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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "9633aea7-5c45-44f9-a78b-b5bc39984754",
"metadata": {},
"outputs": [],
"source": [
"from langchain_google_genai import ChatGoogleGenerativeAI\n",
"from langchain.prompts import PromptTemplate\n",
"from langchain.chains import LLMChain\n",
"\n",
"import os\n",
"\n",
"import google.generativeai as genai\n",
"from langchain.document_loaders import PyPDFLoader\n",
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
"from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings\n",
"from langchain.vectorstores import FAISS\n",
"import gradio as gr\n",
"\n",
"\n",
"os.environ[\"MY_SECRET_KEY\"] = \"AIzaSyDRj3wAgqOCjc_D45W_u-G3y9dk5YDgxEo\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "41abde7b-366d-427e-8938-35ce7a4ed778",
"metadata": {},
"outputs": [],
"source": [
"#pip install pypdf\n",
"#!pip install faiss-cpu"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "b7e3810f-c5fb-44d7-b4b7-a30ac507d78b",
"metadata": {},
"outputs": [],
"source": [
"google_api_key = os.environ[\"MY_SECRET_KEY\"]\n",
"\n",
"# Check if the API key was found\n",
"if google_api_key:\n",
" # Set the environment variable if the API key was found\n",
" os.environ[\"GOOGLE_API_KEY\"] = google_api_key\n",
"\n",
" llm = ChatGoogleGenerativeAI(\n",
" model=\"gemini-pro\", # Specify the model name\n",
" google_api_key=os.environ[\"GOOGLE_API_KEY\"]\n",
" )\n",
"else:\n",
" print(\"Error: GOOGLE_API_KEY not found in Colab secrets. Please store your API key.\")\n",
"\n",
"\n",
"\n",
"genai.configure(api_key=google_api_key)\n",
"model = genai.GenerativeModel(\"gemini-pro\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "ef330936-8c45-4aff-b2cf-fe9dfaaf2764",
"metadata": {},
"outputs": [],
"source": [
"work_dir=os.getcwd()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "a55af811-7758-4090-a5f8-748b6192971b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Current Working Directory: /Users/saurabhverma/GENAI\n"
]
}
],
"source": [
"# Verify file existence\n",
"assert \"Team1.pdf\" in os.listdir(work_dir), \"Team1.pdf not found in the specified directory!\"\n",
"print(f\"Current Working Directory: {os.getcwd()}\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "7a0a4457-2f9c-40db-9dd4-d57e3edf1fd0",
"metadata": {},
"outputs": [],
"source": [
"# Load PDF and split text\n",
"pdf_path = \"Team1.pdf\" # Ensure this file is uploaded to Colab\n",
"loader = PyPDFLoader(pdf_path)\n",
"documents = loader.load()\n",
"\n",
"# Split text into chunks\n",
"text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=10)\n",
"text_chunks = text_splitter.split_documents(documents)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "b5387499-a756-49de-86b0-96a5ce712ba7",
"metadata": {},
"outputs": [],
"source": [
"# Generate embeddings\n",
"embeddings = GoogleGenerativeAIEmbeddings(model=\"models/embedding-001\")\n",
"\n",
"# Store embeddings in FAISS index\n",
"vectorstore = FAISS.from_documents(text_chunks, embeddings)\n",
"retriever = vectorstore.as_retriever(search_kwargs={\"k\": 4})"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "35554163-75cd-4f0b-a538-565a48700245",
"metadata": {},
"outputs": [],
"source": [
"# Set up Gemini model\n",
"llm = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash-001\", temperature=0)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "e95b424b-11c1-46f3-9b4e-9e2d42d1f05d",
"metadata": {},
"outputs": [],
"source": [
"import gradio as gr\n",
"from langchain.prompts import PromptTemplate\n",
"from langchain.chains import LLMChain\n",
"\n",
"def rag_query(query):\n",
" # Retrieve relevant documents\n",
