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SQL Query and Response 2.txt
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SQL Query and Response 2.txt
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Prompt1 Observation Sql query Sql Result GPT Response InputImageAvailable Prompt in different language styles
Find me images of Aurelia aurita "To find the images from the database you need to format the output such that it is a json and has species array that has species concept as concept, image url as url, image id as image_id and species bounding box id as id
Since the prompt does not specify number of images. Limit the query to top 10 images" "SELECT TOP 10 i.url, i.id as image_id, b.concept, b.id
FROM images i
JOIN bounding_boxes b ON i.id = b.image_id
WHERE b.concept = 'Aurelia aurita'" "[
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_03_31_28.png"",
""image_id"": 2586149,
""concept"": ""Aurelia aurita"",
""id"": 2586151
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_03_31_28.png"",
""image_id"": 2586149,
""concept"": ""Aurelia aurita"",
""id"": 2586152
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_03_31_28.png"",
""image_id"": 2586149,
""concept"": ""Aurelia aurita"",
""id"": 2586153
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_03_31_28.png"",
""image_id"": 2586149,
""concept"": ""Aurelia aurita"",
""id"": 2586154
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_03_31_28.png"",
""image_id"": 2586149,
""concept"": ""Aurelia aurita"",
""id"": 2586155
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_03_31_28.png"",
""image_id"": 2586149,
""concept"": ""Aurelia aurita"",
""id"": 2586156
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Doc%20Ricketts\/images\/0199\/00_01_04_07.png"",
""image_id"": 2586296,
""concept"": ""Aurelia aurita"",
""id"": 2586298
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Ventana\/images\/3082\/04_59_42_11.png"",
""image_id"": 2587742,
""concept"": ""Aurelia aurita"",
""id"": 2587744
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/0893\/06_13_15_15.png"",
""image_id"": 2588036,
""concept"": ""Aurelia aurita"",
""id"": 2588038
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Ventana\/images\/2934\/05_23_41_12.png"",
""image_id"": 2589496,
""concept"": ""Aurelia aurita"",
""id"": 2589498
}
]" "{
""prompt"": ""Find me images of Aurelia aurita"",
""findings"": ""1. Since prompt asks to find images, I need to set the output type as image. 2. Image should be of specific species 'Aurlia Aurita' 3. Image output must have id, concept of species and url of it's image."",
""outputType"": ""images"",
""sqlServerQuery"": ""SELECT TOP 10 i.url, i.id as image_id, b.concept, b.id FROM images i JOIN bounding_boxes b ON i.id = b.image_id WHERE b.concept = 'Aurelia aurita'"",
""responseText"": ""Here are some images of Aurelia aurita:""
}" FALSE
"Create a bar chart with temperature ranges for Aurelia Aurita and Pycnopodia helianthoides from 0 C to 100 C in 20 C increments, color-coded for each species." "The prompt asks to display a chart, I will be using vega lite to display it. I should first generate the vega lite schema and then generate a sql query that outputs the data that will be used by vega lite according to the schema" "WITH TemperatureRanges AS (
SELECT 0 AS Temperature
UNION ALL
SELECT Temperature + 5
FROM TemperatureRanges
WHERE Temperature < 15
)
SELECT
CASE
WHEN i.temperature_celsius >= 0 AND i.temperature_celsius < 5 THEN '0-5 C'
WHEN i.temperature_celsius >= 5 AND i.temperature_celsius < 10 THEN '5-10 C'
WHEN i.temperature_celsius >= 10 AND i.temperature_celsius < 15 THEN '10-15 C'
WHEN i.temperature_celsius >= 15 AND i.temperature_celsius < 20 THEN '15-20 C'
END AS TemperatureRange,
COUNT(CASE WHEN b.concept = 'Aurelia aurita' THEN 1 END) AS AureliaAuritaCount,
COUNT(CASE WHEN b.concept = 'Pycnopodia helianthoides' THEN 1 END) AS PycnopodiaHelianthoidesCount
FROM
dbo.images i
JOIN
dbo.bounding_boxes b ON i.id = b.image_id
JOIN
TemperatureRanges tr ON i.temperature_celsius >= tr.Temperature AND i.temperature_celsius < tr.Temperature + 20
WHERE
b.concept IN ('Aurelia aurita', 'Pycnopodia helianthoides')
GROUP BY
CASE
WHEN i.temperature_celsius >= 0 AND i.temperature_celsius < 5 THEN '0-5 C'
WHEN i.temperature_celsius >= 5 AND i.temperature_celsius < 10 THEN '5-10 C'
WHEN i.temperature_celsius >= 10 AND i.temperature_celsius < 15 THEN '10-15 C'
WHEN i.temperature_celsius >= 15 AND i.temperature_celsius < 20 THEN '15-20 C'
END
ORDER BY
TemperatureRange;" "{
""prompt"": ""Create a bar chart with temperature ranges for Aurelia Aurita and Pycnopodia helianthoides from 0 C to 20 C in 5 C increments, color-coded for each species."",
""findings"": ""1. The prompt specifies to generate chart, so the output type is a visualization since it is used to generate the chart. 2. Chart must show temperature ranges for Aurelia Aurita and Pycnopodia helianthoides from 0 C to 20 C in 5 C increments. 3. The temperature levels can thus be 0-5, 5-10, 10-15, 15-20. 4. For each species the visualization must be color coded. 4. The response type is visualization, so sample data must be generated."",
""outputType"": ""visualization"",
""sqlServerQuery"": ""WITH TemperatureRanges AS ( SELECT 0 AS Temperature UNION ALL SELECT Temperature + 5 FROM TemperatureRanges WHERE Temperature < 15 ) SELECT CASE WHEN i.temperature_celsius >= 0 AND i.temperature_celsius < 5 THEN '0-5 C' WHEN i.temperature_celsius >= 5 AND i.temperature_celsius < 10 THEN '5-10 C' WHEN i.temperature_celsius >= 10 AND i.temperature_celsius < 15 THEN '10-15 C' WHEN i.temperature_celsius >= 15 AND i.temperature_celsius < 20 THEN '15-20 C' END AS TemperatureRange, COUNT(CASE WHEN b.concept = 'Aurelia aurita' THEN 1 END) AS AureliaAuritaCount, COUNT(CASE WHEN b.concept = 'Pycnopodia helianthoides' THEN 1 END) AS PycnopodiaHelianthoidesCount FROM dbo.images i JOIN dbo.bounding_boxes b ON i.id = b.image_id JOIN TemperatureRanges tr ON i.temperature_celsius >= tr.Temperature AND i.temperature_celsius < tr.Temperature + 20 WHERE b.concept IN ('Aurelia aurita', 'Pycnopodia helianthoides') GROUP BY CASE WHEN i.temperature_celsius >= 0 AND i.temperature_celsius < 5 THEN '0-5 C' WHEN i.temperature_celsius >= 5 AND i.temperature_celsius < 10 THEN '5-10 C' WHEN i.temperature_celsius >= 10 AND i.temperature_celsius < 15 THEN '10-15 C' WHEN i.temperature_celsius >= 15 AND i.temperature_celsius < 20 THEN '15-20 C' END ORDER BY TemperatureRange;"",
""sampleData"": ""{'TemperatureRange':['0-5','5-10','10-15','15-20'],'AureliaAuritaCount':[23,10,5,3],'PycnopodiaHelianthoidesCount':[2,55,7,3]}"",
""plotlyCode"": ""import plotly.graph_objs as go\ndef drawVisualization(data):\n\ttrace1 = go.Bar(\n\t\tx=data['TemperatureRange'],\n\t\ty=data['AureliaAuritaCount'],\n\t\tname='Aurelia Aurita',\n\t\tmarker=dict(color='rgb(55, 83, 109)')\n\t)\n\ttrace2 = go.Bar(\n\t\tx=data['TemperatureRange'],\n\t\ty=data['PycnopodiaHelianthoidesCount'],\n\t\tname='Pycnopodia helianthoides',\n\t\tmarker=dict(color='rgb(26, 118, 255)')\n\t)\n\tdata = [trace1, trace2]\n\tlayout = go.Layout(\n\t\ttitle='Temperature Range Counts for Aurelia Aurita and Pycnopodia helianthoides',\n\t\txaxis=dict(title='Temperature Range ( C)'),\n\t\tyaxis=dict(title='Count'),\n\t\tbarmode='group'\n\t)\n\tfig = go.Figure(data=data, layout=layout)\n\treturn fig"",
