langchain/docs/docs/integrations/retrievers/self_query/astradb.ipynb

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    "# Astra DB (Cassandra)\n",
    "\n",
    ">[DataStax Astra DB](https://docs.datastax.com/en/astra/home/astra.html) is a serverless vector-capable database built on `Cassandra` and made conveniently available through an easy-to-use JSON API.\n",
    "\n",
    "In the walkthrough, we'll demo the `SelfQueryRetriever` with an `Astra DB` vector store."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating an Astra DB vector store\n",
    "First we'll want to create an Astra DB VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n",
    "\n",
    "NOTE: The self-query retriever requires you to have `lark` installed (`pip install lark`). We also need the `astrapy` package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install --upgrade --quiet lark astrapy langchain-openai"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from getpass import getpass\n",
    "\n",
    "from langchain_openai.embeddings import OpenAIEmbeddings\n",
    "\n",
    "os.environ[\"OPENAI_API_KEY\"] = getpass(\"OpenAI API Key:\")\n",
    "\n",
    "embeddings = OpenAIEmbeddings()"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Create the Astra DB VectorStore:\n",
    "\n",
    "- the API Endpoint looks like `https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com`\n",
    "- the Token looks like `AstraCS:6gBhNmsk135....`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ASTRA_DB_API_ENDPOINT = input(\"ASTRA_DB_API_ENDPOINT = \")\n",
    "ASTRA_DB_APPLICATION_TOKEN = getpass(\"ASTRA_DB_APPLICATION_TOKEN = \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.vectorstores import AstraDB\n",
    "from langchain_core.documents import Document\n",
    "\n",
    "docs = [\n",
    "    Document(\n",
    "        page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\",\n",
    "        metadata={\"year\": 1993, \"rating\": 7.7, \"genre\": \"science fiction\"},\n",
    "    ),\n",
    "    Document(\n",
    "        page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\",\n",
    "        metadata={\"year\": 2010, \"director\": \"Christopher Nolan\", \"rating\": 8.2},\n",
    "    ),\n",
    "    Document(\n",
    "        page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\",\n",
    "        metadata={\"year\": 2006, \"director\": \"Satoshi Kon\", \"rating\": 8.6},\n",
    "    ),\n",
    "    Document(\n",
    "        page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\",\n",
    "        metadata={\"year\": 2019, \"director\": \"Greta Gerwig\", \"rating\": 8.3},\n",
    "    ),\n",
    "    Document(\n",
    "        page_content=\"Toys come alive and have a blast doing so\",\n",
    "        metadata={\"year\": 1995, \"genre\": \"animated\"},\n",
    "    ),\n",
    "    Document(\n",
    "        page_content=\"Three men walk into the Zone, three men walk out of the Zone\",\n",
    "        metadata={\n",
    "            \"year\": 1979,\n",
    "            \"director\": \"Andrei Tarkovsky\",\n",
    "            \"genre\": \"science fiction\",\n",
    "            \"rating\": 9.9,\n",
    "        },\n",
    "    ),\n",
    "]\n",
    "\n",
    "vectorstore = AstraDB.from_documents(\n",
    "    docs,\n",
    "    embeddings,\n",
    "    collection_name=\"astra_self_query_demo\",\n",
    "    api_endpoint=ASTRA_DB_API_ENDPOINT,\n",
    "    token=ASTRA_DB_APPLICATION_TOKEN,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating our self-querying retriever\n",
    "Now we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": [
    "from langchain.chains.query_constructor.base import AttributeInfo\n",
    "from langchain.llms import OpenAI\n",
    "from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
    "\n",
    "metadata_field_info = [\n",
    "    AttributeInfo(\n",
    "        name=\"genre\",\n",
    "        description=\"The genre of the movie\",\n",
    "        type=\"string or list[string]\",\n",
    "    ),\n",
    "    AttributeInfo(\n",
    "        name=\"year\",\n",
    "        description=\"The year the movie was released\",\n",
    "        type=\"integer\",\n",
    "    ),\n",
    "    AttributeInfo(\n",
    "        name=\"director\",\n",
    "        description=\"The name of the movie director\",\n",
    "        type=\"string\",\n",
    "    ),\n",
    "    AttributeInfo(\n",
    "        name=\"rating\", description=\"A 1-10 rating for the movie\", type=\"float\"\n",
    "    ),\n",
    "]\n",
    "document_content_description = \"Brief summary of a movie\"\n",
    "llm = OpenAI(temperature=0)\n",
    "\n",
    "retriever = SelfQueryRetriever.from_llm(\n",
    "    llm, vectorstore, document_content_description, metadata_field_info, verbose=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing it out\n",
    "And now we can try actually using our retriever!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This example only specifies a relevant query\n",
    "retriever.invoke(\"What are some movies about dinosaurs?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This example specifies a filter\n",
    "retriever.invoke(\"I want to watch a movie rated higher than 8.5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This example only specifies a query and a filter\n",
    "retriever.invoke(\"Has Greta Gerwig directed any movies about women\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This example specifies a composite filter\n",
    "retriever.invoke(\"What's a highly rated (above 8.5), science fiction movie ?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This example specifies a query and composite filter\n",
    "retriever.invoke(\n",
    "    \"What's a movie about toys after 1990 but before 2005, and is animated\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter k\n",
    "\n",
    "We can also use the self query retriever to specify `k`: the number of documents to fetch.\n",
    "\n",
    "We can do this by passing `enable_limit=True` to the constructor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "retriever = SelfQueryRetriever.from_llm(\n",
    "    llm,\n",
    "    vectorstore,\n",
    "    document_content_description,\n",
    "    metadata_field_info,\n",
    "    verbose=True,\n",
    "    enable_limit=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This example only specifies a relevant query\n",
    "retriever.invoke(\"What are two movies about dinosaurs?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "## Cleanup\n",
    "\n",
    "If you want to completely delete the collection from your Astra DB instance, run this.\n",
    "\n",
    "_(You will lose the data you stored in it.)_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "vectorstore.delete_collection()"
   ]
  }
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