Name | Main files | Compose name | URLs | Description | |---|---|---|---|---| | Support Bot | `bot.py` | `bot` | http://localhost:8501 | Main usecase. Fullstack Python application. | | Stack Overflow Loader | `loader.py` | `loader` | http://localhost:8502 | Load SO data into the database (create vector embeddings etc). Fullstack Python application. | | PDF Reader | `pdf_bot.py` | `pdf_bot` | http://localhost:8503 | Read local PDF and ask it questions. Fullstack Python application. | | Standalone Bot API | `api.py` | `api` | http://localhost:8504 | Standalone HTTP API streaming (SSE) + non-streaming endpoints Python. | | Standalone Bot UI | `front-end/` | `front-end` | http://localhost:8505 | Standalone client that uses the Standalone Bot API to interact with the model. JavaScript (Svelte) front-end. | The database can be explored at http://localhost:7474. ## App 1 - Support Agent Bot UI: http://localhost:8501 DB client: http://localhost:7474 - answer support question based on recent entries - provide summarized answers with sources - demonstrate difference between - RAG Disabled (pure LLM response) - RAG Enabled (vector + knowledge graph context) - allow to generate a high quality support ticket for the current conversation based on the style of highly rated questions in the database. *(Chat input + RAG mode selector)* | | | |---|---| | | | | *(CTA to auto generate support ticket draft)* | *(UI of the auto generated support ticket draft)* | --- ## App 2 - Loader UI: http://localhost:8502 DB client: http://localhost:7474 - import recent Stack Overflow data for certain tags into a KG - embed questions and answers and store them in vector index - UI: choose tags, run import, see progress, some stats of data in the database - Load high ranked questions (regardless of tags) to support the ticket generation feature of App 1. | | | |---|---| | | | ## App 3 Question / Answer with a local PDF UI: http://localhost:8503 DB client: http://localhost:7474 This application lets you load a local PDF into text chunks and embed it into Neo4j so you can ask questions about its contents and have the LLM answer them using vector similarity search. ## App 4 Standalone HTTP API Endpoints: - http://localhost:8504/query?text=hello&rag=false (non streaming) - http://localhost:8504/query-stream?text=hello&rag=false (SSE streaming) Example cURL command: ```bash curl http://localhost:8504/query-stream\?text\=minimal%20hello%20world%20in%20python\&rag\=false ``` Exposes the functionality to answer questions in the same way as App 1 above. Uses same code and prompts. ## App 5 Static front-end UI: http://localhost:8505 This application has the same features as App 1, but is built separate from the back-end code using modern best practices (Vite, Svelte, Tailwind). The auto-reload on changes are instant using the Docker watch `sync` config. --- ## File: Readme.md # GenAI Stack The GenAI Stack will get you started building your own GenAI application in no time. The demo applications can serve as inspiration or as a starting point. Learn more about the details in the [introduction blog post](https://neo4j.com/blog/introducing-genai-stack-developers/). # Configure Create a `.env` file from the environment template file `env.example` Available variables: | Variable Name | Default value | Description | |------------------------|------------------------------------|-------------------------------------------------------------------------| | OLLAMA_BASE_URL | http://host.docker.internal:11434 | REQUIRED - URL to Ollama LLM API | | NEO4J_URI | neo4j://database:7687 | REQUIRED - URL to Neo4j database | | NEO4J_USERNAME | neo4j | REQUIRED - Username for Neo4j database | | NEO4J_PASSWORD | password | REQUIRED - Password for Neo4j database | | LLM | llama2 | REQUIRED - Can be any Ollama model tag, or gpt-4 or gpt-3.5 or claudev2 | | EMBEDDING_MODEL | sentence_transformer | REQUIRED - Can be sentence_transformer, openai, aws, ollama or google-genai-embedding-001| | AWS_ACCESS_KEY_ID | | REQUIRED - Only if LLM=claudev2 or embedding_model=aws | | AWS_SECRET_ACCESS_KEY | | REQUIRED - Only if LLM=claudev2 