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Browse files- .gitattributes +4 -0
- Equinix-Big.jpg +3 -0
- GenAI.ipynb +433 -0
- GenAI_1.ipynb +315 -0
- RAG.pdf +3 -0
- Team.pdf +3 -0
- Team1.pdf +3 -0
- equinix-sign.jpg +0 -0
.gitattributes
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@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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Equinix-Big.jpg filter=lfs diff=lfs merge=lfs -text
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RAG.pdf filter=lfs diff=lfs merge=lfs -text
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Team.pdf filter=lfs diff=lfs merge=lfs -text
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Team1.pdf filter=lfs diff=lfs merge=lfs -text
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Equinix-Big.jpg
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Git LFS Details
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GenAI.ipynb
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@@ -0,0 +1,433 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "3a800e93-1e4a-40de-a211-4244e8d1a161",
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"metadata": {},
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"outputs": [],
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"source": [
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"#!pip install -qU langchain-google-genai"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "764db45e-0ed0-480b-b338-2d7747d7746d",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_google_genai import ChatGoogleGenerativeAI\n",
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"from langchain.prompts import PromptTemplate\n",
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"from langchain.chains import LLMChain\n",
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"import os\n",
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"#from google.colab import userdata "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "d5b8529b-7206-4f51-804c-0a49b3242310",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"os.environ[\"MY_SECRET_KEY\"] = \"AIzaSyDRj3wAgqOCjc_D45W_u-G3y9dk5YDgxEo\"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "f484e386-e7a8-44a9-9ad5-0ec977a8b618",
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"metadata": {},
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"outputs": [],
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"source": [
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"#pip install fastapi uvicorn google-generativeai"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "bcb32fd9-805c-409c-aa27-2745867daf41",
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"metadata": {},
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"outputs": [],
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"source": [
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"from fastapi import FastAPI\n",
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"import google.generativeai as genai\n",
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"from fastapi.middleware.cors import CORSMiddleware\n",
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"\n",
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"# Configure Google Gemini API\n",
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"from langchain_google_genai import ChatGoogleGenerativeAI"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "34b28dd0-5675-48ac-b07a-5ee27b5a04dd",
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"metadata": {},
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"outputs": [],
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"source": [
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"google_api_key = os.environ[\"MY_SECRET_KEY\"]\n",
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"\n",
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"# Check if the API key was found\n",
