{"id":1807,"date":"2025-07-04T21:46:37","date_gmt":"2025-07-04T21:46:37","guid":{"rendered":"https:\/\/sparkonelabs.com\/home\/?p=1807"},"modified":"2025-07-07T00:52:29","modified_gmt":"2025-07-07T00:52:29","slug":"proof-of-concept-small-model-accurate-csv-parsing-with-auto-debugging-code-execution-workflow","status":"publish","type":"post","link":"https:\/\/sparkonelabs.com\/home\/2025\/07\/04\/proof-of-concept-small-model-accurate-csv-parsing-with-auto-debugging-code-execution-workflow\/","title":{"rendered":"Proof of Concept: Small Model Accurate CSV Parsing with Auto Debugging Code Execution Workflow"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Using Ollama models and a CSV data file, it was troublesome to accurately parse for values. (The LLMs would almost get it right, but skip values present in a search, except for under certain prompts and conditions.)<br><br>So, I started wondering, would it be easier to generate code to parse the CSV directly using the LLM, perhaps with an auto-debugging workflow, so that general user queries can be translated into exact data parsing algorithms on the fly?<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">After much hard debugging, and learning a lot about experimental state machine driven execution of node based workflows, I was able to get a successful result:<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1634\" height=\"593\" src=\"https:\/\/i0.wp.com\/sparkonelabs.com\/home\/wp-content\/uploads\/2025\/07\/Screenshot_20250704_173924.png?fit=1024%2C372&amp;ssl=1\" alt=\"\" class=\"wp-image-1811\" srcset=\"https:\/\/i0.wp.com\/sparkonelabs.com\/home\/wp-content\/uploads\/2025\/07\/Screenshot_20250704_173924.png?w=1634&amp;ssl=1 1634w, https:\/\/i0.wp.com\/sparkonelabs.com\/home\/wp-content\/uploads\/2025\/07\/Screenshot_20250704_173924.png?resize=300%2C109&amp;ssl=1 300w, https:\/\/i0.wp.com\/sparkonelabs.com\/home\/wp-content\/uploads\/2025\/07\/Screenshot_20250704_173924.png?resize=1024%2C372&amp;ssl=1 1024w, https:\/\/i0.wp.com\/sparkonelabs.com\/home\/wp-content\/uploads\/2025\/07\/Screenshot_20250704_173924.png?resize=768%2C279&amp;ssl=1 768w, https:\/\/i0.wp.com\/sparkonelabs.com\/home\/wp-content\/uploads\/2025\/07\/Screenshot_20250704_173924.png?resize=1536%2C557&amp;ssl=1 1536w, https:\/\/i0.wp.com\/sparkonelabs.com\/home\/wp-content\/uploads\/2025\/07\/Screenshot_20250704_173924.png?resize=600%2C218&amp;ssl=1 600w\" sizes=\"(max-width: 1170px) 100vw, 1170px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">So here is the CSV Data:<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1755\" height=\"870\" src=\"https:\/\/i0.wp.com\/sparkonelabs.com\/home\/wp-content\/uploads\/2025\/07\/Screenshot_20250704_174338.png?fit=1024%2C508&amp;ssl=1\" alt=\"\" class=\"wp-image-1812\" srcset=\"https:\/\/i0.wp.com\/sparkonelabs.com\/home\/wp-content\/uploads\/2025\/07\/Screenshot_20250704_174338.png?w=1755&amp;ssl=1 1755w, https:\/\/i0.wp.com\/sparkonelabs.com\/home\/wp-content\/uploads\/2025\/07\/Screenshot_20250704_174338.png?resize=300%2C149&amp;ssl=1 300w, https:\/\/i0.wp.com\/sparkonelabs.com\/home\/wp-content\/uploads\/2025\/07\/Screenshot_20250704_174338.png?resize=1024%2C508&amp;ssl=1 1024w, https:\/\/i0.wp.com\/sparkonelabs.com\/home\/wp-content\/uploads\/2025\/07\/Screenshot_20250704_174338.png?resize=768%2C381&amp;ssl=1 768w, https:\/\/i0.wp.com\/sparkonelabs.com\/home\/wp-content\/uploads\/2025\/07\/Screenshot_20250704_174338.png?resize=1536%2C761&amp;ssl=1 1536w, https:\/\/i0.wp.com\/sparkonelabs.com\/home\/wp-content\/uploads\/2025\/07\/Screenshot_20250704_174338.png?resize=600%2C297&amp;ssl=1 600w\" sizes=\"(max-width: 1170px) 100vw, 1170px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">And here is the output of the Workflow:<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1237\" height=\"607\" src=\"https:\/\/i0.wp.com\/sparkonelabs.com\/home\/wp-content\/uploads\/2025\/07\/Screenshot_20250704_174526.png?fit=1024%2C502&amp;ssl=1\" alt=\"\" class=\"wp-image-1813\" srcset=\"https:\/\/i0.wp.com\/sparkonelabs.com\/home\/wp-content\/uploads\/2025\/07\/Screenshot_20250704_174526.png?w=1237&amp;ssl=1 1237w, https:\/\/i0.wp.com\/sparkonelabs.com\/home\/wp-content\/uploads\/2025\/07\/Screenshot_20250704_174526.png?resize=300%2C147&amp;ssl=1 300w, https:\/\/i0.wp.com\/sparkonelabs.com\/home\/wp-content\/uploads\/2025\/07\/Screenshot_20250704_174526.png?resize=1024%2C502&amp;ssl=1 1024w, https:\/\/i0.wp.com\/sparkonelabs.com\/home\/wp-content\/uploads\/2025\/07\/Screenshot_20250704_174526.png?resize=768%2C377&amp;ssl=1 768w, https:\/\/i0.wp.com\/sparkonelabs.com\/home\/wp-content\/uploads\/2025\/07\/Screenshot_20250704_174526.png?resize=600%2C294&amp;ssl=1 600w\" sizes=\"(max-width: 1170px) 100vw, 1170px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">As you can see, the user prompt directing the process produced a successful result, capturing all the values and formatting the response in a way the user can easily see where the unknown values resided in the data.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Using Ollama models and a CSV data file, it was troublesome to accurately parse for values. (The LLMs would almost get it right, but skip values present in a search, except for under certain prompts and conditions.) So, I started wondering, would it be easier to generate code to parse the CSV directly using the<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"nf_dc_page":"","om_disable_all_campaigns":false,"pagelayer_contact_templates":[],"_pagelayer_content":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_hide_featured_image":false,"_jetpack_featured_image_duplicate_dismissed":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1807","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Proof of Concept: Small Model Accurate CSV Parsing with Auto Debugging Code Execution Workflow - SparkOne() Labs Official Site<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sparkonelabs.com\/home\/2025\/07\/04\/proof-of-concept-small-model-accurate-csv-parsing-with-auto-debugging-code-execution-workflow\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Proof of Concept: Small Model Accurate CSV Parsing with Auto Debugging Code Execution Workflow - SparkOne() Labs Official Site\" \/>\n<meta property=\"og:description\" content=\"Using Ollama models and a CSV data file, it was troublesome to accurately parse for values. 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