""responseText"": ""Below is the bar chart with temperature ranges for Aurelia Aurita and Pycnopodia helianthoides from 0 C to 20 C in 5 C increments""
}" FALSE
What is the maximum and minumum latitude and longitude of Atlantic Ocean The prompt asks to find maximum and minumum latitude and longitude of Atlantic Ocean. It can be found based on the marine_regions table "SELECT min_latitude, min_longitude, max_latitude, max_longitude
From marine_regions WHERE name = 'Atlantic Ocean'" "min_latitude min_longitude max_latitude max_longitude
-60 -83.008056640625 68.62359619140625 20" "{
""prompt"": ""What is the maximum and minumum latitude and longitude of Atlantic Ocean"",
""findings"": ""1. Need to find maximum and minimum latitude and longitude, which is number, so output is text. 2. The sql result should include the maximum and minimum latitude as a json. 3. The response text should be output so that maximum and minimum latitude and longitude result can be formatted into the text."",
""outputType"": ""text"",
""sqlServerQuery"": ""SELECT min_latitude, min_longitude, max_latitude, max_longitude From marine_regions WHERE name = 'Atlantic Ocean'"",
""responseText"": ""The maximum latitude and longitude for Atlantic Ocean are {max_latitude} and {max_longitude} respectively and the minimum latitude and longitude for Atlantic Ocean are {min_latitude} and {min_longitude} respectively""
}
" FALSE
Find me images similar for species with bounding box id 2290084 "IF OBJECT_ID('tempdb..#InputFeatureVectors') IS NOT NULL
DROP TABLE #InputFeatureVectors;
CREATE TABLE #InputFeatureVectors (
vector_index INT,
vector_value DECIMAL(18,5)
);
INSERT INTO #InputFeatureVectors (vector_index, vector_value)
VALUES
{imageFeatureVector};
WITH FeatureVectors AS (
SELECT
BBFV.bounding_box_id AS BoxID,
SUM(IFV.vector_value * BBFV.vector_value) AS DotProduct
FROM #InputFeatureVectors IFV
INNER JOIN bounding_box_image_feature_vectors BBFV
ON IFV.vector_index = BBFV.vector_index
GROUP BY BBFV.bounding_box_id
),
Magnitude AS (
SELECT
id AS BoxID,
magnitude
FROM bounding_boxes
),
InputMagnitude AS (
SELECT SQRT(SUM(POWER(vector_value, 2))) AS magnitude
FROM #InputFeatureVectors
),
SimilaritySearch AS (
SELECT TOP 10
FV.BoxID as id,
FV.DotProduct / (IM.magnitude * M.magnitude) AS CosineSimilarity
FROM FeatureVectors FV
CROSS JOIN InputMagnitude IM
INNER JOIN Magnitude M
ON FV.BoxID = M.BoxID
WHERE IM.magnitude IS NOT NULL
AND M.magnitude IS NOT NULL
AND IM.magnitude > 0
AND M.magnitude > 0
ORDER BY CosineSimilarity DESC
)
SELECT
SS.id AS id,
BB.concept,
IMG.url,
SS.CosineSimilarity
FROM SimilaritySearch SS
INNER JOIN bounding_boxes BB
ON BB.id = SS.id
INNER JOIN images IMG
ON BB.image_id = IMG.id;
DROP TABLE #InputFeatureVectors;" """[
{
""""id"""": 2281439,
""""concept"""": """"Brachyura"""",
""""url"""": """",
}
]""" "{
""prompt"": ""Find me images similar to image found in bounding box 2290084"",
""findings"": ""1. The result of the prompt would be images, thus the output type is images. 2. Image output must have id, concept of species and url of it's image."",
""outputType"": ""images"",
""similarImageIDs"": [2290084],
""similarBoundingBoxIDs"": [],
""similarImageSearch"": true,
""sqlServerQuery"": ""SELECT TOP 10 SS.bb1, SS.bb2, BB.concept, IMG.url, SS.CosineSimilarity FROM SimilaritySearch SS INNER JOIN bounding_boxes BB ON BB.id = SS.bb2 INNER JOIN images IMG ON BB.image_id = IMG.id ORDER BY SS.CosineSimilarity DESC;"",
""responseText"": ""Here are the images similar to image found in bounding box 2290084.""
}" TRUE "DECLARE @InputBoundingBoxID INT; SET @InputBoundingBoxID = 2290084; WITH InputFeatureVectors AS ( SELECT vector_index, vector_value FROM bounding_box_image_feature_vectors WHERE bounding_box_id = @InputBoundingBoxID ), FeatureVectors AS ( SELECT BBFV.bounding_box_id AS BoxID, SUM(IFV.vector_value * BBFV.vector_value) AS DotProduct FROM InputFeatureVectors IFV INNER JOIN bounding_box_image_feature_vectors BBFV ON IFV.vector_index = BBFV.vector_index GROUP BY BBFV.bounding_box_id ), Magnitude AS ( SELECT id AS BoxID, magnitude FROM bounding_boxes ), InputMagnitude AS ( SELECT bounding_box_id AS BoxID, SQRT(SUM(POWER(vector_value, 2))) AS magnitude FROM bounding_box_image_feature_vectors WHERE bounding_box_id = @InputBoundingBoxID GROUP BY bounding_box_id ), SimilaritySearch AS ( SELECT TOP 10 FV.BoxID as id, FV.DotProduct / (IM.magnitude * M.magnitude) AS CosineSimilarity FROM FeatureVectors FV INNER JOIN InputMagnitude IM ON FV.BoxID = IM.BoxID INNER JOIN Magnitude M ON FV.BoxID = M.BoxID WHERE IM.magnitude IS NOT NULL AND M.magnitude IS NOT NULL AND IM.magnitude > 0 AND M.magnitude > 0 ORDER BY CosineSimilarity DESC ) SELECT SS.id AS id, BB.concept, IMG.url, SS.CosineSimilarity FROM SimilaritySearch SS INNER JOIN bounding_boxes BB ON BB.id = SS.id INNER JOIN images IMG ON BB.image_id = IMG.id;"
Generate a comparative box plot for environmental parameters for Anuropus and Eulimidae "{
""prompt"": ""Generate a comparative box plot for environmental parameters for Anuropus and Eulimidae"",
""findings"": ""1. The prompt says to generate comparative box plot for environmental parameters for Anuropus and Eulimidae, so the output is a visualization. 2. The prompt only says Environmental Conditions, so using the database schema we can say that there are 4 environmental factors and they are Temperature, Salinity, Oxygen and Pressure. 3.Since the output is a visualization, the sample data and plotlyCode needs to be generated"",
""outputType"": ""visualization"",
""sqlServerQuery"": ""SELECT bb.concept, i.depth_meters, i.temperature_celsius, i.salinity, i.oxygen_ml_l FROM dbo.bounding_boxes bb JOIN dbo.images i ON bb.image_id = i.id WHERE bb.concept IN ('Anuropus', 'Eulimidae');"",
""sampleData"": ""{ 'concept': ['Anuropus', 'Anuropus', 'Eulimidae', 'Eulimidae'], 'depth_meters': [100, 200, 150, 250], 'temperature_celsius': [10, 15, 12, 16], 'salinity': [35, 36, 35, 37], 'oxygen_ml_l': [5, 6, 7, 8]}"",
""plotlyCode"": ""import pandas as pd\nimport plotly.graph_objs as go\nfrom plotly.subplots import make_subplots\ndef drawVisualization(data):\n\tdf = pd.DataFrame(data)\n\tfig = make_subplots(rows=2, cols=2, subplot_titles=('Depth', 'Temperature', 'Salinity', 'Oxygen'))\n\tfig.add_trace(go.Box(y=df['depth_meters'], x=df['concept'], name='Depth'), row=1, col=1)\n\tfig.add_trace(go.Box(y=df['temperature_celsius'], x=df['concept'], name='Temperature'), row=1, col=2)\n\tfig.add_trace(go.Box(y=df['salinity'], x=df['concept'], name='Salinity'), row=2, col=1)\n\tfig.add_trace(go.Box(y=df['oxygen_ml_l'], x=df['concept'], name='Oxygen'), row=2, col=2)\n\tfig.update_layout(\n\t\ttitle='Comparative Box Plot for Environmental Parameters for Anuropus and Eulimidae',\n\t\tshowlegend=False\n\t)\n\treturn fig"",
""responseText"": ""Below is a comparative box plot for environmental parameters for Anuropus and Eulimidae""
}
" FALSE
Find me images of Aurelia aurita in Monterey Bay "To find the images from the database so you need to format the output such that it is a json and has species array that has species concept as concept, image url as url, image id as image_id and species bounding box id as id
The prompt is asking image for a particular region, so its better to add the latitude and longitude information to the species data too. Since you need to find the images of Aurelia aurita in Monterey Bay, use the marine_regions table to lookup the Monterey Bay min/max latitude/longitude values to search for images.