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"if google_api_key:\n",
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" # Set the environment variable if the API key was found\n",
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" os.environ[\"GOOGLE_API_KEY\"] = google_api_key\n",
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"\n",
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" llm = ChatGoogleGenerativeAI(\n",
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" model=\"gemini-pro\", # Specify the model name\n",
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" google_api_key=os.environ[\"GOOGLE_API_KEY\"]\n",
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" )\n",
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"else:\n",
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" print(\"Error: GOOGLE_API_KEY not found in Colab secrets. Please store your API key.\")\n",
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"\n",
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"\n",
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"\n",
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"genai.configure(api_key=google_api_key)\n",
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"model = genai.GenerativeModel(\"gemini-pro\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "c452755f-fad6-455f-9822-7c66b36d724f",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Initialize FastAPI\n",
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"app = FastAPI()\n",
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"\n",
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"# Enable CORS for frontend access\n",
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"app.add_middleware(\n",
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" CORSMiddleware,\n",
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" allow_origins=[\"*\"],\n",
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" allow_credentials=True,\n",
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" allow_methods=[\"*\"],\n",
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" allow_headers=[\"*\"],\n",
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")\n",
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"\n",
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"@app.get(\"/chat\")\n",
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"def chat(query: str):\n",
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" response = model.generate_content(query)\n",
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" return {\"response\": response.text}\n",
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"\n",
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"# Run the server: uvicorn backend:app --reload"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "cde9c8d0-80a4-4943-a78a-a75ca4825e34",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/var/folders/y9/krs1m7td1p33yj75p9f1s5740000gn/T/ipykernel_11301/2071011701.py:7: LangChainDeprecationWarning: The class `LLMChain` was deprecated in LangChain 0.1.17 and will be removed in 1.0. Use :meth:`~RunnableSequence, e.g., `prompt | llm`` instead.\n",
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" chain = LLMChain(llm=llm, prompt=prompt)\n",
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"/var/folders/y9/krs1m7td1p33yj75p9f1s5740000gn/T/ipykernel_11301/2071011701.py:11: LangChainDeprecationWarning: The method `Chain.run` was deprecated in langchain 0.1.0 and will be removed in 1.0. Use :meth:`~invoke` instead.\n",
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" response = chain.run(query=query)\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Chatbot Response: The capital of France is Paris.\n"
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]
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}
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],
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"source": [
