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 +}