27 |

Create new internal ticket

28 |

29 | Title
31 | 33 |

34 |

35 | Body
37 |

</label
38 |                 >
39 |             </div>
40 |         </div>
41 |         <button type="submit" class="bg-indigo-500 text-white rounded-lg px-4 py-2">Submit</button>
42 |     </form>
43 |     {#if $generationStore.state === generationStates.LOADING}
44 |         <div class="absolute inset-0 bg-indigo-100 bg-opacity-90 flex justify-center items-center">
45 |             Generating title and question body...
46 |         </div>
47 |     {/if}
48 |     <div class="absolute top-0 right-2 text-gray-300 hover:text-gray-900">
49 |         <button class="text-2xl" on:click={onClose}>×</button>
50 |     </div>
51 | </dialog>
52 | 
53 | <style>
54 |     dialog::backdrop {
55 |         @apply bg-gradient-to-t from-white to-indigo-500;
56 |         opacity: 0.75;
57 |     }
58 | </style>
59 |

--------------------------------------------------------------------------------
/front-end/src/lib/chat.store.js:
--------------------------------------------------------------------------------
 1 | import { writable } from "svelte/store";
 2 | 
 3 | const API_ENDPOINT = "http://localhost:8504/query-stream";
 4 | 
 5 | export const chatStates = {
 6 |     IDLE: "idle",
 7 |     RECEIVING: "receiving",
 8 | };
 9 | 
10 | function createChatStore() {
11 |     const { subscribe, update } = writable({ state: chatStates.IDLE, data: [] });
12 | 
13 |     function addMessage(from, text, rag) {
14 |         const newId = Math.random().toString(36).substring(2, 9);
15 |         update((state) => {
16 |             const message = { id: newId, from, text, rag };
17 |             state.data.push(message);
18 |             return state;
19 |         });
20 |         return newId;
21 |     }
22 | 
23 |     function updateMessage(existingId, text, model = null) {
24 |         if (!existingId) {
25 |             return;
26 |         }
27 |         update((state) => {
28 |             const messages = state.data;
29 |             const existingIdIndex = messages.findIndex((m) => m.id === existingId);
30 |             if (existingIdIndex === -1) {
31 |                 return state;
32 |             }
33 |             messages[existingIdIndex].text += text;
34 |             if (model) {
35 |                 messages[existingIdIndex].model = model;
36 |             }
37 |             return { ...state, data: messages };
38 |         });
39 |     }
40 | 
41 |     async function send(question, ragMode = false) {
42 |         if (!question.trim().length) {
43 |             return;
44 |         }
45 |         update((state) => ({ ...state, state: chatStates.RECEIVING }));
46 |         addMessage("me", question, ragMode);
47 |         const messageId = addMessage("bot", "", ragMode);
48 |         try {
49 |             const evt = new EventSource(`${API_ENDPOINT}?text=${encodeURI(question)}&rag=${ragMode}`);
50 |             question = "";
51 |             evt.onmessage = (e) => {
52 |                 if (e.data) {
53 |                     const data = JSON.parse(e.data);
54 |                     if (data.init) {
55 |                         updateMessage(messageId, "", data.model);
56 |                         return;
57 |                     }
58 |                     updateMessage(messageId, data.token);
59 |                 }
60 |             };
61 |             evt.onerror = (e) => {
62 |                 // Stream will end with an error
63 |                 // and we want to close the connection on end (otherwise it will keep reconnecting)
64 |                 evt.close();
65 |                 update((state) => ({ ...state, state: chatStates.IDLE }));
66 |             };
67 |         } catch (e) {
68 |             updateMessage(messageId, "Error: " + e.message);
69 |             update((state) => ({ ...state, state: chatStates.IDLE }));
70 |         }
71 |     }
72 | 
73 |     return {
74 |         subscribe,
75 |         send,
76 |     };
77 | }
78 | 
79 | export const chatStore = createChatStore();
80 |

--------------------------------------------------------------------------------
/pdf_bot.py:
--------------------------------------------------------------------------------
 1 | import os
 2 | 
 3 | import streamlit as st
 4 | from langchain.chains import RetrievalQA
 5 | from PyPDF2 import PdfReader
 6 | from langchain.text_splitter import RecursiveCharacterTextSplitter
 7 | from langchain.callbacks.base import BaseCallbackHandler
 8 | from langchain.vectorstores.neo4j_vector import Neo4jVector
 9 | from streamlit.logger import get_logger
10 | from chains import (
11 |     load_embedding_model,
12 |     load_llm,
13 | )
14 | 
15 | # load api key lib
16 | from dotenv import load_dotenv
17 | 
18 | load_dotenv(".env")
19 | 
20 | 
21 | url = os.getenv("NEO4J_URI")
22 | username = os.getenv("NEO4J_USERNAME")
23 | password = os.getenv("NEO4J_PASSWORD")
24 | ollama_base_url = os.getenv("OLLAMA_BASE_URL")
25 | embedding_model_name = os.getenv("EMBEDDING_MODEL")
26 | llm_name = os.getenv("LLM")
27 | # Remapping for Langchain Neo4j integration
28 | os.environ["NEO4J_URL"] = url
29 | 
30 | logger = get_logger(__name__)
31 | 
32 | 
33 | embeddings, dimension = load_embedding_model(
34 |     embedding_model_name, config={"ollama_base_url": ollama_base_url}, logger=logger
35 | )
36 | 
37 | 
38 | class StreamHandler(BaseCallbackHandler):
39 |     def __init__(self, container, initial_text=""):
40 |         self.container = container
41 |         self.text = initial_text
42 | 
43 |     def on_llm_new_token(self, token: str, **kwargs) -> None:
44 |         self.text += token
45 |         self.container.markdown(self.text)
46 | 
47 | 
48 | llm = load_llm(llm_name, logger=logger, config={"ollama_base_url": ollama_base_url})
49 | 
50 | 
51 | def main():
52 |     st.header("📄Chat with your pdf file")
53 | 
54 |     # upload a your pdf file
55 |     pdf = st.file_uploader("Upload your PDF", type="pdf")
56 | 
57 |     if pdf is not None:
58 |         pdf_reader = PdfReader(pdf)
59 | 
60 |         text = ""
61 |         for page in pdf_reader.pages:
62 |             text += page.extract_text()
63 | 
64 |         # langchain_textspliter
65 |         text_splitter = RecursiveCharacterTextSplitter(
66 |             chunk_size=1000, chunk_overlap=200, length_function=len
67 |         )
68 | 
69 |         chunks = text_splitter.split_text(text=text)
70 | 
71 |         # Store the chunks part in db (vector)
72 |         vectorstore = Neo4jVector.from_texts(
73 |             chunks,
74 |             url=url,
75 |             username=username,
76 |             password=password,
77 |             embedding=embeddings,
78 |             index_name="pdf_bot",
79 |             node_label="PdfBotChunk",
80 |             pre_delete_collection=True,  # Delete existing PDF data
81 |         )
82 |         qa = RetrievalQA.from_chain_type(
83 |             llm=llm, chain_type="stuff", retriever=vectorstore.as_retriever()
84 |         )
85 | 
86 |         # Accept user questions/query
87 |         query = st.text_input("Ask questions about your PDF file")
88 | 
89 |         if query:
90 |             stream_handler = StreamHandler(st.empty())
91 |             qa.run(query, callbacks=[stream_handler])
92 | 
93 | 
94 | if __name__ == "__main__":
95 |     main()
96 |

--------------------------------------------------------------------------------
/front-end/README.md:
--------------------------------------------------------------------------------
 1 | # Svelte + Vite
 2 | 
 3 | This template should help get you started developing with Svelte in Vite.
 4 | 
 5 | ## Recommended IDE Setup
 6 | 
 7 | [VS Code](https://code.visualstudio.com/) + [Svelte](https://marketplace.visualstudio.com/items?itemName=svelte.svelte-vscode).
 8 | 
 9 | ## Need an official Svelte framework?
10 | 
11 | 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.
12 | 
13 | ## Technical considerations
14 | 
15 | **Why use this over SvelteKit?**
16 | 
17 | - It brings its own routing solution which might not be preferable for some users.
18 | - It is first and foremost a framework that just happens to use Vite under the hood, not a Vite app.
19 | 
20 | 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.
21 | 
22 | 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.
23 | 
24 | **Why `global.d.ts` instead of `compilerOptions.types` inside `jsconfig.json` or `tsconfig.json`?**
25 | 
26 | 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.
27 | 
28 | **Why include `.vscode/extensions.json`?**
29 | 
30 | 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.
31 | 
32 | **Why enable `checkJs` in the JS template?**
33 | 
34 | 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.
35 | 
36 | **Why is HMR not preserving my local component state?**
37 | 
38 | 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).
39 | 
40 | 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.
41 | 
42 | ```js
43 | // store.js
44 | // An extremely simple external store
45 | import { writable } from 'svelte/store'
46 | export default writable(0)
47 | ```
48 |

