From e7bb24eed2858f66d216f5c4fcc1b54079a3e886 Mon Sep 17 00:00:00 2001
From: Laura Gutierrez Funderburk <lgutierrwr@gmail.com>
Date: Fri, 17 Nov 2023 15:18:07 -0800
Subject: [PATCH] correct diagram

---
 ch3/jupyter-notebooks/components.ipynb        | 1151 +++++++++++++++++
 .../images/data-structures.png                |  Bin 43393 -> 58813 bytes
 2 files changed, 1151 insertions(+)

diff --git a/ch3/jupyter-notebooks/components.ipynb b/ch3/jupyter-notebooks/components.ipynb
index b80370f..d13e040 100644
--- a/ch3/jupyter-notebooks/components.ipynb
+++ b/ch3/jupyter-notebooks/components.ipynb
@@ -25,6 +25,1157 @@
     "\n",
     "![](./images/haystack-components.png)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Embedding components\n",
+    "\n",
+    "In this example, `docs` is a list of `Document` objects with text content to be embedded. The `OpenAIDocumentEmbedder` is initialized with an OpenAI API key and is used to generate embeddings for each document. The embeddings are then printed out for each document in the `docs` list."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### OpenAIDocumentEmbedder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating embeddings:   0%|          | 0/1 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating embeddings: 100%|██████████| 1/1 [00:00<00:00,  3.18it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from haystack.preview import Document\n",
+    "from haystack.preview.components.embedders import OpenAIDocumentEmbedder\n",
+    "from dotenv import load_dotenv\n",
+    "import os\n",
+    "\n",
+    "# Load the .env file\n",
+    "load_dotenv(\"./../../.env\")\n",
+    "api_key = os.getenv(\"OPENAI_API_KEY\")\n",
+    "\n",
+    "# List of documents to embed\n",
+    "docs = [Document(content=\"The quick brown fox jumps over the lazy dog.\"), \n",
+    "        Document(content=\"To be or not to be, that is the question.\")]\n",
+    "\n",
+    "# Initialize the embedder with your OpenAI API key\n",
+    "document_embedder = OpenAIDocumentEmbedder(api_key=api_key)\n",
+    "\n",
+    "# Run the embedder to get embeddings\n",
+    "result = document_embedder.run(docs)\n",
+    "\n",
+    "# Access the embeddings stored in the documents\n",
+    "for doc in result['documents']:\n",
+    "    print(doc.embedding)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Taking a look at the embeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.0016260817646980286,\n",
+       " 0.005972211714833975,\n",
+       " -0.014899228699505329,\n",
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+       " -0.011499950662255287,\n",
+       " 0.015537766739726067,\n",
+       " -0.021434856578707695,\n",
+       " -0.008682869374752045,\n",
+       " -0.010905234143137932,\n",
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+       " 0.02130965329706669,\n",
+       " 0.017703790217638016,\n",
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+       " 0.0007449614349752665,\n",
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+       " 0.007975469343364239,\n",
+       " 0.021046726033091545,\n",
+       " 0.010254175402224064,\n",
+       " 0.0007926953257992864,\n",
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+       " -0.04657573997974396,\n",
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+       " -0.005872048903256655,\n",
+       " 0.0022818357683718204,\n",
+       " 0.013872559182345867,\n",
+       " 0.010648566298186779,\n",
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+       " 0.03728563338518143,\n",
+       " -0.009208724834024906,\n",
+       " 0.03142610564827919,\n",
+       " 0.0203330647200346,\n",
+       " -0.0028562070801854134,\n",
+       " -0.007217987906187773,\n",
+       " 0.021585101261734962,\n",
+       " -0.005092656705528498,\n",
+       " 0.010711167939007282,\n",
+       " -0.020595991984009743,\n",
+       " 0.009490433149039745,\n",
+       " 0.017303137108683586,\n",
+       " 0.010204093530774117,\n",
+       " -0.005236640572547913,\n",
+       " -0.0009374619694426656,\n",
+       " -0.012032066471874714,\n",
+       " -0.03019910864531994,\n",
+       " -0.010354338213801384,\n",
+       " -0.003737327642738819,\n",
+       " -0.014786545187234879,\n",
+       " -0.006980100646615028,\n",
+       " 0.005696763750165701,\n",
+       " 0.018116962164640427,\n",
+       " -0.00928384717553854,\n",
+       " 0.0025619787629693747,\n",
+       " 0.028696665540337563,\n",
+       " 0.0126393036916852,\n",
+       " -0.010523363016545773,\n",
+       " 0.00014770112466067076,\n",
+       " -0.007925388403236866,\n",
+       " 0.020696155726909637,\n",
+       " -0.012589221820235252,\n",
+       " 0.004369605798274279,\n",
+       " 0.014836627058684826,\n",
+       " 0.02152249962091446,\n",
+       " 0.007130345329642296,\n",
+       " 0.00021323739201761782,\n",
+       " 0.019469160586595535,\n",
+       " 0.0011252673575654626,\n",
+       " -0.005089526530355215,\n",
+       " -0.013860038481652737,\n",
+       " 0.012689385563135147,\n",
+       " 0.02350071631371975,\n",
+       " ...]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result['documents'][0].embedding"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### OpenAITextEmbedder\n",
+    "\n",
+    "In this snippet, `text_embedder` is created with an OpenAI API key and used to generate an embedding for the string \"I love pizza!\". The resulting embedding and associated metadata are then printed out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'embedding': [0.017020374536514282, -0.023255806416273117, 0.007667226251214743, -0.033693648874759674, -0.029528429731726646, -0.011646497994661331, -0.006254027131944895, -0.009080425836145878, 0.026429306715726852, -0.03461098670959473, 0.030222631990909576, 0.009644465520977974, -0.00958248320966959, -0.00684905843809247, -0.001529416418634355, -0.011026673950254917, 0.030445769429206848, -0.0059379166923463345, 0.010679572820663452, -0.02620617114007473, -0.012904741801321507, -0.014565871097147465, 0.004394554067403078, -0.020726924762129784, -0.008547376841306686, 0.02843753807246685, -0.0006543020135723054, -0.0023971705231815577, 0.008144491352140903, -0.010425444692373276, 0.04705705866217613, 0.0069234371185302734, -0.02453264594078064, -0.01952446438372135, -0.010419245809316635, -0.005048468708992004, -0.00459909625351429, -0.010208506137132645, -0.009185795672237873, -0.012557639740407467, 0.010196109302341938, -0.012941931374371052, -0.009886197745800018, 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+     ]
+    }
+   ],
+   "source": [
+    "from haystack.preview.components.embedders import OpenAITextEmbedder\n",
+    "\n",
+    "# Initialize the text embedder with your OpenAI API key\n",
+    "text_embedder = OpenAITextEmbedder(api_key=api_key)\n",
+    "\n",
+    "# Text you want to embed\n",
+    "text_to_embed = \"I love pizza!\"\n",
+    "\n",
+    "# Embed the text and print the result\n",
+    "result = text_embedder.run(text_to_embed)\n",
+    "print(result)\n",
+    "\n",
+    "# Output will include the embedding vector and metadata\n",
+    "# {'embedding': [0.017020374536514282, -0.023255806416273117, ...],\n",
+    "#  'metadata': {'model': 'text-embedding-ada-002',\n",
+    "#               'usage': {'prompt_tokens': 4, 'total_tokens': 4}}}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/ch3/jupyter-notebooks/images/data-structures.png b/ch3/jupyter-notebooks/images/data-structures.png
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