or embedding_model=aws | | AWS_DEFAULT_REGION | | REQUIRED - Only if LLM=claudev2 or embedding_model=aws | | OPENAI_API_KEY | | REQUIRED - Only if LLM=gpt-4 or LLM=gpt-3.5 or embedding_model=openai | | GOOGLE_API_KEY | | REQUIRED - Only required when using GoogleGenai LLM or embedding model google-genai-embedding-001| | LANGCHAIN_ENDPOINT | "https://api.smith.langchain.com" | OPTIONAL - URL to Langchain Smith API | | LANGCHAIN_TRACING_V2 | false | OPTIONAL - Enable Langchain tracing v2 | | LANGCHAIN_PROJECT | | OPTIONAL - Langchain project name | | LANGCHAIN_API_KEY | | OPTIONAL - Langchain API key | ## LLM Configuration MacOS and Linux users can use any LLM that's available via Ollama. Check the "tags" section under the model page you want to use on https://ollama.ai/library and write the tag for the value of the environment variable `LLM=` in the `.env` file. All platforms can use GPT-3.5-turbo and GPT-4 (bring your own API keys for OpenAI models). **MacOS** Install [Ollama](https://ollama.ai) on MacOS and start it before running `docker compose up` using `ollama serve` in a separate terminal. **Linux** No need to install Ollama manually, it will run in a container as part of the stack when running with the Linux profile: run `docker compose --profile linux up`. Make sure to set the `OLLAMA_BASE_URL=http://llm:11434` in the `.env` file when using Ollama docker container. To use the Linux-GPU profile: run `docker compose --profile linux-gpu up`. Also change `OLLAMA_BASE_URL=http://llm-gpu:11434` in the `.env` file. **Windows** Ollama now supports Windows. Install [Ollama](https://ollama.ai) on Windows and start it before running `docker compose up` using `ollama serve` in a separate terminal. Alternatively, Windows users can generate an OpenAI API key and configure the stack to use `gpt-3.5` or `gpt-4` in the `.env` file. # Develop > [!WARNING] > There is a performance issue that impacts python applications in the `4.24.x` releases of Docker Desktop. Please upgrade to the latest release before using this stack. **To start everything** ``` docker compose up ``` If changes to build scripts have been made, **rebuild**. ``` docker compose up --build ``` To enter **watch mode** (auto rebuild on file changes). First start everything, then in new terminal: ``` docker compose watch ``` **Shutdown** If health check fails or containers don't start up as expected, shutdown completely to start up again. ``` docker compose down ``` # Applications Here's what's in this repo: | Name | Main files | Compose name | URLs | Description | |---|---|---|---|---| | Support Bot | `bot.py` | `bot` | http://localhost:8501 | Main usecase. Fullstack Python application. | | Stack Overflow Loader | `loader.py` | `loader` | http://localhost:8502 | Load SO data into the database (create vector embeddings etc). Fullstack Python application. | | PDF Reader | `pdf_bot.py` | `pdf_bot` | http://localhost:8503 | Read local PDF and ask it questions. Fullstack Python application. | | Standalone Bot API | `api.py` | `api` | http://localhost:8504 | Standalone HTTP API streaming (SSE) + non-streaming endpoints Python. | | Standalone Bot UI | `front-end/` | `front-end` | http://localhost:8505 | Standalone client that uses the Standalone Bot API to interact with the model. JavaScript (Svelte) front-end. | The database can be explored at http://localhost:7474. ## App 1 - Support Agent Bot UI: http://localhost:8501 DB client: http://localhost:7474 - answer support question based on recent entries - provide summarized answers with sources - demonstrate difference between - RAG Disabled (pure LLM response) - RAG Enabled (vector + knowledge graph context) - allow to generate a high quality support ticket for the current conversation based on the style of highly rated questions in the database. *(Chat input + RAG mode selector)* | | | |---|---| | | | | *(CTA to auto generate support ticket draft)* | *(UI of the auto generated support ticket draft)* | --- ## App 2 - Loader UI: http://localhost:8502 DB client: http://localhost:7474 - import recent Stack Overflow data for certain tags into a KG - embed