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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false},"responsivegrid":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn 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Repo"}},"flexible_column_cont":{"id":"flexible-column-container-08ed790c73","propertiesId":"quickstart-template-main-flexible-container","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"id":"container-c3dcd1adca","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"contentfragment":{"id":"contentfragment-d30a3a9f68","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EThis first half of this guide blazes through the setup of a premade UDF so you can start playing with text embedding in Snowflake as quickly as possible!\u003C/p\u003E\n","\u003Cp\u003EFor those who are interested in exploring a little deeper how everything works, stick around for the second half, which discusses how to make your own variant of the UDF coverd in the first half of the guide.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFamiliarity with Snowpark Python UDFs\n\u003Cul\u003E\u003Cli\u003EThe \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark/python/index\"\u003ESnowpark Developer Guide for Python\u003C/a\u003E serves as an excellent overview and reference for all things Snowpark Python\u003C/li\u003E\u003Cli\u003EIf you want a holistic bootcamp, consider spending an hour or two with the \u003Ca href=\"/en/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python/\"\u003EGetting Started with Data Engineering and ML using Snowpark for Python Quickstart\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EFamiliarity with running Jupyter notebooks\n\u003Cul\u003E\u003Cli\u003EBefore proceeding, make sure you've got Python 3.8+ and Jupyter Lab installed in a Python environment that you feel comfortable installing more Python packages into.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EA conceptual grasp of text embedding\n\u003Cul\u003E\u003Cli\u003EThere is no need to follow ever word of technical descriptions like the \u003Ca href=\"https://en.wikipedia.org/wiki/Sentence_embedding\"\u003E\u003Cem\u003ESentence embedding\u003C/em\u003E Wikipedia article\u003C/a\u003E, but you will get the most out of this guide if you are able to follow along plain-language material like \u003Ca href=\"https://platform.openai.com/docs/guides/embeddings\"\u003EOpenAI documentation on Embeddings\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Learn\u003C/h3\u003E\n","\u003Cp\u003EYou will learn how to install a premade text embedding Python UDF into your Snowflake environment, and (optionally) get some tips on creating your own text embedding UDFs.\u003C/p\u003E\n","\u003Ch3\u003EWhat You’ll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake account with \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages.html#using-third-party-packages-from-anaconda\"\u003EAnaconda Packages enabled by ORGADMIN\u003C/a\u003E.\n\u003Cul\u003E\u003Cli\u003EIf you do not have a Snowflake account, or your account does not have Anaconda Packages enabled, you can register for a \u003Ca href=\"https://signup.snowflake.com/?utm_cta=quickstarts_\"\u003Efree trial account\u003C/a\u003E.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EA Snowflake account login with ACCOUNTADMIN role.\n\u003Cul\u003E\u003Cli\u003EIf you do not have this role in your environment, you may register for a free trial.\u003C/li\u003E\u003Cli\u003EAlternatively, it is possible to\n\u003Col\u003E\u003Cli\u003EUse a different role that has the ability to create database, schema, tables, stages, and user-defined functions.\u003C/li\u003E\u003Cli\u003EUse an existing database and schema in which you are able to create the aforementioned objects.\u003C/li\u003E\u003C/ol\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EPython 3.8+ with \u003Ca href=\"https://jupyter.org/\"\u003EJupyter Lab\u003C/a\u003E installed.\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPart One: Loading A Text Embedding Model Into Snowflake\u003C/h2\u003E\n","\u003Ch3\u003EFollow Along In Jupyter\u003C/h3\u003E\n","\u003Cp\u003ERemember how we said you'd need a Python 3.8+ installation with Jupyter Lab installed in it? Well, now it's time to use it! Fire up Jupyter Lab, download a copy of the \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/text_embedding_as_snowpark_python_udf/assets/notebook.ipynb\"\u003Enotebook for this quickstart from GitHub\u003C/a\u003E and pop it open.