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"prompt = PromptTemplate.from_template(\"Answer the following query: {query}\")\n",
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"\n",
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"# Initialize LLM\n",
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"llm = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash\", temperature=0)\n",
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"\n",
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"# Create an LLM Chain\n",
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"chain = LLMChain(llm=llm, prompt=prompt)\n",
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"\n",
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"# Run chatbot\n",
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"query = \"What is the capital of France?\"\n",
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"response = chain.run(query=query)\n",
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"print(\"Chatbot Response:\", response)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "da1d9899-328b-47c2-87a5-4175519bacdc",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Chatbot Response: The capital of India is **New Delhi**.\n"
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]
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}
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],
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"source": [
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"# Run chatbot\n",
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"query = \"What is the capital of India?\"\n",
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"response = chain.run(query=query)\n",
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"print(\"Chatbot Response:\", response)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"id": "d42c4cf2-8b96-4310-8692-350d0d9c85b4",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'/Users/saurabhverma/GENAI'"
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]
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},
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"execution_count": 24,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"os.getcwd()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "161cd070-2ac4-422a-8199-2a7cc42ac335",
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"metadata": {},
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"outputs": [],
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"source": [
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"# UI with Gradio\n",
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"def chat_interface(question):\n",
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" return rag_pipeline(question)\n",
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"\n",
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"ui = gr.Interface(fn=chat_interface, inputs=\"text\", outputs=\"text\", title=\"RAG Chat with Gemini\")\n",
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"ui.launch()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "cc81ff71-c152-4780-bb90-31f1df623f7e",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Collecting gradio\n",
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" Downloading gradio-5.20.1-py3-none-any.whl.metadata (16 kB)\n",
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+
"Collecting aiofiles<24.0,>=22.0 (from gradio)\n",
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+
" Downloading aiofiles-23.2.1-py3-none-any.whl.metadata (9.7 kB)\n",
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"Requirement already satisfied: anyio<5.0,>=3.0 in /opt/anaconda3/lib/python3.12/site-packages (from gradio) (4.2.0)\n",
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"Requirement already satisfied: fastapi<1.0,>=0.115.2 in /opt/anaconda3/lib/python3.12/site-packages (from gradio) (0.115.11)\n",
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"Collecting ffmpy (from gradio)\n",