questions and answers and store them in vector index - UI: choose tags, run import, see progress, some stats of data in the database - Load high ranked questions (regardless of tags) to support the ticket generation feature of App 1. | | | |---|---| | | | ## App 3 Question / Answer with a local PDF UI: http://localhost:8503 DB client: http://localhost:7474 This application lets you load a local PDF into text chunks and embed it into Neo4j so you can ask questions about its contents and have the LLM answer them using vector similarity search. ## App 4 Standalone HTTP API Endpoints: - http://localhost:8504/query?text=hello&rag=false (non streaming) - http://localhost:8504/query-stream?text=hello&rag=false (SSE streaming) Example cURL command: ```bash curl http://localhost:8504/query-stream\?text\=minimal%20hello%20world%20in%20python\&rag\=false ``` Exposes the functionality to answer questions in the same way as App 1 above. Uses same code and prompts. ## App 5 Static front-end UI: http://localhost:8505 This application has the same features as App 1, but is built separate from the back-end code using modern best practices (Vite, Svelte, Tailwind). The auto-reload on changes are instant using the Docker watch `sync` config. --- ## File: readme.md # GenAI Stack The GenAI Stack will get you started building your own GenAI application in no time. The demo applications can serve as inspiration or as a starting point. Learn more about the details in the [introduction blog post](https://neo4j.com/blog/introducing-genai-stack-developers/). # Configure Create a `.env` file from the environment template file `env.example` Available variables: | Variable Name | Default value | Description | |------------------------|------------------------------------|-------------------------------------------------------------------------| | OLLAMA_BASE_URL | http://host.docker.internal:11434 | REQUIRED - URL to Ollama LLM API | | NEO4J_URI | neo4j://database:7687 | REQUIRED - URL to Neo4j database | | NEO4J_USERNAME | neo4j | REQUIRED - Username for Neo4j database | | NEO4J_PASSWORD | password | REQUIRED - Password for Neo4j database | | LLM | llama2 | REQUIRED - Can be any Ollama model tag, or gpt-4 or gpt-3.5 or claudev2 | | EMBEDDING_MODEL | sentence_transformer | REQUIRED - Can be sentence_transformer, openai, aws, ollama or google-genai-embedding-001| | AWS_ACCESS_KEY_ID | | REQUIRED - Only if LLM=claudev2 or embedding_model=aws | | AWS_SECRET_ACCESS_KEY | | REQUIRED - Only if LLM=claudev2 or embedding_model=aws | | AWS_DEFAULT_REGION | | REQUIRED - Only if LLM=claudev2 or embedding_model=aws | | OPENAI_API_KEY | | REQUIRED - Only if LLM=gpt-4 or LLM=gpt-3.5 or embedding_model=openai | | GOOGLE_API_KEY | | REQUIRED - Only required when using GoogleGenai LLM or embedding model google-genai-embedding-001| | LANGCHAIN_ENDPOINT | "https://api.smith.langchain.com" | OPTIONAL - URL to Langchain Smith API | | LANGCHAIN_TRACING_V2 | false | OPTIONAL - Enable Langchain tracing v2 | | LANGCHAIN_PROJECT | | OPTIONAL - Langchain project name | | LANGCHAIN_API_KEY | | OPTIONAL - Langchain API key | ## LLM Configuration MacOS and Linux users can use any LLM that's available via Ollama. Check the "tags" section under the model page you want to use on https://ollama.ai/library and write the tag for the value of the environment variable `LLM=` in the `.env` file. All platforms can use GPT-3.5-turbo and GPT-4 (bring your own API keys for OpenAI models). **MacOS** Install [Ollama](https://ollama.ai) on MacOS and start it before running `docker compose up` using `ollama serve` in a separate terminal. **Linux** No need to install Ollama manually, it will run in a container as part of the stack when running with the Linux profile: run `docker compose --profile linux up`. Make sure to set the `OLLAMA_BASE_URL=http://llm:11434` in the `.env` file when using Ollama docker container. To use the Linux-GPU profile: run `docker compose --profile linux-gpu up`. Also change `OLLAMA_BASE_URL=http://llm-gpu:11434` in the `.env` file. **Windows** Ollama now supports Windows. Install [Ollama](https://ollama.ai) on