\u003C/p\u003E\n","\u003Cp\u003EGo ahead and start by installing the prerequisites.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/text-embedding-as-snowpark-python-udf/install_prerequisites.png\" alt=\"install prerequisites\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EA Note About Our Embedding Model\u003C/h3\u003E\n","\u003Cp\u003EIn this guide, we will be using the \u003Ca href=\"https://huggingface.co/intfloat/e5-base-v2\"\u003E\u003Ccode\u003Ebase\u003C/code\u003E size of the E5 text embedding model series (version 2)\u003C/a\u003E, which was dominating the \u003Ca href=\"https://github.com/embeddings-benchmark/mteb\"\u003EMassive Text Embedding Benchmark (MTEB)\u003C/a\u003E leaderboard until the Alibaba DAMO Academy released the \u003Ca href=\"https://huggingface.co/thenlper/gte-large\"\u003EGTE series\u003C/a\u003E in late July 2023. Although GTE slightly edges out E5 on the leaderboard, we see in the August 04, 2023 screenshot below above that both series of models compete favorably with OpenAI's proprietary Ada 002 model and can be considered state-of-the-art text embedding systems.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/text-embedding-as-snowpark-python-udf/2023-08-04_MTEB_leaderboard.png\" alt=\"MTEB leaderboard\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EUploading Model Weights To Snowflake.\u003C/h3\u003E\n","\u003Cp\u003EFor security reasons, Snowpark Python UDFs are not generally permitted to access the internet. This means that even if you want to use a \u003Ca href=\"https://huggingface.co/models?pipeline_tag=sentence-similarity&sort=trending\"\u003Epublicly-available text embedding model from Huggingface\u003C/a\u003E, you will need to store a copy of your model weights in a Snowflake stage.\u003C/p\u003E\n","\u003Ch4\u003EDownloading The Model Weights From Huggingface\u003C/h4\u003E\n","\u003Cp\u003EGo ahead and download the E5-base-v2 model from Hugginface and save it to a tarfile on disk by running the first few cells of the notebook.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/text-embedding-as-snowpark-python-udf/download_model.png\" alt=\"download model\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EUploading The Model Weights\u003C/h4\u003E\n","\u003Cp\u003ENow that we have the weights downloaded, we can upload them to a Snowflake stage via the Python connector. The next block of cells in the notebook establishes a connection to Snowflake, sets up a new warehouse, database, schema, and stage for this quickstart, and uploads the model weights to our new stage. Go ahead and run those cells now -- and maybe take a coffee break, too, becuse the upload can take several minutes unless your internet connection is particularly snappy.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/text-embedding-as-snowpark-python-udf/upload_model.png\" alt=\"upload model\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EImplementing A UDF To Run The Model\u003C/h3\u003E\n","\u003Cp\u003EIn the second half of this quickstart we'll revisit how to implement your own UDF for running a text embedding model as a Snowpark Python UDF, but for now let's just use the premade UDF baked into the notebook. Simply run the cell with the \u003Ccode\u003E%%writefile\u003C/code\u003E magic to write the UDF implementation to disk.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/text-embedding-as-snowpark-python-udf/write_udf_to_disk.png\" alt=\"write udf to disk\"\u003E\u003C/p\u003E\n","\u003Cp\u003ETo make sure this UDF implementation runs as expected, go ahead and execute the next block of cells.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/text-embedding-as-snowpark-python-udf/locally_test_udf.png\" alt=\"locally test udf\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EInstall The UDF And Run Text Embedding Via SQL!\u003C/h3\u003E\n","\u003Cp\u003EWe're almost ready to run text embedding directly on Snowflake warehouse compute. All that's left is to upload our UDF implementation file and define the UDF via a \u003Ccode\u003Ecreate function\u003C/code\u003E SQL query. Run the last couple cells to make this happen!