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

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  {
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   "source": [
    "# Elasticsearch\n",
    "\n",
    "> [Elasticsearch](https://www.elastic.co/elasticsearch/) is a distributed, RESTful search and analytics engine.\n",
    "> It provides a distributed, multi-tenant-capable full-text search engine with an HTTP web interface and schema-free\n",
    "> JSON documents.\n",
    "\n",
    "In this notebook, we'll demo the `SelfQueryRetriever` with an `Elasticsearch` vector store."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68e75fb9",
   "metadata": {},
   "source": [
    "## Creating an Elasticsearch vector store\n",
    "\n",
    "First, we'll want to create an `Elasticsearch` vector store 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 `elasticsearch` package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "63a8af5b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 23.3 is available.\n",
      "You should consider upgrading via the '/Users/joe/projects/elastic/langchain/libs/langchain/.venv/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "%pip install --upgrade --quiet  U lark langchain langchain-elasticsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cb4a5787",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import getpass\n",
    "import os\n",
    "\n",
    "from langchain_core.documents import Document\n",
    "from langchain_elasticsearch import ElasticsearchStore\n",
    "from langchain_openai import OpenAIEmbeddings\n",
    "\n",
    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
    "\n",
    "embeddings = OpenAIEmbeddings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bcbe04d9",
   "metadata": {
    "tags": []
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   "outputs": [],
   "source": [
    "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",
    "vectorstore = ElasticsearchStore.from_documents(\n",
    "    docs,\n",
    "    embeddings,\n",
    "    index_name=\"elasticsearch-self-query-demo\",\n",
    "    es_url=\"http://localhost:9200\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ecaab6d",
   "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": 3,
   "id": "86e34dbf",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.chains.query_constructor.base import AttributeInfo\n",
    "from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
    "from langchain_openai import OpenAI\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",
    "retriever = SelfQueryRetriever.from_llm(\n",
    "    llm, vectorstore, document_content_description, metadata_field_info, verbose=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea9df8d4",
   "metadata": {},
   "source": [
    "## Testing it out\n",
    "And now we can try actually using our retriever!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "38a126e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'science fiction'}),\n",
       " Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'}),\n",
       " Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction'}),\n",
       " Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6})]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This example only specifies a relevant query\n",
    "retriever.invoke(\"What are some movies about dinosaurs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b19d4da0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'year': 2019, 'director': 'Greta Gerwig', 'rating': 8.3})]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This example specifies a query and a filter\n",
    "retriever.invoke(\"Has Greta Gerwig directed any movies about women\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39bd1de1-b9fe-4a98-89da-58d8a7a6ae51",
   "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": 6,
   "id": "bff36b88-b506-4877-9c63-e5a1a8d78e64",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "retriever = SelfQueryRetriever.from_llm(\n",
    "    llm,\n",
    "    vectorstore,\n",
    "    document_content_description,\n",
    "    metadata_field_info,\n",
    "    enable_limit=True,\n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2758d229-4f97-499c-819f-888acaf8ee10",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'science fiction'}),\n",
       " Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This example only specifies a relevant query\n",
    "retriever.invoke(\"what are two movies about dinosaurs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61a10294",
   "metadata": {},
   "source": [
    "## Complex queries in Action!\n",
    "We've tried out some simple queries, but what about more complex ones? Let's try out a few more complex queries that utilize the full power of Elasticsearch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e460da93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retriever.invoke(\n",
    "    \"what animated or comedy movies have been released in the last 30 years about animated toys?\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0851fc42",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorstore.client.indices.delete(index=\"elasticsearch-self-query-demo\")"
   ]
  }
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