--------------------------------------------------------------------------------
/api.py:
--------------------------------------------------------------------------------
  1 | import os
  2 | 
  3 | from langchain.graphs import Neo4jGraph
  4 | from dotenv import load_dotenv
  5 | from utils import (
  6 |     create_vector_index,
  7 |     BaseLogger,
  8 | )
  9 | from chains import (
 10 |     load_embedding_model,
 11 |     load_llm,
 12 |     configure_llm_only_chain,
 13 |     configure_qa_rag_chain,
 14 |     generate_ticket,
 15 | )
 16 | from fastapi import FastAPI, Depends
 17 | from pydantic import BaseModel
 18 | from langchain.callbacks.base import BaseCallbackHandler
 19 | from threading import Thread
 20 | from queue import Queue, Empty
 21 | from collections.abc import Generator
 22 | from sse_starlette.sse import EventSourceResponse
 23 | from fastapi.middleware.cors import CORSMiddleware
 24 | import json
 25 | 
 26 | load_dotenv(".env")
 27 | 
 28 | url = os.getenv("NEO4J_URI")
 29 | username = os.getenv("NEO4J_USERNAME")
 30 | password = os.getenv("NEO4J_PASSWORD")
 31 | ollama_base_url = os.getenv("OLLAMA_BASE_URL")
 32 | embedding_model_name = os.getenv("EMBEDDING_MODEL")
 33 | llm_name = os.getenv("LLM")
 34 | # Remapping for Langchain Neo4j integration
 35 | os.environ["NEO4J_URL"] = url
 36 | 
 37 | embeddings, dimension = load_embedding_model(
 38 |     embedding_model_name,
 39 |     config={"ollama_base_url": ollama_base_url},
 40 |     logger=BaseLogger(),
 41 | )
 42 | 
 43 | # if Neo4j is local, you can go to http://localhost:7474/ to browse the database
 44 | neo4j_graph = Neo4jGraph(url=url, username=username, password=password)
 45 | create_vector_index(neo4j_graph, dimension)
 46 | 
 47 | llm = load_llm(
 48 |     llm_name, logger=BaseLogger(), config={"ollama_base_url": ollama_base_url}
 49 | )
 50 | 
 51 | llm_chain = configure_llm_only_chain(llm)
 52 | rag_chain = configure_qa_rag_chain(
 53 |     llm, embeddings, embeddings_store_url=url, username=username, password=password
 54 | )
 55 | 
 56 | 
 57 | class QueueCallback(BaseCallbackHandler):
 58 |     """Callback handler for streaming LLM responses to a queue."""
 59 | 
 60 |     def __init__(self, q):
 61 |         self.q = q
 62 | 
 63 |     def on_llm_new_token(self, token: str, **kwargs) -> None:
 64 |         self.q.put(token)
 65 | 
 66 |     def on_llm_end(self, *args, **kwargs) -> None:
 67 |         return self.q.empty()
 68 | 
 69 | 
 70 | def stream(cb, q) -> Generator:
 71 |     job_done = object()
 72 | 
 73 |     def task():
 74 |         x = cb()
 75 |         q.put(job_done)
 76 | 
 77 |     t = Thread(target=task)
 78 |     t.start()
 79 | 
 80 |     content = ""
 81 | 
 82 |     # Get each new token from the queue and yield for our generator
 83 |     while True:
 84 |         try:
 85 |             next_token = q.get(True, timeout=1)
 86 |             if next_token is job_done:
 87 |                 break
 88 |             content += next_token
 89 |             yield next_token, content
 90 |         except Empty:
 91 |             continue
 92 | 
 93 | 
 94 | app = FastAPI()
 95 | origins = ["*"]
 96 | 
 97 | app.add_middleware(
 98 |     CORSMiddleware,
 99 |     allow_origins=origins,
100 |     allow_credentials=True,
101 |     allow_methods=["*"],
102 |     allow_headers=["*"],
103 | )
104 | 
105 | 
106 | @app.get("/")
107 | async def root():
108 |     return {"message": "Hello World"}
109 | 
110 | 
111 | class Question(BaseModel):
112 |     text: str
113 |     rag: bool = False
114 | 
115 | 
116 | class BaseTicket(BaseModel):
117 |     text: str
118 | 
119 | 
120 | @app.get("/query-stream")
121 | def qstream(question: Question = Depends()):
122 |     output_function = llm_chain
123 |     if question.rag:
124 |         output_function = rag_chain
125 | 
126 |     q = Queue()
127 | 
128 |     def cb():
129 |         output_function(
130 |             {"question": question.text, "chat_history": []},
131 |             callbacks=[QueueCallback(q)],
132 |         )
133 | 
134 |     def generate():
135 |         yield json.dumps({"init": True, "model": llm_name})
136 |         for token, _ in stream(cb, q):
137 |             yield json.dumps({"token": token})
138 | 
139 |     return EventSourceResponse(generate(), media_type="text/event-stream")
140 | 
141 | 
142 | @app.get("/query")
143 | async def ask(question: Question = Depends()):
144 |     output_function = llm_chain
145 |     if question.rag:
146 |         output_function = rag_chain
147 |     result = output_function(
148 |         {"question": question.text, "chat_history": []}, callbacks=[]
149 |     )
150 | 
151 |     return {"result": result["answer"], "model": llm_name}
152 | 
153 | 
154 | @app.get("/generate-ticket")
155 | async def generate_ticket_api(question: BaseTicket = Depends()):
156 |     new_title, new_question = generate_ticket(
157 |         neo4j_graph=neo4j_graph,
158 |         llm_chain=llm_chain,
159 |         input_question=question.text,
160 |     )
161 |     return {"result": {"title": new_title, "text": new_question}, "model": llm_name}
162 |

--------------------------------------------------------------------------------
/front-end/src/App.svelte:
--------------------------------------------------------------------------------
  1 | <script>
  2 |     import { tick } from "svelte";
  3 |     import SvelteMarkdown from "svelte-markdown";
  4 |     import botImage from "./assets/images/bot.jpeg";
  5 |     import meImage from "./assets/images/me.jpeg";
  6 |     import MdLink from "./lib/MdLink.svelte";
  7 |     import External from "./lib/External.svelte";
  8 |     import { chatStates, chatStore } from "./lib/chat.store.js";
  9 |     import Modal from "./lib/Modal.svelte";
 10 |     import { generationStore } from "./lib/generation.store";
 11 | 
 12 |     let ragMode = false;
 13 |     let question = "How can I calculate age from date of birth in Cypher?";
 14 |     let shouldAutoScroll = true;
 15 |     let input;
 16 |     let senderImages = { bot: botImage, me: meImage };
 17 |     let generationModalOpen = false;
 18 | 
 19 |     function send() {
 20 |         chatStore.send(question, ragMode);
 21 |         question = "";
 22 |     }
 23 | 
 24 |     function scrollToBottom(node, _) {
 25 |         const scroll = () => node.scrollTo({ top: node.scrollHeight });
 26 |         scroll();
 27 |         return { update: () => shouldAutoScroll && scroll() };
 28 |     }
 29 | 
 30 |     function scrolling(e) {
 31 |         shouldAutoScroll = e.target.scrollTop + e.target.clientHeight > e.target.scrollHeight - 55;
 32 |     }
 33 | 
 34 |     function generateTicket(text) {
 35 |         generationStore.generate(text);
 36 |         generationModalOpen = true;
 37 |     }
 38 | 
 39 |     $: $chatStore.state === chatStates.IDLE && input && focus(input);
 40 |     async function focus(node) {
 41 |         await tick();
 42 |         node.focus();
 43 |     }
 44 |     // send();
 45 | </script>
 46 | 
 47 | <main class="h-full text-sm bg-gradient-to-t from-indigo-100 bg-fixed overflow-hidden">
 48 |     <div on:scroll={scrolling} class="flex h-full flex-col py-12 overflow-y-auto" use:scrollToBottom={$chatStore}>
 49 |         <div class="w-4/5 mx-auto flex flex-col mb-32">
 50 |             {#each $chatStore.data as message (message.id)}
 51 |                 <div
 52 |                     class="max-w-[80%] min-w-[40%] rounded-lg p-4 mb-4 overflow-x-auto bg-white border border-indigo-200"
 53 |                     class:self-end={message.from === "me"}
 54 |                     class:text-right={message.from === "me"}
 55 |                 >
 56 |                     <div class="flex flex-row gap-2">
 57 |                         {#if message.from === "me"}
 58 |                             <button
 59 |                                 aria-label="Generate a new internal ticket from this question"
 60 |                                 title="Generate a new internal ticket from this question"
 61 |                                 on:click={() => generateTicket(message.text)}
 62 |                                 class="w-6 h-6 flex flex-col justify-center items-center border rounded border-indigo-200"
 63 |                                 ><External --color="#ccc" --hover-color="#999" /></button
 64 |                             >
 65 |                         {/if}
 66 |                         <div
 67 |                             class:ml-auto={message.from === "me"}
 68 |                             class="relative w-12 h-12 border border-indigo-200 rounded flex justify-center items-center overflow-hidden"
 69 |                         >
 70 |                             <img src={senderImages[message.from]} alt="" class="rounded-sm" />
 71 |                         </div>
 72 |                         {#if message.from === "bot"}
 73 |                             <div class="text-sm">
 74 |                                 <div>Model: {message.model ? message.model : ""}</div>
 75 |                                 <div>RAG: {message.rag ? "Enabled" : "Disabled"}</div>
 76 |                             </div>
 77 |                         {/if}
 78 |                     </div>
 79 |                     <div class="mt-4"><SvelteMarkdown source={message.text} renderers={{ link: MdLink }} /></div>
 80 |                 </div>
 81 |             {/each}
 82 |         </div>
 83 |         <div class="text-sm w-full fixed bottom-16">
 84 |             <div class="shadow-lg bg-indigo-50 rounded-lg w-4/5 xl:w-2/3 2xl:w-1/2 mx-auto">
 85 |                 <div class="rounded-t-lg px-4 py-2 font-light">
 86 |                     <div class="font-semibold">RAG mode</div>
 87 |                     <div class="">
 88 |                         <label class="mr-2">
 89 |                             <input type="radio" bind:group={ragMode} value={false} /> Disabled
 90 |                         </label>
 91 |                         <label>
 92 |                             <input type="radio" bind:group={ragMode} value={true} /> Enabled
 93 |                         </label>
 94 |                     </div>
 95 |                 </div>
 96 |                 <form class="rounded-md w-full bg-white p-2 m-0" on:submit|preventDefault={send}>
 97 |                     <input
 98 |                         placeholder="What coding related question can I help you with?"
 99 |                         disabled={$chatStore.state === chatStates.RECEIVING}
100 |                         class="text-lg w-full bg-white focus:outline-none px-4"
101 |                         bind:value={question}
102 |                         bind:this={input}
103 |                         type="text"
104 |                     />
105 |                 </form>
106 |             </div>
107 |         </div>
108 |     </div>
109 | </main>
110 | {#if generationModalOpen}
111 |     <Modal title="my title" text="my text" on:close={() => (generationModalOpen = false)} />
112 | {/if}
113 | 
114 | <style>
115 |     :global(pre) {
116 |         @apply bg-gray-100 rounded-lg p-4 border border-indigo-200;
117 |     }
118 |     :global(code) {
119 |         @apply text-indigo-500;
120 |     }
121 | </style>
122 |