\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/text-embedding-as-snowpark-python-udf/create_udf.png\" alt=\"create udf\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EA Note On Storing Vectors As BINARY Vs. ARRAY\u003C/h3\u003E\n","\u003Cp\u003EFor storage and computational efficiency, our text embedding UDF stores embedding vectors as BINARY blobs constructed by concatenating all of the 32-bit floating point numbers in the vector together in order. While this is how numerical computing libraries like Numpy and Pytorch "see" vectors, sometimes it may be useful to take a more JSON-like perspective and treat your embedding vectors as a Snowflake ARRAY.\u003C/p\u003E\n","\u003Cp\u003ETo accomplish this, we could modify our UDF to make it always return an ARRAY, but for greater flexibility we can instead add a new Snowpark UDF to enable a conversion from our BINARY form to ARRAY form. In fact, all it takes is one line of Python to make this possible!\u003C/p\u003E\n","\u003Cp\u003EFeel free to give it a shot by running the last couple cells of the notebook.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/text-embedding-as-snowpark-python-udf/unpack_binary_vectors.png\" alt=\"unpacking example\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EInterlude: A Reminder To Clean Up\u003C/h2\u003E\n","\u003Cp\u003EIf you don't plan to use the warehouse, database, schema, stage, or UDFs we've defined in this Quickstart, now might be a good time to do some deletion. Below are the SQL commands to wipe out everything we've built so far.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\u003C/p\u003E\n","\u003Cp\u003EIf you're excited to do part two, it might be handy to hold off on deleting our files and instead come back to this interlude section later when you're sure you don't want any of the files/UDFs/etc.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Edrop database text_embedding_quickstart_db;\ndrop warehouse text_embedding_quickstart_wh;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPart Two: Tips For Building Your Own Text Embedding UDF\u003C/h2\u003E\n","\u003Cp\u003EI promised that after rushing to get something up and running in part one, I would come back with a part two to help you craft your own text embedding UDFs. While the source code example from part one is probably the most useful reference I can provide, I'll use this section to explain some "tricks of the trade" from the implementation.\u003C/p\u003E\n","\u003Ch3\u003ESaving Yourself From Unprotected File Access In A Parallel Universe\u003C/h3\u003E\n","\u003Cp\u003EApologies for the pun, but in a Snowflake warehouse, your Python Snowflake UDF lives in a \u003Cem\u003Eparallel universe\u003C/em\u003E. At any moment during a query, the warehouse may be invoking multiple copies of your UDF on the same machine, and as a result, we need to make sure to avoid concurrency-related bugs like unprotected file access.\u003C/p\u003E\n","\u003Cp\u003EThis is not actually as hard as it might sound. The official \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-examples#unzipping-a-staged-file\"\u003Edeveloper guide offers a great example\u003C/a\u003E of using file-based locking to ensure that even if two or more copies of your code are running at the same time, only one messes with your files at a time. In our UDF example, this translates to these lines:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eclass FileLock:\n    def __enter__(self):\n        self._lock = threading.Lock()\n        self._lock.acquire()\n        self._fd = open("/tmp/lockfile.LOCK", "w+")\n        fcntl.lockf(self._fd, fcntl.LOCK_EX)\n\n    def __exit__(self, type, value, traceback):\n        self._fd.close()\n        self._lock.release()\n\n\ndef _load_assets(archive_path: Path) -> Tuple[BertTokenizerFast, BertModel]:\n    # Config.\n    tmp = Path("/tmp")\n    extracted_dir = tmp / SAVE_DIR_NAME\n    tokenizer_dir = extracted_dir / "tokenizer"\n    model_dir = extracted_dir / "model"\n\n    # Extract and load, with a lock placed for concurrency sanity.\n    with FileLock():\n        assert archive_path.exists(), f"{archive_path} not found!"\n        shutil.unpack_archive(archive_path, tmp)\n        assert tokenizer_dir.exists(), "failed to extract tokenizer dir"\n        assert model_dir.exists(), "failed to extract model dir"\n        tokenizer = BertTokenizerFast.from_pretrained(\n            tokenizer_dir, local_files_only=True\n        )\n        model = BertModel.from_pretrained(model_dir, local_files_only=True)\n    assert isinstance(model, BertModel)  # Appease typechecker.