Windows and start it before running `docker compose up` using `ollama serve` in a separate terminal. Alternatively, Windows users can generate an OpenAI API key and configure the stack to use `gpt-3.5` or `gpt-4` in the `.env` file. # Develop > [!WARNING] > There is a performance issue that impacts python applications in the `4.24.x` releases of Docker Desktop. Please upgrade to the latest release before using this stack. **To start everything** ``` docker compose up ``` If changes to build scripts have been made, **rebuild**. ``` docker compose up --build ``` To enter **watch mode** (auto rebuild on file changes). First start everything, then in new terminal: ``` docker compose watch ``` **Shutdown** If health check fails or containers don't start up as expected, shutdown completely to start up again. ``` docker compose down ``` # Applications Here's what's in this repo: | Name | Main files | Compose name | URLs | Description | |---|---|---|---|---| | Support Bot | `bot.py` | `bot` | http://localhost:8501 | Main usecase. Fullstack Python application. | | Stack Overflow Loader | `loader.py` | `loader` | http://localhost:8502 | Load SO data into the database (create vector embeddings etc). Fullstack Python application. | | PDF Reader | `pdf_bot.py` | `pdf_bot` | http://localhost:8503 | Read local PDF and ask it questions. Fullstack Python application. | | Standalone Bot API | `api.py` | `api` | http://localhost:8504 | Standalone HTTP API streaming (SSE) + non-streaming endpoints Python. | | Standalone Bot UI | `front-end/` | `front-end` | http://localhost:8505 | Standalone client that uses the Standalone Bot API to interact with the model. JavaScript (Svelte) front-end. | The database can be explored at http://localhost:7474. ## App 1 - Support Agent Bot UI: http://localhost:8501 DB client: http://localhost:7474 - answer support question based on recent entries - provide summarized answers with sources - demonstrate difference between - RAG Disabled (pure LLM response) - RAG Enabled (vector + knowledge graph context) - allow to generate a high quality support ticket for the current conversation based on the style of highly rated questions in the database. *(Chat input + RAG mode selector)* | | | |---|---| | | | | *(CTA to auto generate support ticket draft)* | *(UI of the auto generated support ticket draft)* | --- ## App 2 - Loader UI: http://localhost:8502 DB client: http://localhost:7474 - import recent Stack Overflow data for certain tags into a KG - embed questions and answers and store them in vector index - UI: choose tags, run import, see progress, some stats of data in the database - Load high ranked questions (regardless of tags) to support the ticket generation feature of App 1. | | | |---|---| | | | ## App 3 Question / Answer with a local PDF UI: http://localhost:8503 DB client: http://localhost:7474 This application lets you load a local PDF into text chunks and embed it into Neo4j so you can ask questions about its contents and have the LLM answer them using vector similarity search. ## App 4 Standalone HTTP API Endpoints: - http://localhost:8504/query?text=hello&rag=false (non streaming) - http://localhost:8504/query-stream?text=hello&rag=false (SSE streaming) Example cURL command: ```bash curl http://localhost:8504/query-stream\?text\=minimal%20hello%20world%20in%20python\&rag\=false ``` Exposes the functionality to answer questions in the same way as App 1 above. Uses same code and prompts. ## App 5 Static front-end UI: http://localhost:8505 This application has the same features as App 1, but is built separate from the back-end code using modern best practices (Vite, Svelte, Tailwind). The auto-reload on changes are instant using the Docker watch `sync` config. --- ## File: front-end/README.md # Svelte + Vite This template should help get you started developing with Svelte in Vite. ## Recommended IDE Setup [VS Code](https://code.visualstudio.com/) + [Svelte](https://marketplace.visualstudio.com/items?itemName=svelte.svelte-vscode). ## Need an official Svelte framework? Check out [SvelteKit](https://github.com/sveltejs/kit#readme), which is also powered by Vite. Deploy anywhere with its serverless-first approach and adapt to various platforms, with out of the box support for TypeScript, SCSS and Less and easily-added support for mdsvex, GraphQL, PostCSS, Tailwind CSS, and more. ## Technical considerations **Why use this over SvelteKit?