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   "source": [
    "# Using Chroma for Embeddings Search\n",
    "\n",
    "This notebook takes you through a simple flow to download some data, embed it, and then index and search it using a selection of vector databases. This is a common requirement for customers who want to store and search our embeddings with their own data in a secure environment to support production use cases such as chatbots, topic modelling and more.\n",
    "\n",
    "### What is a Vector Database\n",
    "\n",
    "A vector database is a database made to store, manage and search embedding vectors. The use of embeddings to encode unstructured data (text, audio, video and more) as vectors for consumption by machine-learning models has exploded in recent years, due to the increasing effectiveness of AI in solving use cases involving natural language, image recognition and other unstructured forms of data. Vector databases have emerged as an effective solution for enterprises to deliver and scale these use cases.\n",
    "\n",
    "### Why use a Vector Database\n",
    "\n",
    "Vector databases enable enterprises to take many of the embeddings use cases we've shared in this repo (question and answering, chatbot and recommendation services, for example), and make use of them in a secure, scalable environment. Many of our customers make embeddings solve their problems at small scale but performance and security hold them back from going into production - we see vector databases as a key component in solving that, and in this guide we'll walk through the basics of embedding text data, storing it in a vector database and using it for semantic search.\n",
    "\n",
    "\n",
    "### Demo Flow\n",
    "The demo flow is:\n",
    "- **Setup**: Import packages and set any required variables\n",
    "- **Load data**: Load a dataset and embed it using OpenAI embeddings\n",
    "- **Chroma**:\n",
    "    - *Setup*: Here we'll set up the Python client for Chroma. For more details go [here](https://docs.trychroma.com/usage-guide)\n",
    "    - *Index Data*: We'll create collections with vectors for __titles__ and __content__\n",
    "    - *Search Data*: We'll run a few searches to confirm it works\n",
    "\n",
    "Once you've run through this notebook you should have a basic understanding of how to setup and use vector databases, and can move on to more complex use cases making use of our embeddings."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2b59250",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "Import the required libraries and set the embedding model that we'd like to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8d8810f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Installing collected packages: tokenizers, pytz, pypika, mpmath, monotonic, flatbuffers, websockets, uvloop, tzdata, sympy, sniffio, pyyaml, python-dotenv, pydantic, pulsar-client, protobuf, overrides, numpy, importlib-resources, humanfriendly, httptools, h11, exceptiongroup, click, backoff, uvicorn, posthog, pandas, coloredlogs, chroma-hnswlib, anyio, watchfiles, starlette, onnxruntime, fastapi, chromadb\n",
      "Successfully installed anyio-3.7.1 backoff-2.2.1 chroma-hnswlib-0.7.1 chromadb-0.4.2 click-8.1.6 coloredlogs-15.0.1 exceptiongroup-1.1.2 fastapi-0.99.1 flatbuffers-23.5.26 h11-0.14.0 httptools-0.6.0 humanfriendly-10.0 importlib-resources-6.0.0 monotonic-1.6 mpmath-1.3.0 numpy-1.25.1 onnxruntime-1.15.1 overrides-7.3.1 pandas-2.0.3 posthog-3.0.1 protobuf-4.23.4 pulsar-client-3.2.0 pydantic-1.10.11 pypika-0.48.9 python-dotenv-1.0.0 pytz-2023.3 pyyaml-6.0.1 sniffio-1.3.0 starlette-0.27.0 sympy-1.12 tokenizers-0.13.3 tzdata-2023.3 uvicorn-0.23.1 uvloop-0.17.0 watchfiles-0.19.0 websockets-11.0.3\n",
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      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "# Make sure the OpenAI library is installed\n",
    "%pip install openai\n",
    "\n",
    "# We'll need to install the Chroma client\n",
    "%pip install chromadb\n",
    "\n",
    "# Install wget to pull zip file\n",
    "%pip install wget\n",
    "\n",
    "# Install numpy for data manipulation\n",
    "%pip install numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5be94df6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import openai\n",
    "import pandas as pd\n",
    "import os\n",
    "import wget\n",
    "from ast import literal_eval\n",
    "\n",
    "# Chroma's client library for Python\n",
    "import chromadb\n",
    "\n",
    "# I've set this to our new embeddings model, this can be changed to the embedding model of your choice\n",
    "EMBEDDING_MODEL = \"text-embedding-ada-002\"\n",
    "\n",
    "# Ignore unclosed SSL socket warnings - optional in case you get these errors\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(action=\"ignore\", message=\"unclosed\", category=ResourceWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5d9d2e1",
   "metadata": {},
   "source": [
    "## Load data\n",
    "\n",
