{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3a800e93-1e4a-40de-a211-4244e8d1a161",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install -qU langchain-google-genai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "764db45e-0ed0-480b-b338-2d7747d7746d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_google_genai import ChatGoogleGenerativeAI\n",
    "from langchain.prompts import PromptTemplate\n",
    "from langchain.chains import LLMChain\n",
    "import os\n",
    "#from google.colab import userdata "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d5b8529b-7206-4f51-804c-0a49b3242310",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"MY_SECRET_KEY\"] = \"AIzaSyDRj3wAgqOCjc_D45W_u-G3y9dk5YDgxEo\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f484e386-e7a8-44a9-9ad5-0ec977a8b618",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install fastapi uvicorn google-generativeai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bcb32fd9-805c-409c-aa27-2745867daf41",
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastapi import FastAPI\n",
    "import google.generativeai as genai\n",
    "from fastapi.middleware.cors import CORSMiddleware\n",
    "\n",
    "# Configure Google Gemini API\n",
    "from langchain_google_genai import ChatGoogleGenerativeAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "34b28dd0-5675-48ac-b07a-5ee27b5a04dd",
   "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": 7,
   "id": "c452755f-fad6-455f-9822-7c66b36d724f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize FastAPI\n",
    "app = FastAPI()\n",
    "\n",
    "# Enable CORS for frontend access\n",
    "app.add_middleware(\n",
    "    CORSMiddleware,\n",
    "    allow_origins=[\"*\"],\n",
    "    allow_credentials=True,\n",
    "    allow_methods=[\"*\"],\n",
    "    allow_headers=[\"*\"],\n",
    ")\n",
    "\n",
    "@app.get(\"/chat\")\n",
    "def chat(query: str):\n",
    "    response = model.generate_content(query)\n",
    "    return {\"response\": response.text}\n",
    "\n",
    "# Run the server: uvicorn backend:app --reload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cde9c8d0-80a4-4943-a78a-a75ca4825e34",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/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",
      "  chain = LLMChain(llm=llm, prompt=prompt)\n",
      "/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",
      "  response = chain.run(query=query)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chatbot Response: The capital of France is Paris.\n"
     ]
    }
   ],
   "source": [
    "prompt = PromptTemplate.from_template(\"Answer the following query: {query}\")\n",
    "\n",
    "# Initialize LLM\n",
    "llm = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash\", temperature=0)\n",
    "\n",
    "# Create an LLM Chain\n",
    "chain = LLMChain(llm=llm, prompt=prompt)\n",
    "\n",
    "# Run chatbot\n",
    "query = \"What is the capital of France?\"\n",
    "response = chain.run(query=query)\n",
    "print(\"Chatbot Response:\", response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "da1d9899-328b-47c2-87a5-4175519bacdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chatbot Response: The capital of India is **New Delhi**.\n"
     ]
    }
   ],
   "source": [
    "# Run chatbot\n",
    "query = \"What is the capital of India?\"\n",
    "response = chain.run(query=query)\n",
    "print(\"Chatbot Response:\", response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d42c4cf2-8b96-4310-8692-350d0d9c85b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/saurabhverma/GENAI'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "161cd070-2ac4-422a-8199-2a7cc42ac335",
   "metadata": {},
   "outputs": [],
   "source": [
    "# UI with Gradio\n",
    "def chat_interface(question):\n",
    "    return rag_pipeline(question)\n",
    "\n",
    "ui = gr.Interface(fn=chat_interface, inputs=\"text\", outputs=\"text\", title=\"RAG Chat with Gemini\")\n",
    "ui.launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cc81ff71-c152-4780-bb90-31f1df623f7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "\u001b[?25hInstalling collected packages: pydub, websockets, tomlkit, semantic-version, ruff, python-multipart, groovy, ffmpy, aiofiles, huggingface-hub, typer, safehttpx, gradio-client, gradio\n",
      "  Attempting uninstall: tomlkit\n",
      "    Found existing installation: tomlkit 0.11.1\n",
      "    Uninstalling tomlkit-0.11.1:\n",
      "      Successfully uninstalled tomlkit-0.11.1\n",
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      "    Uninstalling typer-0.9.0:\n",
      "      Successfully uninstalled typer-0.9.0\n",
      "Successfully installed aiofiles-23.2.1 ffmpy-0.5.0 gradio-5.20.1 gradio-client-1.7.2 groovy-0.1.2 huggingface-hub-0.29.2 pydub-0.25.1 python-multipart-0.0.20 ruff-0.9.10 safehttpx-0.1.6 semantic-version-2.10.0 tomlkit-0.13.2 typer-0.15.2 websockets-15.0.1\n"
     ]
    }
   ],
   "source": [
    "#pip install -U langchain-community\n",
    "!pip install gradio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8c816fe4-5454-4e30-bbf9-cda7214943f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/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",
      "  vector_store = Chroma.from_documents(docs, embedding=OpenAIEmbeddings())\n"
     ]
    },
    {
     "ename": "ValidationError",
     "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",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValidationError\u001b[0m                           Traceback (most recent call last)",
      "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",
      "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",
      "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",
      "\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"
     ]
    }
   ],
   "source": [
    "import google.generativeai as genai\n",
    "from langchain.vectorstores import Chroma\n",
    "from langchain.embeddings import OpenAIEmbeddings\n",
    "from langchain.schema import Document\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "from langchain_community.document_loaders import TextLoader\n",
    "import gradio as gr\n",
    "\n",
    "# Configure Google Gemini API\n",
    "GOOGLE_API_KEY = \"AIzaSyDRj3wAgqOCjc_D45W_u-G3y9dk5YDgxEo\"\n",
    "genai.configure(api_key=GOOGLE_API_KEY)\n",
    "model = genai.GenerativeModel(\"gemini-pro\")\n",
    "\n",
    "# Load and process documents\n",
    "def load_docs():\n",
    "    raw_text = \"\"\"\n",
    "    Machine learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time.\n",
    "    Supervised learning uses labeled data, while unsupervised learning finds hidden patterns.\n",
    "    Reinforcement learning is based on rewards and penalties.\n",
    "    \"\"\"\n",
    "    text_splitter = RecursiveCharacterTextSplitter(chunk_size=100, chunk_overlap=10)\n",
    "    docs = [Document(page_content=text) for text in text_splitter.split_text(raw_text)]\n",
    "    return docs\n",
    "\n",
    "# Create Vector Database\n",
    "docs = load_docs()\n",
    "vector_store = Chroma.from_documents(docs, embedding=OpenAIEmbeddings())\n",
    "\n",
    "# RAG Pipeline: Retrieve and Generate\n",
    "def rag_pipeline(query):\n",
    "    results = vector_store.similarity_search(query, k=2)\n",
    "    context = \" \".join([doc.page_content for doc in results])\n",
    "    \n",
    "    # Pass context + query to Gemini\n",
    "    full_prompt = f\"Context: {context}\\n\\nQuestion: {query}\\nAnswer:\"\n",
    "    response = model.generate_content(full_prompt)\n",
    "    \n",
    "    return response.text\n",
    "\n",
    "# UI with Gradio\n",
    "def chat_interface(question):\n",
    "    return rag_pipeline(question)\n",
    "\n",
    "ui = gr.Interface(fn=chat_interface, inputs=\"text\", outputs=\"text\", title=\"RAG Chat with Gemini\")\n",
    "ui.launch()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93bcb8cd-9202-45c1-98e3-9f9b06387fc2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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