Testing Document extraction in Snowflake
Testing on Document AI and AI_PARSE_DOCUMENT features in Snowflake.
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Once upon a time
…I was struggling with the problem of parsing PDF documents or images into structured tables. Bank statements, credit statements, you name it.
As usual, idealistic me integrated learning python into resolving this fancy problem. After a not-so-short while of experimenting, I realized a Venn diagram:
Despite big beautiful Venn, I still learned that:
image-based PDF is particularly time-consuming to solve and customize across different templates (you would have to dabble into OCR-verse).
if you run into PDF files that have no table borders/lines, you are living life on hard mode with the open-source python packages out there (hint: pay for plug and play solutions).
and of course, if you have money, there are always paid, managed solutions (think evil Adobe, among others). Now I’m not that rich to pay hundreds of dollars to parse hundred pages of PDF documents into table, so I didn’t opt for this.
there are also “free” tools on the cloud of course, but they are as free as that Nigerian prince who promised me a mountain of gold. And I’m dealing with financial statements here.
Fast-forward to today
Who would have thought my procrastination would pay off one day. Long story short, after some extraction tests, my lazy self is quite happy with the result I got from built-in no-tweaking-required features in Snowflake.
I tested on my personal bank statements - from 2 banks, lets call them bank A and bank B. Bank A’s statements are properly border-lined-ed (thank you), while bank B’s (booo) are not1. There are two ways I can tackle this in Snowflake, I tried both:
✅ AI_PARSE_DOCUMENT on jpeg & pdf.
✅ Document AI on jpeg
AI_PARSE_DOCUMENT is an AI SQL feature of Snowflake, and Document AI is basically a proprietary model of Snowflake. Both can extract information from your files or images with zero to minimal training involved. Both can solve text-based as well as image-based problems, and so far they have not bankrupted me2 (yet).
The process
The whole high-level process looks like this:
Input files
So here is an example of the statements. You will see that their structures and layout are different:
Bank A: 10 columns, properly border-lined
Bank B: 9 columns, no lines until I draw squiggly lines on it to improve the result of parsing3.
I tested first with Bank A using AI_PARSE_DOCUMENT
I do have a script of the whole processing workflow on multiple files, but to illustrate this simply, let’s just examine single-page processing steps in this post.
After uploading the image into an internal stage in Snowflake, I just need to type the standard AI_PARSE_DOCUMENT SQL statement, blink me eyes 3 to 5 times, depends on the internet connection and how much texts there are in the image, and voila 🎉 the pdf/image file is extracted into a single-row json record, with a pipe separator.
| -- standard SQL statement | |
| SELECT AI_PARSE_DOCUMENT ( | |
| TO_FILE(’@my_pdf_stage_bank_a’,’{file_name}’), | |
| {’mode’: ‘LAYOUT’, ‘page_split’: false}) AS content | |
| ); | |
| -- `content` result is a json record | |
| ```json | |
| { | |
| “content”: “censored stuff \n\n| Ngày giao dịch\nTransaction Date | Đối tác\nRemi\nter | NH Đối tác\nRemi\nter | Diễn giải\nDetails | Sổ bút toán\nTransaction No. | Nợ\nDebit | Có\nCredit | Phí/Lãi\nFee/Interest | Thuế VAT\nTax | Sổ dư\nBalance |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| 04/01/2020 | blabla | blabla | blabla ck | blabla/BNK | xx,000,000.00 | …”, | |
| “metadata”: { “pageCount”: 2} | |
| } | |
| ``` |
I then transform that single-row record into intended table structure that matches BankA’s table format. You will notice that the number of columns is reflected in the code here.
| with joined as ( | |
| select * from a_single_page_parsed --persist that AI_PARSE_DOCUMENT output into a table is recommended | |
| ), | |
| --lateral split to table from the single-row content of each page | |
| cleansed as ( | |
| select | |
| which_bank, | |
| file_source, --page indicator | |
| index, | |
| mod(index - 1, 11) as position_in_group, --the number 11 = number of columns you have in the table + 1 | |
| value, | |
| row_number() over(partition by file_source, position_in_group order by index) as record_group --this is row indicator for the eventual table | |
| from joined, | |
| lateral split_to_table(content['content']::string,'|') | |
| order by file_source,index | |
| ), | |
| transformed_data AS ( | |
| SELECT | |
| file_source, | |
| record_group, | |
| --this is where we group the values in single record_group into a row | |
| MAX(CASE WHEN position_in_group = 1 THEN trim(value) END) AS transaction_date, | |
| ... | |
| MAX(CASE WHEN position_in_group = 10 THEN trim(value) END) AS balance | |
| from cleansed | |
| group by all | |
| having len(transaction_date) = 10 --some condition here to filter out irrelevant records | |
| ) | |
| select | |
| file_source, | |
| --transforming data type | |
| from transformed_data | |
| order by file_source, record_group |
This script would give you a neat output like so:
Fast and accurate! But when I tried the same approach with Bank B, it didn’t give me accurate result. Even after adding border lines onto it, the layout of the A4 being portrait instead of landscape (leading to some text rows overflowing to the next lines) led to some messy result (the column value being shifted to another one).
