1 files changed, 1 insertions, 1 deletions
diff --git a/src/client/apis/gpt/GPT.ts b/src/client/apis/gpt/GPT.ts
index 0d1d77f06..ba8106c4d 100644
--- a/src/client/apis/gpt/GPT.ts
+++ b/src/client/apis/gpt/GPT.ts
@@ -58,7 +58,7 @@ const callTypeMap: { [type: string]: GPTCallOpts } = {
     },
     template: { model: 'gpt-4-turbo', maxTokens: 512, temp: 0.5, prompt: 'You will be given a list of field descriptions for multiple templates in the format {field #0: “description”}{field #1: “description”}{...}, and a list of column descriptions in the format {“title”: “description}{...}. Your job is to match columns with fields based on their descriptions. Your output should be in the following JSON format: {“Template title”:{“#”: “title”, “#”: “title”, “#”: “title” …}, “Template title”:{“#”: “title”, “#”: “title”, “#”: “title” …}} where “Template title” represents the template,  # represents the field # and “title” the title of the column assigned to it. A filled out example might look like {“fivefield2”:{0:”Name”, 1:”Image”, 2:”Caption”, 3:”Position”, 4:”Stats”}, “fivefield3”:{0:”Image”, 1:”Name”, 2:”Caption”, 3:”Stats”, 4:”Position”}. Include one object for each template. IT IS VERY IMPORTANT THAT YOU ONLY INCLUDE TEXT IN THE FORMAT ABOVE, WITH NO ADDITIONS WHATSOEVER. Do not include extraneous ‘#’ characters, ‘column’ for columns, or ‘template’ for templates: ONLY THE TITLES AND NUMBERS. Do this for each template whose fields are described. The descriptions are as follows:  ' },
     vizsum: { model: 'gpt-4-turbo', maxTokens: 512, temp: 0.5, prompt: 'Your job is to provide brief descriptions for columns in a dataset based on example rows. Your descriptions should be geared towards how each column’s data might fit together into a visual template. Would they make good titles, main focuses, captions, descriptions, etc. Pay special attention to connections between columns, i.e. is there one column that specifically seems to describe/be related to another more than the rest? You should provide your analysis in JSON format like so: {“col1”:”description”, “col2”:”description”, …}. DO NOT INCLUDE ANY OTHER TEXT, ONLY THE JSON.'},
-    vizsum2: { model: 'gpt-4-turbo', maxTokens: 512, temp: 0.5, prompt: 'Your job is to provide structured information on columns in a dataset based on example rows. You will categorize each column in two ways: by type and size. The size categories are as follows: tiny (one or two words), small (a sentence/multiple words), medium (a few sentences), large (a longer paragraph), and huge (a very long or multiple paragraphs). The type categories are as follows: visual (links/file paths to images, pdfs, maps, or any other visual media type), and text (plain text that isn’t a link/file path). Visual media should be assumed to have size “medium” “large” or “huge”. You will give your responses in JSON format, like so: {“col1”:{“type”:”text”, “size”:”small”}, “col2”:{“type”:”visual”, “size”:”medium”}, …}. DO NOT INCLUDE ANY OTHER TEXT, ONLY THE JSON.'} 
+    vizsum2: { model: 'gpt-4-turbo', maxTokens: 512, temp: 0.5, prompt: 'Your job is to provide structured information on columns in a dataset based on example rows. You will categorize each column in two ways: by type and size. The size categories are as follows: tiny (one or two words), small (a sentence/multiple words), medium (a few sentences), large (a longer paragraph), and huge (a very long or multiple paragraphs). The type categories are as follows: visual (links/file paths to images, pdfs, maps, or any other visual media type), and text (plain text that isn’t a link/file path). Visual media should be assumed to have size “medium” “large” or “huge”. You will give your responses in JSON format, like so: {“title (of column)”:{“type”:”text”, “size”:”small”}, “title (of column)”:{“type”:”visual”, “size”:”medium”}, …}. DO NOT INCLUDE ANY OTHER TEXT, ONLY THE JSON.'} 
 };
 let lastCall = '';
 let lastResp = '';