langchain-02.ipynb

{
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R2-i8jBl9GRH"
      },
      "source": [
        "![Redis](https://redis.com/wp-content/themes/wpx/assets/images/logo-redis.svg?auto=webp&quality=85,75&width=120)\n",
        "# RAG with LangChain\n",
        "\n",
        "This notebook uses [LangChain](https://python.langchain.com/docs/get_started/introduction) and [Redis](https://redis.com) to perform document + embedding indexing and semantic search tasks. It also shows how to integrate with an LLM like AWS Bedrock models."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ctOVb_LZ1vmk"
      },
      "source": [
        "## Environment Setup\n",
        "\n",
        "### Pull Github Materials\n",
        "Because you are likely running this notebook in **Google Colab**, we need to first\n",
        "pull the necessary dataset and materials directly from GitHub.\n",
        "\n",
        "**If you are running this notebook locally**, FYI you may not need to perform this\n",
        "step at all."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UQezgPCG1vml",
        "outputId": "97b9bc03-da1b-439a-c37b-be6fdb58ab21"
      },
      "outputs": [],
      "source": [
        "# This clones the supporting git repository into a directory named 'temp_repo'.\n",
        "!git clone https://github.com/redis-developer/financial-vss.git temp_repo\n",
        "\n",
        "# This command moves the 'resources' directory from 'temp_repo' to your current directory.\n",
        "!mv temp_repo/resources .\n",
        "!mv temp_repo/requirements.txt .\n",
        "\n",
        "# This deletes the 'temp_repo' directory, cleaning up the unwanted files.\n",
        "!rm -rf temp_repo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9eieJowO1vmo"
      },
      "source": [
        "### Install Python Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B3v1wUzX1vmq",
        "outputId": "84a3feff-e7c1-41ba-9ab1-8c975074552e"
      },
      "outputs": [],
      "source": [
        "!pip install -r requirements.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import warnings\n",
        "\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Install Redis Stack\n",
        "\n",
        "Later in this tutorial, Redis will be used to store, index, and query vector\n",
        "embeddings created from PDF document chunks. **We need to make sure we have a Redis\n",
        "instance available.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Method 1: localized Redis Stack\n",
        "Use the shell script below to download, extract, and install [Redis Stack](https://redis.io/docs/getting-started/install-stack/) directly\n",
        "from the Redis package archive."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#%%sh\n",
        "#curl -fsSL https://packages.redis.io/gpg | sudo gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg\n",
        "#echo \"deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb $(lsb_release -cs) main\" | sudo tee /etc/apt/sources.list.d/redis.list\n",
        "#sudo apt-get update  > /dev/null 2>&1\n",
        "#sudo apt-get install redis-stack-server  > /dev/null 2>&1\n",
        "#redis-stack-server --daemonize yes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Method 2: Redis Cloud\n",
        "Instead of using the in-notebook, localized Redis Stack, you can quickly deploy a\n",
        "[FREE instance of Redis in the cloud](https://redis.com/try-free/). Or, if you have your\n",
        "own version of Redis Enterprise running, that works too!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Define the Redis Connection URL\n",
        "\n",
        "By default this notebook connects to the local instance of Redis Stack. **If you have your own Redis Enterprise instance** - replace REDIS_PASSWORD, REDIS_HOST and REDIS_PORT values with your own."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "# Replace values below with your own if using Redis Cloud instance\n",
        "#REDIS_HOST = os.getenv(\"REDIS_HOST\", \"localhost\")\n",
        "#REDIS_PORT = os.getenv(\"REDIS_PORT\", \"6379\")\n",
        "#REDIS_PASSWORD = os.getenv(\"REDIS_PASSWORD\", \"\")\n",
        "\n",
        "# Replace values below with your own if you are using a Redis Cloud instance.\n",
        "REDIS_HOST = \"redis-17834.c322.us-east-1-2.ec2.cloud.redislabs.com\"\n",
        "REDIS_PORT = 17834\n",
        "REDIS_PASSWORD = \"XzJR9ewHpp9hdMVNERloYCMFoavPkgH8\"\n",
        "REDIS_REQUIRE_PASS = True\n",
        "\n",
        "# If SSL is enabled on the endpoint, use rediss:// as the URL prefix\n",
        "REDIS_URL = f\"redis://:{REDIS_PASSWORD}@{REDIS_HOST}:{REDIS_PORT}\"\n",
        "os.environ[\"REDIS_URL\"] = REDIS_URL"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## RAG with LangChain"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7MaVqU8Y1vms"
      },
      "source": [
        "### Dataset Preparation (PDF Documents)\n",
        "\n",
        "To best demonstrate Redis as a vector database layer, we will load a single\n",
        "financial (10k filings) doc and preprocess it using some helpers from LangChain:\n",
        "\n",