Since the prompt does not specify number of images. Limit the query to top 10 images" "SELECT i.urlSELECT TOP 10 i.url, i.id as image_id, b.concept, b.id, i.latitude, i.longitude
FROM images i
JOIN bounding_boxes b ON i.id = b.image_id
JOIN marine_regions mr ON i.latitude BETWEEN mr.min_latitude AND mr.max_latitude AND
i.longitude BETWEEN mr.min_longitude AND mr.max_longitude
WHERE b.concept = 'Aurelia aurita'
AND mr.name = 'Monterey Bay'
FROM images i
JOIN bounding_boxes b ON i.id = b.image_id
JOIN marine_regions mr ON i.latitude BETWEEN mr.min_latitude AND mr.max_latitude
WHERE b.concept = 'Aurelia aurita'
AND mr.name = 'Pacific Ocean'" "[
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_04_55_15.png"",
""image_id"": 2590409,
""concept"": ""Aurelia aurita"",
""id"": 2590411,
""latitude"": 3.669970800000000e+001,
""longitude"": -1.220502030000000e+002
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_04_55_15.png"",
""image_id"": 2590409,
""concept"": ""Aurelia aurita"",
""id"": 2590412,
""latitude"": 3.669970800000000e+001,
""longitude"": -1.220502030000000e+002
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_04_55_15.png"",
""image_id"": 2590409,
""concept"": ""Aurelia aurita"",
""id"": 2590413,
""latitude"": 3.669970800000000e+001,
""longitude"": -1.220502030000000e+002
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_04_55_15.png"",
""image_id"": 2590409,
""concept"": ""Aurelia aurita"",
""id"": 2590414,
""latitude"": 3.669970800000000e+001,
""longitude"": -1.220502030000000e+002
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_04_55_15.png"",
""image_id"": 2590409,
""concept"": ""Aurelia aurita"",
""id"": 2590415,
""latitude"": 3.669970800000000e+001,
""longitude"": -1.220502030000000e+002
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_04_55_15.png"",
""image_id"": 2590409,
""concept"": ""Aurelia aurita"",
""id"": 2590416,
""latitude"": 3.669970800000000e+001,
""longitude"": -1.220502030000000e+002
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_04_55_15.png"",
""image_id"": 2590409,
""concept"": ""Aurelia aurita"",
""id"": 2590417,
""latitude"": 3.669970800000000e+001,
""longitude"": -1.220502030000000e+002
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_04_55_15.png"",
""image_id"": 2590409,
""concept"": ""Aurelia aurita"",
""id"": 2590418,
""latitude"": 3.669970800000000e+001,
""longitude"": -1.220502030000000e+002
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_04_55_15.png"",
""image_id"": 2590409,
""concept"": ""Aurelia aurita"",
""id"": 2590419,
""latitude"": 3.669970800000000e+001,
""longitude"": -1.220502030000000e+002
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_04_55_15.png"",
""image_id"": 2590409,
""concept"": ""Aurelia aurita"",
""id"": 2590420,
""latitude"": 3.669970800000000e+001,
""longitude"": -1.220502030000000e+002
}
]" "{
""prompt"": ""Find me images of Aurelia aurita in Monterey Bay"",
""findings"": ""1. Since prompt asks to generate images, I need to generate image type response. 2. Image should be of specific species 'Aurlia Aurita' in Monterey Bay. 3. Image output must have id, concept of species and url of it's image. 4.Since the prompt asks images from place named Monterey Bay, I should also add the latitude and longitude detail."",
""outputType"": ""images"",
""sqlServerQuery"": ""SELECT TOP 10 i.url, i.id as image_id, b.concept, b.id, i.latitude, i.longitude FROM images i JOIN bounding_boxes b ON i.id = b.image_id JOIN marine_regions mr ON i.latitude BETWEEN mr.min_latitude AND mr.max_latitude AND i.longitude BETWEEN mr.min_longitude AND mr.max_longitude WHERE b.concept = 'Aurelia aurita' AND mr.name = 'Monterey Bay'"",
""responseText"": ""Images of Aurelia aurita in Monterey Bay are provided below""
}" FALSE
Which location has the most Aurelia aurita "The prompt asks for location that has the most Aurelia aurita. The prompt does not provide detail on what location would mean. So, use the marine_regions table and find the count of bounding boxes of Aurelia aurita in the regions, sort them from highest and smallest and output the top 15 results
" "SELECT TOP 10 r.name, COUNT(b.id) AS count
FROM dbo.bounding_boxes b
JOIN dbo.images i ON b.image_id = i.id
JOIN dbo.marine_regions r ON i.latitude BETWEEN r.min_latitude AND r.max_latitude
AND i.longitude BETWEEN r.min_longitude AND r.max_longitude
WHERE b.concept = 'Aurelia aurita'
GROUP BY r.name
ORDER BY count DESC" "name count
Global 110
California Current 110
North America 110
SeaVox Sea Areas 110
United States Exclusive Economic Zone 110
World 110
World Oceans 110
United States 109
California Borderland 109
Greater Monterey Bay 109
Monterey Bay 108
Monterey Bay Region 108
Pacific Northwest 1
Canadian Exclusive Economic Zone 1" "{
""prompt"": ""Which location has the most Aurelia aurita"",
""findings"": ""1. The prompt asks the location of Aurelia Aurita, thus output should have name of places which can be shown in a table making the output type table. 2. The output data should have the location name and the species count."",
""outputType"": ""table"",
""sqlServerQuery"": ""SELECT TOP 10 r.name, COUNT(b.id) AS count FROM dbo.bounding_boxes b JOIN dbo.images i ON b.image_id = i.id JOIN dbo.marine_regions r ON i.latitude BETWEEN r.min_latitude AND r.max_latitude AND i.longitude BETWEEN r.min_longitude AND r.max_longitude WHERE b.concept = 'Aurelia aurita' GROUP BY r.name ORDER BY count DESC"",
""responseText"": ""The locations where Aurelia aurita are found and their count is shown in the table below""
}" FALSE
What species are frequently found near Aurelia aurita? "The prompt asks for frequently found species near Aurelia aurita without providing detail what frequent means numerically. So, find the number of images that have Aurelia aurita and another species. Use the bounding_boxes table to find another species that is near Aurelia aurita and output the name and the frequency
" "SELECT b2.concept AS species, COUNT(*) AS frequency
FROM dbo.bounding_boxes b1
JOIN dbo.bounding_boxes b2 ON b1.image_id = b2.image_id
WHERE b1.concept = 'Aurelia aurita' AND b2.concept <> 'Aurelia aurita'
GROUP BY b2.concept
ORDER BY frequency DESC;" "species frequency
Chrysaora fuscescens 211
Siphonophorae 20
Pyrosoma 4
Chrysaora 2
Mitrocomidae 1" "{
""prompt"": ""What species are frequently found near Aurelia aurita?"",
""findings"": ""1. The prompt asks the species that are frequently found near Aurelia Aurita, thus output should have name of species which can be shown in a table making the output type table. 2. The output data should have the name of the species and their count. 3. When the output is sorted in descending order, it will easier to view the data."",
""outputType"": ""table"",
""sqlServerQuery"": ""SELECT b2.concept AS species, COUNT(*) AS frequency FROM dbo.bounding_boxes b1 JOIN dbo.bounding_boxes b2 ON b1.image_id = b2.image_id WHERE b1.concept = 'Aurelia aurita' AND b2.concept <> 'Aurelia aurita' GROUP BY b2.concept ORDER BY frequency DESC;"",
""responseText"": ""The table below shows the frequency of species frequently found near Aurelia aurita""
}" FALSE
Generate a bar chart showing the year the images were taken "The prompt asks to display a chart, I will be using vega lite to display it. I should first generate the vega lite schema and then generate a sql query that outputs the data that will be used by vega lite according to the schema" "{
""outputType"": ""vegaLite"",
""responseText"": ""A bar chart showing the year the images were taken"",
""vegaSchema"": {
""$schema"": ""https://vega.github.io/schema/vega-lite/v4.json"",
""mark"": ""bar"",
""encoding"": {
""x"": {""field"": ""Year"", ""type"": ""ordinal""},