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" Downloading ffmpy-0.5.0-py3-none-any.whl.metadata (3.0 kB)\n",
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"Collecting gradio-client==1.7.2 (from gradio)\n",
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"#pip install -U langchain-community\n",
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"!pip install gradio"
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"id": "8c816fe4-5454-4e30-bbf9-cda7214943f5",
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"/var/folders/y9/krs1m7td1p33yj75p9f1s5740000gn/T/ipykernel_16004/3095841870.py:27: LangChainDeprecationWarning: The class `OpenAIEmbeddings` was deprecated in LangChain 0.0.9 and will be removed in 1.0. An updated version of the class exists in the :class:`~langchain-openai package and should be used instead. To use it run `pip install -U :class:`~langchain-openai` and import as `from :class:`~langchain_openai import OpenAIEmbeddings``.\n",
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" vector_store = Chroma.from_documents(docs, embedding=OpenAIEmbeddings())\n"
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{
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"ename": "ValidationError",
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"evalue": "1 validation error for OpenAIEmbeddings\n Value error, Did not find openai_api_key, please add an environment variable `OPENAI_API_KEY` which contains it, or pass `openai_api_key` as a named parameter. [type=value_error, input_value={'model_kwargs': {}, 'cli...20, 'http_client': None}, input_type=dict]\n For further information visit https://errors.pydantic.dev/2.10/v/value_error",
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mValidationError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[11], line 27\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;66;03m# Create Vector Database\u001b[39;00m\n\u001b[1;32m 26\u001b[0m docs \u001b[38;5;241m=\u001b[39m load_docs()\n\u001b[0;32m---> 27\u001b[0m vector_store \u001b[38;5;241m=\u001b[39m Chroma\u001b[38;5;241m.\u001b[39mfrom_documents(docs, embedding\u001b[38;5;241m=\u001b[39mOpenAIEmbeddings())\n\u001b[1;32m 29\u001b[0m \u001b[38;5;66;03m# RAG Pipeline: Retrieve and Generate\u001b[39;00m\n\u001b[1;32m 30\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrag_pipeline\u001b[39m(query):\n",
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"File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/langchain_core/_api/deprecation.py:214\u001b[0m, in \u001b[0;36mdeprecated.<locals>.deprecate.<locals>.finalize.<locals>.warn_if_direct_instance\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 212\u001b[0m warned \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 213\u001b[0m emit_warning()\n\u001b[0;32m--> 214\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m wrapped(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
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"File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pydantic/main.py:214\u001b[0m, in \u001b[0;36mBaseModel.__init__\u001b[0;34m(self, **data)\u001b[0m\n\u001b[1;32m 212\u001b[0m \u001b[38;5;66;03m# `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks\u001b[39;00m\n\u001b[1;32m 213\u001b[0m __tracebackhide__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 214\u001b[0m validated_self \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__pydantic_validator__\u001b[38;5;241m.\u001b[39mvalidate_python(data, self_instance\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m 215\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m validated_self:\n\u001b[1;32m 216\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 217\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mA custom validator is returning a value other than `self`.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 218\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReturning anything other than `self` from a top level model validator isn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt supported when validating via `__init__`.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 219\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSee the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 220\u001b[0m stacklevel\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m,\n\u001b[1;32m 221\u001b[0m )\n",