\n    return tokenizer, model\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EUsing Conservative Batch Sizes To Avoid Memory Problems And Timeouts\u003C/h3\u003E\n","\u003Cp\u003EWhen you want to run a Python function on your laptop, you can generally run just one copy of the function at a time with no timeout. In Snowpark, on the other hand, the official developer guide \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-designing#write-single-threaded-udf-handlers\"\u003Erecommends writing single-threaded UDF handlers\u003C/a\u003E, since the warehouse handles query parallelization for you. Additionally, (at least at the time of writing) vectorized Python UDFs have a \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-batch#setting-a-target-batch-size\"\u003E180-second timeout per invocation\u003C/a\u003E. While these aspects of Snowpark's design make life better for a lot of queries (faster execution and limited hanging), they can cause some unexpected problems as well.\u003C/p\u003E\n","\u003Cp\u003ERecall that the PyTorch library powering our text embedding UDF is optimized to use all available CPU cores, regardless of how many copies of it are being run at the same time. While this should accelerate execution, it is also a violation of the single-threaded handler recommendation I mentioned earlier, and in parallelized query execution this can potentially trigger CPU contention, slowing down individual UDF handler invocations and risking timeout issues that you woudln't otherwise expect.\u003C/p\u003E\n","\u003Cp\u003ERemember, too, that our UDF handler includes code to load a sizeable chunk of model weights from disk to memory, so automatic query parallelization can potentially lead to more memory pressure than expected if it involves multiple copies of the model being loaded into memory concurrently. If you do hit memory issues, a \u003Ca href=\"https://docs.snowflake.com/en/user-guide/warehouses-snowpark-optimized\"\u003ESnowpark optimized warehouse\u003C/a\u003E may offer a quick fix, but we can also do our part in our handler implementation to conserve RAM by keeping batches small.\u003C/p\u003E\n","\u003Cp\u003EOkay, that's a lot of information on \u003Cem\u003Ewhy\u003C/em\u003E we should limit batch size to avoid timeouts and minimize memory impact. Let's now talk about \u003Cem\u003Ehow\u003C/em\u003E to implement these limits.\u003C/p\u003E\n","\u003Cp\u003ETo start, it's a good idea to limit how many input can be put into a single batch of UDF inputs. For example, a batch size of 32 will give the UDF over five seconds on average to embed each input before hitting the 180 second timeout.\u003C/p\u003E\n","\u003Cp\u003ETo do this, we can simply set the \u003Ccode\u003E_sf_max_batch_size\u003C/code\u003E attribute of our handler function.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eembed._sf_max_batch_size = 32  # type: ignore\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\u003C/p\u003E\n","\u003Cp\u003EIf you have a really slow model and want to limit to batch size of one, you could also switch out to a non-vectorized UDF.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EWe may also want to separately limit how many inputs from that batch are actually pushed through the model at a time. Doing so won't help us avoid timeouts any better, but it should reduce our memory usage, since the internal neural network activation values produced by running text embedding models tend to be far larger than the input strings themselves. We can accomplish this internal limitation by implimenting minibatching within the UDF itself.\u003C/p\u003E\n","\u003Cp\u003EHere is the internal minibatching implementation from my UDF implementation.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E    # Do internal batching according to the `batch_size` constant.\n    input_iter = iter(inputs)\n    batched_iter = iter(lambda: list(itertools.islice(input_iter, MAX_BATCH_SIZE)), [])\n\n    # Run the embedding.\n    # Note: We're byte-packing our float32 embedding vectors into binary scalars\n    # so that we have a scalar output compatible with Snowflake BINARY type.