""y"": {""field"": ""ImageCount"", ""type"": ""quantitative""}
}
},
""sqlServerQuery"": ""SELECT YEAR([timestamp]) AS Year, COUNT(*) AS ImageCount FROM dbo.images GROUP BY YEAR([timestamp]) ORDER BY Year; data: {""message"": ""Formatting response""}""
}" "{
""prompt"": ""Generate a bar chart showing the year the images were taken"",
""findings"": ""1. The prompt specifies to generate bar chart, so the output type is a visualization. 2. Chart must show the year the images were taken. 3. The response type is visualization, so sampleData must be generated along with the plotlyCode."",
""outputType"": ""visualization"",
""sqlServerQuery"": ""SELECT YEAR([timestamp]) AS Year, COUNT(*) AS ImageCount FROM dbo.images GROUP BY YEAR([timestamp]) ORDER BY Year;"",
""sampleData"": ""{'Year': ['2024', '2023', '2022'], 'ImageCount':[10,12,5]}"",
""plotlyCode"": ""import plotly.graph_objs as go\ndef drawVisualization(data):\n\ttrace = go.Bar(\n\t\tx=data['Year'],\n\t\ty=data['ImageCount'],\n\t\tmarker=dict(color='rgb(158,202,225)', line=dict(color='rgb(8,48,107)', width=1.5))\n\t)\n\tdata = [trace]\n\tlayout = go.Layout(\n\t\ttitle='Image Count per Year',\n\t\txaxis=dict(title='Year'),\n\t\tyaxis=dict(title='Image Count')\n\t)\n\tfig = go.Figure(data=data, layout=layout)\n\treturn fig"",
""responseText"": ""A bar chart below shows the year the images were taken""
}" FALSE
How often are Aurelia aurita found near other species? "The prompt does not provide what often would mean numerically, so first find all distinct images in the database that has Aurelia aurita. Now, find the images that have Aurelia aurita and another species. Now output the total AureliaImages and images that have other species
" "WITH AureliaImages AS (
SELECT DISTINCT image_id
FROM dbo.bounding_boxes
WHERE concept = 'Aurelia aurita'
),
OtherSpeciesInAureliaImages AS (
SELECT DISTINCT bb1.image_id
FROM dbo.bounding_boxes AS bb1
JOIN dbo.bounding_boxes AS bb2 ON bb1.image_id = bb2.image_id
WHERE bb1.concept = 'Aurelia aurita' AND bb2.concept != 'Aurelia aurita'
)
SELECT
COUNT(*) AS 'Frequency',
(SELECT COUNT(*) FROM AureliaImages) AS 'TotalAureliaImages'
FROM OtherSpeciesInAureliaImages;" "Frequency TotalAureliaImages
9 45" "{
""prompt"": ""How often are Aurelia aurita found near other species?"",
""findings"": ""1. The prompt asks the species that are frequently found near Aurelia Aurita, thus output should have frequency of other species which can be shown in a table making the output type table. 2. The prompt asks the fequency so it would be better to put the total images of Aurelia Aurita images along with the frquency of other distinct species."",
""outputType"": ""table"",
""sqlServerQuery"": ""WITH AureliaImages AS ( SELECT DISTINCT image_id FROM dbo.bounding_boxes WHERE concept = 'Aurelia aurita' ), OtherSpeciesInAureliaImages AS ( SELECT DISTINCT bb1.image_id FROM dbo.bounding_boxes AS bb1 JOIN dbo.bounding_boxes AS bb2 ON bb1.image_id = bb2.image_id WHERE bb1.concept = 'Aurelia aurita' AND bb2.concept != 'Aurelia aurita' ) SELECT COUNT(*) AS 'Frequency', (SELECT COUNT(*) FROM AureliaImages) AS 'TotalAureliaImages' FROM OtherSpeciesInAureliaImages;"",
""responseText"": ""The table below shows the frequency of other species found near Aurelia Aurita""
}" FALSE
Find me similar images of species that are found depth level of 5000m and lower "{
""prompt"": ""Find me similar images of species that are found depth level of 5000m and lower"",
""findings"": ""1. The result of the prompt would be images, thus the output type is images. 2. The sql generated should have a place to add the input image data with name {imageFeatureVector} 3. The bounding boxes have the feature vector for the image of species and images table has depth column, thus we need to find the bounding boxes using the images table as per the depth condition provided. 4.Image output must have id, concept of species and url of it's image."",
""outputType"": ""images"",
""similarImageIDs"": [],
""similarBoundingBoxIDs"": [],
""similarImageSearch"": true,
""sqlServerQuery"": ""SELECT TOP 10 SS.bb1, SS.bb2, BB.concept, IMG.url, SS.CosineSimilarity FROM SimilaritySearch SS INNER JOIN bounding_boxes BB ON BB.id = SS.bb2 INNER JOIN images IMG ON BB.image_id = IMG.id WHERE IMG.depth_meters < 5000 ORDER BY SS.CosineSimilarity DESC;"",
""responseText"": ""Here are the similar images of species that are found below depth level of 5000m and lower""
}" TRUE "IF OBJECT_ID('tempdb..#InputFeatureVectors') IS NOT NULL DROP TABLE #InputFeatureVectors; CREATE TABLE #InputFeatureVectors ( vector_index INT, vector_value DECIMAL(18,5) ); INSERT INTO #InputFeatureVectors (vector_index, vector_value) VALUES {imageFeatureVector}; WITH FeatureVectors AS ( SELECT BBFV.bounding_box_id AS BoxID, SUM(IFV.vector_value * BBFV.vector_value) AS DotProduct FROM #InputFeatureVectors IFV INNER JOIN bounding_box_image_feature_vectors BBFV ON IFV.vector_index = BBFV.vector_index GROUP BY BBFV.bounding_box_id ), Magnitude AS ( SELECT id AS BoxID, magnitude FROM bounding_boxes ), InputMagnitude AS ( SELECT SQRT(SUM(POWER(vector_value, 2))) AS magnitude FROM #InputFeatureVectors ), SimilaritySearch AS ( SELECT TOP 10 FV.BoxID as id, FV.DotProduct / (IM.magnitude * M.magnitude) AS CosineSimilarity FROM FeatureVectors FV CROSS JOIN InputMagnitude IM INNER JOIN Magnitude M ON FV.BoxID = M.BoxID WHERE IM.magnitude IS NOT NULL AND M.magnitude IS NOT NULL AND IM.magnitude > 0 AND M.magnitude > 0 ORDER BY CosineSimilarity DESC ) SELECT SS.id AS id, BB.concept, IMG.url, SS.CosineSimilarity FROM SimilaritySearch SS INNER JOIN bounding_boxes BB ON BB.id = SS.id INNER JOIN images IMG ON BB.image_id = IMG.id; DROP TABLE #InputFeatureVectors;"
Generate a cluster plot of Observations Based on Environmental Conditions "{
""prompt"": ""Generate a cluster plot of Observations Based on Environmental Conditions"",
""findings"": ""1. The prompt says to create a cluster plot, so the output is a visualization. 2. The database has environment conditions specified in depth_meters, temperature_celsius, salinity and oxygen_ml_l. 3.Since the output is a visualization, the sample data and plotlyCode needs to be generated"",
""outputType"": ""visualization"",
""sqlServerQuery"": ""SELECT i.depth_meters, i.temperature_celsius, i.salinity, i.oxygen_ml_l FROM dbo.images i WHERE i.depth_meters IS NOT NULL AND i.temperature_celsius IS NOT NULL AND i.salinity IS NOT NULL AND i.oxygen_ml_l IS NOT NULL;"",
""sampleData"": ""{ 'depth_meters': [100, 200, 300, 400], 'temperature_celsius': [10, 15, 20, 25], 'salinity': [35, 36, 37, 38], 'oxygen_ml_l': [6, 5, 4, 3]}"",
""plotlyCode"": ""import plotly.graph_objs as go\nfrom sklearn.cluster import KMeans\nimport numpy as np\ndef drawVisualization(data):\n\tX = np.column_stack((data['depth_meters'], data['temperature_celsius'], data['salinity'], data['oxygen_ml_l']))\n\tkmeans = KMeans(n_clusters=3)\n\tkmeans.fit(X)\n\tlabels = kmeans.predict(X)\n\ttrace = go.Scatter3d(\n\t\tx=data['depth_meters'],\n\t\ty=data['temperature_celsius'],\n\t\tz=data['salinity'],\n\t\tmode='markers',\n\t\tmarker=dict(\n\t\t\tsize=12,\n\t\t\tcolor=labels, \n\t\t\tcolorscale='Viridis', \n\t\t\topacity=0.8\n\t\t)\n\t)\n\tdata = [trace]\n\tlayout = go.Layout(\n\t\ttitle='Cluster Analysis of Observations Based on Environmental Conditions',\n\t\tscene=dict(\n\t\t\txaxis=dict(title='Depth (meters)'),\n\t\t\tyaxis=dict(title='Temperature (Celsius)'),\n\t\t\tzaxis=dict(title='Salinity')\n\t\t),\n\t\tmargin=dict(l=0, r=0, b=0, t=0)\n\t)\n\tfig = go.Figure(data=data, layout=layout)\n\treturn fig"",
""responseText"": ""Below is the cluster plot of Observations Based on depth level, temperature level, salinity level and oxygen level.""
