langchain/docs/docs/integrations/vectorstores/rockset.ipynb

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   "source": [
    "# Rockset\n",
    "\n",
    ">[Rockset](https://rockset.com/) is a real-time search and analytics database built for the cloud. Rockset uses a [Converged Index™](https://rockset.com/blog/converged-indexing-the-secret-sauce-behind-rocksets-fast-queries/) with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and  handles real-time ingestion for constantly updating, streaming data.\n",
    "\n",
    "This notebook demonstrates how to use `Rockset` as a vector store in LangChain. Before getting started, make sure you have access to a `Rockset` account and an API key available. [Start your free trial today.](https://rockset.com/create/)\n"
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    "## Setting Up Your Environment\n",
    "\n",
    "1. Leverage the `Rockset` console to create a [collection](https://rockset.com/docs/collections/) with the Write API as your source. In this walkthrough, we create a collection named `langchain_demo`. \n",
    "    \n",
    "    Configure the following [ingest transformation](https://rockset.com/docs/ingest-transformation/) to mark your embeddings field and take advantage of performance and storage optimizations:\n",
    "\n",
    "\n",
    "   (We used OpenAI `text-embedding-ada-002` for this examples, where #length_of_vector_embedding = 1536)"
   ]
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   "source": [
    "```\n",
    "SELECT _input.* EXCEPT(_meta), \n",
    "VECTOR_ENFORCE(_input.description_embedding, #length_of_vector_embedding, 'float') as description_embedding \n",
    "FROM _input\n",
    "```\n",
    "\n",
    "2. After creating your collection, use the console to retrieve an [API key](https://rockset.com/docs/iam/#users-api-keys-and-roles). For the purpose of this notebook, we assume you are using the `Oregon(us-west-2)` region.\n",
    "\n",
    "3. Install the [rockset-python-client](https://github.com/rockset/rockset-python-client) to enable LangChain to communicate directly with `Rockset`."
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00d16b83",
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   "outputs": [],
   "source": [
    "%pip install --upgrade --quiet  rockset"
   ]
  },
  {
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   "id": "e79550eb",
   "metadata": {},
   "source": [
    "## LangChain Tutorial\n",
    "\n",
    "Follow along in your own Python notebook to generate and store vector embeddings in Rockset.\n",
    "Start using Rockset to search for documents similar to your search queries.\n",
    "\n",
    "### 1. Define Key Variables"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "import os\n",
    "\n",
    "import rockset\n",
    "\n",
    "ROCKSET_API_KEY = os.environ.get(\n",
    "    \"ROCKSET_API_KEY\"\n",
    ")  # Verify ROCKSET_API_KEY environment variable\n",
    "ROCKSET_API_SERVER = rockset.Regions.usw2a1  # Verify Rockset region\n",
    "rockset_client = rockset.RocksetClient(ROCKSET_API_SERVER, ROCKSET_API_KEY)\n",
    "\n",
    "COLLECTION_NAME = \"langchain_demo\"\n",
    "TEXT_KEY = \"description\"\n",
    "EMBEDDING_KEY = \"description_embedding\""
   ]
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  {
   "cell_type": "markdown",
   "id": "07625be2",
   "metadata": {},
   "source": [
    "### 2. Prepare Documents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9740d8c4",
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   "outputs": [],
   "source": [
    "from langchain_community.document_loaders import TextLoader\n",
    "from langchain_community.vectorstores import Rockset\n",
    "from langchain_openai import OpenAIEmbeddings\n",
    "from langchain_text_splitters import CharacterTextSplitter\n",
    "\n",
    "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n",
    "documents = loader.load()\n",
    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
    "docs = text_splitter.split_documents(documents)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a068be18",
   "metadata": {},
   "source": [
    "### 3. Insert Documents"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "id": "85b6a6c5",
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   "outputs": [],
   "source": [
    "embeddings = OpenAIEmbeddings()  # Verify OPENAI_API_KEY environment variable\n",
    "\n",
    "docsearch = Rockset(\n",
    "    client=rockset_client,\n",
    "    embeddings=embeddings,\n",
    "    collection_name=COLLECTION_NAME,\n",
    "    text_key=TEXT_KEY,\n",
    "    embedding_key=EMBEDDING_KEY,\n",
    ")\n",
    "\n",
    "ids = docsearch.add_texts(\n",
    "    texts=[d.page_content for d in docs],\n",
    "    metadatas=[d.metadata for d in docs],\n",
    ")"
   ]
  },
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   "cell_type": "markdown",
   "id": "56eef48d",
   "metadata": {},
   "source": [
    "### 4. Search for Similar Documents"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "0bbf3df0",
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   "outputs": [],
   "source": [
    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
    "output = docsearch.similarity_search_with_relevance_scores(\n",
    "    query, 4, Rockset.DistanceFunction.COSINE_SIM\n",
    ")\n",
    "print(\"output length:\", len(output))\n",
    "for d, dist in output:\n",
    "    print(dist, d.metadata, d.page_content[:20] + \"...\")\n",
    "\n",
    "##\n",
    "# output length: 4\n",
    "# 0.764990692109871 {'source': '../../../state_of_the_union.txt'} Madam Speaker, Madam...\n",
    "# 0.7485416901622112 {'source': '../../../state_of_the_union.txt'} And I’m taking robus...\n",
    "# 0.7468678973398306 {'source': '../../../state_of_the_union.txt'} And so many families...\n",
    "# 0.7436231261419488 {'source': '../../../state_of_the_union.txt'} Groups of citizens b..."
   ]
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  {
   "cell_type": "markdown",
   "id": "7037a22f",
   "metadata": {},
   "source": [
    "### 5. Search for Similar Documents with Filtering"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "output = docsearch.similarity_search_with_relevance_scores(\n",
    "    query,\n",
    "    4,\n",
    "    Rockset.DistanceFunction.COSINE_SIM,\n",
    "    where_str=\"{} NOT LIKE '%citizens%'\".format(TEXT_KEY),\n",
    ")\n",
    "print(\"output length:\", len(output))\n",
    "for d, dist in output:\n",
    "    print(dist, d.metadata, d.page_content[:20] + \"...\")\n",
    "\n",
    "##\n",
    "# output length: 4\n",
    "# 0.7651359650263554 {'source': '../../../state_of_the_union.txt'} Madam Speaker, Madam...\n",
    "# 0.7486265516824893 {'source': '../../../state_of_the_union.txt'} And I’m taking robus...\n",
    "# 0.7469625542348115 {'source': '../../../state_of_the_union.txt'} And so many families...\n",
    "# 0.7344177777547739 {'source': '../../../state_of_the_union.txt'} We see the unity amo..."
   ]
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   "source": [
    "### 6. [Optional] Delete Inserted Documents\n",
    "\n",
    "You must have the unique ID associated with each document to delete them from your collection.\n",
    "Define IDs when inserting documents with `Rockset.add_texts()`. Rockset will otherwise generate a unique ID for each document. Regardless, `Rockset.add_texts()` returns the IDs of inserted documents.\n",
    "\n",
    "To delete these docs, simply use the `Rockset.delete_texts()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f755924",
   "metadata": {},
   "outputs": [],
   "source": [
    "docsearch.delete_texts(ids)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d468f431",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "In this tutorial, we successfully created a `Rockset` collection, `inserted` documents with  OpenAI embeddings, and searched for similar documents with and without metadata filters.\n",
    "\n",
    "Keep an eye on https://rockset.com/ for future updates in this space."
   ]
  }
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