examples/notebooks/beam-ml/rag_usecase/beam_rag_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "af127b51-1c7e-4e56-9759-aee40d9df194",
   "metadata": {},
   "outputs": [],
   "source": [
    "# @title ###### Licensed to the Apache Software Foundation (ASF), Version 2.0 (the \"License\")\n",
    "\n",
    "# Licensed to the Apache Software Foundation (ASF) under one\n",
    "# or more contributor license agreements. See the NOTICE file\n",
    "# distributed with this work for additional information\n",
    "# regarding copyright ownership. The ASF licenses this file\n",
    "# to you under the Apache License, Version 2.0 (the\n",
    "# \"License\"); you may not use this file except in compliance\n",
    "# with the License. You may obtain a copy of the License at\n",
    "#\n",
    "#   http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing,\n",
    "# software distributed under the License is distributed on an\n",
    "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
    "# KIND, either express or implied. See the License for the\n",
    "# specific language governing permissions and limitations\n",
    "# under the License"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "160b9fee-00e9-4dd1-b1db-3d050e1bc710",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.1.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49m/usr/local/opt/python@3.10/bin/python3.10 -m pip install --upgrade pip\u001b[0m\n",
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      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49m/usr/local/opt/python@3.10/bin/python3.10 -m pip install --upgrade pip\u001b[0m\n",
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      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.1.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49m/usr/local/opt/python@3.10/bin/python3.10 -m pip install --upgrade pip\u001b[0m\n",
      "Requirement already satisfied: redis==5.0.1 in /usr/local/lib/python3.10/site-packages (5.0.1)\n",
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      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.1.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49m/usr/local/opt/python@3.10/bin/python3.10 -m pip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "#installing dependencies\n",
    "!pip install pandas==1.4.4\n",
    "!pip install numpy==1.24.4\n",
    "!pip install apache_beam==2.56.0\n",
    "!pip install redis==5.0.1\n",
    "!pip install langchain==0.1.14 #used for chunking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bb8f59b0-254f-4b8e-a3dc-9015f35ef798",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Imports Required for the notebook\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import apache_beam as beam\n",
    "from apache_beam.ml.transforms.base import MLTransform\n",
    "from apache_beam.transforms.enrichment import Enrichment\n",
    "from apache_beam.ml.transforms.embeddings.huggingface import SentenceTransformerEmbeddings\n",
    "import tempfile\n",
    "import redis\n",
    "import redis_connector\n",
    "import redis_enrichment\n",
    "from redis_connector import *\n",
    "from redis_enrichment import *\n",
    "from redis.commands.search.indexDefinition import (IndexDefinition,IndexType)\n",
    "from redis.commands.search.query import Query\n",
    "from redis.commands.search.field import (TextField,VectorField)\n",
    "from chunks_generation import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3d274890-4e6b-4a3d-b682-9fc6e21e5cca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.56.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To check beam version installed \n",
    "beam.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c62d2ac3-36f5-42f2-8560-2e72421a1ff9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "with open('hf_small_wikipedia.json', 'r') as j:\n",
    "     contents = json.loads(j.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19c1c652-b9df-4f7e-bcb5-7ee2d290e091",
   "metadata": {},
   "source": [
    "# For now Reading json data locally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fe0d6dc7-1809-44c9-9a36-b0781ec6731a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'id': '12', 'url': 'https://en.wikipedia.org/wiki/Anarchism', 'title': 'Anarchism', 'text': 'Anarchism is a political philosophy and movement that is skeptical of all  ... \\nSocial theories\\nSocialism'}]\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "with open('hf_small_wikipedia.json', 'r') as j:\n",
    "     contents = json.loads(j.read())\n",
    "\n",
    "\n",
    "print(contents[:1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "947974b9-0218-4cb0-bd5a-1d57fd37c2f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa06d33f-ed94-4bea-8b33-04c947a99034",
   "metadata": {},
   "source": [
    "# Create Redis Client for connecting to Redis Vector Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "43342378-18cd-4fd3-849c-2c6f8dc9a5ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "REDIS_HOST =  \"localhost\"\n",
    "REDIS_PORT = 6379\n",
    "REDIS_PASSWORD = \"\" # default for passwordless Redis\n",
    "\n",
    "# Connect to Redis\n",