    "In this section we'll load embedded data that we've prepared previous to this session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5dff8b55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'vector_database_wikipedia_articles_embedded.zip'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings_url = 'https://cdn.openai.com/API/examples/data/vector_database_wikipedia_articles_embedded.zip'\n",
    "\n",
    "# The file is ~700 MB so this will take some time\n",
    "wget.download(embeddings_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "21097972",
   "metadata": {},
   "outputs": [],
   "source": [
    "import zipfile\n",
    "with zipfile.ZipFile(\"vector_database_wikipedia_articles_embedded.zip\",\"r\") as zip_ref:\n",
    "    zip_ref.extractall(\"../data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "70bbd8ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "article_df = pd.read_csv('../data/vector_database_wikipedia_articles_embedded.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1721e45d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>url</th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
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       "      <th>content_vector</th>\n",
       "      <th>vector_id</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>https://simple.wikipedia.org/wiki/April</td>\n",
       "      <td>April</td>\n",
       "      <td>April is the fourth month of the year in the J...</td>\n",
       "      <td>[0.001009464613161981, -0.020700545981526375, ...</td>\n",
       "      <td>[-0.011253940872848034, -0.013491976074874401,...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>https://simple.wikipedia.org/wiki/August</td>\n",
       "      <td>August</td>\n",
       "      <td>August (Aug.) is the eighth month of the year ...</td>\n",
       "      <td>[0.0009286514250561595, 0.000820168002974242, ...</td>\n",
       "      <td>[0.0003609954728744924, 0.007262262050062418, ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>https://simple.wikipedia.org/wiki/Art</td>\n",
       "      <td>Art</td>\n",
       "      <td>Art is a creative activity that expresses imag...</td>\n",
       "      <td>[0.003393713850528002, 0.0061537534929811954, ...</td>\n",
       "      <td>[-0.004959689453244209, 0.015772193670272827, ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>https://simple.wikipedia.org/wiki/A</td>\n",
       "      <td>A</td>\n",
       "      <td>A or a is the first letter of the English alph...</td>\n",
       "      <td>[0.0153952119871974, -0.013759135268628597, 0....</td>\n",
       "      <td>[0.024894846603274345, -0.022186409682035446, ...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>https://simple.wikipedia.org/wiki/Air</td>\n",
       "      <td>Air</td>\n",
       "      <td>Air refers to the Earth's atmosphere. Air is a...</td>\n",
       "      <td>[0.02224554680287838, -0.02044147066771984, -0...</td>\n",
       "      <td>[0.021524671465158463, 0.018522677943110466, -...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                                       url   title  \\\n",
       "0   1   https://simple.wikipedia.org/wiki/April   April   \n",
       "1   2  https://simple.wikipedia.org/wiki/August  August   \n",
       "2   6     https://simple.wikipedia.org/wiki/Art     Art   \n",
       "3   8       https://simple.wikipedia.org/wiki/A       A   \n",
       "4   9     https://simple.wikipedia.org/wiki/Air     Air   \n",
       "\n",
       "                                                text  \\\n",
       "0  April is the fourth month of the year in the J...   \n",
       "1  August (Aug.) is the eighth month of the year ...   \n",
       "2  Art is a creative activity that expresses imag...   \n",
       "3  A or a is the first letter of the English alph...   \n",
       "4  Air refers to the Earth's atmosphere. Air is a...   \n",
       "\n",
       "                                        title_vector  \\\n",
       "0  [0.001009464613161981, -0.020700545981526375, ...   \n",
       "1  [0.0009286514250561595, 0.000820168002974242, ...   \n",
       "2  [0.003393713850528002, 0.0061537534929811954, ...   \n",
       "3  [0.0153952119871974, -0.013759135268628597, 0....   \n",
       "4  [0.02224554680287838, -0.02044147066771984, -0...   \n",
       "\n",
       "                                      content_vector  vector_id  \n",
       "0  [-0.011253940872848034, -0.013491976074874401,...          0  \n",
       "1  [0.0003609954728744924, 0.007262262050062418, ...          1  \n",
       "2  [-0.004959689453244209, 0.015772193670272827, ...          2  \n",
       "3  [0.024894846603274345, -0.022186409682035446, ...          3  \n",
       "4  [0.021524671465158463, 0.018522677943110466, -...          4  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "article_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "960b82af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read vectors from strings back into a list\n",
    "article_df['title_vector'] = article_df.title_vector.apply(literal_eval)\n",
    "article_df['content_vector'] = article_df.content_vector.apply(literal_eval)\n",
    "\n",
    "# Set vector_id to be a string\n",