}" FALSE
Find me images of species 'Aurelia aurita' in Monterey bay and depth less than 5k meters "You need to find the images from the database so you need to format the output such that it is a json and has species array that has species concept as concept, image url as url, image id as image_id and species bounding box id as id
The prompt asks for species at depth less than 5k, so add the depth data too.
Use the bounding_boxes, images and marine_regions table to find the species images" "SELECT TOP 10 i.url, i.id as image_id, b.concept, b.id, i.depth_meters
FROM images i
JOIN bounding_boxes b ON i.id = b.image_id
JOIN marine_regions mr ON i.latitude BETWEEN mr.min_latitude AND mr.max_latitude
WHERE b.concept = 'Aurelia aurita'
AND mr.name = 'Monterey bay'
AND i.depth_meters < 5000" "[
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_04_55_15.png"",
""image_id"": 2590409,
""concept"": ""Aurelia aurita"",
""id"": 2590411,
""depth_meters"": 4.447999954223633e+001
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_04_55_15.png"",
""image_id"": 2590409,
""concept"": ""Aurelia aurita"",
""id"": 2590412,
""depth_meters"": 4.447999954223633e+001
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_04_55_15.png"",
""image_id"": 2590409,
""concept"": ""Aurelia aurita"",
""id"": 2590413,
""depth_meters"": 4.447999954223633e+001
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_04_55_15.png"",
""image_id"": 2590409,
""concept"": ""Aurelia aurita"",
""id"": 2590414,
""depth_meters"": 4.447999954223633e+001
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_04_55_15.png"",
""image_id"": 2590409,
""concept"": ""Aurelia aurita"",
""id"": 2590415,
""depth_meters"": 4.447999954223633e+001
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_04_55_15.png"",
""image_id"": 2590409,
""concept"": ""Aurelia aurita"",
""id"": 2590416,
""depth_meters"": 4.447999954223633e+001
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_04_55_15.png"",
""image_id"": 2590409,
""concept"": ""Aurelia aurita"",
""id"": 2590417,
""depth_meters"": 4.447999954223633e+001
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_04_55_15.png"",
""image_id"": 2590409,
""concept"": ""Aurelia aurita"",
""id"": 2590418,
""depth_meters"": 4.447999954223633e+001
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_04_55_15.png"",
""image_id"": 2590409,
""concept"": ""Aurelia aurita"",
""id"": 2590419,
""depth_meters"": 4.447999954223633e+001
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_04_55_15.png"",
""image_id"": 2590409,
""concept"": ""Aurelia aurita"",
""id"": 2590420,
""depth_meters"": 4.447999954223633e+001
}
]" "
{
""prompt"": ""Find me images of species 'Aurelia aurita' in Monterey bay and depth less than 5k meters"",
""findings"": ""1. Since prompt asks to generate images, I need to generate image type response. 2. Image should be of specific species 'Aurlia Aurita' 3. Images of the species should be from Monterey Bay. 4.The depth the species are should be less than 5000 meters. 5.Since the prompt asks images from place named Monterey Bay, I should also add the latitude and longitude detail."",
""outputType"": ""images"",
""sqlServerQuery"": ""SELECT TOP 10 i.url, i.id as image_id, b.concept, b.id FROM images i JOIN bounding_boxes b ON i.id = b.image_id WHERE b.concept = 'Aurelia aurita'"",
""responseText"": ""Here are some images of Aurelia aurita in Monterey bay and depth less than 5k meters""
}" FALSE
Find me similar looking fish found in Atlantic Ocean "{
""prompt"": ""Find me similar looking fish found in Atlantic Ocean"",
""findings"": ""1. The result of the prompt would be images, thus the output type is images. 2. The sql generated should have a place to add the input image data with name {imageFeatureVector} 3.Image output must have id, concept of species and url of it's image."",
""outputType"": ""images"",
""similarImageIDs"": [],
""similarBoundingBoxIDs"": [],
""similarImageSearch"": true,
""sqlServerQuery"": ""SELECT TOP 10 SS.bb1, SS.bb2, BB.concept, IMG.url, SS.CosineSimilarity FROM SimilaritySearch SS INNER JOIN bounding_boxes BB ON BB.id = SS.bb2 INNER JOIN images IMG ON BB.image_id = IMG.id INNER JOIN marine_regions MR ON IMG.latitude BETWEEN MR.min_latitude AND MR.max_latitude AND IMG.longitude BETWEEN MR.min_longitude AND MR.max_longitude WHERE MR.name = 'Atlantic Ocean' ORDER BY SS.CosineSimilarity DESC;"",
""responseText"": ""Below are similar looking fish found in Atlantic Ocean.""
}" TRUE
Find me an image containing both Aurelia aurita and Siphonophorae "The prompt asks for an image of individual species so, You need to find the images from the database so you need to format the output such that it is a json and has species array that has species concept as concept, image url as url, image id as image_id and species bounding box id as id.
Also since you are showing images that has more than one species, add the bounding box data x, y, width, height.