" docs = retriever.get_relevant_documents(query)\n",
" \n",
" # Otherwise, use RAG\n",
" context = \"\\n\".join([doc.page_content for doc in docs])\n",
" prompt = f\"Context:\\n{context}\\n\\nQuestion: {query}\\nAnswer directly and concisely:\"\n",
"\n",
" try:\n",
" response = llm.invoke(prompt)\n",
" except Exception as e:\n",
" response = f\"Error in RAG processing: {str(e)}\"\n",
"\n",
" return response.content\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "552ff2fa-3c70-4054-803e-633efc7601f4",
"metadata": {},
"outputs": [],
"source": [
"import gradio as gr\n",
"from langchain.prompts import PromptTemplate\n",
"from langchain.chains import LLMChain\n",
"from langchain_google_genai import ChatGoogleGenerativeAI\n",
"\n",
"# Initialize LLM once (avoid repeated initialization)\n",
"llm = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash\", temperature=0)\n",
"\n",
"# Define the general query function\n",
"def general_query(query):\n",
" try:\n",
" # Define the prompt correctly\n",
" prompt = PromptTemplate.from_template(\"Answer the following query: {query}\")\n",
" \n",
" # Create an LLM Chain\n",
" chain = LLMChain(llm=llm, prompt=prompt)\n",
" \n",
" # Run chatbot and return response\n",
" response = chain.run(query=query)\n",
" \n",
" return response # Return response directly (not response.content)\n",
" \n",
" except Exception as e:\n",
" return f\"Error: {str(e)}\"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "ab63a509-e927-405a-985b-d07039e05e9f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Running on local URL: http://127.0.0.1:7860\n",
"* Running on public URL: https://efeff91c52754b11ed.gradio.live\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"https://efeff91c52754b11ed.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import gradio as gr\n",
"\n",
"\n",
"# Function to call the selected query method\n",
"def query_router(query, method):\n",
" if method == \"Team Query\": # Ensure exact match with dropdown options\n",
" return rag_query(query)\n",
" elif method == \"General Query\":\n",
" return general_query(query)\n",
" return \"Invalid selection!\"\n",
"\n",
"# Define local image paths\n",
"logo_path = \"equinix-sign.jpg\" # Ensure this file exists\n",
"\n",
"# Custom CSS for background styling\n",
"custom_css = \"\"\"\n",
".gradio-container {\n",
" background-color: #f0f0f0;\n",
" text-align: center;\n",
"}\n",
"#logo img {\n",
" display: block;\n",
" margin: 0 auto;\n",
" max-width: 200px; /* Adjust size */\n",
"}\n",
"\"\"\"\n",
"\n",
"# Create Gradio UI\n",
"with gr.Blocks(css=custom_css) as ui:\n",
" gr.Image(logo_path, elem_id=\"logo\", show_label=False, height=100, width=200) # Display Logo\n",
" \n",
" # Title & Description\n",
" gr.Markdown(\"<h1 style='text-align: center; color: black;'>Equinix Chatbot for Automation Team</h1>\")\n",
" gr.Markdown(\"<p style='text-align: center; color: black;'>Ask me anything!</p>\")\n",
"\n",
" # Input & Dropdown Section\n",
" with gr.Row():\n",
" query_input = gr.Textbox(label=\"Enter your query\")\n",
" query_method = gr.Dropdown([\"Team Query\", \"General Query\"], label=\"Select Query Type\")\n",
" \n",
" # Button for submitting query\n",
" submit_button = gr.Button(\"Submit\")\n",
"\n",
" # Output Textbox\n",
" output_box = gr.Textbox(label=\"Response\", interactive=False)\n",
"\n",
" # Button Click Event\n",
" submit_button.click(query_router, inputs=[query_input, query_method], outputs=output_box)\n",
"\n",
"# Launch UI\n",
"ui.launch(share=True)\n"
]
}
],
"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": "3.12.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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