        "- `UnstructuredFileLoader` is not the only document loader type that LangChain provides. Docs: https://python.langchain.com/docs/integrations/document_loaders/unstructured_file\n",
        "- `RecursiveCharacterTextSplitter` is what we use to create smaller chunks of text from the doc. Docs: https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/recursive_text_splitter"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I7VS1bPr1vmt",
        "outputId": "72299230-26c4-4d80-d61f-138616e2173b"
      },
      "outputs": [],
      "source": [
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain.document_loaders import UnstructuredFileLoader\n",
        "\n",
        "# Load list of pdfs from a folder\n",
        "data_path = \"resources/\"\n",
        "docs = [os.path.join(data_path, file) for file in os.listdir(data_path)]\n",
        "\n",
        "print(\"Listing available documents ...\")\n",
        "for d in docs:\n",
        "    print(d)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Search for a document containing the string \"nke\" (representing \"Nike\") \n",
        "# within a collection of documents named docs.\n",
        "doc = [doc for doc in docs if \"nke\" in doc][0]\n",
        "\n",
        "# Once the Nike document is found, sets up a text splitter and \n",
        "# file loader/extractor for processing the document.\n",
        "# \n",
        "# The RecursiveCharacterTextSplitter is configured to split the text \n",
        "# into chunks of 2500 characters with no overlap between chunks.\n",
        "text_splitter = RecursiveCharacterTextSplitter(\n",
        "    chunk_size=2500, chunk_overlap=0\n",
        ")\n",
        "\n",
        "# The UnstructuredFileLoader is configured to load the Nike document in a single mode \n",
        "# (meaning it's loading one document at a time) using a fast strategy.\n",
        "loader = UnstructuredFileLoader(\n",
        "    doc, mode=\"single\", strategy=\"fast\"\n",
        ")\n",
        "\n",
        "# extracts the content from the Nike document, load it, and split it \n",
        "# into chunks using the previously configured text splitter.\n",
        "chunks = loader.load_and_split(text_splitter)\n",
        "\n",
        "print(\"Done preprocessing. Created\", len(chunks), \"chunks of the original pdf\", doc)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "96BzcyW31vmw"
      },
      "source": [
        "### Initialize Embeddings Engine\n",
        "Here we will use LangChain's built in embedding engine so that it will work seemlessly with the LangChain VectorStore classes."
      ]
    },
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      "outputs": [],
      "source": [
        "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
        "\n",
        "embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qrj-jeGmBRTL"
      },
      "source": [
        "## Vector Search with Redis & LangChain\n",
        "### Create Redis vector store instance\n",
        "\n",
        "We also need to create a schema for the vector index so we can take advantage of the metadata along with the vectors.\n",
        "\n",
        "**Important Note**: LangChain does not support JSON data types yet. Only supports HASH for now. This update should be coming soon.\n",
        "\n",
        "**Important Note**: The below code segment throws a warning `index_schema does not match generated metadata schema.`, notifying users when they are overriding the default behavior. You may ignore it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yY69FViAjNv1",
        "outputId": "ab7b212b-3c55-44b1-cf72-6eb926cf302f"
      },
      "outputs": [],
      "source": [
        "from langchain.vectorstores.redis import Redis\n",
        "\n",
        "# set the index name for this example\n",
        "index_name = \"langchain\"\n",
        "\n",
        "# with langchain we can manually modify the default vector schema configuration\n",
        "vector_schema = {\n",
        "    \"name\": \"chunk_vector\",        # name of the vector field in langchain\n",
        "    \"algorithm\": \"HNSW\",           # could use HNSW instead\n",
        "    \"dims\": 384,                   # set based on the HF model embedding dimension\n",
        "    \"distance_metric\": \"COSINE\",   # could use EUCLIDEAN or IP\n",
        "    \"datatype\": \"FLOAT32\",\n",
        "}\n",
        "\n",
        "# here we can define the entire schema spec for our index in LangChain\n",
        "index_schema = {\n",
        "    \"vector\": [vector_schema],\n",
        "    \"text\": [{\"name\": \"content\"}],\n",
        "    \"content_vector_key\": \"chunk_vector\"    # name of the vector field in langchain\n",
        "}\n",
        "\n",
        "# construct the vector store class from texts and metadata\n",
        "rds = Redis.from_documents(\n",
        "    documents=chunks,\n",
        "    embedding=embeddings,\n",
        "    index_name=index_name,\n",
        "    redis_url=REDIS_URL,\n",
        "    index_schema=index_schema,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_Z3KpL8q1vmw"
      },
      "outputs": [],
      "source": [
        "# If you wish to connect to an existing Redis vector store instance\n",
