Added pdf, env and notebook

Author: rahuljoshi078

.DS_Store

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install pinecone-client\n",
+    "#!pip install llama-index-llms-gemini\n",
+    "#!pip install llama-index-vector-stores-pinecone\n",
+    "#!pip install llama-index\n",
+    "#!pip install llama-index-embeddings-gemini"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 1: Import libraries and define API keys\n",
+    "We'll need to import a few libraries and take care of some basics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from pinecone import Pinecone\n",
+    "from llama_index.llms.gemini import Gemini\n",
+    "from llama_index.vector_stores.pinecone import PineconeVectorStore\n",
+    "from llama_index.core import StorageContext\n",
+    "from llama_index.embeddings.gemini import GeminiEmbedding\n",
+    "from llama_index.core import ServiceContext, VectorStoreIndex, SimpleDirectoryReader, download_loader, set_global_service_context\n",
+    "from llama_index.core import Settings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set API keys and set Gemini as llm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "GOOGLE_API_KEY = \"AIzaSyDEvLkqFWDGcNEdfej5nGtGk_gqELwini4\"\n",
+    "PINECONE_API_KEY = \"953c33e9-4c8e-4c61-868a-64b246640ef4\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ[\"GOOGLE_API_KEY\"] = GOOGLE_API_KEY\n",
+    "os.environ[\"PINECONE_API_KEY\"] = PINECONE_API_KEY"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set llm as Gemini Pro\n",
+    "llm = Gemini()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 2: Create a Pinecone client\n",
+    "To send data back and forth between the app and Pinecone, we'll need to instantiate a Pinecone client. It's a one-liner:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#pinecone_client = Pinecone(api_key=os.environ[\"PINECONE_API_KEY\"])\n",
+    "pinecone_client = Pinecone(api_key=PINECONE_API_KEY)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "testindex\n"
+     ]
+    }
+   ],
+   "source": [
+    "# list pinecone indexes\n",
+    "for index in pinecone_client.list_indexes():\n",
+    "    print(index['name'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 3: Select the Pinecone index\n",
+    "Using our Pinecone client, we can select the Index that we previously created and assign it to the variable pinecone_index:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pinecone_index = pinecone_client.Index(\"testindex\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 4: Call the documents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "documents = SimpleDirectoryReader(\"data\").load_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 5: Generate embeddings using GeminiEmbedding\n",
+    "\n",
+    "By default, LlamaIndex assumes you are using OpenAI to generate embeddings.\n",
+    "To configure it to use Gemini instead, we need to set up the service context which lets LlamaIndex know which llm and which embedding model to use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embed_model = GeminiEmbedding(model_name=\"models/embedding-001\")\n",
+    "\n",
+    "Settings.llm = llm\n",
+    "Settings.embed_model = embed_model\n",
+    "Settings.chunk_size = 512"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 6: Generate and store embeddings in the Pinecone index\n",
+    "Using the VectorStoreIndex class, LlamaIndex takes care of sending the data chunks to the embedding model and then handles storing the vectorized data into the Pinecone index."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Upserted vectors: 100%|██████████| 32/32 [00:01<00:00, 26.58it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# store embeddings in pinecone index\n",
+    "vector_store = PineconeVectorStore(pinecone_index=pinecone_index)\n",
+    "\n",
+    "# Create a StorageContext using the created PineconeVectorStore\n",
+    "storage_context = StorageContext.from_defaults(\n",
+    "    vector_store=vector_store\n",
+    ")\n",
+    "\n",
+    "# Use the chunks of documents and the storage_context to create the index\n",
+    "index = VectorStoreIndex.from_documents(\n",
+    "    documents, \n",
+    "    storage_context=storage_context\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 7: Query Pinecone vector store\n",
+    "\n",
+    "Now the contents of the URL are converted to embeddings and stored in the Pinecone index.\n",
+    "Let's perform a similarity search by querying the index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# query pinecone index for similar embeddings\n",
+    "query_engine = index.as_query_engine()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gemini_response = query_engine.query(\"What are the plans covered under Rooftop solarization and muft bijli?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Through rooftop solarization, one crore households will be enabled to obtain up to 300 units free electricity every month.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print response\n",
+    "print(gemini_response)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

data/budget_speech.pdf

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+llama-index
+llama-index-llms-gemini
+llama-index-vector-stores-pinecone
+llama-index-embeddings-gemini
+llama-index-readers-file