\n    i = 0\n    result = np.ndarray(shape=len(inputs), dtype=EMBEDDING_AS_BYTES_DTYPE)\n    for batch in batched_iter:\n        n_in_batch = len(batch)\n        embedding_matrix = _embed_batch(tokenizer=tokenizer, model=model, texts=batch)\n        result[i : i + n_in_batch] = _byte_pack_embedding_matrix_rows(embedding_matrix)\n        i = i + n_in_batch\n\n    return result\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EOver To You!\u003C/h3\u003E\n","\u003Cp\u003EWith the above pointers and the original code as a starting point, you are now well equipped to try implementing your own text embedding UDF! Consider trying to package up a different model like the brand-new and leaderboard-topping \u003Ca href=\"https://huggingface.co/thenlper/gte-large\"\u003EGTE-large\u003C/a\u003E. Or maybe even experiment with a homegrown model, a different UDF implementation, or something else. Whatever you choose, I wish you the best of luck!\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion\u003C/h2\u003E\n","\u003Cp\u003ECongratulations, you've made it all the way through! I hope this guide has helped kickstart your journey to securely applying text embedding on your data without anything having to leave the warehouse.\u003C/p\u003E"],"description":"","title":"Text Embedding As A Snowpark Python UDF",":type":"snowflake-site/components/contentfragment","isDeveloperGuidesPage":false,":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","value":"\u003C!-- ------------------------ --\u003E\r\n## Overview\r\n\r\n\r\nThis first half of this guide blazes through the setup of a premade UDF so you can start playing with text embedding in Snowflake as quickly as possible!\r\n\r\nFor those who are interested in exploring a little deeper how everything works, stick around for the second half, which discusses how to make your own variant of the UDF coverd in the first half of the guide.\r\n\r\n### Prerequisites\r\n\r\n- Familiarity with Snowpark Python UDFs\r\n  - The [Snowpark Developer Guide for Python](https://docs.snowflake.com/en/developer-guide/snowpark/python/index) serves as an excellent overview and reference for all things Snowpark Python\r\n  - If you want a holistic bootcamp, consider spending an hour or two with the [Getting Started with Data Engineering and ML using Snowpark for Python Quickstart](/en/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python/)\r\n- Familiarity with running Jupyter notebooks\r\n  - Before proceeding, make sure you've got Python 3.8+ and Jupyter Lab installed in a Python environment that you feel comfortable installing more Python packages into.\r\n- A conceptual grasp of text embedding\r\n  - There is no need to follow ever word of technical descriptions like the [*Sentence embedding* Wikipedia article](https://en.wikipedia.org/wiki/Sentence_embedding), but you will get the most out of this guide if you are able to follow along plain-language material like [OpenAI documentation on Embeddings](https://platform.openai.com/docs/guides/embeddings)\r\n\r\n### What You’ll Learn\r\n\r\nYou will learn how to install a premade text embedding Python UDF into your Snowflake environment, and (optionally) get some tips on creating your own text embedding UDFs.\r\n\r\n### What You’ll Need\r\n\r\n- A Snowflake account with [Anaconda Packages enabled by ORGADMIN](https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages.html#using-third-party-packages-from-anaconda).\r\n  - If you do not have a Snowflake account, or your account does not have Anaconda Packages enabled, you can register for a [free trial account](https://signup.snowflake.com/?utm_cta=quickstarts_).\r\n- A Snowflake account login with ACCOUNTADMIN role.\r\n  - If you do not have this role in your environment, you may register for a free trial.\r\n  - Alternatively, it is possible to\r\n    1. Use a different role that has the ability to create database, schema, tables, stages, and user-defined functions.\r\n    2. Use an existing database and schema in which you are able to create the aforementioned objects.\r\n- Python 3.8+ with [Jupyter Lab](https://jupyter.org/) installed.\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Part One: Loading A Text Embedding Model Into Snowflake\r\n\r\n### Follow Along In Jupyter\r\n\r\nRemember how we said you'd need a Python 3.8+ installation with Jupyter Lab installed in it? Well, now it's time to use it! Fire up Jupyter Lab, download a copy of the [notebook for this quickstart from GitHub](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/text_embedding_as_snowpark_python_udf/assets/notebook.ipynb) and pop it open.