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{
"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)"
]
}
],
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"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",
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