** - It brings its own routing solution which might not be preferable for some users. - It is first and foremost a framework that just happens to use Vite under the hood, not a Vite app. This template contains as little as possible to get started with Vite + Svelte, while taking into account the developer experience with regards to HMR and intellisense. It demonstrates capabilities on par with the other `create-vite` templates and is a good starting point for beginners dipping their toes into a Vite + Svelte project. Should you later need the extended capabilities and extensibility provided by SvelteKit, the template has been structured similarly to SvelteKit so that it is easy to migrate. **Why `global.d.ts` instead of `compilerOptions.types` inside `jsconfig.json` or `tsconfig.json`?** Setting `compilerOptions.types` shuts out all other types not explicitly listed in the configuration. Using triple-slash references keeps the default TypeScript setting of accepting type information from the entire workspace while also adding `svelte` and `vite/client` type information. **Why include `.vscode/extensions.json`?** Other templates indirectly recommend extensions via the README, but this file allows VS Code to prompt the user to install the recommended extension upon opening the project. **Why enable `checkJs` in the JS template?** It is likely that most cases of changing variable types in runtime are likely to be accidental rather than deliberate. This provides advanced typechecking out of the box. Should you like to take advantage of the dynamically-typed nature of JavaScript, it is trivial to change the configuration. **Why is HMR not preserving my local component state?** HMR state preservation comes with a number of gotchas! It has been disabled by default in both `svelte-hmr` and `@sveltejs/vite-plugin-svelte` due to its often surprising behavior. You can read the details [here](https://github.com/sveltejs/svelte-hmr/tree/master/packages/svelte-hmr#preservation-of-local-state). If you have a state that's important to retain within a component, consider creating an external store that would not be replaced by HMR. ```js // store.js // An extremely simple external store import { writable } from 'svelte/store' export default writable(0) ``` ## 2. Official Technical Reference & Guides (docker/docs) # Docs @ Docker Welcome to the Docker Documentation repository. This is the source for the [Docker Docs Website](https://docs.docker.com/). Feel free to open pull requests or issues. Our docs are completely open source, and we deeply appreciate contributions from the Docker community! ## Provide feedback We’d love to hear your feedback! To submit feedback: - Click **[New issue](https://github.com/docker/docs/issues/new)** on the docs repository, or - Click **Request changes** in the right column of every page on [docs.docker.com](https://docs.docker.com/), or - Click **Give feedback** on every page in the docs. To get community support, use the [Docker Community Slack](https://dockr.ly/comm-slack). Personalized support is available through the Docker Pro, Team, and Business subscriptions. See [Docker Pricing](https://www.docker.com/pricing?ref=Docs&refAction=DocsReadme) for details. If you have an idea for a new feature or behavior change in a specific aspect of Docker or have found a product bug, file an issue in the project's repository. ## Contribute to Docker docs We welcome contributions! To get started: - [CONTRIBUTING.md](CONTRIBUTING.md) - Contribution workflow and setup - [STYLE.md](STYLE.md) - Writing style and content guidelines - [COMPONENTS.md](COMPONENTS.md) - Component and shortcode usage ## Copyright and license Copyright 2013-2026 Docker, Inc., released under the [Apache 2.0 license](https://github.com/docker/docs/blob/main/LICENSE).