demo/llama_demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.legacy import SimpleDirectoryReader, StorageContext, ServiceContext\n",
    "from llama_index.legacy.indices.vector_store import VectorStoreIndex\n",
    "from llama_iris import IRISVectorStore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Document ID: 3f842572-22ec-4138-8e98-5b6bf547d415\n"
     ]
    }
   ],
   "source": [
    "documents = SimpleDirectoryReader(\"../data/paul_graham\").load_data()\n",
    "print(\"Document ID:\", documents[0].doc_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bb70ebf13a724d779fd911cbdadfd3ff",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Parsing nodes:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3cc70b123e8a4786a9d8abbe1b80babc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating embeddings:   0%|          | 0/22 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This creates a persistent vector store (as a SQL table) under the hood and stores the document embeddings. This should only be called once!\n",
    "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": 11,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To add documents to an existing vector store:\n",
    "\n",
    "# storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
    "# for doc in documents:\n",
    "#     index.insert(document=doc, 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 wrote essays and programmed before college.\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 things that influenced the author were a novel by\n",
      "Heinlein called The Moon is a Harsh Mistress, which featured an intelligent computer called Mike,\n",
      "and a PBS documentary showing Terry Winograd using SHRDLU.\n"
     ]
    }
   ],
   "source": [
    "response = query_engine.query(\"What happened in the mid 1980s?\")\n",
    "print(textwrap.fill(str(response), 100))"
   ]
  }
 ],
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