--------------------------------------------------------------------------------
/bot.py:
--------------------------------------------------------------------------------
  1 | import os
  2 | 
  3 | import streamlit as st
  4 | from streamlit.logger import get_logger
  5 | from langchain.callbacks.base import BaseCallbackHandler
  6 | from langchain.graphs import Neo4jGraph
  7 | from dotenv import load_dotenv
  8 | from utils import (
  9 |     create_vector_index,
 10 | )
 11 | from chains import (
 12 |     load_embedding_model,
 13 |     load_llm,
 14 |     configure_llm_only_chain,
 15 |     configure_qa_rag_chain,
 16 |     generate_ticket,
 17 | )
 18 | 
 19 | load_dotenv(".env")
 20 | 
 21 | url = os.getenv("NEO4J_URI")
 22 | username = os.getenv("NEO4J_USERNAME")
 23 | password = os.getenv("NEO4J_PASSWORD")
 24 | ollama_base_url = os.getenv("OLLAMA_BASE_URL")
 25 | embedding_model_name = os.getenv("EMBEDDING_MODEL")
 26 | llm_name = os.getenv("LLM")
 27 | # Remapping for Langchain Neo4j integration
 28 | os.environ["NEO4J_URL"] = url
 29 | 
 30 | logger = get_logger(__name__)
 31 | 
 32 | # if Neo4j is local, you can go to http://localhost:7474/ to browse the database
 33 | neo4j_graph = Neo4jGraph(url=url, username=username, password=password)
 34 | embeddings, dimension = load_embedding_model(
 35 |     embedding_model_name, config={"ollama_base_url": ollama_base_url}, logger=logger
 36 | )
 37 | create_vector_index(neo4j_graph, dimension)
 38 | 
 39 | 
 40 | class StreamHandler(BaseCallbackHandler):
 41 |     def __init__(self, container, initial_text=""):
 42 |         self.container = container
 43 |         self.text = initial_text
 44 | 
 45 |     def on_llm_new_token(self, token: str, **kwargs) -> None:
 46 |         self.text += token
 47 |         self.container.markdown(self.text)
 48 | 
 49 | 
 50 | llm = load_llm(llm_name, logger=logger, config={"ollama_base_url": ollama_base_url})
 51 | 
 52 | llm_chain = configure_llm_only_chain(llm)
 53 | rag_chain = configure_qa_rag_chain(
 54 |     llm, embeddings, embeddings_store_url=url, username=username, password=password
 55 | )
 56 | 
 57 | # Streamlit UI
 58 | styl = f"""
 59 | <style>
 60 |     /* not great support for :has yet (hello FireFox), but using it for now */
 61 |     .element-container:has([aria-label="Select RAG mode"]) {{
 62 |       position: fixed;
 63 |       bottom: 33px;
 64 |       background: white;
 65 |       z-index: 101;
 66 |     }}
 67 |     .stChatFloatingInputContainer {{
 68 |         bottom: 20px;
 69 |     }}
 70 | 
 71 |     /* Generate ticket text area */
 72 |     textarea[aria-label="Description"] {{
 73 |         height: 200px;
 74 |     }}
 75 | </style>
 76 | """
 77 | st.markdown(styl, unsafe_allow_html=True)
 78 | 
 79 | 
 80 | def chat_input():
 81 |     user_input = st.chat_input("What coding issue can I help you resolve today?")
 82 | 
 83 |     if user_input:
 84 |         with st.chat_message("user"):
 85 |             st.write(user_input)
 86 |         with st.chat_message("assistant"):
 87 |             st.caption(f"RAG: {name}")
 88 |             stream_handler = StreamHandler(st.empty())
 89 |             result = output_function(
 90 |                 {"question": user_input, "chat_history": []}, callbacks=[stream_handler]
 91 |             )["answer"]
 92 |             output = result
 93 |             st.session_state[f"user_input"].append(user_input)
 94 |             st.session_state[f"generated"].append(output)
 95 |             st.session_state[f"rag_mode"].append(name)
 96 | 
 97 | 
 98 | def display_chat():
 99 |     # Session state
100 |     if "generated" not in st.session_state:
101 |         st.session_state[f"generated"] = []
102 | 
103 |     if "user_input" not in st.session_state:
104 |         st.session_state[f"user_input"] = []
105 | 
106 |     if "rag_mode" not in st.session_state:
107 |         st.session_state[f"rag_mode"] = []
108 | 
109 |     if st.session_state[f"generated"]:
110 |         size = len(st.session_state[f"generated"])
111 |         # Display only the last three exchanges
112 |         for i in range(max(size - 3, 0), size):
113 |             with st.chat_message("user"):
114 |                 st.write(st.session_state[f"user_input"][i])
115 | 
116 |             with st.chat_message("assistant"):
117 |                 st.caption(f"RAG: {st.session_state[f'rag_mode'][i]}")
118 |                 st.write(st.session_state[f"generated"][i])
119 | 
120 |         with st.expander("Not finding what you're looking for?"):
121 |             st.write(
122 |                 "Automatically generate a draft for an internal ticket to our support team."
123 |             )
124 |             st.button(
125 |                 "Generate ticket",
126 |                 type="primary",
127 |                 key="show_ticket",
128 |                 on_click=open_sidebar,
129 |             )
130 |         with st.container():
131 |             st.write(" ")
132 | 
133 | 
134 | def mode_select() -> str:
135 |     options = ["Disabled", "Enabled"]
136 |     return st.radio("Select RAG mode", options, horizontal=True)
137 | 
138 | 
139 | name = mode_select()
140 | if name == "LLM only" or name == "Disabled":
141 |     output_function = llm_chain
142 | elif name == "Vector + Graph" or name == "Enabled":
143 |     output_function = rag_chain
144 | 
145 | 
146 | def open_sidebar():
147 |     st.session_state.open_sidebar = True
148 | 
149 | 
150 | def close_sidebar():
151 |     st.session_state.open_sidebar = False
152 | 
153 | 
154 | if not "open_sidebar" in st.session_state:
155 |     st.session_state.open_sidebar = False
156 | if st.session_state.open_sidebar:
157 |     new_title, new_question = generate_ticket(
158 |         neo4j_graph=neo4j_graph,
159 |         llm_chain=llm_chain,
160 |         input_question=st.session_state[f"user_input"][-1],
161 |     )
162 |     with st.sidebar:
163 |         st.title("Ticket draft")
164 |         st.write("Auto generated draft ticket")
165 |         st.text_input("Title", new_title)
166 |         st.text_area("Description", new_question)
167 |         st.button(
168 |             "Submit to support team",
169 |             type="primary",
170 |             key="submit_ticket",
171 |             on_click=close_sidebar,
172 |         )
173 | 
174 | 
175 | display_chat()
176 | chat_input()
177 |