Since you need to output data as species array, first you will get the image id that has both concepts. After that you can search the database using the image id to generate the data in json format with id, concept, url, image_id, x, y, width and height values
" "WITH ImageConcepts AS (
SELECT TOP 1 i.id as image_id
FROM dbo.images i
WHERE
EXISTS (
SELECT 1
FROM dbo.bounding_boxes b
WHERE b.image_id = i.id AND b.concept = 'Aurelia aurita'
)
AND EXISTS (
SELECT 1
FROM dbo.bounding_boxes b
WHERE b.image_id = i.id AND b.concept = 'Siphonophorae'
)
GROUP BY i.id)
SELECT
b.id,
b.concept,
i.url,
i.id as image_id,
b.x,
b.y,
b.width,
b.height
FROM
dbo.images i
JOIN
dbo.bounding_boxes b ON i.id = b.image_id
JOIN
ImageConcepts c ON c.image_id = i.id
WHERE
b.concept IN ('Aurelia aurita', 'Siphonophorae')" "[
{
""id"": 2600146,
""concept"": ""Aurelia aurita"",
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Doc%20Ricketts\/images\/0971\/03_42_04_04.png"",
""image_id"": 2600144,
""x"": 1200,
""y"": 686,
""width"": 72,
""height"": 96
},
{
""id"": 2600148,
""concept"": ""Aurelia aurita"",
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Doc%20Ricketts\/images\/0971\/03_42_04_04.png"",
""image_id"": 2600144,
""x"": 170,
""y"": 364,
""width"": 72,
""height"": 66
},
{
""id"": 2600149,
""concept"": ""Aurelia aurita"",
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Doc%20Ricketts\/images\/0971\/03_42_04_04.png"",
""image_id"": 2600144,
""x"": 1357,
""y"": 118,
""width"": 51,
""height"": 57
},
{
""id"": 2600150,
""concept"": ""Aurelia aurita"",
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Doc%20Ricketts\/images\/0971\/03_42_04_04.png"",
""image_id"": 2600144,
""x"": 1434,
""y"": 715,
""width"": 50,
""height"": 74
},
{
""id"": 2600151,
""concept"": ""Aurelia aurita"",
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Doc%20Ricketts\/images\/0971\/03_42_04_04.png"",
""image_id"": 2600144,
""x"": 126,
""y"": 863,
""width"": 66,
""height"": 64
},
{
""id"": 2600152,
""concept"": ""Aurelia aurita"",
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Doc%20Ricketts\/images\/0971\/03_42_04_04.png"",
""image_id"": 2600144,
""x"": 9,
""y"": 805,
""width"": 82,
""height"": 76
},
{
""id"": 2600153,
""concept"": ""Aurelia aurita"",
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Doc%20Ricketts\/images\/0971\/03_42_04_04.png"",
""image_id"": 2600144,
""x"": 831,
""y"": 161,
""width"": 41,
""height"": 53
},
{
""id"": 2600158,
""concept"": ""Aurelia aurita"",
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Doc%20Ricketts\/images\/0971\/03_42_04_04.png"",
""image_id"": 2600144,
""x"": 1598,
""y"": 69,
""width"": 77,
""height"": 111
},
{
""id"": 2600159,
""concept"": ""Aurelia aurita"",
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Doc%20Ricketts\/images\/0971\/03_42_04_04.png"",
""image_id"": 2600144,
""x"": 1,
""y"": 279,
""width"": 41,
""height"": 75
},
{
""id"": 2600160,
""concept"": ""Siphonophorae"",
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Doc%20Ricketts\/images\/0971\/03_42_04_04.png"",
""image_id"": 2600144,
""x"": 928,
""y"": 555,
""width"": 192,
""height"": 125
},
{
""id"": 2600161,
""concept"": ""Aurelia aurita"",
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Doc%20Ricketts\/images\/0971\/03_42_04_04.png"",
""image_id"": 2600144,
""x"": 1018,
""y"": 372,
""width"": 80,
""height"": 82
},
{
""id"": 2600162,
""concept"": ""Aurelia aurita"",
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Doc%20Ricketts\/images\/0971\/03_42_04_04.png"",
""image_id"": 2600144,
""x"": 728,
""y"": 962,
""width"": 57,
""height"": 63
},
{
""id"": 2600163,
""concept"": ""Aurelia aurita"",
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Doc%20Ricketts\/images\/0971\/03_42_04_04.png"",
""image_id"": 2600144,
""x"": 963,
""y"": 65,
""width"": 39,
""height"": 64
},
{
""id"": 2600164,
""concept"": ""Aurelia aurita"",
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Doc%20Ricketts\/images\/0971\/03_42_04_04.png"",
""image_id"": 2600144,
""x"": 703,
""y"": 136,
""width"": 48,
""height"": 65
},
{
""id"": 2600165,
""concept"": ""Aurelia aurita"",
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Doc%20Ricketts\/images\/0971\/03_42_04_04.png"",
""image_id"": 2600144,
""x"": 796,
""y"": 359,
""width"": 57,
""height"": 53
},
{
""id"": 2600167,
""concept"": ""Aurelia aurita"",
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Doc%20Ricketts\/images\/0971\/03_42_04_04.png"",
""image_id"": 2600144,
""x"": 1777,
""y"": 171,
""width"": 53,
""height"": 70
},
{
""id"": 2600169,
""concept"": ""Aurelia aurita"",
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Doc%20Ricketts\/images\/0971\/03_42_04_04.png"",
""image_id"": 2600144,
""x"": 1154,
""y"": 583,
""width"": 35,
""height"": 48
},
{
""id"": 2600171,
""concept"": ""Aurelia aurita"",
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Doc%20Ricketts\/images\/0971\/03_42_04_04.png"",
""image_id"": 2600144,
""x"": 1527,
""y"": 453,
""width"": 42,
""height"": 56
},
{
""id"": 2600174,
""concept"": ""Aurelia aurita"",
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Doc%20Ricketts\/images\/0971\/03_42_04_04.png"",
""image_id"": 2600144,
""x"": 1314,
""y"": 183,
""width"": 40,
""height"": 62
},
{
""id"": 2600175,
""concept"": ""Aurelia aurita"",
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Doc%20Ricketts\/images\/0971\/03_42_04_04.png"",
""image_id"": 2600144,
""x"": 392,
""y"": 224,
""width"": 56,
""height"": 58
},
{
""id"": 2600176,
""concept"": ""Aurelia aurita"",
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Doc%20Ricketts\/images\/0971\/03_42_04_04.png"",
""image_id"": 2600144,
""x"": 1389,
""y"": 231,
""width"": 42,
""height"": 57
}
]" "{
""prompt"": ""Find me an image containing both Aurelia aurita and Siphonophorae"",
""findings"": ""1. Since prompt asks to generate images, the response type is image. 2. Image should be of specific species 'Aurlia Aurita' and 'Siphonophorae' 3. Images should contain both the species. 4. Image output must have id, concept of species and url of it's image."",
""outputType"": ""images"",
""sqlServerQuery"": ""WITH ImageConcepts AS ( SELECT TOP 1 i.id as image_id FROM dbo.images i WHERE EXISTS ( SELECT 1 FROM dbo.bounding_boxes b WHERE b.image_id = i.id AND b.concept = 'Aurelia aurita' ) AND EXISTS ( SELECT 1 FROM dbo.bounding_boxes b WHERE b.image_id = i.id AND b.concept = 'Siphonophorae' ) GROUP BY i.id) SELECT b.id, b.concept, i.url, i.id as image_id, b.x, b.y, b.width, b.height FROM dbo.images i JOIN dbo.bounding_boxes b ON i.id = b.image_id JOIN ImageConcepts c ON c.image_id = i.id WHERE b.concept IN ('Aurelia aurita', 'Siphonophorae') "",
""responseText"": ""Here are some images containing both Aurelia aurita and Siphonophorae""
}" FALSE
What species are frequently found at 1000m depth? "The prompt is asking the frequency of species found at depth of 1000m, so output freuqency data of species name with the frequency using the bounding_boxes and images table
" "SELECT b.concept AS species, COUNT(*) AS frequency
FROM dbo.bounding_boxes AS b
JOIN dbo.images AS i ON b.image_id = i.id
WHERE i.depth_meters = 1000
GROUP BY b.concept
ORDER BY frequency DESC;" "species frequency
Isosicyonis 24
Sebastolobus 6
Embassichthys bathybius 4
Actiniaria 3
Ceriantharia 2
Chionoecetes tanneri 2
Corallimorphus 2
Eptatretus 2
Microstomus pacificus 2
Umbellula sp. 2 2" "{
""prompt"": ""What species are frequently found at 1000m depth?"",
""findings"": ""1. Since prompt is asking the frequently found species at 1000m, the output would be the name of the species, which is best shown in a table so output type is a table. 2. Since the species are selected based on how frequent they are, it would be best to show the frequency of the species too."",
""outputType"": ""table"",
""sqlServerQuery"": ""SELECT b.concept AS species, COUNT(*) AS frequency FROM dbo.bounding_boxes AS b JOIN dbo.images AS i ON b.image_id = i.id WHERE i.depth_meters = 1000 GROUP BY b.concept ORDER BY frequency DESC;"",
""responseText"": ""Table shows the frequently found species at 1000m depth and their count.""
