langchain/docs/extras/modules/data_connection/vectorstores/integrations/cassandra.ipynb

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    "# Cassandra\n",
    "\n",
    ">[Apache Cassandra®](https://cassandra.apache.org) is a NoSQL, row-oriented, highly scalable and highly available database.\n",
    "\n",
    "Newest Cassandra releases natively [support](https://cwiki.apache.org/confluence/display/CASSANDRA/CEP-30%3A+Approximate+Nearest+Neighbor(ANN)+Vector+Search+via+Storage-Attached+Indexes) Vector Similarity Search.\n",
    "\n",
    "To run this notebook you need either a running Cassandra cluster equipped with Vector Search capabilities (in pre-release at the time of writing) or a DataStax Astra DB instance running in the cloud (you can get one for free at [datastax.com](https://astra.datastax.com)). Check [cassio.org](https://cassio.org/start_here/) for more information."
   ]
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   "source": [
    "!pip install \"cassio>=0.0.5\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7e46bb0",
   "metadata": {},
   "source": [
    "### Please provide database connection parameters and secrets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36128a32",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import getpass\n",
    "\n",
    "database_mode = (input('\\n(L)ocal Cassandra or (A)stra DB? ')).upper()\n",
    "\n",
    "keyspace_name = input('\\nKeyspace name? ')\n",
    "\n",
    "if database_mode == 'A':\n",
    "    ASTRA_DB_APPLICATION_TOKEN = getpass.getpass('\\nAstra DB Token (\"AstraCS:...\") ')\n",
    "    #\n",
    "    ASTRA_DB_SECURE_BUNDLE_PATH = input('Full path to your Secure Connect Bundle? ')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f22aac2",
   "metadata": {},
   "source": [
    "#### depending on whether local or cloud-based Astra DB, create the corresponding database connection \"Session\" object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "677f8576",
   "metadata": {},
   "outputs": [],
   "source": [
    "from cassandra.cluster import Cluster\n",
    "from cassandra.auth import PlainTextAuthProvider\n",
    "\n",
    "if database_mode == 'L':\n",
    "    cluster = Cluster()\n",
    "    session = cluster.connect()\n",
    "elif database_mode == 'A':\n",
    "    ASTRA_DB_CLIENT_ID = \"token\"\n",
    "    cluster = Cluster(\n",
    "        cloud={\n",
    "            \"secure_connect_bundle\": ASTRA_DB_SECURE_BUNDLE_PATH,\n",
    "        },\n",
    "        auth_provider=PlainTextAuthProvider(\n",
    "            ASTRA_DB_CLIENT_ID,\n",
    "            ASTRA_DB_APPLICATION_TOKEN,\n",
    "        ),\n",
    "    )\n",
    "    session = cluster.connect()\n",
    "else:\n",
    "    raise NotImplementedError"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "320af802-9271-46ee-948f-d2453933d44b",
   "metadata": {},
   "source": [
    "### Please provide OpenAI access key\n",
    "\n",
    "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffea66e4-bc23-46a9-9580-b348dfe7b7a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e98a139b",
   "metadata": {},
   "source": [
    "### Creation and usage of the Vector Store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aac9563e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
    "from langchain.text_splitter import CharacterTextSplitter\n",
    "from langchain.vectorstores import Cassandra\n",
    "from langchain.document_loaders import TextLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3c3999a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.document_loaders import TextLoader\n",
    "loader = TextLoader('../../../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)\n",
    "\n",
    "embedding_function = OpenAIEmbeddings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e104aee",
   "metadata": {},
   "outputs": [],
   "source": [
    "table_name = 'my_vector_db_table'\n",
    "\n",
    "docsearch = Cassandra.from_documents(\n",
    "    documents=docs,\n",
    "    embedding=embedding_function,\n",
    "    session=session,\n",
    "    keyspace=keyspace_name,\n",
    "    table_name=table_name,\n",
    ")\n",
    "\n",
    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
    "docs = docsearch.similarity_search(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f509ee02",
   "metadata": {},
   "outputs": [],
   "source": [
    "## if you already have an index, you can load it and use it like this:\n",
    "\n",
    "# docsearch_preexisting = Cassandra(\n",
    "#     embedding=embedding_function,\n",
    "#     session=session,\n",
    "#     keyspace=keyspace_name,\n",
    "#     table_name=table_name,\n",
    "# )\n",
    "\n",
    "# docsearch_preexisting.similarity_search(query, k=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c608226",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(docs[0].page_content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d46d1452",
   "metadata": {},
   "source": [
    "### Maximal Marginal Relevance Searches\n",
    "\n",
    "In addition to using similarity search in the retriever object, you can also use `mmr` as retriever.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a359ed74",
   "metadata": {},
   "outputs": [],
   "source": [
    "retriever = docsearch.as_retriever(search_type=\"mmr\")\n",
    "matched_docs = retriever.get_relevant_documents(query)\n",
    "for i, d in enumerate(matched_docs):\n",
    "    print(f\"\\n## Document {i}\\n\")\n",
    "    print(d.page_content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c477287",
   "metadata": {},
   "source": [
    "Or use `max_marginal_relevance_search` directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ca82740",
   "metadata": {},
   "outputs": [],
   "source": [
    "found_docs = docsearch.max_marginal_relevance_search(query, k=2, fetch_k=10)\n",
    "for i, doc in enumerate(found_docs):\n",
    "    print(f\"{i + 1}.\", doc.page_content, \"\\n\")"
   ]
  }
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