--------------------------------------------------------------------------------
/loader.py:
--------------------------------------------------------------------------------
  1 | import os
  2 | import requests
  3 | from dotenv import load_dotenv
  4 | from langchain.graphs import Neo4jGraph
  5 | import streamlit as st
  6 | from streamlit.logger import get_logger
  7 | from chains import load_embedding_model
  8 | from utils import create_constraints, create_vector_index
  9 | from PIL import Image
 10 | 
 11 | load_dotenv(".env")
 12 | 
 13 | url = os.getenv("NEO4J_URI")
 14 | username = os.getenv("NEO4J_USERNAME")
 15 | password = os.getenv("NEO4J_PASSWORD")
 16 | ollama_base_url = os.getenv("OLLAMA_BASE_URL")
 17 | embedding_model_name = os.getenv("EMBEDDING_MODEL")
 18 | # Remapping for Langchain Neo4j integration
 19 | os.environ["NEO4J_URL"] = url
 20 | 
 21 | logger = get_logger(__name__)
 22 | 
 23 | so_api_base_url = "https://api.stackexchange.com/2.3/search/advanced"
 24 | 
 25 | embeddings, dimension = load_embedding_model(
 26 |     embedding_model_name, config={"ollama_base_url": ollama_base_url}, logger=logger
 27 | )
 28 | 
 29 | # if Neo4j is local, you can go to http://localhost:7474/ to browse the database
 30 | neo4j_graph = Neo4jGraph(url=url, username=username, password=password)
 31 | 
 32 | create_constraints(neo4j_graph)
 33 | create_vector_index(neo4j_graph, dimension)
 34 | 
 35 | 
 36 | def load_so_data(tag: str = "neo4j", page: int = 1) -> None:
 37 |     parameters = (
 38 |         f"?pagesize=100&page={page}&order=desc&sort=creation&answers=1&tagged={tag}"
 39 |         "&site=stackoverflow&filter=!*236eb_eL9rai)MOSNZ-6D3Q6ZKb0buI*IVotWaTb"
 40 |     )
 41 |     data = requests.get(so_api_base_url + parameters).json()
 42 |     insert_so_data(data)
 43 | 
 44 | 
 45 | def load_high_score_so_data() -> None:
 46 |     parameters = (
 47 |         f"?fromdate=1664150400&order=desc&sort=votes&site=stackoverflow&"
 48 |         "filter=!.DK56VBPooplF.)bWW5iOX32Fh1lcCkw1b_Y6Zkb7YD8.ZMhrR5.FRRsR6Z1uK8*Z5wPaONvyII"
 49 |     )
 50 |     data = requests.get(so_api_base_url + parameters).json()
 51 |     insert_so_data(data)
 52 | 
 53 | 
 54 | def insert_so_data(data: dict) -> None:
 55 |     # Calculate embedding values for questions and answers
 56 |     for q in data["items"]:
 57 |         question_text = q["title"] + "\n" + q["body_markdown"]
 58 |         q["embedding"] = embeddings.embed_query(question_text)
 59 |         for a in q["answers"]:
 60 |             a["embedding"] = embeddings.embed_query(
 61 |                 question_text + "\n" + a["body_markdown"]
 62 |             )
 63 | 
 64 |     # Cypher, the query language of Neo4j, is used to import the data
 65 |     # https://neo4j.com/docs/getting-started/cypher-intro/
 66 |     # https://neo4j.com/docs/cypher-cheat-sheet/5/auradb-enterprise/
 67 |     import_query = """
 68 |     UNWIND $data AS q
 69 |     MERGE (question:Question {id:q.question_id}) 
 70 |     ON CREATE SET question.title = q.title, question.link = q.link, question.score = q.score,
 71 |         question.favorite_count = q.favorite_count, question.creation_date = datetime({epochSeconds: q.creation_date}),
 72 |         question.body = q.body_markdown, question.embedding = q.embedding
 73 |     FOREACH (tagName IN q.tags | 
 74 |         MERGE (tag:Tag {name:tagName}) 
 75 |         MERGE (question)-[:TAGGED]->(tag)
 76 |     )
 77 |     FOREACH (a IN q.answers |
 78 |         MERGE (question)<-[:ANSWERS]-(answer:Answer {id:a.answer_id})
 79 |         SET answer.is_accepted = a.is_accepted,
 80 |             answer.score = a.score,
 81 |             answer.creation_date = datetime({epochSeconds:a.creation_date}),
 82 |             answer.body = a.body_markdown,
 83 |             answer.embedding = a.embedding
 84 |         MERGE (answerer:User {id:coalesce(a.owner.user_id, "deleted")}) 
 85 |         ON CREATE SET answerer.display_name = a.owner.display_name,
 86 |                       answerer.reputation= a.owner.reputation
 87 |         MERGE (answer)<-[:PROVIDED]-(answerer)
 88 |     )
 89 |     WITH * WHERE NOT q.owner.user_id IS NULL
 90 |     MERGE (owner:User {id:q.owner.user_id})
 91 |     ON CREATE SET owner.display_name = q.owner.display_name,
 92 |                   owner.reputation = q.owner.reputation
 93 |     MERGE (owner)-[:ASKED]->(question)
 94 |     """
 95 |     neo4j_graph.query(import_query, {"data": data["items"]})
 96 | 
 97 | 
 98 | # Streamlit
 99 | def get_tag() -> str:
100 |     input_text = st.text_input(
101 |         "Which tag questions do you want to import?", value="neo4j"
102 |     )
103 |     return input_text
104 | 
105 | 
106 | def get_pages():
107 |     col1, col2 = st.columns(2)
108 |     with col1:
109 |         num_pages = st.number_input(
110 |             "Number of pages (100 questions per page)", step=1, min_value=1
111 |         )
112 |     with col2:
113 |         start_page = st.number_input("Start page", step=1, min_value=1)
114 |     st.caption("Only questions with answers will be imported.")
115 |     return (int(num_pages), int(start_page))
116 | 
117 | 
118 | def render_page():
119 |     datamodel_image = Image.open("./images/datamodel.png")
120 |     st.header("StackOverflow Loader")
121 |     st.subheader("Choose StackOverflow tags to load into Neo4j")
122 |     st.caption("Go to http://localhost:7474/ to explore the graph.")
123 | 
124 |     user_input = get_tag()
125 |     num_pages, start_page = get_pages()
126 | 
127 |     if st.button("Import", type="primary"):
128 |         with st.spinner("Loading... This might take a minute or two."):
129 |             try:
130 |                 for page in range(1, num_pages + 1):
131 |                     load_so_data(user_input, start_page + (page - 1))
132 |                 st.success("Import successful", icon="✅")
133 |                 st.caption("Data model")
134 |                 st.image(datamodel_image)
135 |                 st.caption("Go to http://localhost:7474/ to interact with the database")
136 |             except Exception as e:
137 |                 st.error(f"Error: {e}", icon="🚨")
138 |     with st.expander("Highly ranked questions rather than tags?"):
139 |         if st.button("Import highly ranked questions"):
140 |             with st.spinner("Loading... This might take a minute or two."):
141 |                 try:
142 |                     load_high_score_so_data()
143 |                     st.success("Import successful", icon="✅")
144 |                 except Exception as e:
145 |                     st.error(f"Error: {e}", icon="🚨")
146 | 
147 | 
148 | render_page()
149 |