    "redis_client = redis.Redis(\n",
    "    host=REDIS_HOST,\n",
    "    port=REDIS_PORT,\n",
    "    password=REDIS_PASSWORD\n",
    ")\n",
    "redis_client.ping()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72cd4ad1-6577-453a-a2f7-947ae3149993",
   "metadata": {},
   "source": [
    "# Creating a Search Index\n",
    "Below cells will show how to specify and create a search index in Redis vector DB. Below are the following steps:\n",
    "\n",
    "1) Set some constants for defining our index like the distance metric and the index name\n",
    "2) Define the index schema with RediSearch fields\n",
    "3) Create the index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0a1b7250-6bb1-4f29-81fd-9934e7a457cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Constants\n",
    "EMBEDDING_MODEL = 'all-MiniLM-L6-v2' # Embedding model name to be use with ML Transform\n",
    "VECTOR_DIM = 384                     # length of the vector for above embedding model\n",
    "VECTOR_NUMBER = 2                    # initial number of vectors\n",
    "INDEX_NAME = \"embeddings-index\"      # name of the search index      \n",
    "PREFIX = \"doc\"                       # prefix for the document keys           \n",
    "DISTANCE_METRIC = \"COSINE\"           # distance metric for the vectors (ex. COSINE, IP, L2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "78ef7e80-680e-424d-b018-be3bd71008ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define RediSearch fields for each of the columns in the dataset\n",
    "url = TextField(name=\"url\")\n",
    "title = TextField(name=\"title\")\n",
    "title_embedding = VectorField(\"title_vector\",\n",
    "    \"FLAT\", {\n",
    "        \"TYPE\": \"FLOAT32\",\n",
    "        \"DIM\": VECTOR_DIM,\n",
    "        \"DISTANCE_METRIC\": DISTANCE_METRIC,\n",
    "        \"INITIAL_CAP\": VECTOR_NUMBER,\n",
    "    }\n",
    ")\n",
    "\n",
    "text = TextField(name=\"text\")\n",
    "text_embedding = VectorField(\"text_vector\",\n",
    "    \"FLAT\", {\n",
    "        \"TYPE\": \"FLOAT32\",\n",
    "        \"DIM\": VECTOR_DIM,\n",
    "        \"DISTANCE_METRIC\": DISTANCE_METRIC,\n",
    "        \"INITIAL_CAP\": VECTOR_NUMBER,\n",
    "    }\n",
    ")\n",
    "fields = [url, title, title_embedding, text, text_embedding]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "120eabcf-a87a-4fdf-ba29-3117dec9d858",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index already exists\n"
     ]
    }
   ],
   "source": [
    "# Check if index exists\n",
    "try:\n",
    "    redis_client.ft(INDEX_NAME).info()\n",
    "    print(\"Index already exists\")\n",
    "except:\n",
    "    # Create RediSearch Index\n",
    "    redis_client.ft(INDEX_NAME).create_index(\n",
    "        fields = fields,\n",
    "        definition = IndexDefinition(prefix=[PREFIX], index_type=IndexType.HASH)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f93ea35f-47d3-43b3-ab32-5dba16612337",
   "metadata": {},
   "source": [
    "# Creating Knowledge Base in Redis Vector Database\n",
    "After creating a search index, we can load documents into it. We will use the same documents we used in the previous cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5736710f-b16d-405e-a1fe-f504e753b024",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Missing pipeline option (runner). Executing pipeline using the default runner: DirectRunner.\n",
      "WARNING:apache_beam.runners.interactive.interactive_environment:Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features.\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (typeof window.interactive_beam_jquery == 'undefined') {\n",
       "          var jqueryScript = document.createElement('script');\n",
       "          jqueryScript.src = 'https://code.jquery.com/jquery-3.4.1.slim.min.js';\n",
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     "text": [
      "WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Union[Tuple[apache_beam.pvalue.PCollection[~MLTransformOutputT], apache_beam.pvalue.PCollection[apache_beam.pvalue.Row]], apache_beam.pvalue.PCollection[~MLTransformOutputT]] instead.\n",
      "WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Union[Tuple[apache_beam.pvalue.PCollection[~MLTransformOutputT], apache_beam.pvalue.PCollection[apache_beam.pvalue.Row]], apache_beam.pvalue.PCollection[~MLTransformOutputT]] instead.\n",
      "WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Union[Tuple[apache_beam.pvalue.PCollection[~MLTransformOutputT], apache_beam.pvalue.PCollection[apache_beam.pvalue.Row]], apache_beam.pvalue.PCollection[~MLTransformOutputT]] instead.\n",
      "WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Union[Tuple[apache_beam.pvalue.PCollection[~MLTransformOutputT], apache_beam.pvalue.PCollection[apache_beam.pvalue.Row]], apache_beam.pvalue.PCollection[~MLTransformOutputT]] instead.\n",
      "INFO:apache_beam.runners.worker.statecache:Creating state cache with size 104857600\n",
      "INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: all-MiniLM-L6-v2\n",
      "INFO:sentence_transformers.SentenceTransformer:Use pytorch device_name: mps\n",
