From 058f6e78c97ea9c599bab649cb6c4b7c56821e66 Mon Sep 17 00:00:00 2001
From: samt07 <miscapps111@gmail.com>
Date: Wed, 16 Apr 2025 23:06:15 -0400
Subject: [PATCH] Added Auto body shop AI

---
 week5/community-contributions/auto_shop.json  |  74 ++++++
 .../day5-autoshop-AI.ipynb                    | 229 ++++++++++++++++++
 2 files changed, 303 insertions(+)
 create mode 100644 week5/community-contributions/auto_shop.json
 create mode 100644 week5/community-contributions/day5-autoshop-AI.ipynb

diff --git a/week5/community-contributions/auto_shop.json b/week5/community-contributions/auto_shop.json
new file mode 100644
index 0000000..f5d5d55
--- /dev/null
+++ b/week5/community-contributions/auto_shop.json
@@ -0,0 +1,74 @@
+[
+  {
+    "id": "service_001",
+    "content": "We offer tire services including rotation, balancing, flat repair, and new tire sales and installation.",
+    "metadata": {
+      "source": "service_page",
+      "category": "tire_services",
+      "tags": "tire, rotation, repair, installation"
+    }
+  },
+  {
+    "id": "service_002",
+    "content": "Brake services include pad replacement, rotor resurfacing, and ABS diagnostics.",
+    "metadata": {
+      "source": "service_page",
+      "category": "brake_services",
+      "tags": "brake, pads, rotors, abs"
+    }
+  },
+  {
+    "id": "faq_001",
+    "content": "Walk-ins are welcome, but appointments are recommended for faster service.",
+    "metadata": {
+      "source": "faq",
+      "category": "appointments",
+      "tags": "appointment, walk-in"
+    }
+  },
+  {
+    "id": "faq_002",
+    "content": "Most oil changes are completed within 30–45 minutes.",
+    "metadata": {
+      "source": "faq",
+      "category": "oil_change",
+      "tags": "oil change, duration"
+    }
+  },
+  {
+    "id": "general_001",
+    "content": "Pinkys Auto Care is located at Rte 112, Yorkjuh, JH 98746. We're open Monday through Friday from 8am to 6pm, and Saturday from 9am to 2pm.",
+    "metadata": {
+      "source": "general_info",
+      "category": "location_hours",
+      "tags": "location, hours, contact"
+    }
+  },
+  {
+    "id": "promo_001",
+    "content": "At Pinkys Auto Care, we combine modern diagnostics with friendly, small-town service. Our ASE-certified mechanics serve Springfield with over 15 years of experience.",
+    "metadata": {
+      "source": "about_us",
+      "category": "branding",
+      "tags": "promo, about us, experience"
+    }
+  },
+  {
+    "id": "customer_query_001",
+    "content": "My car shakes when braking—do I need new rotors?",
+    "metadata": {
+      "source": "user_query",
+      "category": "brake_services",
+      "tags": "brake, rotor, vibration"
+    }
+  },
+  {
+    "id": "customer_query_002",
+    "content": "Can you align wheels on a 2021 Subaru Outback?",
+    "metadata": {
+      "source": "user_query",
+      "category": "wheel_alignment",
+      "tags": "wheel alignment, vehicle-specific"
+    }
+  }
+]
diff --git a/week5/community-contributions/day5-autoshop-AI.ipynb b/week5/community-contributions/day5-autoshop-AI.ipynb
new file mode 100644
index 0000000..deb6b73
--- /dev/null
+++ b/week5/community-contributions/day5-autoshop-AI.ipynb
@@ -0,0 +1,229 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ba2779af-84ef-4227-9e9e-6eaf0df87e77",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "\n",
+    "import os\n",
+    "import glob\n",
+    "from dotenv import load_dotenv\n",
+    "import gradio as gr\n",
+    "import json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "802137aa-8a74-45e0-a487-d1974927d7ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports for langchain, plotly and Chroma\n",
+    "\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "from langchain.schema import Document\n",
+    "from langchain_openai import OpenAIEmbeddings, ChatOpenAI\n",
+    "from langchain_chroma import Chroma\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.manifold import TSNE\n",
+    "import numpy as np \n",
+    "import plotly.graph_objects as go\n",
+    "from langchain.memory import ConversationBufferMemory\n",
+    "from langchain.chains import ConversationalRetrievalChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58c85082-e417-4708-9efe-81a5d55d1424",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# price is a factor for our company, so we're going to use a low cost model\n",
