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   "source": [
    "# John Snow Labs\n",
    "\n",
    ">[John Snow Labs](https://nlp.johnsnowlabs.com/) NLP & LLM ecosystem includes software libraries for state-of-the-art AI at scale, Responsible AI, No-Code AI, and access to over 20,000 models for Healthcare, Legal, Finance, etc.\n",
    ">\n",
    ">Models are loaded with [nlp.load](https://nlp.johnsnowlabs.com/docs/en/jsl/load_api) and spark session is started >with [nlp.start()](https://nlp.johnsnowlabs.com/docs/en/jsl/start-a-sparksession) under the hood.\n",
    ">For all 24.000+ models, see the [John Snow Labs Model Models Hub](https://nlp.johnsnowlabs.com/models)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": [
    "%pip install --upgrade --quiet  johnsnowlabs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "# If you have a enterprise license, you can run this to install enterprise features\n",
    "# from johnsnowlabs import nlp\n",
    "# nlp.install()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
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   },
   "outputs": [],
   "source": [
    "from langchain_community.embeddings.johnsnowlabs import JohnSnowLabsEmbeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
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    }
   },
   "source": [
    "Initialize Johnsnowlabs Embeddings and Spark Session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "embedder = JohnSnowLabsEmbeddings(\"en.embed_sentence.biobert.clinical_base_cased\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
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    }
   },
   "source": [
    "Define some example texts . These could be any documents that you want to analyze - for example, news articles, social media posts, or product reviews."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
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   },
   "outputs": [],
   "source": [
    "texts = [\"Cancer is caused by smoking\", \"Antibiotics aren't painkiller\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "Generate and print embeddings for the texts . The JohnSnowLabsEmbeddings class generates an embedding for each document, which is a numerical representation of the document's content. These embeddings can be used for various natural language processing tasks, such as document similarity comparison or text classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "embeddings = embedder.embed_documents(texts)\n",
    "for i, embedding in enumerate(embeddings):\n",
    "    print(f\"Embedding for document {i+1}: {embedding}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "Generate and print an embedding for a single piece of text. You can also generate an embedding for a single piece of text, such as a search query. This can be useful for tasks like information retrieval, where you want to find documents that are similar to a given query."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "query = \"Cancer is caused by smoking\"\n",
    "query_embedding = embedder.embed_query(query)\n",
    "print(f\"Embedding for query: {query_embedding}\")"
   ]
  }
 ],
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