      "2024-08-09 13:01:57.330902: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: all-MiniLM-L6-v2\n",
      "INFO:sentence_transformers.SentenceTransformer:Use pytorch device_name: mps\n",
      "INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: all-MiniLM-L6-v2\n",
      "INFO:sentence_transformers.SentenceTransformer:Use pytorch device_name: mps\n",
      "INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: all-MiniLM-L6-v2\n",
      "INFO:sentence_transformers.SentenceTransformer:Use pytorch device_name: mps\n",
      "INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: all-MiniLM-L6-v2\n",
      "INFO:sentence_transformers.SentenceTransformer:Use pytorch device_name: mps\n",
      "INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: all-MiniLM-L6-v2\n",
      "INFO:sentence_transformers.SentenceTransformer:Use pytorch device_name: mps\n",
      "INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: all-MiniLM-L6-v2\n",
      "INFO:sentence_transformers.SentenceTransformer:Use pytorch device_name: mps\n",
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      "INFO:sentence_transformers.SentenceTransformer:Use pytorch device_name: mps\n",
      "INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: all-MiniLM-L6-v2\n",
      "INFO:sentence_transformers.SentenceTransformer:Use pytorch device_name: mps\n",
      "INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: all-MiniLM-L6-v2\n",
      "INFO:sentence_transformers.SentenceTransformer:Use pytorch device_name: mps\n",
      "INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: all-MiniLM-L6-v2\n",
      "INFO:sentence_transformers.SentenceTransformer:Use pytorch device_name: mps\n",
      "INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: all-MiniLM-L6-v2\n",
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     "metadata": {},
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:BatchElements statistics: element_count=349 batch_count=35 next_batch_size=20 timings=[(1, 0.14947915077209473), (2, 0.4767780303955078), (1, 0.1831817626953125), (2, 0.2000288963317871), (1, 0.14716172218322754), (2, 0.19555902481079102), (4, 0.568000078201294), (3, 0.49921202659606934), (1, 0.15181899070739746), (2, 0.2397456169128418), (2, 0.19758391380310059), (3, 0.259962797164917), (6, 0.6725080013275146), (7, 0.7242469787597656), (7, 0.6019473075866699), (7, 0.47342967987060547), (10, 0.9236979484558105), (13, 1.1121737957000732), (14, 1.130352258682251), (12, 0.9904649257659912), (19, 1.336167812347412), (13, 0.8261549472808838), (15, 1.156790018081665), (17, 1.272038221359253), (15, 1.0295050144195557), (14, 0.973175048828125), (16, 1.2297940254211426), (15, 0.8902089595794678), (23, 1.609447956085205), (19, 1.1090152263641357), (22, 1.5956041812896729), (18, 1.3837130069732666), (25, 1.715193748474121), (17, 1.1607699394226074)]\n"
     ]
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   ],
   "source": [
    "#Insertion Pipeline\n",
    "\n",
    "artifact_location = tempfile.mkdtemp()\n",
    "generate_embedding_fn = SentenceTransformerEmbeddings(model_name='all-MiniLM-L6-v2',\n",
    "                                                               columns=['title','text'])\n",
    "with beam.Pipeline() as p:\n",
    "    embeddings = (\n",
    "        p  \n",
    "        | \"Read data\" >> beam.Create(contents) \n",
    "        | \"Generate text chunks\" >> ChunksGeneration(chunk_size = 500, chunk_overlap = 0, chunking_strategy = ChunkingStrategy.SPLIT_BY_TOKENS)\n",
    "        | \"Insert document in Redis\" >> InsertDocInRedis(host='127.0.0.1',port=6379, batch_size=10)\n",
    "        | \"Generate Embeddings\" >> MLTransform(write_artifact_location=artifact_location).with_transform(generate_embedding_fn) \n",
    "        | \"Insert Embedding in Redis\" >> InsertEmbeddingInRedis(host='127.0.0.1',port=6379, batch_size=10,embedded_columns=['title','text'])\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6206f83e-d3b6-4869-a7f2-2f662d3968f0",
   "metadata": {},
   "source": [
    "## Pipeline Steps:\n",
    "\n",
    "Now that we have ingested the documents in Redis, we will create a embeddings transform, which is used for storing the text and its embedding in redis vector db\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f71e0cad-c062-4c12-9ba6-17010758f6db",
   "metadata": {},
   "source": [
    "# Running Search Queries/ Perform Enrichment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42697871-a5be-48cc-b961-799d69fc750b",
   "metadata": {},
   "source": [
    "## Pipeline Steps:\n",
    "Create a search transform, which emits the document Id, vector score along with the matching text from knowledge base\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bc447d60-0588-4c6d-8a5c-b3f97e12461e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Missing pipeline option (runner). Executing pipeline using the default runner: DirectRunner.\n",
      "WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Union[Tuple[apache_beam.pvalue.PCollection[~MLTransformOutputT], apache_beam.pvalue.PCollection[apache_beam.pvalue.Row]], apache_beam.pvalue.PCollection[~MLTransformOutputT]] instead.\n",