        "rds = Redis.from_existing_index(\n",
        "    embedding=embeddings,\n",
        "    index_name=index_name,\n",
        "    schema=index_schema,\n",
        "    redis_url=REDIS_URL,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KO0QVNtE1vmw",
        "outputId": "849451ed-c908-4e9a-ae49-bf734d33d036"
      },
      "outputs": [],
      "source": [
        "# checkout out the schema we created\n",
        "rds.schema"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gntcxF1P1vmx",
        "outputId": "3bef4861-8778-4ab7-fb61-710aba655f2c"
      },
      "outputs": [],
      "source": [
        "# access underlying redis client to see how many docs have been stores\n",
        "rds.client.dbsize()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vaXF7Gj-1vmy",
        "outputId": "082cc73c-5fe3-465e-aa50-52170ce14f50"
      },
      "outputs": [],
      "source": [
        "# do NOT run this command in production\n",
        "keys = rds.client.keys()\n",
        "\n",
        "rds.client.hgetall(keys[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "61xV5qyp1vmy"
      },
      "source": [
        "### Query the database\n",
        "Now we can use the LangChain vector store class to perform similarity search operations on Redis\n",
        "\n",
        "[Hybrid Filtering](https://python.langchain.com/docs/integrations/vectorstores/redis/#hybrid-filtering)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Gv6SxKOB1vmy"
      },
      "outputs": [],
      "source": [
        "from langchain.vectorstores.redis import RedisText"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q3pH3MPD1vmy",
        "outputId": "2754347f-be31-4a38-d356-31ecc235ffe8"
      },
      "outputs": [],
      "source": [
        "# basic \"top 4\" vector search on a given query\n",
        "\n",
        "rds.similarity_search_with_score(query=\"Profit margins\", k=4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "od6Wqmya1vmy",
        "outputId": "5dbac581-4a07-45b0-d630-995342e91dc7"
      },
      "outputs": [],
      "source": [
        "# vector search with metadata filtering\n",
        "\n",
        "f = RedisText(\"content\") % \"profit\"\n",
        "rds.similarity_search_with_score(query=\"Profit margins\", k=4, filter=f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DT9sPHw51vmy",
        "outputId": "7d2b8969-91b8-4ff7-81ba-9d0cb6280edc"
      },
      "outputs": [],
      "source": [
        "# vector search with combinations of metadata filtering\n",
        "\n",
        "f = (RedisText(\"content\") % \"profit\") | (RedisText(\"content\") % \"revenue\")\n",
        "rds.similarity_search_with_score(query=\"Nike company revenue\", k=4, filter=f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kIoGJOop1vmz",
        "outputId": "9abbf5fd-77de-4094-a2e8-fba2fc5a3a05"
      },
      "outputs": [],
      "source": [
        "# filter results to a certain distance threshold\n",
        "rds.similarity_search_with_score(query=\"Nike company revenue\", k=4, distance_threshold=0.14)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XdzQa112Bf2b"
      },
      "source": [
        "## RAG with Redis, AWS Bedrock and Langchain\n",
        "LangChain makes it easy to now take this vector store and build retireval augmented generation (RAG) applications over your data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "CU2aFQPW1vmz"
      },
      "source": [
        "### Initialize Bedrock"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import boto3\n",
        "import json \n",
        "\n",
        "region = 'us-east-1'\n",
        "\n",
        "#Create the connection to Bedrock\n",
        "bedrock = boto3.client(\n",
        "    service_name='bedrock',\n",
        "    region_name=region,\n",
        ")\n",
        "\n",
        "bedrock_runtime = boto3.client(\n",
        "    service_name='bedrock-runtime',\n",
        "    region_name=region,\n",
        ")\n",
        "\n",
        "# Let's see all available Amazon Models\n",
        "available_models = bedrock.list_foundation_models()\n",
        "\n",
        "# Print field names\n",
        "print(\"Model ID, Model Name, Provider Name, Input Modalities, Output Modalities\")\n",
        "\n",
        "# Print a line of dashes after the header row\n",
        "print(\"-\" * 80)\n",
        "\n",
        "count = 1\n",
        "\n",
        "# Print specific fields for each model\n",
        "for model in available_models['modelSummaries']:\n",
        "    if 'amazon' in model['modelId']:\n",
        "        model_id = model['modelId']\n",
        "        model_name = model['modelName']\n",
        "        provider_name = model['providerName']\n",
        "        input_modalities = ', '.join(model['inputModalities'])\n",
        "        output_modalities = ', '.join(model['outputModalities'])\n",
        "    \n",
        "        print(f\"{count}. {model_id}, {model_name}, {provider_name}, {input_modalities}, {output_modalities}\")\n",
        "        \n",
        "         # Increment count\n",
        "        count += 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from langchain.llms import Bedrock\n",
        "from langchain.embeddings import BedrockEmbeddings\n",