So, I have no choice but to test Document AI for Bank B
I was under the assumption that Document AI would require more effort, but it turned out the steps were quite simple if I just followed the documentation properly.
Build a Document AI model
Upload sample files into Document (choose Table extraction! - very important)
Enter key, column names, then click Extract
Validate the tables extracted4
Go to the build and Publish if satisfactory
After this step is done you can try out the model on new documents uploaded onto your stage.
Once published, you can now call the function to extract other similar files (identical structure) - in my case it’s bank B’s statements.
| -- the statement to apply the model just built onto documents | |
| select | |
| relative_path as file_name, | |
| size as file_size, | |
| last_modified, | |
| file_url AS snowflake_file_url, | |
| <Document AI build model name>!PREDICT(GET_PRESIGNED_URL('@my_pdf_stage', RELATIVE_PATH), 1) AS json_content | |
| from directory(@my_pdf_stage) |
The result of json content would look like so for each file, you will see that every key is denoted by the Table Key and column name, and contains multiple score/value dictionaries in them. Of course to turn this into table would require some SQL transformation. But that’s about it.
Accuracy evaluation
For both approaches I’m quite happy with the result, I have not actually nailed down every single cell to give you a quantified result of accuracy. But I would say it’s accurate where-it-matters: in my case it’s the bank transaction and balance amounts.
In case of AI_PARSE_DOCUMENT:
Having reliable consistent layout and border lines is important. I realize that the challenge isn’t that AI_PARSE_DOCUMENT cannot extract the information (it can), but the challenge is in transformation step afterwards to turn it into the table with accurate value position in each column.
In case of Document AI:
I do notice some fuzzy output on effective date being identical to transaction date in the output, but reality is they are sometimes 1 day apart, which is interesting. I suppose Document AI has some “fast-tracking” logic behind the scene here that results in this outcome.
There are OCR scores returned for each record in json output as well as on the entire file parsed, which you can factor into a production workflow.
Discoveries
Fun fact I tried Document AI on Bank A afterwards to cross check the result of AI_PARSE_DOCUMENT, and realized that if your image is upside down or 90 degree rotated, it can still recognize the columns correctly! Though it did miss out on some rows, ironically.
You cannot change the extraction mode (Table vs entity) after publishing - I realized this after wrongly chose Entity for first tries because I did not read documents and just went with the UI flow. Don’t be like me.
The bi-lingual headers did not bother Snowflake at all.
Cost: so basing on the fine prints in here and elsewhere in Snowflake documentations, documents are charged per page & depends on text-density. But just to give you a feel - for this whole trial & error process it cost me:
Document AI: 17.07 snowflake credits for 170 pages of document —> 0.1004117647 credit per page.
AI_PARSE_DOCUMENT: 0.60323 snowflake credits for 182 pages —> 0.0033144505 credit per page.
Remember that you still need to multiply the snowflake credits to USD/credits for the dollar amount, and this number varies depends on your cloud platform and region, ranging from 3-4(?) USD/credit.
Conclusions
It was a success! The only painful part is where I didn’t read documentations. But overall it is way less painful than trying to customize open source OCR python packages (full data privacy), or paying for some expensive solutions on subscription mode (your data is stored either way).
Ultimately it did not bankrupt me because I used a trial account to test - and this is still way under the trial budget. Though, I can imagine in a large scale deployment, if only bank B isn’t annoying about the border lines and layout, how much CO2 emission & dollars I/we could have avoided.
Maybe the whole takeaway from this post is at some point there would be job posts for humans titled “PDF Line manager” on LinkedIn.
After testing on different stuff, I realized that AI_PARSE_DOCUMENT function in Snowflake is my best bet out of big beautiful Venn, so I added lines into the borderless tables in bank B’s statements to get better results, which might have added a bit of advantage for Document AI (which I tested afterwards).
But also to bankruptcy-proof, I created a trial account for this, where I could use free credits to burn.
Because I tested first with AI_PARSE_DOCUMENT thinking it would be my fastest way to solve (but it later dawned on me Document AI isn’t that much work either - I wonder what would happen if I just feed Document AI borderless tables though).
This is where you actually have to use human intelligence and eyes, to cross check the result and type in overriding values if something is wrong. If your tables are extra confusing I suppose this would be the most labor-intensive task of the whole process.