      "WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Union[Tuple[apache_beam.pvalue.PCollection[~MLTransformOutputT], apache_beam.pvalue.PCollection[apache_beam.pvalue.Row]], apache_beam.pvalue.PCollection[~MLTransformOutputT]] instead.\n",
      "WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Union[Tuple[apache_beam.pvalue.PCollection[~MLTransformOutputT], apache_beam.pvalue.PCollection[apache_beam.pvalue.Row]], apache_beam.pvalue.PCollection[~MLTransformOutputT]] instead.\n",
      "WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Union[Tuple[apache_beam.pvalue.PCollection[~MLTransformOutputT], apache_beam.pvalue.PCollection[apache_beam.pvalue.Row]], apache_beam.pvalue.PCollection[~MLTransformOutputT]] instead.\n",
      "INFO:apache_beam.runners.worker.statecache:Creating state cache with size 104857600\n",
      "INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: all-MiniLM-L6-v2\n",
      "INFO:sentence_transformers.SentenceTransformer:Use pytorch device_name: mps\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0230c48548aa4229aaecabdea860a5f3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:BatchElements statistics: element_count=1 batch_count=1 next_batch_size=1 timings=[]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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anarchist thinkers advocated anarchist federalism. another criticism of anarchism is the belief that it is inherently unstable : that an anarchist society would inevitably evolve back into a state. thomas hobbes and other early social contract theorists argued that the state emerges in response to natural anarchy in order to protect the people\\'s interests and keep order. philosopher robert nozick argued that a \" night - watchman state \", or minarchy, would emerge from anarchy through the process of an invisible hand, in which people would exercise their liberty and buy protection from protection agencies, evolving into a minimal state. anarchists reject these criticisms by arguing that humans in a state of nature would not just be in a state of war. anarcho - primitivists in particular argue that humans were better off in a state of nature in small tribes living close to the land, while anarchists in general argue that the negatives of state organization, such as hierarchies, monopolies and inequality'}, Document {'id': 'doc_12_section_34', 'payload': None, 'title': 'Anarchism', 'url': 'https://en.wikipedia.org/wiki/Anarchism', 'text': 'a revolution is by itself authoritarian. academic john molyneux writes in his book anarchism : a marxist criticism that \" anarchism cannot win \", believing that it lacks the ability to properly implement its ideas. the marxist criticism of anarchism is that it has a utopian character because all individuals should have anarchist views and values. according to the marxist view, that a social idea would follow directly from this human ideal and out of the free will of every individual formed its essence. marxists state that this contradiction was responsible for their inability to act. in the anarchist vision, the conflict between liberty and equality was resolved through coexistence and intertwining. see also anarchism by country governance without government list of anarchist political ideologies list of books about anarchism references explanatory notes citations general and cited sources primary sources secondary sources tertiary sources further reading criticism of philosophical anarchism. a defence of philosophical anarchism, stating that \" both kinds of\\'anarchism\\'[ i. e. philosophical and political anarchism ] are philosophical and political claims. \" ( p. 137 ) anarchistic popular fiction novel. an argument for philosophical anarchism. external links anarchy archives – an online research center on the history'}])\n"
     ]
    }
   ],
   "source": [
    "#  Enchriment Pipeline \n",
    "\n",
    "\n",
    "data = [{'text':'What is Anarchy ?'}]\n",
    "\n",
    "artifact_location = tempfile.mkdtemp()\n",
    "generate_embedding_fn = SentenceTransformerEmbeddings(model_name='all-MiniLM-L6-v2',\n",
    "                                                                 columns=['text'])\n",
    "\n",
    "redis_handler = RedisEnrichmentHandler(redis_host='127.0.0.1', redis_port=6379)\n",
    "                                       \n",
    "\n",
    "with beam.Pipeline() as p:\n",
    "  _ = (\n",
    "      p\n",
    "      | \"Create\" >> beam.Create(data)\n",
    "      | \"Generate Embedding\" >> MLTransform(write_artifact_location=artifact_location).with_transform(generate_embedding_fn)\n",
    "      | \"Enrich W/ Redis\" >> Enrichment(redis_handler)\n",
    "      | \"Print\" >> beam.Map(print)\n",
    "  )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9126f5a9-179e-4059-b868-0838b0944902",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "Here we have demonstrated how we can implement Ingestion and Enrichment pipeline using redis vector DB by using ML Transfrom's SentenceTransformerEmbeddings for generating the embeddings of the text chunks."
   ]
  }
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