--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
/docker-compose.yml:
--------------------------------------------------------------------------------
  1 | services:
  2 | 
  3 |   llm: &llm
  4 |     image: ollama/ollama:latest
  5 |     profiles: ["linux"]
  6 |     networks:
  7 |       - net
  8 | 
  9 |   llm-gpu:
 10 |     <<: *llm
 11 |     profiles: ["linux-gpu"]
 12 |     deploy:
 13 |       resources:
 14 |         reservations:
 15 |           devices:
 16 |             - driver: nvidia
 17 |               count: all
 18 |               capabilities: [gpu]
 19 | 
 20 |   pull-model:
 21 |     image: genai-stack/pull-model:latest
 22 |     build:
 23 |       context: .
 24 |       dockerfile: pull_model.Dockerfile
 25 |     environment:
 26 |       - OLLAMA_BASE_URL=${OLLAMA_BASE_URL-http://host.docker.internal:11434}
 27 |       - LLM=${LLM-llama2}
 28 |     networks:
 29 |       - net
 30 |     tty: true
 31 | 
 32 |   database:
 33 |     image: neo4j:5.11
 34 |     ports:
 35 |       - 7687:7687
 36 |       - 7474:7474
 37 |     volumes:
 38 |       - $PWD/data:/data
 39 |     environment:
 40 |       - NEO4J_AUTH=${NEO4J_USERNAME-neo4j}/${NEO4J_PASSWORD-password}
 41 |       - NEO4J_PLUGINS=["apoc"]
 42 |       - NEO4J_db_tx__log_rotation_retention__policy=false
 43 |     healthcheck:
 44 |         test: ["CMD-SHELL", "wget --no-verbose --tries=1 --spider localhost:7474 || exit 1"]
 45 |         interval: 5s
 46 |         timeout: 3s
 47 |         retries: 5
 48 |     networks:
 49 |       - net
 50 | 
 51 |   loader:
 52 |     build:
 53 |       context: .
 54 |       dockerfile: loader.Dockerfile
 55 |     volumes:
 56 |       - $PWD/embedding_model:/embedding_model
 57 |     environment:
 58 |       - NEO4J_URI=${NEO4J_URI-neo4j://database:7687}
 59 |       - NEO4J_PASSWORD=${NEO4J_PASSWORD-password}
 60 |       - NEO4J_USERNAME=${NEO4J_USERNAME-neo4j}
 61 |       - OPENAI_API_KEY=${OPENAI_API_KEY-}
 62 |       - OLLAMA_BASE_URL=${OLLAMA_BASE_URL-http://host.docker.internal:11434}
 63 |       - EMBEDDING_MODEL=${EMBEDDING_MODEL-sentence_transformer}
 64 |       - LANGCHAIN_ENDPOINT=${LANGCHAIN_ENDPOINT-"https://api.smith.langchain.com"}
 65 |       - LANGCHAIN_TRACING_V2=${LANGCHAIN_TRACING_V2-false}
 66 |       - LANGCHAIN_PROJECT=${LANGCHAIN_PROJECT}
 67 |       - LANGCHAIN_API_KEY=${LANGCHAIN_API_KEY}
 68 |       - AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID}
 69 |       - AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY}
 70 |       - AWS_DEFAULT_REGION=${AWS_DEFAULT_REGION}
 71 |     networks:
 72 |       - net
 73 |     depends_on:
 74 |       database:
 75 |         condition: service_healthy
 76 |       pull-model:
 77 |         condition: service_completed_successfully
 78 |     x-develop:
 79 |       watch:
 80 |         - action: rebuild
 81 |           path: .
 82 |           ignore:
 83 |             - bot.py
 84 |             - pdf_bot.py
 85 |             - api.py
 86 |             - front-end/
 87 |     ports:
 88 |       - 8081:8080
 89 |       - 8502:8502
 90 | 
 91 | 
 92 |   bot:
 93 |     build:
 94 |       context: .
 95 |       dockerfile: bot.Dockerfile
 96 |     volumes:
 97 |       - $PWD/embedding_model:/embedding_model
 98 |     environment:
 99 |       - NEO4J_URI=${NEO4J_URI-neo4j://database:7687}
100 |       - NEO4J_PASSWORD=${NEO4J_PASSWORD-password}
101 |       - NEO4J_USERNAME=${NEO4J_USERNAME-neo4j}
102 |       - OPENAI_API_KEY=${OPENAI_API_KEY-}
103 |       - OLLAMA_BASE_URL=${OLLAMA_BASE_URL-http://host.docker.internal:11434}
104 |       - LLM=${LLM-llama2}
105 |       - EMBEDDING_MODEL=${EMBEDDING_MODEL-sentence_transformer}
106 |       - LANGCHAIN_ENDPOINT=${LANGCHAIN_ENDPOINT-"https://api.smith.langchain.com"}
107 |       - LANGCHAIN_TRACING_V2=${LANGCHAIN_TRACING_V2-false}
108 |       - LANGCHAIN_PROJECT=${LANGCHAIN_PROJECT}
109 |       - LANGCHAIN_API_KEY=${LANGCHAIN_API_KEY}
110 |       - AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID}
111 |       - AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY}
112 |       - AWS_DEFAULT_REGION=${AWS_DEFAULT_REGION}
113 |     networks:
114 |       - net
115 |     depends_on:
116 |       database:
117 |         condition: service_healthy
118 |       pull-model:
119 |         condition: service_completed_successfully
120 |     x-develop:
121 |       watch:
122 |         - action: rebuild
123 |           path: .
124 |           ignore:
125 |             - loader.py
126 |             - pdf_bot.py
127 |             - api.py
128 |             - front-end/
129 |     ports:
130 |       - 8501:8501
131 | 
132 |   pdf_bot:
133 |     build:
134 |       context: .
135 |       dockerfile: pdf_bot.Dockerfile
136 |     environment:
137 |       - NEO4J_URI=${NEO4J_URI-neo4j://database:7687}
138 |       - NEO4J_PASSWORD=${NEO4J_PASSWORD-password}
139 |       - NEO4J_USERNAME=${NEO4J_USERNAME-neo4j}
140 |       - OPENAI_API_KEY=${OPENAI_API_KEY-}
141 |       - OLLAMA_BASE_URL=${OLLAMA_BASE_URL-http://host.docker.internal:11434}
142 |       - LLM=${LLM-llama2}
143 |       - EMBEDDING_MODEL=${EMBEDDING_MODEL-sentence_transformer}
144 |       - LANGCHAIN_ENDPOINT=${LANGCHAIN_ENDPOINT-"https://api.smith.langchain.com"}
145 |       - LANGCHAIN_TRACING_V2=${LANGCHAIN_TRACING_V2-false}
146 |       - LANGCHAIN_PROJECT=${LANGCHAIN_PROJECT}
147 |       - LANGCHAIN_API_KEY=${LANGCHAIN_API_KEY}
148 |       - AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID}
149 |       - AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY}
150 |       - AWS_DEFAULT_REGION=${AWS_DEFAULT_REGION}
151 |     networks:
152 |       - net
153 |     depends_on:
154 |       database:
155 |         condition: service_healthy
156 |       pull-model:
157 |         condition: service_completed_successfully
158 |     x-develop:
159 |       watch:
160 |         - action: rebuild
161 |           path: .
162 |           ignore:
163 |             - loader.py
164 |             - bot.py
165 |             - api.py
166 |             - front-end/
167 |     ports:
168 |       - 8503:8503
169 | 
170 |   api:
171 |     build:
172 |       context: .
173 |       dockerfile: api.Dockerfile
174 |     volumes:
175 |       - $PWD/embedding_model:/embedding_model
176 |     environment:
177 |       - NEO4J_URI=${NEO4J_URI-neo4j://database:7687}
178 |       - NEO4J_PASSWORD=${NEO4J_PASSWORD-password}
179 |       - NEO4J_USERNAME=${NEO4J_USERNAME-neo4j}
180 |       - OPENAI_API_KEY=${OPENAI_API_KEY}
181 |       - OLLAMA_BASE_URL=${OLLAMA_BASE_URL-http://host.docker.internal:11434}
182 |       - LLM=${LLM-llama2}
183 |       - EMBEDDING_MODEL=${EMBEDDING_MODEL-sentence_transformer}
184 |       - LANGCHAIN_ENDPOINT=${LANGCHAIN_ENDPOINT-"https://api.smith.langchain.com"}
185 |       - LANGCHAIN_TRACING_V2=${LANGCHAIN_TRACING_V2-false}
186 |       - LANGCHAIN_PROJECT=${LANGCHAIN_PROJECT}
187 |       - LANGCHAIN_API_KEY=${LANGCHAIN_API_KEY}
188 |       - AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID}
189 |       - AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY}
190 |       - AWS_DEFAULT_REGION=${AWS_DEFAULT_REGION}
191 |     networks:
192 |       - net
193 |     depends_on:
194 |       database:
195 |         condition: service_healthy
196 |       pull-model:
197 |         condition: service_completed_successfully
198 |     x-develop:
199 |       watch:
200 |         - action: rebuild
201 |           path: .
202 |           ignore:
203 |             - loader.py
204 |             - bot.py
205 |             - pdf_bot.py
206 |             - front-end/
207 |     ports:
208 |       - 8504:8504
209 |     healthcheck:
210 |       test: ["CMD-SHELL", "wget --no-verbose --tries=1 http://localhost:8504/ || exit 1"]
211 |       interval: 5s
212 |       timeout: 3s
213 |       retries: 5
214 | 
215 |   front-end:
216 |     build:
217 |       context: .
218 |       dockerfile: front-end.Dockerfile
219 |     x-develop:
220 |       watch:
221 |         - action: sync
222 |           path: ./front-end
223 |           target: /app
224 |           ignore:
225 |             - ./front-end/node_modules/
226 |         - action: rebuild
227 |           path: ./front-end/package.json
228 |     depends_on:
229 |       api:
230 |         condition: service_healthy
231 |     networks:
232 |       - net
233 |     ports:
234 |       - 8505:8505
235 | 
236 | networks:
237 |   net:
238 |