+    "\n",
+    "MODEL = \"gpt-4o-mini\"\n",
+    "db_name = \"vector_db\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee78efcb-60fe-449e-a944-40bab26261af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load environment variables in a file called .env\n",
+    "\n",
+    "load_dotenv(override=True)\n",
+    "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b14e6c30-37c6-4eac-845b-5471aa75f587",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##Load json\n",
+    "with open(\"knowledge-base/auto_shop.json\", 'r') as f: #place auto_shop.json file inside your knowledge-base folder\n",
+    "    data = json.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "408bc620-477f-47fd-b9e8-ab9d21843ecd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Convert to Langchain\n",
+    "documents = []\n",
+    "for item in data:\n",
+    "    content = item[\"content\"]\n",
+    "    metadata = item.get(\"metadata\", {})\n",
+    "    documents.append(Document(page_content=content, metadata=metadata))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0371d472-cd14-4967-bc09-9b78e233809f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Chunk documents\n",
+    "splitter = RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=50, separators=[\"\\n\\n\", \"\\n\", \",\", \" \", \"\"])\n",
+    "chunks = splitter.split_documents(documents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91c2404b-b3c9-4c7f-b199-9895e429a3da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "doc_types = set(chunk.metadata['source'] for chunk in chunks)\n",
+    "#print(f\"Document types found: {', '.join(doc_types)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78998399-ac17-4e28-b15f-0b5f51e6ee23",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embeddings = OpenAIEmbeddings()\n",
+    "\n",
+    "# Delete if already exists\n",
+    "\n",
+    "if os.path.exists(db_name):\n",
+    "    Chroma(persist_directory=db_name, embedding_function=embeddings).delete_collection()\n",
+    "\n",
+    "# Create vectorstore\n",
+    "\n",
+    "vectorstore = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=db_name)\n",
+    "#print(f\"Vectorstore created with {vectorstore._collection.count()} documents\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ff2e7687-60d4-4920-a1d7-a34b9f70a250",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Let's investigate the vectors. Use for debugging if needed\n",
+    "\n",
+    "# collection = vectorstore._collection\n",
+    "# count = collection.count()\n",
+    "\n",
+    "# sample_embedding = collection.get(limit=1, include=[\"embeddings\"])[\"embeddings\"][0]\n",
+    "# dimensions = len(sample_embedding)\n",
+    "# print(f\"There are {count:,} vectors with {dimensions:,} dimensions in the vector store\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "129c7d1e-0094-4479-9459-f9360b95f244",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a new Chat with OpenAI\n",
+    "llm = ChatOpenAI(temperature=0.7, model_name=MODEL)\n",
+    "\n",
+    "\n",
+    "memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n",
+    "\n",
+    "# the retriever is an abstraction over the VectorStore that will be used during RAG\n",
+    "retriever = vectorstore.as_retriever()\n",
+    "\n",
+    "# putting it together: set up the conversation chain with the GPT 3.5 LLM, the vector store and memory\n",
+    "conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbbcb659-13ce-47ab-8a5e-01b930494964",
+   "metadata": {},
+   "source": [
+    "## Now we will bring this up in Gradio using the Chat interface -"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3536590-85c7-4155-bd87-ae78a1467670",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Wrapping that in a function\n",
+    "\n",
+    "def chat(question, history):\n",
+    "    result = conversation_chain.invoke({\"question\": question})\n",
+    "    return result[\"answer\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b252d8c1-61a8-406d-b57a-8f708a62b014",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# And in Gradio:\n",
+    "\n",
+    "view = gr.ChatInterface(chat, type=\"messages\").launch(inbrowser=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}