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"\u001b[0;31mValidationError\u001b[0m: 1 validation error for OpenAIEmbeddings\n Value error, Did not find openai_api_key, please add an environment variable `OPENAI_API_KEY` which contains it, or pass `openai_api_key` as a named parameter. [type=value_error, input_value={'model_kwargs': {}, 'cli...20, 'http_client': None}, input_type=dict]\n For further information visit https://errors.pydantic.dev/2.10/v/value_error"
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"source": [
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"import google.generativeai as genai\n",
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357 |
+
"from langchain.vectorstores import Chroma\n",
|
358 |
+
"from langchain.embeddings import OpenAIEmbeddings\n",
|
359 |
+
"from langchain.schema import Document\n",
|
360 |
+
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
|
361 |
+
"from langchain_community.document_loaders import TextLoader\n",
|
362 |
+
"import gradio as gr\n",
|
363 |
+
"\n",
|
364 |
+
"# Configure Google Gemini API\n",
|
365 |
+
"GOOGLE_API_KEY = \"AIzaSyDRj3wAgqOCjc_D45W_u-G3y9dk5YDgxEo\"\n",
|
366 |
+
"genai.configure(api_key=GOOGLE_API_KEY)\n",
|
367 |
+
"model = genai.GenerativeModel(\"gemini-pro\")\n",
|
368 |
+
"\n",
|
369 |
+
"# Load and process documents\n",
|
370 |
+
"def load_docs():\n",
|
371 |
+
" raw_text = \"\"\"\n",
|
372 |
+
" Machine learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time.\n",
|
373 |
+
" Supervised learning uses labeled data, while unsupervised learning finds hidden patterns.\n",
|
374 |
+
" Reinforcement learning is based on rewards and penalties.\n",
|
375 |
+
" \"\"\"\n",
|
376 |
+
" text_splitter = RecursiveCharacterTextSplitter(chunk_size=100, chunk_overlap=10)\n",
|
377 |
+
" docs = [Document(page_content=text) for text in text_splitter.split_text(raw_text)]\n",
|
378 |
+
" return docs\n",
|
379 |
+
"\n",
|
380 |
+
"# Create Vector Database\n",
|
381 |
+
"docs = load_docs()\n",
|
382 |
+
"vector_store = Chroma.from_documents(docs, embedding=OpenAIEmbeddings())\n",
|
383 |
+
"\n",
|
384 |
+
"# RAG Pipeline: Retrieve and Generate\n",
|
385 |
+
"def rag_pipeline(query):\n",
|
386 |
+
" results = vector_store.similarity_search(query, k=2)\n",
|
387 |
+
" context = \" \".join([doc.page_content for doc in results])\n",
|
388 |
+
" \n",
|
389 |
+
" # Pass context + query to Gemini\n",
|
390 |
+
" full_prompt = f\"Context: {context}\\n\\nQuestion: {query}\\nAnswer:\"\n",
|
391 |
+
" response = model.generate_content(full_prompt)\n",
|
392 |
+
" \n",
|
393 |
+
" return response.text\n",
|
394 |
+
"\n",
|
395 |
+
"# UI with Gradio\n",
|
396 |
+
"def chat_interface(question):\n",
|
397 |
+
" return rag_pipeline(question)\n",
|
398 |
+
"\n",
|
399 |
+
"ui = gr.Interface(fn=chat_interface, inputs=\"text\", outputs=\"text\", title=\"RAG Chat with Gemini\")\n",
|
400 |
+
"ui.launch()\n"
|
401 |
+
]
|
402 |
+
},
|
403 |
+
{
|
404 |
+
"cell_type": "code",
|
405 |
+
"execution_count": null,
|
406 |
+
"id": "93bcb8cd-9202-45c1-98e3-9f9b06387fc2",
|
407 |
+
"metadata": {},
|
408 |
+
"outputs": [],
|
409 |
+
"source": []
|
410 |
+
}
|
411 |
+
],
|
412 |
+
"metadata": {
|
413 |
+
"kernelspec": {
|
414 |
+
"display_name": "Python 3 (ipykernel)",
|
415 |
+
"language": "python",
|
416 |
+
"name": "python3"
|
417 |
+
},
|
418 |
+
"language_info": {
|
419 |
+
"codemirror_mode": {
|
420 |
+
"name": "ipython",
|
421 |
+
"version": 3
|
422 |
+
},
|
423 |
+
"file_extension": ".py",
|
424 |
+
"mimetype": "text/x-python",
|
425 |
+