\r\n\r\nGo ahead and start by installing the prerequisites.\r\n\r\n![install prerequisites](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/text-embedding-as-snowpark-python-udf/install_prerequisites.png)\r\n\r\n### A Note About Our Embedding Model\r\n\r\n\r\nIn this guide, we will be using the [`base` size of the E5 text embedding model series (version 2)](https://huggingface.co/intfloat/e5-base-v2), which was dominating the [Massive Text Embedding Benchmark (MTEB)](https://github.com/embeddings-benchmark/mteb) leaderboard until the Alibaba DAMO Academy released the [GTE series](https://huggingface.co/thenlper/gte-large) in late July 2023. Although GTE slightly edges out E5 on the leaderboard, we see in the August 04, 2023 screenshot below above that both series of models compete favorably with OpenAI's proprietary Ada 002 model and can be considered state-of-the-art text embedding systems.\r\n\r\n![MTEB leaderboard](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/text-embedding-as-snowpark-python-udf/2023-08-04_MTEB_leaderboard.png)\r\n\r\n\r\n### Uploading Model Weights To Snowflake.\r\n\r\nFor security reasons, Snowpark Python UDFs are not generally permitted to access the internet. This means that even if you want to use a [publicly-available text embedding model from Huggingface](https://huggingface.co/models?pipeline_tag=sentence-similarity&sort=trending), you will need to store a copy of your model weights in a Snowflake stage.\r\n\r\n#### Downloading The Model Weights From Huggingface\r\n\r\nGo ahead and download the E5-base-v2 model from Hugginface and save it to a tarfile on disk by running the first few cells of the notebook.\r\n\r\n![download model](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/text-embedding-as-snowpark-python-udf/download_model.png)\r\n\r\n#### Uploading The Model Weights\r\n\r\nNow that we have the weights downloaded, we can upload them to a Snowflake stage via the Python connector. The next block of cells in the notebook establishes a connection to Snowflake, sets up a new warehouse, database, schema, and stage for this quickstart, and uploads the model weights to our new stage. Go ahead and run those cells now -- and maybe take a coffee break, too, becuse the upload can take several minutes unless your internet connection is particularly snappy.\r\n\r\n![upload model](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/text-embedding-as-snowpark-python-udf/upload_model.png)\r\n\r\n### Implementing A UDF To Run The Model\r\n\r\nIn the second half of this quickstart we'll revisit how to implement your own UDF for running a text embedding model as a Snowpark Python UDF, but for now let's just use the premade UDF baked into the notebook. Simply run the cell with the `%%writefile` magic to write the UDF implementation to disk.\r\n\r\n![write udf to disk](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/text-embedding-as-snowpark-python-udf/write_udf_to_disk.png)\r\n\r\nTo make sure this UDF implementation runs as expected, go ahead and execute the next block of cells.\r\n\r\n![locally test udf](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/text-embedding-as-snowpark-python-udf/locally_test_udf.png)\r\n\r\n\r\n### Install The UDF And Run Text Embedding Via SQL!\r\n\r\nWe're almost ready to run text embedding directly on Snowflake warehouse compute. All that's left is to upload our UDF implementation file and define the UDF via a `create function` SQL query. Run the last couple cells to make this happen!\r\n\r\n![create udf](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/text-embedding-as-snowpark-python-udf/create_udf.png)\r\n\r\n\r\n### A Note On Storing Vectors As BINARY Vs. ARRAY\r\n\r\nFor storage and computational efficiency, our text embedding UDF stores embedding vectors as BINARY blobs constructed by concatenating all of the 32-bit floating point numbers in the vector together in order. While this is how numerical computing libraries like Numpy and Pytorch \"see\" vectors, sometimes it may be useful to take a more JSON-like perspective and treat your embedding vectors as a Snowflake ARRAY.\r\n\r\nTo accomplish this, we could modify our UDF to make it always return an ARRAY, but for greater flexibility we can instead add a new Snowpark UDF to enable a conversion from our BINARY form to ARRAY form. In fact, all it takes is one line of Python to make this possible!