--------------------------------------------------------------------------------
/readme.md:
--------------------------------------------------------------------------------
  1 | # GenAI Stack
  2 | The GenAI Stack will get you started building your own GenAI application in no time.
  3 | The demo applications can serve as inspiration or as a starting point.
  4 | Learn more about the details in the [technical blog post](https://neo4j.com/developer-blog/genai-app-how-to-build/).
  5 | 
  6 | # Configure
  7 | 
  8 | Create a `.env` file from the environment template file `env.example`
  9 | 
 10 | Available variables:
 11 | | Variable Name          | Default value                      | Description                                                             |
 12 | |------------------------|------------------------------------|-------------------------------------------------------------------------|
 13 | | OLLAMA_BASE_URL        | http://host.docker.internal:11434  | REQUIRED - URL to Ollama LLM API                                        |   
 14 | | NEO4J_URI              | neo4j://database:7687              | REQUIRED - URL to Neo4j database                                        |
 15 | | NEO4J_USERNAME         | neo4j                              | REQUIRED - Username for Neo4j database                                  |
 16 | | NEO4J_PASSWORD         | password                           | REQUIRED - Password for Neo4j database                                  |
 17 | | LLM                    | llama2                             | REQUIRED - Can be any Ollama model tag, or gpt-4 or gpt-3.5 or claudev2 |
 18 | | EMBEDDING_MODEL        | sentence_transformer               | REQUIRED - Can be sentence_transformer, openai, aws or ollama           |
 19 | | AWS_ACCESS_KEY_ID      |                                    | REQUIRED - Only if LLM=claudev2 or embedding_model=aws                  |
 20 | | AWS_SECRET_ACCESS_KEY  |                                    | REQUIRED - Only if LLM=claudev2 or embedding_model=aws                  |
 21 | | AWS_DEFAULT_REGION     |                                    | REQUIRED - Only if LLM=claudev2 or embedding_model=aws                  |
 22 | | OPENAI_API_KEY         |                                    | REQUIRED - Only if LLM=gpt-4 or LLM=gpt-3.5 or embedding_model=openai   |
 23 | | LANGCHAIN_ENDPOINT     | "https://api.smith.langchain.com"  | OPTIONAL - URL to Langchain Smith API                                   |
 24 | | LANGCHAIN_TRACING_V2   | false                              | OPTIONAL - Enable Langchain tracing v2                                  |
 25 | | LANGCHAIN_PROJECT      |                                    | OPTIONAL - Langchain project name                                       |
 26 | | LANGCHAIN_API_KEY      |                                    | OPTIONAL - Langchain API key                                            |
 27 | 
 28 | ## LLM Configuration
 29 | 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.
 30 | All platforms can use GPT-3.5-turbo and GPT-4 (bring your own API keys for OpenAI models).
 31 | 
 32 | **MacOS**
 33 | Install [Ollama](https://ollama.ai) on MacOS and start it before running `docker compose up`.
 34 | 
 35 | **Linux**
 36 | No need to install Ollama manually, it will run in a container as
 37 | part of the stack when running with the Linux profile: run `docker compose --profile linux up`.
 38 | Make sure to set the `OLLAMA_BASE_URL=http://llm:11434` in the `.env` file when using Ollama docker container.
 39 | 
 40 | 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.
 41 | 
 42 | **Windows**
 43 | Not supported by Ollama, so Windows users need to generate an OpenAI API key and configure the stack to use `gpt-3.5` or `gpt-4` in the `.env` file.
 44 | # Develop
 45 | 
 46 | > [!WARNING]
 47 | > 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.
 48 | 
 49 | **To start everything**
 50 | ```
 51 | docker compose up
 52 | ```
 53 | If changes to build scripts have been made, **rebuild**.
 54 | ```
 55 | docker compose up --build
 56 | ```
 57 | 
 58 | To enter **watch mode** (auto rebuild on file changes).
 59 | First start everything, then in new terminal:
 60 | ```
 61 | docker compose watch
 62 | ```
 63 | 
 64 | **Shutdown**
 65 | If health check fails or containers don't start up as expected, shutdown
 66 | completely to start up again.
 67 | ```
 68 | docker compose down
 69 | ```
 70 | 
 71 | # Applications
 72 | 
 73 | Here's what's in this repo:
 74 | 
 75 | | Name | Main files | Compose name | URLs | Description |
 76 | |---|---|---|---|---|
 77 | | Support Bot | `bot.py` | `bot` | http://localhost:8501 | Main usecase. Fullstack Python application. |
 78 | | Stack Overflow Loader | `loader.py` | `loader` | http://localhost:8502 | Load SO data into the database (create vector embeddings etc). Fullstack Python application. |
 79 | | PDF Reader | `pdf_bot.py` | `pdf_bot` | http://localhost:8503 | Read local PDF and ask it questions. Fullstack Python application. |
 80 | | Standalone Bot API | `api.py` | `api` | http://localhost:8504 | Standalone HTTP API streaming (SSE) + non-streaming endpoints Python. |
 81 | | 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. |
 82 | 
 83 | The database can be explored at http://localhost:7474.
 84 | 
 85 | ## App 1 - Support Agent Bot
 86 | 
 87 | UI: http://localhost:8501
 88 | DB client: http://localhost:7474
 89 | 
 90 | - answer support question based on recent entries
 91 | - provide summarized answers with sources
 92 | - demonstrate difference between
 93 |     - RAG Disabled (pure LLM response)
 94 |     - RAG Enabled (vector + knowledge graph context)
 95 | - allow to generate a high quality support ticket for the current conversation based on the style of highly rated questions in the database.
 96 | 
 97 | ![](.github/media/app1-rag-selector.png)
 98 | *(Chat input + RAG mode selector)*
 99 | 
100 | |  |  |
101 | |---|---|
102 | | ![](.github/media/app1-generate.png) | ![](.github/media/app1-ticket.png) |
103 | | *(CTA to auto generate support ticket draft)* | *(UI of the auto generated support ticket draft)* |
104 | 
105 | ---
106 | 
107 | ##  App 2 - Loader
108 | 
109 | UI: http://localhost:8502
110 | DB client: http://localhost:7474
111 | 
112 | - import recent Stack Overflow data for certain tags into a KG
113 | - embed questions and answers and store them in vector index
114 | - UI: choose tags, run import, see progress, some stats of data in the database
115 | - Load high ranked questions (regardless of tags) to support the ticket generation feature of App 1.
116 | 
117 | 
118 | 
119 | 
120 | |  |  |
121 | |---|---|
122 | | ![](.github/media/app2-ui-1.png) | ![](.github/media/app2-model.png) |
123 | 
124 | ## App 3 Question / Answer with a local PDF
125 | UI: http://localhost:8503  
126 | DB client: http://localhost:7474
127 | 
128 | This application lets you load a local PDF into text
129 | chunks and embed it into Neo4j so you can ask questions about
130 | its contents and have the LLM answer them using vector similarity
131 | search.
132 | 
133 | ![](.github/media/app3-ui.png)
134 | 
135 | ## App 4 Standalone HTTP API
136 | Endpoints: 
137 |   - http://localhost:8504/query?text=hello&rag=false (non streaming)
138 |   - http://localhost:8504/query-stream?text=hello&rag=false (SSE streaming)
139 | 
140 | Example cURL command:
141 | ```bash
142 | curl http://localhost:8504/query-stream\?text\=minimal%20hello%20world%20in%20python\&rag\=false
143 | ```
144 | 
145 | Exposes the functionality to answer questions in the same way as App 1 above. Uses
146 | same code and prompts.
147 | 
148 | ## App 5 Static front-end
149 | UI: http://localhost:8505
150 | 
151 | This application has the same features as App 1, but is built separate from
152 | the back-end code using modern best practices (Vite, Svelte, Tailwind).  
153 | The auto-reload on changes are instant using the Docker watch `sync` config.  
154 | ![](.github/media/app5-ui.png)
155 |