    "article_df['vector_id'] = article_df['vector_id'].apply(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a334ab8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25000 entries, 0 to 24999\n",
      "Data columns (total 7 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   id              25000 non-null  int64 \n",
      " 1   url             25000 non-null  object\n",
      " 2   title           25000 non-null  object\n",
      " 3   text            25000 non-null  object\n",
      " 4   title_vector    25000 non-null  object\n",
      " 5   content_vector  25000 non-null  object\n",
      " 6   vector_id       25000 non-null  object\n",
      "dtypes: int64(1), object(6)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "article_df.info(show_counts=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81bf5349",
   "metadata": {},
   "source": [
    "# Chroma\n",
    "\n",
    "We'll index these embedded documents in a vector database and search them. The first option we'll look at is **Chroma**, an easy to use open-source self-hosted in-memory vector database, designed for working with embeddings together with LLMs. \n",
    "\n",
    "In this section, we will:\n",
    "- Instantiate the Chroma client\n",
    "- Create collections for each class of embedding \n",
    "- Query each collection "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37d1f693",
   "metadata": {},
   "source": [
    "### Instantiate the Chroma client\n",
    "\n",
    "Create the Chroma client. By default, Chroma is ephemeral and runs in memory. \n",
    "However, you can easily set up a persistent configuration which writes to disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "159d9646",
   "metadata": {},
   "outputs": [],
   "source": [
    "chroma_client = chromadb.EphemeralClient() # Equivalent to chromadb.Client(), ephemeral.\n",
    "# Uncomment for persistent client\n",
    "# chroma_client = chromadb.PersistentClient()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cd61943",
   "metadata": {},
   "source": [
    "### Create collections\n",
    "\n",
    "Chroma collections allow you to store and filter with arbitrary metadata, making it easy to query subsets of the embedded data. \n",
    "\n",
    "Chroma is already integrated with OpenAI's embedding functions. The best way to use them is on construction of a collection, as follows.\n",
    "Alternatively, you can 'bring your own embeddings'. More information can be found [here](https://docs.trychroma.com/embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ad2d1bce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OPENAI_API_KEY is ready\n"
     ]
    }
   ],
   "source": [
    "from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction\n",
    "\n",
    "# Test that your OpenAI API key is correctly set as an environment variable\n",
    "# Note. if you run this notebook locally, you will need to reload your terminal and the notebook for the env variables to be live.\n",
    "\n",
    "# Note. alternatively you can set a temporary env variable like this:\n",
    "# os.environ[\"OPENAI_API_KEY\"] = 'sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'\n",
    "\n",
    "if os.getenv(\"OPENAI_API_KEY\") is not None:\n",
    "    openai.api_key = os.getenv(\"OPENAI_API_KEY\")\n",
    "    print (\"OPENAI_API_KEY is ready\")\n",
    "else:\n",
    "    print (\"OPENAI_API_KEY environment variable not found\")\n",
    "\n",
    "\n",
    "embedding_function = OpenAIEmbeddingFunction(api_key=os.environ.get('OPENAI_API_KEY'), model_name=EMBEDDING_MODEL)\n",
    "\n",
    "wikipedia_content_collection = chroma_client.create_collection(name='wikipedia_content', embedding_function=embedding_function)\n",
    "wikipedia_title_collection = chroma_client.create_collection(name='wikipedia_titles', embedding_function=embedding_function)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02887b52",
   "metadata": {},
   "source": [
    "### Populate the collections\n",
    "\n",
    "Chroma collections allow you to populate, and filter on, whatever metadata you like. Chroma can also store the text alongside the vectors, and return everything in a single `query` call, when this is more convenient. \n",
    "\n",
    "For this use-case, we'll just store the embeddings and IDs, and use these to index the original dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "84885fec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the content vectors\n",
    "wikipedia_content_collection.add(\n",
    "    ids=article_df.vector_id.tolist(),\n",
    "    embeddings=article_df.content_vector.tolist(),\n",
    ")\n",
    "\n",
    "# Add the title vectors\n",
    "wikipedia_title_collection.add(\n",
    "    ids=article_df.vector_id.tolist(),\n",
    "    embeddings=article_df.title_vector.tolist(),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79122c6b",
   "metadata": {},
   "source": [
    "### Search the collections\n",
    "\n",
    "Chroma handles embedding queries for you if an embedding function is set, like in this example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "273b8b4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def query_collection(collection, query, max_results, dataframe):\n",