        "\n",
        "# fumbles on Is Nike an ethical company?\n",
        "#model = 'amazon.titan-tg1-large'\n",
        "\n",
        "model = 'amazon.titan-text-lite-v1'\n",
        "#model = 'amazon.titan-text-express-v1:0:8k'\n",
        "#model = 'amazon.titan-embed-text-v1'\n",
        "\n",
        "embeddings = BedrockEmbeddings(client=bedrock_runtime)\n",
        "llm = Bedrock(model_id=model, client=bedrock_runtime)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GSpH-cur1vmz",
        "outputId": "b00de5c7-1684-4e8e-c146-6d402910cdc5"
      },
      "outputs": [],
      "source": [
        "# You need to supply an OpenAI API key (starts with `sk-...`) when prompted. \n",
        "# If the key is in your env -- great, otherwise enter it when prompted below. \n",
        "# You can find your API key at https://platform.openai.com/account/api-keys\n",
        "\n",
        "#import getpass\n",
        "#from langchain.llms import OpenAI\n",
        "#\n",
        "#llm = OpenAI(openai_api_key=os.getenv(\"OPENAI_API_KEY\") or getpass.getpass(prompt=\"OpenAI API Key:\"))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-SQXQB-c1vmz"
      },
      "source": [
        "### Setup prompt\n",
        "PromptTemplate defines the exect text of the response that would be fed to the LLM. This step is optional, but the defaults usually work well for OpenAI and might fall short for other models."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yKKn2KKp3TQ4"
      },
      "outputs": [],
      "source": [
        "def get_prompt():\n",
        "    \"\"\"Create the QA chain.\"\"\"\n",
        "    from langchain.prompts import PromptTemplate\n",
        "\n",
        "    # Define our prompt\n",
        "    prompt_template = \"\"\"Use the following pieces of context from financial 10k filings data to answer the user question at the end. If you don't know the answer, say that you don't know, don't try to make up an answer.\n",
        "\n",
        "    This should be in the following format:\n",
        "\n",
        "    Question: [question here]\n",
        "    Answer: [answer here]\n",
        "\n",
        "    Begin!\n",
        "\n",
        "    Context:\n",
        "    ---------\n",
        "    {context}\n",
        "    ---------\n",
        "    Question: {question}\n",
        "    Answer:\"\"\"\n",
        "\n",
        "    prompt = PromptTemplate(\n",
        "        template=prompt_template,\n",
        "        input_variables=[\"context\", \"question\"]\n",
        "    )\n",
        "    return prompt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xgEXBujxG1dO"
      },
      "source": [
        "### Putting it all together\n",
        "\n",
        "This is where the Langchain brings all the components together in a form of a simple RAG application with the financial PDF document."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RKNSP0zqZq98"
      },
      "outputs": [],
      "source": [
        "from langchain.chains import RetrievalQA\n",
        "\n",
        "qa = RetrievalQA.from_chain_type(\n",
        "    llm=llm,\n",
        "    chain_type=\"stuff\",\n",
        "    retriever=rds.as_retriever(search_type=\"similarity_distance_threshold\",search_kwargs={\"distance_threshold\":0.5}),\n",
        "    return_source_documents=True,\n",
        "    chain_type_kwargs={\"prompt\": get_prompt()},\n",
        "    #verbose=True\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SURTtVbYBFGc"
      },
      "source": [
        "### Finally - let's ask questions!\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "0JkswfOHZu9h",
        "outputId": "813fffe6-3d8b-4df7-a035-c54e421c0856"
      },
      "outputs": [],
      "source": [
        "query = \"What was Nike's revenue last year compared to this year??\"\n",
        "res=qa(query)\n",
        "res['result']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "ZvioUduS1vm0",
        "outputId": "a8ba0e5d-c06b-4848-d066-c26884b3caee"
      },
      "outputs": [],
      "source": [
        "query = \"How many products does Nike offer? What is the industry that Nike is part of?\"\n",
        "res=qa(query)\n",
        "res['result']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Q9mE5z-61vm1",
        "outputId": "e3860d3e-e68b-489b-9897-c7118377c7bb"
      },
      "outputs": [],
      "source": [
        "query = \"Is Nike an ethical company?\"\n",
        "res=qa(query)\n",
        "res['result']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "kjTO0RtH1vm1",
        "outputId": "f415c57b-1612-450a-db33-6b9616bff84b"
      },
      "outputs": [],
      "source": [
        "query = \"How many employees work at Nike???\"\n",
        "res=qa(query)\n",
        "res['result']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5itk4UPF1vm1"
      },
      "source": [
        "## Cleanup\n",
        "\n",
        "Cleanup the index and data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DtZi-mQ61vm-"
      },
      "outputs": [],
      "source": [
        "#rds.drop_index(index_name=index_name, redis_url=REDIS_URL, delete_documents=True)"
      ]
    }
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