demo/llama_demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index import SimpleDirectoryReader, StorageContext, ServiceContext\n",
    "from llama_index.indices.vector_store import VectorStoreIndex\n",
    "from llama_iris import IRISVectorStore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import getpass\n",
    "import os\n",
    "from dotenv import load_dotenv\n",
    "\n",
    "load_dotenv(override=True)\n",
    "\n",
    "if not os.environ.get(\"OPENAI_API_KEY\"):\n",
    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Document ID: 3d9c136b-3be6-4c64-b875-9c086357482e\n"
     ]
    }
   ],
   "source": [
    "documents = SimpleDirectoryReader(\"../data/paul_graham\").load_data()\n",
    "print(\"Document ID:\", documents[0].doc_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "username = 'demo'\n",
    "password = 'demo' \n",
    "hostname = os.getenv('IRIS_HOSTNAME', 'localhost')\n",
    "port = '1972' \n",
    "namespace = 'USER'\n",
    "CONNECTION_STRING = f\"iris://{username}:{password}@{hostname}:{port}/{namespace}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "vector_store = IRISVectorStore.from_params(\n",
    "    connection_string=CONNECTION_STRING,\n",
    "    table_name=\"paul_graham_essay\",\n",
    "    embed_dim=1536,  # openai embedding dimension\n",
    ")\n",
    "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
    "# service_context = ServiceContext.from_defaults(\n",
    "#     embed_model=embed_model, llm=None\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aryanput/anaconda3/envs/iris-vector-search/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "Parsing nodes: 100%|██████████| 1/1 [00:00<00:00, 16.96it/s]\n",
      "Generating embeddings: 100%|██████████| 22/22 [00:00<00:00, 39.46it/s]\n"
     ]
    }
   ],
   "source": [
    "index = VectorStoreIndex.from_documents(\n",
    "    documents, \n",
    "    storage_context=storage_context, \n",
    "    show_progress=True, \n",
    "    # service_context=service_context,\n",
    ")\n",
    "query_engine = index.as_query_engine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # If reconnecting to the vector store, use this: \n",
    "\n",
    "# index = VectorStoreIndex.from_vector_store(vector_store=vector_store)\n",
    "# storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
    "# query_engine = index.as_query_engine()\n",
    "\n",
    "# # Adding documents to existing index\n",
    "\n",
    "# for d in documents:\n",
    "#     index.insert(document=d, storage_context=storage_context)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "response = query_engine.query(\"What did the author do?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The author worked on writing essays and programming before college. Initially, the author wrote\n",
      "short stories and experimented with programming on an IBM 1401 using Fortran. Later, with the\n",
      "introduction of microcomputers, the author's interest in programming grew. The author's first\n",
      "personal computer was a TRS-80, on which they wrote simple games, a rocket flight prediction\n",
      "program, and a word processor. In college, the author initially planned to study philosophy but\n",
      "eventually switched to studying AI due to the influence of a novel and a PBS documentary.\n"
     ]
    }
   ],
   "source": [
    "import textwrap\n",
    "print(textwrap.fill(str(response), 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AI was in the air in the mid 1980s, and two specific events influenced the individual's interest in\n",
      "working on it.\n"
     ]
    }
   ],
   "source": [
    "response = query_engine.query(\"What happened in the mid 1980s?\")\n",
    "print(textwrap.fill(str(response), 100))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}