    "    results = collection.query(query_texts=query, n_results=max_results, include=['distances']) \n",
    "    df = pd.DataFrame({\n",
    "                'id':results['ids'][0], \n",
    "                'score':results['distances'][0],\n",
    "                'title': dataframe[dataframe.vector_id.isin(results['ids'][0])]['title'],\n",
    "                'content': dataframe[dataframe.vector_id.isin(results['ids'][0])]['text'],\n",
    "                })\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e84cf47f",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.279306</td>\n",
       "      <td>Byzantine art</td>\n",
       "      <td>Byzantine art is a form of Christian Greek art...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13215</th>\n",
       "      <td>11777</td>\n",
       "      <td>0.294415</td>\n",
       "      <td>Art film</td>\n",
       "      <td>Art films are a type of movie that is very dif...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15436</th>\n",
       "      <td>22108</td>\n",
       "      <td>0.305937</td>\n",
       "      <td>Renaissance art</td>\n",
       "      <td>Many of the most famous and best-loved works o...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id     score            title  \\\n",
       "2      23266  0.249646              Art   \n",
       "11777  15436  0.271688  Hellenistic art   \n",
       "12178  23265  0.279306    Byzantine art   \n",
       "13215  11777  0.294415         Art film   \n",
       "15436  22108  0.305937  Renaissance art   \n",
       "\n",
       "                                                 content  \n",
       "2      Art is a creative activity that expresses imag...  \n",
       "11777  The art of the Hellenistic time (from 400 B.C....  \n",
       "12178  Byzantine art is a form of Christian Greek art...  \n",
       "13215  Art films are a type of movie that is very dif...  \n",
       "15436  Many of the most famous and best-loved works o...  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title_query_result = query_collection(\n",
    "    collection=wikipedia_title_collection,\n",
    "    query=\"modern art in Europe\",\n",
    "    max_results=10,\n",
    "    dataframe=article_df\n",
    ")\n",
    "title_query_result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f4db910a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>score</th>\n",
       "      <th>title</th>\n",
       "      <th>content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2923</th>\n",
       "      <td>13135</td>\n",
       "      <td>0.261328</td>\n",
       "      <td>1651</td>\n",
       "      <td>\\n\\nEvents \\n January 1 – Charles II crowned K...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3694</th>\n",
       "      <td>13571</td>\n",
       "      <td>0.277058</td>\n",
       "      <td>Stirling</td>\n",
       "      <td>Stirling () is a city in the middle of Scotlan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6248</th>\n",
       "      <td>2923</td>\n",
       "      <td>0.294823</td>\n",
       "      <td>841</td>\n",
       "      <td>\\n\\nEvents \\n June 25: Battle of Fontenay – Lo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6297</th>\n",
       "      <td>13568</td>\n",
       "      <td>0.300756</td>\n",
       "      <td>1746</td>\n",
       "      <td>\\n\\nEvents \\n January 8 – Bonnie Prince Charli...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11702</th>\n",
       "      <td>11708</td>\n",
       "      <td>0.307572</td>\n",
       "      <td>William Wallace</td>\n",
       "      <td>William Wallace was a Scottish knight who foug...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id     score            title  \\\n",
       "2923   13135  0.261328             1651   \n",
       "3694   13571  0.277058         Stirling   \n",
       "6248    2923  0.294823              841   \n",
       "6297   13568  0.300756             1746   \n",
       "11702  11708  0.307572  William Wallace   \n",
       "\n",
       "                                                 content  \n",
       "2923   \\n\\nEvents \\n January 1 – Charles II crowned K...  \n",
       "3694   Stirling () is a city in the middle of Scotlan...  \n",
       "6248   \\n\\nEvents \\n June 25: Battle of Fontenay – Lo...  \n",
       "6297   \\n\\nEvents \\n January 8 – Bonnie Prince Charli...  \n",
       "11702  William Wallace was a Scottish knight who foug...  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content_query_result = query_collection(\n",
    "    collection=wikipedia_content_collection,\n",
    "    query=\"Famous battles in Scottish history\",\n",
    "    max_results=10,\n",
    "    dataframe=article_df\n",
    ")\n",
    "content_query_result.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a03e7645",
   "metadata": {},
   "source": [
    "Now that you've got a basic embeddings search running, you can [hop over to the Chroma docs](https://docs.trychroma.com/usage-guide#using-where-filters) to learn more about how to add filters to your query, update/delete data in your collections, and deploy Chroma."
   ]
  }
 ],
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