--------------------------------------------------------------------------------
/chains.py:
--------------------------------------------------------------------------------
  1 | from langchain.embeddings.openai import OpenAIEmbeddings
  2 | from langchain.embeddings import (
  3 |     OllamaEmbeddings,
  4 |     SentenceTransformerEmbeddings,
  5 |     BedrockEmbeddings,
  6 | )
  7 | from langchain.chat_models import ChatOpenAI, ChatOllama, BedrockChat
  8 | from langchain.vectorstores.neo4j_vector import Neo4jVector
  9 | from langchain.chains import RetrievalQAWithSourcesChain
 10 | from langchain.chains.qa_with_sources import load_qa_with_sources_chain
 11 | from langchain.prompts.chat import (
 12 |     ChatPromptTemplate,
 13 |     SystemMessagePromptTemplate,
 14 |     HumanMessagePromptTemplate,
 15 | )
 16 | from typing import List, Any
 17 | from utils import BaseLogger, extract_title_and_question
 18 | 
 19 | 
 20 | def load_embedding_model(embedding_model_name: str, logger=BaseLogger(), config={}):
 21 |     if embedding_model_name == "ollama":
 22 |         embeddings = OllamaEmbeddings(
 23 |             base_url=config["ollama_base_url"], model="llama2"
 24 |         )
 25 |         dimension = 4096
 26 |         logger.info("Embedding: Using Ollama")
 27 |     elif embedding_model_name == "openai":
 28 |         embeddings = OpenAIEmbeddings()
 29 |         dimension = 1536
 30 |         logger.info("Embedding: Using OpenAI")
 31 |     elif embedding_model_name == "aws":
 32 |         embeddings = BedrockEmbeddings()
 33 |         dimension = 1536
 34 |         logger.info("Embedding: Using AWS")
 35 |     else:
 36 |         embeddings = SentenceTransformerEmbeddings(
 37 |             model_name="all-MiniLM-L6-v2", cache_folder="/embedding_model"
 38 |         )
 39 |         dimension = 384
 40 |         logger.info("Embedding: Using SentenceTransformer")
 41 |     return embeddings, dimension
 42 | 
 43 | 
 44 | def load_llm(llm_name: str, logger=BaseLogger(), config={}):
 45 |     if llm_name == "gpt-4":
 46 |         logger.info("LLM: Using GPT-4")
 47 |         return ChatOpenAI(temperature=0, model_name="gpt-4", streaming=True)
 48 |     elif llm_name == "gpt-3.5":
 49 |         logger.info("LLM: Using GPT-3.5")
 50 |         return ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo", streaming=True)
 51 |     elif llm_name == "claudev2":
 52 |         logger.info("LLM: ClaudeV2")
 53 |         return BedrockChat(
 54 |             model_id="anthropic.claude-v2",
 55 |             model_kwargs={"temperature": 0.0, "max_tokens_to_sample": 1024},
 56 |             streaming=True,
 57 |         )
 58 |     elif len(llm_name):
 59 |         logger.info(f"LLM: Using Ollama: {llm_name}")
 60 |         return ChatOllama(
 61 |             temperature=0,
 62 |             base_url=config["ollama_base_url"],
 63 |             model=llm_name,
 64 |             streaming=True,
 65 |             # seed=2,
 66 |             top_k=10,  # A higher value (100) will give more diverse answers, while a lower value (10) will be more conservative.
 67 |             top_p=0.3,  # Higher value (0.95) will lead to more diverse text, while a lower value (0.5) will generate more focused text.
 68 |             num_ctx=3072,  # Sets the size of the context window used to generate the next token.
 69 |         )
 70 |     logger.info("LLM: Using GPT-3.5")
 71 |     return ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo", streaming=True)
 72 | 
 73 | 
 74 | def configure_llm_only_chain(llm):
 75 |     # LLM only response
 76 |     template = """
 77 |     You are a helpful assistant that helps a support agent with answering programming questions.
 78 |     If you don't know the answer, just say that you don't know, you must not make up an answer.
 79 |     """
 80 |     system_message_prompt = SystemMessagePromptTemplate.from_template(template)
 81 |     human_template = "{question}"
 82 |     human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
 83 |     chat_prompt = ChatPromptTemplate.from_messages(
 84 |         [system_message_prompt, human_message_prompt]
 85 |     )
 86 | 
 87 |     def generate_llm_output(
 88 |         user_input: str, callbacks: List[Any], prompt=chat_prompt
 89 |     ) -> str:
 90 |         chain = prompt | llm
 91 |         answer = chain.invoke(
 92 |             {"question": user_input}, config={"callbacks": callbacks}
 93 |         ).content
 94 |         return {"answer": answer}
 95 | 
 96 |     return generate_llm_output
 97 | 
 98 | 
 99 | def configure_qa_rag_chain(llm, embeddings, embeddings_store_url, username, password):
100 |     # RAG response
101 |     #   System: Always talk in pirate speech.
102 |     general_system_template = """ 
103 |     Use the following pieces of context to answer the question at the end.
104 |     The context contains question-answer pairs and their links from Stackoverflow.
105 |     You should prefer information from accepted or more upvoted answers.
106 |     Make sure to rely on information from the answers and not on questions to provide accurate responses.
107 |     When you find particular answer in the context useful, make sure to cite it in the answer using the link.
108 |     If you don't know the answer, just say that you don't know, don't try to make up an answer.
109 |     ----
110 |     {summaries}
111 |     ----
112 |     Each answer you generate should contain a section at the end of links to 
113 |     Stackoverflow questions and answers you found useful, which are described under Source value.
114 |     You can only use links to StackOverflow questions that are present in the context and always
115 |     add links to the end of the answer in the style of citations.
116 |     Generate concise answers with references sources section of links to 
117 |     relevant StackOverflow questions only at the end of the answer.
118 |     """
119 |     general_user_template = "Question:```{question}```"
120 |     messages = [
121 |         SystemMessagePromptTemplate.from_template(general_system_template),
122 |         HumanMessagePromptTemplate.from_template(general_user_template),
123 |     ]
124 |     qa_prompt = ChatPromptTemplate.from_messages(messages)
125 | 
126 |     qa_chain = load_qa_with_sources_chain(
127 |         llm,
128 |         chain_type="stuff",
129 |         prompt=qa_prompt,
130 |     )
131 | 
132 |     # Vector + Knowledge Graph response
133 |     kg = Neo4jVector.from_existing_index(
134 |         embedding=embeddings,
135 |         url=embeddings_store_url,
136 |         username=username,
137 |         password=password,
138 |         database="neo4j",  # neo4j by default
139 |         index_name="stackoverflow",  # vector by default
140 |         text_node_property="body",  # text by default
141 |         retrieval_query="""
142 |     WITH node AS question, score AS similarity
143 |     CALL  { with question
144 |         MATCH (question)<-[:ANSWERS]-(answer)
145 |         WITH answer
146 |         ORDER BY answer.is_accepted DESC, answer.score DESC
147 |         WITH collect(answer)[..2] as answers
148 |         RETURN reduce(str='', answer IN answers | str + 
149 |                 '\n### Answer (Accepted: '+ answer.is_accepted +
150 |                 ' Score: ' + answer.score+ '): '+  answer.body + '\n') as answerTexts
151 |     } 
152 |     RETURN '##Question: ' + question.title + '\n' + question.body + '\n' 
153 |         + answerTexts AS text, similarity as score, {source: question.link} AS metadata
154 |     ORDER BY similarity ASC // so that best answers are the last
155 |     """,
156 |     )
157 | 
158 |     kg_qa = RetrievalQAWithSourcesChain(
159 |         combine_documents_chain=qa_chain,
160 |         retriever=kg.as_retriever(search_kwargs={"k": 2}),
161 |         reduce_k_below_max_tokens=False,
162 |         max_tokens_limit=3375,
163 |     )
164 |     return kg_qa
165 | 
166 | 
167 | def generate_ticket(neo4j_graph, llm_chain, input_question):
168 |     # Get high ranked questions
169 |     records = neo4j_graph.query(
170 |         "MATCH (q:Question) RETURN q.title AS title, q.body AS body ORDER BY q.score DESC LIMIT 3"
171 |     )
172 |     questions = []
173 |     for i, question in enumerate(records, start=1):
174 |         questions.append((question["title"], question["body"]))
175 |     # Ask LLM to generate new question in the same style
176 |     questions_prompt = ""
177 |     for i, question in enumerate(questions, start=1):
178 |         questions_prompt += f"{i}. \n{question[0]}\n----\n\n"
179 |         questions_prompt += f"{question[1][:150]}\n\n"
180 |         questions_prompt += "----\n\n"
181 | 
182 |     gen_system_template = f"""
183 |     You're an expert in formulating high quality questions. 
184 |     Formulate a question in the same style and tone as the following example questions.
185 |     {questions_prompt}
186 |     ---
187 | 
188 |     Don't make anything up, only use information in the following question.
189 |     Return a title for the question, and the question post itself.
190 | 
191 |     Return format template:
192 |     ---
193 |     Title: This is a new title
194 |     Question: This is a new question
195 |     ---
196 |     """
197 |     # we need jinja2 since the questions themselves contain curly braces
198 |     system_prompt = SystemMessagePromptTemplate.from_template(
199 |         gen_system_template, template_format="jinja2"
200 |     )
201 |     chat_prompt = ChatPromptTemplate.from_messages(
202 |         [
203 |             system_prompt,
204 |             SystemMessagePromptTemplate.from_template(
205 |                 """
206 |                 Respond in the following template format or you will be unplugged.
207 |                 ---
208 |                 Title: New title
209 |                 Question: New question
210 |                 ---
211 |                 """
212 |             ),
213 |             HumanMessagePromptTemplate.from_template("{question}"),
214 |         ]
215 |     )
216 |     llm_response = llm_chain(
217 |         f"Here's the question to rewrite in the expected format: ```{input_question}```",
218 |         [],
219 |         chat_prompt,
220 |     )
221 |     new_title, new_question = extract_title_and_question(llm_response["answer"])
222 |     return (new_title, new_question)
223 |