}" FALSE
Generate an Interactive Time-lapse Map of Marine Species Observations Grouped by Year "{
""prompt"": ""Generate an Interactive Time-lapse Map of Marine Species Observations Grouped by Year"",
""findings"": ""1. The prompt says to generate an Interactive time-lapse map, so the output is a visualization. 2. For the visualization, concept, latitude, longitude and timestamp of every species is needed. 3.Since the output will contain all species data from the database and be very large, the timestamp that is None can be ignored. 4. Since mapbox can't be used, I will use plotly's Scattergeo. 5.Since the output is a visualization, the sample data and plotlyCode needs to be generated"",
""outputType"": ""visualization"",
""sqlServerQuery"": ""SELECT bb.concept, i.latitude, i.longitude, YEAR(i.timestamp) AS ObservationYear FROM dbo.bounding_boxes bb JOIN dbo.images i ON bb.image_id = i.id WHERE i.latitude IS NOT NULL AND i.longitude IS NOT NULL AND i.timestamp IS NOT NULL ORDER BY i.timestamp;"",
""sampleData"": ""{ 'concept': ['Species A', 'Species B', 'Species A', 'Species B'], 'latitude': [35.6895, 35.6895, 35.6895, 35.6895], 'longitude': [139.6917, 139.6917, 139.6917, 139.6917], 'ObservationYear': [2020, 2021, 2022, 2023] }"",
""plotlyCode"": ""import plotly.graph_objs as go\nfrom plotly.subplots import make_subplots\ndef drawVisualization(data):\n\tfig = go.Figure()\n\tfor year in sorted(set(data['ObservationYear'])):\n\t\tfig.add_trace(\n\t\t\tgo.Scattergeo(\n\t\t\t\tlon=[data['longitude'][i] for i in range(len(data['longitude'])) if data['ObservationYear'][i] == year],\n\t\t\t\tlat=[data['latitude'][i] for i in range(len(data['latitude'])) if data['ObservationYear'][i] == year],\n\t\t\t\ttext=[f\""{data['concept'][i]} ({year})\"" for i in range(len(data['concept'])) if data['ObservationYear'][i] == year],\n\t\t\t\tmode='markers',\n\t\t\t\tmarker=dict(\n\t\t\t\t\tsize=8,\n\t\t\t\t\tsymbol='circle',\n\t\t\t\t\tline=dict(width=1, color='rgba(102, 102, 102)')\n\t\t\t\t),\n\t\t\t\tname=f\""Year {year}\"",\n\t\t\t\tvisible=(year == min(data['ObservationYear'])) \n\t\t\t)\n\t\t)\n\tsteps = []\n\tfor i, year in enumerate(sorted(set(data['ObservationYear']))):\n\t\tstep = dict(\n\t\t\tmethod='update',\n\t\t\targs=[{'visible': [False] * len(fig.data)},\n\t\t\t\t{'title': f\""Observations for Year: {year}\""}], \n\t\t)\n\t\tstep['args'][0]['visible'][i] = True i'th trace to \""visible\""\n\t\tsteps.append(step)\n\tsliders = [dict(\n\t\tactive=10,\n\t\tcurrentvalue={\""prefix\"": \""Year: \""},\n\t\tpad={\""t\"": 50},\n\t\tsteps=steps\n\t)]\n\tfig.update_layout(\n\t\tsliders=sliders,\n\t\ttitle='Time-lapse Visualization of Species Observations',\n\t\tgeo=dict(\n\t\t\tscope='world',\n\t\t\tprojection_type='equirectangular',\n\t\t\tshowland=True,\n\t\t\tlandcolor='rgb(243, 243, 243)',\n\t\t\tcountrycolor='rgb(204, 204, 204)',\n\t\t),\n\t)\n\treturn fig"",
""responseText"": ""Below is an Interactive Time-lapse Map of Marine Species Observations Grouped by Year""
}" FALSE
How many distinct species are there at Monterey Bay? "The species is asking for number of distinct species in Monterey Bay, so use the bounding_boxes and marine_regions table from database to find the number of species
" "SELECT COUNT(DISTINCT b.concept) as DistinctSpeciesCount
FROM dbo.bounding_boxes b
WHERE EXISTS (
SELECT 1
FROM dbo.images i
JOIN dbo.marine_regions mr ON i.latitude BETWEEN mr.min_latitude AND mr.max_latitude
AND i.longitude BETWEEN mr.min_longitude AND mr.max_longitude
WHERE mr.name = 'Monterey Bay'
AND i.id = b.image_id
)" "DistinctSpeciesCount
711" "{
""prompt"": ""How many distinct species are there at Monterey Bay?"",
""findings"": ""1. The prompt asks the number of distinct species in Monterey Bay, which would be a number, thus the output type is text. 2. The response text should be output so that the distinct species count result can be formatted into the text."",
""outputType"": ""text"",
""sqlServerQuery"": ""SELECT COUNT(DISTINCT b.concept) as DistinctSpeciesCount FROM dbo.bounding_boxes b WHERE EXISTS ( SELECT 1 FROM dbo.images i JOIN dbo.marine_regions mr ON i.latitude BETWEEN mr.min_latitude AND mr.max_latitude AND i.longitude BETWEEN mr.min_longitude AND mr.max_longitude WHERE mr.name = 'Monterey Bay' AND i.id = b.image_id )"",
""responseText"": ""There are {DistinctSpeciesCount} distinct species at Monterey Bay""
}" FALSE
Find me images of species that looks alike to the provided image and are found in Monterey Bay "{
""prompt"": ""Find me images of species that looks alike to the provided image and are found in Monterey Bay"",
""findings"": ""1. The result of the prompt would be images, thus the output type is images. 2. The sql generated should have a place to add the input image data with name {imageFeatureVector} 3.Image output must have id, concept of species and url of it's image."",
""outputType"": ""images"",
""similarImageIDs"": [],
""similarBoundingBoxIDs"": [],
""similarImageSearch"": true,
""sqlServerQuery"": ""SELECT TOP 10 SS.bb1, SS.bb2, BB.concept, IMG.url, SS.CosineSimilarity FROM SimilaritySearch SS INNER JOIN bounding_boxes BB ON BB.id = SS.bb2 INNER JOIN images IMG ON BB.image_id = IMG.id INNER JOIN marine_regions MR ON IMG.latitude BETWEEN MR.min_latitude AND MR.max_latitude AND IMG.longitude BETWEEN MR.min_longitude AND MR.max_longitude WHERE MR.name = 'Monterey Bay' ORDER BY SS.CosineSimilarity DESC;"",
""responseText"": ""Below are images of species that looks alike to the provided image and are found in Monterey Bay.""
}" TRUE
"Find me images of Aurelia aurita that looks good
" "The prompt asks for images of individual species so, You need to find the images from the database so you need to format the output such that it is a json and has species array that has species concept as concept, image url as url, image id as image_id and species bounding box id as id.
Since the prompt is asking for image that looks good without specifying what type of image would look good, find images that has highest bounding box width, height to image width, height ratio and show 10 top results
" "SELECT TOP 10 i.url, b.concept, b.id, i.id as image_id
FROM dbo.bounding_boxes AS b
JOIN dbo.images AS i ON b.image_id = i.id
WHERE b.concept = 'Aurelia aurita'
ORDER BY (b.width * b.height) / (i.width * i.height) DESC" "[
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Ventana\/images\/3082\/05_02_20_21.png"",
""concept"": ""Aurelia aurita"",
""id"": 2590274,
""image_id"": 2590272
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Ventana\/images\/2934\/05_23_41_12.png"",
""concept"": ""Aurelia aurita"",
""id"": 2589498,
""image_id"": 2589496
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/0893\/06_13_15_15.png"",
""concept"": ""Aurelia aurita"",
""id"": 2588038,
""image_id"": 2588036
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Ventana\/images\/3082\/04_59_42_11.png"",
""concept"": ""Aurelia aurita"",
""id"": 2587744,
""image_id"": 2587742
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Doc%20Ricketts\/images\/0199\/00_01_04_07.png"",
""concept"": ""Aurelia aurita"",
""id"": 2586298,
""image_id"": 2586296
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_03_31_28.png"",
""concept"": ""Aurelia aurita"",
""id"": 2586156,
""image_id"": 2586149
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_03_31_28.png"",
""concept"": ""Aurelia aurita"",
""id"": 2586155,
""image_id"": 2586149
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_03_31_28.png"",
""concept"": ""Aurelia aurita"",
""id"": 2586154,
""image_id"": 2586149
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_03_31_28.png"",
""concept"": ""Aurelia aurita"",
""id"": 2586153,
""image_id"": 2586149
},
{
""url"": ""https:\/\/fathomnet.org\/static\/m3\/framegrabs\/Tiburon\/images\/1111\/00_03_31_28.png"",
""concept"": ""Aurelia aurita"",
""id"": 2586152,
""image_id"": 2586149
}
]" "{
""prompt"": ""Find me images of Aurelia aurita that looks good"",
""findings"": ""1. Since prompt asks to generate images that look good, I need to generate image type response. 2. The prompt does not specify what good means, I should try to find the image with species that has the highest bunding box size. 3. Image output must have id, concept of species and url of it's image."",
""outputType"": ""images"",
""sqlServerQuery"": ""SELECT TOP 10 i.url, b.concept, b.id, i.id as image_id FROM dbo.bounding_boxes AS b JOIN dbo.images AS i ON b.image_id = i.id WHERE b.concept = 'Aurelia aurita' ORDER BY (b.width * b.height) / (i.width * i.height) DESC"",
""responseText"": ""Here is an image of Aurelia aurita that you may find appealing.""