\r\n\r\n\r\nFeel free to give it a shot by running the last couple cells of the notebook.\r\n\r\n![unpacking example](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/text-embedding-as-snowpark-python-udf/unpack_binary_vectors.png)\r\n\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Interlude: A Reminder To Clean Up\r\n\r\nIf you don't plan to use the warehouse, database, schema, stage, or UDFs we've defined in this Quickstart, now might be a good time to do some deletion. Below are the SQL commands to wipe out everything we've built so far.\r\n\r\n\u003E aside positive\r\n\u003E \r\n\u003E  If you're excited to do part two, it might be handy to hold off on deleting our files and instead come back to this interlude section later when you're sure you don't want any of the files/UDFs/etc.\r\n\r\n```sql\r\ndrop database text_embedding_quickstart_db;\r\ndrop warehouse text_embedding_quickstart_wh;\r\n```\r\n\r\n\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Part Two: Tips For Building Your Own Text Embedding UDF\r\n\r\nI promised that after rushing to get something up and running in part one, I would come back with a part two to help you craft your own text embedding UDFs. While the source code example from part one is probably the most useful reference I can provide, I'll use this section to explain some \"tricks of the trade\" from the implementation.\r\n### Saving Yourself From Unprotected File Access In A Parallel Universe\r\n\r\nApologies for the pun, but in a Snowflake warehouse, your Python Snowflake UDF lives in a *parallel universe*. At any moment during a query, the warehouse may be invoking multiple copies of your UDF on the same machine, and as a result, we need to make sure to avoid concurrency-related bugs like unprotected file access.\r\n\r\nThis is not actually as hard as it might sound. The official [developer guide offers a great example](https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-examples#unzipping-a-staged-file) of using file-based locking to ensure that even if two or more copies of your code are running at the same time, only one messes with your files at a time. In our UDF example, this translates to these lines:\r\n\r\n``` python\r\nclass FileLock:\r\n    def __enter__(self):\r\n        self._lock = threading.Lock()\r\n        self._lock.acquire()\r\n        self._fd = open(\"/tmp/lockfile.LOCK\", \"w+\")\r\n        fcntl.lockf(self._fd, fcntl.LOCK_EX)\r\n\r\n    def __exit__(self, type, value, traceback):\r\n        self._fd.close()\r\n        self._lock.release()\r\n\r\n\r\ndef _load_assets(archive_path: Path) -\u003E Tuple[BertTokenizerFast, BertModel]:\r\n    # Config.\r\n    tmp = Path(\"/tmp\")\r\n    extracted_dir = tmp / SAVE_DIR_NAME\r\n    tokenizer_dir = extracted_dir / \"tokenizer\"\r\n    model_dir = extracted_dir / \"model\"\r\n\r\n    # Extract and load, with a lock placed for concurrency sanity.\r\n    with FileLock():\r\n        assert archive_path.exists(), f\"{archive_path} not found!\"\r\n        shutil.unpack_archive(archive_path, tmp)\r\n        assert tokenizer_dir.exists(), \"failed to extract tokenizer dir\"\r\n        assert model_dir.exists(), \"failed to extract model dir\"\r\n        tokenizer = BertTokenizerFast.from_pretrained(\r\n            tokenizer_dir, local_files_only=True\r\n        )\r\n        model = BertModel.from_pretrained(model_dir, local_files_only=True)\r\n    assert isinstance(model, BertModel)  # Appease typechecker.\r\n    return tokenizer, model\r\n```\r\n\r\n### Using Conservative Batch Sizes To Avoid Memory Problems And Timeouts\r\n\r\nWhen you want to run a Python function on your laptop, you can generally run just one copy of the function at a time with no timeout. In Snowpark, on the other hand, the official developer guide [recommends writing single-threaded UDF handlers](https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-designing#write-single-threaded-udf-handlers), since the warehouse handles query parallelization for you. Additionally, (at least at the time of writing) vectorized Python UDFs have a [180-second timeout per invocation](https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-batch#setting-a-target-batch-size). While these aspects of Snowpark's design make life better for a lot of queries (faster execution and limited hanging), they can cause some unexpected problems as well.