"name": "python",
|
426 |
+
"nbconvert_exporter": "python",
|
427 |
+
"pygments_lexer": "ipython3",
|
428 |
+
"version": "3.12.4"
|
429 |
+
}
|
430 |
+
},
|
431 |
+
"nbformat": 4,
|
432 |
+
"nbformat_minor": 5
|
433 |
+
}
|
GenAI_1.ipynb
ADDED
@@ -0,0 +1,315 @@
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|
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|
|
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|
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|
|
|
|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"cells": [
|
3 |
+
{
|
4 |
+
"cell_type": "code",
|
5 |
+
"execution_count": 2,
|
6 |
+
"id": "9633aea7-5c45-44f9-a78b-b5bc39984754",
|
7 |
+
"metadata": {},
|
8 |
+
"outputs": [],
|
9 |
+
"source": [
|
10 |
+
"from langchain_google_genai import ChatGoogleGenerativeAI\n",
|
11 |
+
"from langchain.prompts import PromptTemplate\n",
|
12 |
+
"from langchain.chains import LLMChain\n",
|
13 |
+
"\n",
|
14 |
+
"import os\n",
|
15 |
+
"\n",
|
16 |
+
"import google.generativeai as genai\n",
|
17 |
+
"from langchain.document_loaders import PyPDFLoader\n",
|
18 |
+
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
|
19 |
+
"from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings\n",
|
20 |
+
"from langchain.vectorstores import FAISS\n",
|
21 |
+
"import gradio as gr\n",
|
22 |
+
"\n",
|
23 |
+
"\n",
|
24 |
+
"os.environ[\"MY_SECRET_KEY\"] = \"AIzaSyDRj3wAgqOCjc_D45W_u-G3y9dk5YDgxEo\""
|
25 |
+
]
|
26 |
+
},
|
27 |
+
{
|
28 |
+
"cell_type": "code",
|
29 |
+
"execution_count": 3,
|
30 |
+
"id": "41abde7b-366d-427e-8938-35ce7a4ed778",
|
31 |
+
"metadata": {},
|
32 |
+
"outputs": [],
|
33 |
+
"source": [
|
34 |
+
"#pip install pypdf\n",
|
35 |
+
"#!pip install faiss-cpu"
|
36 |
+
]
|
37 |
+
},
|
38 |
+
{
|
39 |
+
"cell_type": "code",
|
40 |
+
"execution_count": 4,
|
41 |
+
"id": "b7e3810f-c5fb-44d7-b4b7-a30ac507d78b",
|
42 |
+
"metadata": {},
|
43 |
+
"outputs": [],
|
44 |
+
"source": [
|
45 |
+
"google_api_key = os.environ[\"MY_SECRET_KEY\"]\n",
|
46 |
+
"\n",
|
47 |
+
"# Check if the API key was found\n",
|
48 |
+
"if google_api_key:\n",
|
49 |
+
" # Set the environment variable if the API key was found\n",
|
50 |
+
" os.environ[\"GOOGLE_API_KEY\"] = google_api_key\n",
|
51 |
+
"\n",
|
52 |
+
" llm = ChatGoogleGenerativeAI(\n",
|
53 |
+
" model=\"gemini-pro\", # Specify the model name\n",
|
54 |
+
" google_api_key=os.environ[\"GOOGLE_API_KEY\"]\n",
|
55 |
+
" )\n",
|
56 |
+
"else:\n",
|
57 |
+
" print(\"Error: GOOGLE_API_KEY not found in Colab secrets. Please store your API key.\")\n",
|
58 |
+
"\n",
|
59 |
+
"\n",
|
60 |
+
"\n",
|
61 |
+
"genai.configure(api_key=google_api_key)\n",
|
62 |
+
"model = genai.GenerativeModel(\"gemini-pro\")"
|
63 |
+
]
|
64 |
+
},
|
65 |
+
{
|
66 |
+
"cell_type": "code",
|
67 |
+
"execution_count": 5,
|
68 |
+
"id": "ef330936-8c45-4aff-b2cf-fe9dfaaf2764",
|
69 |
+
"metadata": {},
|
70 |
+
"outputs": [],
|
71 |
+
"source": [
|
72 |
+
"work_dir=os.getcwd()"
|
73 |
+
]
|
74 |
+
},
|
75 |
+
{
|
76 |
+
"cell_type": "code",
|
77 |
+
"execution_count": 6,
|
78 |
+
"id": "a55af811-7758-4090-a5f8-748b6192971b",
|
79 |
+
"metadata": {},
|
80 |
+
"outputs": [
|
81 |
+
{
|
82 |
+
"name": "stdout",
|
83 |
+
"output_type": "stream",
|
84 |
+
"text": [
|
85 |
+
"Current Working Directory: /Users/saurabhverma/GENAI\n"
|
86 |
+
]
|
87 |
+
}
|
88 |
+
],
|
89 |
+
"source": [
|
90 |
+
"# Verify file existence\n",
|
91 |
+
"assert \"Team1.pdf\" in os.listdir(work_dir), \"Team1.pdf not found in the specified directory!\"\n",
|
92 |
+
"print(f\"Current Working Directory: {os.getcwd()}\")"
|
93 |
+
]
|
94 |
+
},
|
95 |
+
{
|
96 |
+
"cell_type": "code",
|
97 |
+
"execution_count": 7,
|
98 |
+
"id": "7a0a4457-2f9c-40db-9dd4-d57e3edf1fd0",
|
99 |
+
"metadata": {},
|
100 |
+
"outputs": [],
|
101 |
+
"source": [
|
102 |
+
"# Load PDF and split text\n",
|
103 |
+
"pdf_path = \"Team1.pdf\" # Ensure this file is uploaded to Colab\n",
|
104 |
+
"loader = PyPDFLoader(pdf_path)\n",
|
105 |
+
"documents = loader.load()\n",