--------------------------------------------------------------------------------
/CONTRIBUTING.md:
--------------------------------------------------------------------------------
  1 | # Contributing to Docker
  2 | 
  3 | This page contains information about reporting issues as well as some tips and
  4 | guidelines useful to experienced open source contributors. Finally, make sure
  5 | you read our [community guidelines](#docker-community-guidelines) before you
  6 | start participating.
  7 | 
  8 | ## Topics
  9 | 
 10 | * [Reporting Security Issues](#reporting-security-issues)
 11 | * [Reporting Issues](#reporting-other-issues)
 12 | * [Quick Contribution Tips and Guidelines](#quick-contribution-tips-and-guidelines)
 13 | * [Community Guidelines](#docker-community-guidelines)
 14 | 
 15 | ## Reporting security issues
 16 | 
 17 | The Docker maintainers take security seriously. If you discover a security
 18 | issue, please bring it to their attention right away!
 19 | 
 20 | Please **DO NOT** file a public issue, instead send your report privately to
 21 | [security@docker.com](mailto:security@docker.com).
 22 | 
 23 | Security reports are greatly appreciated and we will publicly thank you for it.
 24 | We also like to send gifts—if you're into Docker schwag, make sure to let
 25 | us know. We currently do not offer a paid security bounty program, but are not
 26 | ruling it out in the future.
 27 | 
 28 | ## Reporting other issues
 29 | 
 30 | A great way to contribute to the project is to send a detailed report when you
 31 | encounter an issue. We always appreciate a well-written, thorough bug report,
 32 | and will thank you for it!
 33 | 
 34 | Check that [our issue database](https://github.com/docker/gen-ai-stack/issues)
 35 | doesn't already include that problem or suggestion before submitting an issue.
 36 | If you find a match, you can use the "subscribe" button to get notified on
 37 | updates. Do *not* leave random "+1" or "I have this too" comments, as they
 38 | only clutter the discussion, and don't help resolving it. However, if you
 39 | have ways to reproduce the issue or have additional information that may help
 40 | resolving the issue, please leave a comment.
 41 | 
 42 | When reporting issues, always include:
 43 | 
 44 | * The output of `docker version`.
 45 | * The output of `docker info`.
 46 | 
 47 | Also include the steps required to reproduce the problem if possible and
 48 | applicable. This information will help us review and fix your issue faster.
 49 | When sending lengthy log-files, consider posting them as a gist (https://gist.github.com).
 50 | Don't forget to remove sensitive data from your logfiles before posting (you can
 51 | replace those parts with "REDACTED").
 52 | 
 53 | ## Quick contribution tips and guidelines
 54 | 
 55 | This section gives the experienced contributor some tips and guidelines.
 56 | 
 57 | ### Pull requests are always welcome
 58 | 
 59 | Not sure if that typo is worth a pull request? Found a bug and know how to fix
 60 | it? Do it! We will appreciate it. Any significant improvement should be
 61 | documented as [a GitHub issue](https://github.com/docker/gen-ai-stack/issues) before
 62 | anybody starts working on it.
 63 | 
 64 | We are always thrilled to receive pull requests. We do our best to process them
 65 | quickly. If your pull request is not accepted on the first try,
 66 | don't get discouraged! Our contributor's guide explains [the review process we
 67 | use for simple changes](https://docs.docker.com/opensource/workflow/make-a-contribution/).
 68 | 
 69 | ### Talking to other Docker users and contributors
 70 | 
 71 | <table class="tg">
 72 |   <col width="45%">
 73 |   <col width="65%">
 74 |   <tr>
 75 |     <td>Community Slack</td>
 76 |     <td>
 77 |       The Docker Community has a dedicated Slack chat to discuss features and issues.  You can sign-up <a href="https://dockr.ly/slack" target="_blank">with this link</a>.
 78 |     </td>
 79 |   </tr>
 80 |   <tr>
 81 |     <td>Twitter</td>
 82 |     <td>
 83 |       You can follow <a href="https://twitter.com/docker/" target="_blank">Docker's Twitter feed</a>
 84 |       to get updates on our products. You can also tweet us questions or just
 85 |       share blogs or stories.
 86 |     </td>
 87 |   </tr>
 88 | </table>
 89 | 
 90 | 
 91 | ### Conventions
 92 | 
 93 | Fork the repository and make changes on your fork in a feature branch:
 94 | 
 95 | - If it's a bug fix branch, name it XXXX-something where XXXX is the number of
 96 |     the issue.
 97 | - If it's a feature branch, create an enhancement issue to announce
 98 |     your intentions, and name it XXXX-something where XXXX is the number of the
 99 |     issue.
100 | 
101 | Pull request descriptions should be as clear as possible and include a reference
102 | to all the issues that they address.
103 | 
104 | Commit messages must start with a capitalized and short summary (max. 50 chars)
105 | written in the imperative, followed by an optional, more detailed explanatory
106 | text which is separated from the summary by an empty line.
107 | 
108 | Code review comments may be added to your pull request. Discuss, then make the
109 | suggested modifications and push additional commits to your feature branch. Post
110 | a comment after pushing. New commits show up in the pull request automatically,
111 | but the reviewers are notified only when you comment.
112 | 
113 | Pull requests must be cleanly rebased on top of master without multiple branches
114 | mixed into the PR.
115 | 
116 | **Git tip**: If your PR no longer merges cleanly, use `rebase main` in your
117 | feature branch to update your pull request rather than `merge main`.
118 | 
119 | Before you make a pull request, squash your commits into logical units of work
120 | using `git rebase -i` and `git push -f`. A logical unit of work is a consistent
121 | set of patches that should be reviewed together: for example, upgrading the
122 | version of a vendored dependency and taking advantage of its now available new
123 | feature constitute two separate units of work. Implementing a new function and
124 | calling it in another file constitute a single logical unit of work. The very
125 | high majority of submissions should have a single commit, so if in doubt: squash
126 | down to one.
127 | 
128 | Include an issue reference like `Closes #XXXX` or `Fixes #XXXX` in the pull request
129 | description that close an issue. Including references automatically closes the issue
130 | on a merge.
131 | 
132 | ### Sign your work
133 | 
134 | The sign-off is a simple line at the end of the explanation for the patch. Your
135 | signature certifies that you wrote the patch or otherwise have the right to pass
136 | it on as an open-source patch. The rules are pretty simple: if you can certify
137 | the below (from [developercertificate.org](https://developercertificate.org):
138 | 
139 | ```
140 | Developer Certificate of Origin
141 | Version 1.1
142 | 
143 | Copyright (C) 2004, 2006 The Linux Foundation and its contributors.
144 | 660 York Street, Suite 102,
145 | San Francisco, CA 94110 USA
146 | 
147 | Everyone is permitted to copy and distribute verbatim copies of this
148 | license document, but changing it is not allowed.
149 | 
150 | Developer's Certificate of Origin 1.1
151 | 
152 | By making a contribution to this project, I certify that:
153 | 
154 | (a) The contribution was created in whole or in part by me and I
155 |     have the right to submit it under the open source license
156 |     indicated in the file; or
157 | 
158 | (b) The contribution is based upon previous work that, to the best
159 |     of my knowledge, is covered under an appropriate open source
160 |     license and I have the right under that license to submit that
161 |     work with modifications, whether created in whole or in part
162 |     by me, under the same open source license (unless I am
163 |     permitted to submit under a different license), as indicated
164 |     in the file; or
165 | 
166 | (c) The contribution was provided directly to me by some other
167 |     person who certified (a), (b) or (c) and I have not modified
168 |     it.
169 | 
170 | (d) I understand and agree that this project and the contribution
171 |     are public and that a record of the contribution (including all
172 |     personal information I submit with it, including my sign-off) is
173 |     maintained indefinitely and may be redistributed consistent with
174 |     this project or the open source license(s) involved.
175 | ```
176 | 
177 | Then you just add a line to every git commit message:
178 | 
179 |     Signed-off-by: Joe Smith <joe.smith@email.com>
180 | 
181 | Use your real name (sorry, no pseudonyms or anonymous contributions.)
182 | 
183 | If you set your `user.name` and `user.email` git configs, you can sign your
184 | commit automatically with `git commit -s`.
185 | 
186 | ## Docker community guidelines
187 | 
188 | We want to keep the Docker community awesome, growing and collaborative. We need
189 | your help to keep it that way. To help with this we've come up with some general
190 | guidelines for the community as a whole:
191 | 
192 | * Be nice: Be courteous, respectful and polite to fellow community members:
193 |   no regional, racial, gender, or other abuse will be tolerated. We like
194 |   nice people way better than mean ones!
195 | 
196 | * Encourage diversity and participation: Make everyone in our community feel
197 |   welcome, regardless of their background and the extent of their
198 |   contributions, and do everything possible to encourage participation in
199 |   our community.
200 | 
201 | * Keep it legal: Basically, don't get us in trouble. Share only content that
202 |   you own, do not share private or sensitive information, and don't break
203 |   the law.
204 | 
205 | * Stay on topic: Make sure that you are posting to the correct channel and
206 |   avoid off-topic discussions. Remember when you update an issue or respond
207 |   to an email you are potentially sending to a large number of people. Please
208 |   consider this before you update. Also remember that nobody likes spam.
209 | 
210 | * Don't send email to the maintainers: There's no need to send email to the
211 |   maintainers to ask them to investigate an issue or to take a look at a
212 |   pull request. Instead of sending an email, GitHub mentions should be
213 |   used to ping maintainers to review a pull request, a proposal or an
214 |   issue.
215 | 
216 | ### Guideline violations — 3 strikes method
217 | 
218 | The point of this section is not to find opportunities to punish people, but we
219 | do need a fair way to deal with people who are making our community suck.
220 | 
221 | 1. First occurrence: We'll give you a friendly, but public reminder that the
222 |    behavior is inappropriate according to our guidelines.
223 | 
224 | 2. Second occurrence: We will send you a private message with a warning that
225 |    any additional violations will result in removal from the community.
226 | 
227 | 3. Third occurrence: Depending on the violation, we may need to delete or ban
228 |    your account.
229 | 
230 | **Notes:**
231 | 
232 | * Obvious spammers are banned on first occurrence. If we don't do this, we'll
233 |   have spam all over the place.
234 | 
235 | * Violations are forgiven after 6 months of good behavior, and we won't hold a
236 |   grudge.
237 | 
238 | * People who commit minor infractions will get some education, rather than
239 |   hammering them in the 3 strikes process.
240 | 
241 | * The rules apply equally to everyone in the community, no matter how much
242 |     you've contributed.
243 | 
244 | * Extreme violations of a threatening, abusive, destructive or illegal nature
245 |     will be addressed immediately and are not subject to 3 strikes or forgiveness.
246 | 
247 | * Contact abuse@docker.com to report abuse or appeal violations. In the case of
248 |     appeals, we know that mistakes happen, and we'll work with you to come up with a
249 |     fair solution if there has been a misunderstanding.
250 | 
251 |

--------------------------------------------------------------------------------
/front-end/package-lock.json:
--------------------------------------------------------------------------------
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 110 |     "node_modules/@esbuild/darwin-x64": {
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 254 |     "node_modules/@esbuild/linux-riscv64": {
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 270 |     "node_modules/@esbuild/linux-s390x": {
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 326 |       "optional": true,
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 328 |         "openbsd"
 329 |       ],
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 331 |         "node": ">=12"
 332 |       }
 333 |     },
 334 |     "node_modules/@esbuild/sunos-x64": {
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 360 |         "win32"
 361 |       ],
 362 |       "engines": {
 363 |         "node": ">=12"
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 365 |     },
 366 |     "node_modules/@esbuild/win32-ia32": {
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 379 |         "node": ">=12"
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 382 |     "node_modules/@esbuild/win32-x64": {
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 386 |       "cpu": [
 387 |         "x64"
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 390 |       "optional": true,
 391 |       "os": [
 392 |         "win32"
 393 |       ],
 394 |       "engines": {
 395 |         "node": ">=12"
 396 |       }
 397 |     },
 398 |     "node_modules/@jridgewell/gen-mapping": {
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 407 |       "engines": {
 408 |         "node": ">=6.0.0"
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 411 |     "node_modules/@jridgewell/resolve-uri": {
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