\r\n\r\nRecall that the PyTorch library powering our text embedding UDF is optimized to use all available CPU cores, regardless of how many copies of it are being run at the same time. While this should accelerate execution, it is also a violation of the single-threaded handler recommendation I mentioned earlier, and in parallelized query execution this can potentially trigger CPU contention, slowing down individual UDF handler invocations and risking timeout issues that you woudln't otherwise expect.\r\n\r\nRemember, too, that our UDF handler includes code to load a sizeable chunk of model weights from disk to memory, so automatic query parallelization can potentially lead to more memory pressure than expected if it involves multiple copies of the model being loaded into memory concurrently. If you do hit memory issues, a [Snowpark optimized warehouse](https://docs.snowflake.com/en/user-guide/warehouses-snowpark-optimized) may offer a quick fix, but we can also do our part in our handler implementation to conserve RAM by keeping batches small.\r\n\r\nOkay, that's a lot of information on *why* we should limit batch size to avoid timeouts and minimize memory impact. Let's now talk about *how* to implement these limits.\r\n\r\nTo start, it's a good idea to limit how many input can be put into a single batch of UDF inputs. For example, a batch size of 32 will give the UDF over five seconds on average to embed each input before hitting the 180 second timeout.\r\n\r\nTo do this, we can simply set the `_sf_max_batch_size` attribute of our handler function.\r\n\r\n``` python\r\nembed._sf_max_batch_size = 32  # type: ignore\r\n```\r\n\r\n\u003E aside positive\r\n\u003E\r\n\u003E If you have a really slow model and want to limit to batch size of one, you could also switch out to a non-vectorized UDF.\r\n\r\nWe may also want to separately limit how many inputs from that batch are actually pushed through the model at a time. Doing so won't help us avoid timeouts any better, but it should reduce our memory usage, since the internal neural network activation values produced by running text embedding models tend to be far larger than the input strings themselves. We can accomplish this internal limitation by implimenting minibatching within the UDF itself.\r\n\r\nHere is the internal minibatching implementation from my UDF implementation.\r\n\r\n``` python\r\n    # Do internal batching according to the `batch_size` constant.\r\n    input_iter = iter(inputs)\r\n    batched_iter = iter(lambda: list(itertools.islice(input_iter, MAX_BATCH_SIZE)), [])\r\n\r\n    # Run the embedding.\r\n    # Note: We're byte-packing our float32 embedding vectors into binary scalars\r\n    # so that we have a scalar output compatible with Snowflake BINARY type.\r\n    i = 0\r\n    result = np.ndarray(shape=len(inputs), dtype=EMBEDDING_AS_BYTES_DTYPE)\r\n    for batch in batched_iter:\r\n        n_in_batch = len(batch)\r\n        embedding_matrix = _embed_batch(tokenizer=tokenizer, model=model, texts=batch)\r\n        result[i : i + n_in_batch] = _byte_pack_embedding_matrix_rows(embedding_matrix)\r\n        i = i + n_in_batch\r\n\r\n    return result\r\n```\r\n\r\n### Over To You!\r\n\r\nWith the above pointers and the original code as a starting point, you are now well equipped to try implementing your own text embedding UDF! Consider trying to package up a different model like the brand-new and leaderboard-topping [GTE-large](https://huggingface.co/thenlper/gte-large). Or maybe even experiment with a homegrown model, a different UDF implementation, or something else. Whatever you choose, I wish you the best of luck!\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Conclusion\r\n\r\nCongratulations, you've made it all the way through! I hope this guide has helped kickstart your journey to securely applying text embedding on your data without anything having to leave the warehouse.","title":"Quickstart Article Body","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-8b217488aa","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"id":"container-5a19c1522b","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-3bd06f77c8","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2024-07-08",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-0ee0648dc5","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake 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