|
106 |
+
"\n",
|
107 |
+
"# Split text into chunks\n",
|
108 |
+
"text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=10)\n",
|
109 |
+
"text_chunks = text_splitter.split_documents(documents)"
|
110 |
+
]
|
111 |
+
},
|
112 |
+
{
|
113 |
+
"cell_type": "code",
|
114 |
+
"execution_count": 8,
|
115 |
+
"id": "b5387499-a756-49de-86b0-96a5ce712ba7",
|
116 |
+
"metadata": {},
|
117 |
+
"outputs": [],
|
118 |
+
"source": [
|
119 |
+
"# Generate embeddings\n",
|
120 |
+
"embeddings = GoogleGenerativeAIEmbeddings(model=\"models/embedding-001\")\n",
|
121 |
+
"\n",
|
122 |
+
"# Store embeddings in FAISS index\n",
|
123 |
+
"vectorstore = FAISS.from_documents(text_chunks, embeddings)\n",
|
124 |
+
"retriever = vectorstore.as_retriever(search_kwargs={\"k\": 4})"
|
125 |
+
]
|
126 |
+
},
|
127 |
+
{
|
128 |
+
"cell_type": "code",
|
129 |
+
"execution_count": 9,
|
130 |
+
"id": "35554163-75cd-4f0b-a538-565a48700245",
|
131 |
+
"metadata": {},
|
132 |
+
"outputs": [],
|
133 |
+
"source": [
|
134 |
+
"# Set up Gemini model\n",
|
135 |
+
"llm = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash-001\", temperature=0)\n",
|
136 |
+
"\n"
|
137 |
+
]
|
138 |
+
},
|
139 |
+
{
|
140 |
+
"cell_type": "code",
|
141 |
+
"execution_count": 10,
|
142 |
+
"id": "e95b424b-11c1-46f3-9b4e-9e2d42d1f05d",
|
143 |
+
"metadata": {},
|
144 |
+
"outputs": [],
|
145 |
+
"source": [
|
146 |
+
"import gradio as gr\n",
|
147 |
+
"from langchain.prompts import PromptTemplate\n",
|
148 |
+
"from langchain.chains import LLMChain\n",
|
149 |
+
"\n",
|
150 |
+
"def rag_query(query):\n",
|
151 |
+
" # Retrieve relevant documents\n",
|
152 |
+
" docs = retriever.get_relevant_documents(query)\n",
|
153 |
+
" \n",
|
154 |
+
" # Otherwise, use RAG\n",
|
155 |
+
" context = \"\\n\".join([doc.page_content for doc in docs])\n",
|
156 |
+
" prompt = f\"Context:\\n{context}\\n\\nQuestion: {query}\\nAnswer directly and concisely:\"\n",
|
157 |
+
"\n",
|
158 |
+
" try:\n",
|
159 |
+
" response = llm.invoke(prompt)\n",
|
160 |
+
" except Exception as e:\n",
|
161 |
+
" response = f\"Error in RAG processing: {str(e)}\"\n",
|
162 |
+
"\n",
|
163 |
+
" return response.content\n",
|
164 |
+
"\n",
|
165 |
+
"\n"
|
166 |
+
]
|
167 |
+
},
|
168 |
+
{
|
169 |
+
"cell_type": "code",
|
170 |
+
"execution_count": 11,
|
171 |
+
"id": "552ff2fa-3c70-4054-803e-633efc7601f4",
|
172 |
+
"metadata": {},
|
173 |
+
"outputs": [],
|
174 |
+
"source": [
|
175 |
+
"import gradio as gr\n",
|
176 |
+
"from langchain.prompts import PromptTemplate\n",
|
177 |
+
"from langchain.chains import LLMChain\n",
|
178 |
+
"from langchain_google_genai import ChatGoogleGenerativeAI\n",
|
179 |
+
"\n",
|
180 |
+
"# Initialize LLM once (avoid repeated initialization)\n",
|
181 |
+
"llm = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash\", temperature=0)\n",
|
182 |
+
"\n",
|
183 |
+
"# Define the general query function\n",
|
184 |
+
"def general_query(query):\n",
|
185 |
+
" try:\n",
|
186 |
+
" # Define the prompt correctly\n",
|
187 |
+
" prompt = PromptTemplate.from_template(\"Answer the following query: {query}\")\n",
|
188 |
+
" \n",
|
189 |
+
" # Create an LLM Chain\n",
|
190 |
+
" chain = LLMChain(llm=llm, prompt=prompt)\n",
|
191 |
+
" \n",
|
192 |
+
" # Run chatbot and return response\n",
|
193 |
+
" response = chain.run(query=query)\n",
|
194 |
+
" \n",
|
195 |
+
" return response # Return response directly (not response.content)\n",
|
196 |
+
" \n",
|
197 |
+
" except Exception as e:\n",
|
198 |
+
" return f\"Error: {str(e)}\"\n",
|
199 |
+
"\n"
|
200 |
+
]
|
201 |
+
},
|
202 |
+
{
|
203 |
+
"cell_type": "code",
|
204 |
+
"execution_count": 12,
|
205 |
+
"id": "ab63a509-e927-405a-985b-d07039e05e9f",
|
206 |
+
"metadata": {},
|
207 |
+
"outputs": [
|
208 |
+
{
|
209 |
+
"name": "stdout",
|
210 |
+
"output_type": "stream",
|
211 |
+
"text": [
|
212 |
+
"* Running on local URL: http://127.0.0.1:7860\n",
|
213 |
+
"* Running on public URL: https://efeff91c52754b11ed.gradio.live\n",
|
214 |
+
"\n",
|
215 |
+
"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"
|
216 |
+
]
|
217 |
+
},
|
218 |
+
{
|
219 |
+
"data": {
|
220 |
+
"text/html": [
|
221 |
+
"<div><iframe src=\"https://efeff91c52754b11ed.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
|
222 |
+
],
|
223 |
+
"text/plain": [
|
224 |
+
"<IPython.core.display.HTML object>"
|
225 |
+
]
|
226 |
+
},
|
227 |
+
"metadata": {},
|
228 |
+
"output_type": "display_data"
|
229 |
+
},
|
230 |
+
{
|
231 |
+
"data": {
|
232 |
+
"text/plain": []
|
233 |
+
},
|
234 |
+
"execution_count": 12,
|
235 |
+
"metadata": {},
|
236 |
+
"output_type": "execute_result"
|
237 |
+
}
|
238 |
+
],
|
239 |
+
"source": [
|
240 |
+
"import gradio as gr\n",
|
241 |
+
"\n",
|
242 |
+
"\n",
|
243 |
+
"# Function to call the selected query method\n",
|
244 |
+
"def query_router(query, method):\n",
|
245 |
+
" if method == \"Team Query\": # Ensure exact match with dropdown options\n",
|
246 |
+
" return rag_query(query)\n",
|
247 |
+
" elif method == \"General Query\":\n",
|
248 |
+
" return general_query(query)\n",
|
249 |
+
" return \"Invalid selection!\"\n",
|
250 |
+
"\n",
|
251 |
+
"# Define local image paths\n",
|
252 |
+
"logo_path = \"equinix-sign.jpg\" # Ensure this file exists\n",
|
253 |
+
"\n",
|
254 |
+
"# Custom CSS for background styling\n",
|
255 |
+
"custom_css = \"\"\"\n",
|
256 |
+
".gradio-container {\n",
|
257 |
+
" background-color: #f0f0f0;\n",
|
258 |
+
" text-align: center;\n",
|
259 |
+
"}\n",
|
260 |
+
"#logo img {\n",
|
261 |
+
" display: block;\n",
|
262 |
+
" margin: 0 auto;\n",
|
263 |
+
" max-width: 200px; /* Adjust size */\n",
|
264 |
+
"}\n",
|
265 |
+
"\"\"\"\n",
|
266 |
+
"\n",
|
267 |
+
"# Create Gradio UI\n",
|
268 |
+
"with gr.Blocks(css=custom_css) as ui:\n",
|
269 |
+
" gr.Image(logo_path, elem_id=\"logo\", show_label=False, height=100, width=200) # Display Logo\n",
|
270 |
+
" \n",
|
271 |
+
" # Title & Description\n",
|
272 |
+
" gr.Markdown(\"<h1 style='text-align: center; color: black;'>Equinix Chatbot for Automation Team</h1>\")\n",
|
273 |
+
" gr.Markdown(\"<p style='text-align: center; color: black;'>Ask me anything!</p>\")\n",
|
274 |
+
"\n",
|
275 |
+
" # Input & Dropdown Section\n",
|
276 |
+
" with gr.Row():\n",
|
277 |
+
" query_input = gr.Textbox(label=\"Enter your query\")\n",
|
278 |
+
" query_method = gr.Dropdown([\"Team Query\", \"General Query\"], label=\"Select Query Type\")\n",
|
279 |
+
" \n",
|
280 |
+
" # Button for submitting query\n",
|
281 |
+
" submit_button = gr.Button(\"Submit\")\n",
|
282 |
+
"\n",
|
283 |
+
" # Output Textbox\n",
|
284 |
+
" output_box = gr.Textbox(label=\"Response\", interactive=False)\n",
|
285 |
+
"\n",
|
286 |
+
" # Button Click Event\n",
|
287 |
+
" submit_button.click(query_router, inputs=[query_input, query_method], outputs=output_box)\n",
|
288 |
+
"\n",
|
289 |
+
"# Launch UI\n",
|
290 |
+
"ui.launch(share=True)\n"
|
291 |
+
]
|
292 |
+
}
|
293 |
+
],
|
294 |
+
"metadata": {
|
295 |
+
"kernelspec": {
|
296 |
+
"display_name": "Python 3 (ipykernel)",
|
297 |
+
"language": "python",
|
298 |
+
"name": "python3"
|
299 |
+
},
|
300 |
+
"language_info": {
|
301 |
+
"codemirror_mode": {
|
302 |
+
"name": "ipython",
|
303 |
+
"version": 3
|
304 |
+
},
|
305 |
+
"file_extension": ".py",
|
306 |
+
"mimetype": "text/x-python",
|
307 |
+
"name": "python",
|
308 |
+
"nbconvert_exporter": "python",
|
309 |
+
"pygments_lexer": "ipython3",
|
310 |
+
"version": "3.12.4"
|
311 |
+
}
|
312 |
+
},
|
313 |
+
"nbformat": 4,
|
314 |
+
"nbformat_minor": 5
|
315 |
+
}
|
RAG.pdf
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:5b88dad7546bc3b74af2f8fa99991933639d8ca6c9d9b29dc40f15f835e7d1ee
|
3 |
+
size 100412
|
Team.pdf
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:35732f9e343ec1705006de208fe38327524bf162a9ef3041c9942f1378f86511
|
3 |
+
size 295956
|
Team1.pdf
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:da809c3a654847a5923d8221a8e008649fd32d725b19c4bca51d138e0823f067
|
3 |
+
size 308946
|
equinix-sign.jpg
ADDED
![]() |