}" FALSE
Display me a heatmap of all species in Monterey Bay "The prompt asks to give data of all species in Monterey Bay to show in a heatmap. So, You need to find the images from the database so you need to format the output such that it is a json and has species array that has species concept as concept and species bounding box id as id.
Also since the data will be shown in a heatmap, location data is very important, so you will add the latitude, longitude and depth data of the species. " "SELECT
b.concept,
b.id,
i.latitude,
i.longitude,
i.depth_meters
FROM
dbo.bounding_boxes b
JOIN dbo.images i ON b.image_id = i.id
JOIN dbo.marine_regions mr ON i.latitude BETWEEN mr.min_latitude AND mr.max_latitude
AND i.longitude BETWEEN mr.min_longitude AND mr.max_longitude
WHERE
mr.name = 'Monterey Bay'" "[
{
""concept"": ""Beringraja rhina"",
""id"": 2343675,
""latitude"": 3.658101500000000e+001,
""longitude"": -1.220103770000000e+002,
""depth_meters"": 2.918299865722656e+002
},
{
""concept"": ""Asteroidea"",
""id"": 2416979,
""latitude"": 3.658115600000000e+001,
""longitude"": -1.220557880000000e+002,
""depth_meters"": 9.054000244140625e+002
},
...
]" "{
""prompt"": ""Display me a heatmap of all species in Monterey Bay"",
""findings"": ""1. The prompt says to generate heatmap of all species in Monterey Bay, so the output is a visualization. 2. Since map box cannot be used, I shall use open street maps for the visualization. 3.Since the output is a visualization, the sample data and plotlyCode needs to be generated"",
""outputType"": ""visualization"",
""sqlServerQuery"": ""SELECT b.concept, b.id, i.latitude, i.longitude, i.depth_meters FROM dbo.bounding_boxes b JOIN dbo.images i ON b.image_id = i.id JOIN dbo.marine_regions mr ON i.latitude BETWEEN mr.min_latitude AND mr.max_latitude AND i.longitude BETWEEN mr.min_longitude AND mr.max_longitude WHERE mr.name = 'Monterey Bay'"",
""sampleData"": ""{ 'concept': ['Species A', 'Species B', 'Species A', 'Species B'], 'id': [1, 2, 3, 4], 'latitude': [36.6002, 36.8002, 36.7002, 36.6502], 'longitude': [-121.8947, -121.7947, -121.9947, -121.8447], 'depth_meters': [10, 20, 30, 40] }"",
""plotlyCode"": ""import pandas as pd\nimport plotly.express as px\ndef drawVisualization(data):\n\tdf = pd.DataFrame(data)\n\tfig = px.density_mapbox(df, lat='latitude', lon='longitude', z='depth_meters', radius=10,\n\t\t\t\t\t\t\tcenter=dict(lat=36.6002, lon=-121.8947), zoom=10,\n\t\t\t\t\t\t\tmapbox_style=\""open-street-map\"",\n\t\t\t\t\t\t\thover_data={'concept': True, 'depth_meters': False}) \n\tfig.update_layout(\n\t\ttitle='Heatmap of Species in Monterey Bay'\n\t)\n\treturn fig"",
""responseText"": ""Below is a heatmap of all species in Monterey Bay""
}" FALSE
Find me a place where Aurelia aurita is found the most "Since the prompt is asking about the place where Aurelia aurita is found the most without specifying any region, you can use the marine_regions table along with the bounding_boxes table to find out the number of that species in a region. After that you can sort the data and get the result.
" "SELECT TOP 1 mr.name as region_name, COUNT(bb.id) as count
FROM dbo.bounding_boxes bb
JOIN dbo.images i ON bb.image_id = i.id
JOIN dbo.marine_regions mr ON i.latitude BETWEEN mr.min_latitude AND mr.max_latitude AND i.longitude BETWEEN mr.min_longitude AND mr.max_longitude
WHERE bb.concept = 'Aurelia aurita'
GROUP BY mr.name
ORDER BY count DESC" "region_name COUNT
California Current 110" "{
""prompt"": ""Find me a place where Aurelia aurita is found the most"",
""findings"": ""1. The result of the prompt would be the name of the place where Aurelia aurita is found the most since the prompt is asking for a single place, thus the output type is text. 2.The response text should have the most found region name as result and it should be be formatted into the text."",
""outputType"": ""text"",
""sqlServerQuery"": ""SELECT TOP 1 mr.name as MostFoundRegionName, COUNT(bb.id) as count FROM dbo.bounding_boxes bb JOIN dbo.images i ON bb.image_id = i.id JOIN dbo.marine_regions mr ON i.latitude BETWEEN mr.min_latitude AND mr.max_latitude AND i.longitude BETWEEN mr.min_longitude AND mr.max_longitude WHERE bb.concept = 'Aurelia aurita' GROUP BY mr.name ORDER BY count DESC"",
""responseText"": ""The region where Aurelia Aurita is found the most is {MostFoundRegionName}.""
}" FALSE
Find me images of species that looks alike that live at oxygen level between 0.5 to 1 ml per liter "{
""prompt"": ""Find me images of species that looks alike that live at oxygen level between 0.5 to 1 ml per liter"",
""findings"": ""1. The result of the prompt would be images, thus the output type is images. 2. The sql generated should have a place to add the input image data with name {imageFeatureVector} 3.Image output must have id, concept of species and url of it's image."",
""outputType"": ""images"",
""similarImageIDs"": [],
""similarBoundingBoxIDs"": [],
""similarImageSearch"": true,
""sqlServerQuery"": ""SELECT TOP 10 SS.bb1, SS.bb2, BB.concept, IMG.url, SS.CosineSimilarity FROM SimilaritySearch SS INNER JOIN bounding_boxes BB ON BB.id = SS.bb2 INNER JOIN images IMG ON BB.image_id = IMG.id WHERE IMG.oxygen_ml_l BETWEEN 0.5 AND 1 ORDER BY SS.CosineSimilarity DESC;"",
""responseText"": ""Below are images of species that looks alike that live at oxygen level between 0.5 to 1 ml per liter.""
}" TRUE
Provide the data that correlates salinity levels with the distribution of Aurelia aurita "The prompt asks to find correlation between salinity levels with the distribution of Aurelia aurita. Since the prompt does not provide the salinity levels, use the salinity level normally used to understand ocean water salinity level. So, use levels <32, 32-33, 33-34, 34-35, 35-36, 36-37, >37
Use these region to find out, the distribution of the species in these regions. Also, make sure you include the 0 values, which will help user better understand that some region donot have the species.
" "WITH SalinityLevels AS (
SELECT '<32' AS salinity_level
UNION ALL
SELECT '32-33'
UNION ALL
SELECT '33-34'
UNION ALL
SELECT '34-35'
UNION ALL
SELECT '35-36'
UNION ALL
SELECT '36-37'
UNION ALL
SELECT '>37'
),
AggregatedData AS (
SELECT
CASE
WHEN images.salinity < 32 THEN '<32'
WHEN images.salinity BETWEEN 32 AND 33 THEN '32-33'
WHEN images.salinity BETWEEN 33 AND 34 THEN '33-34'
WHEN images.salinity BETWEEN 34 AND 35 THEN '34-35'
WHEN images.salinity BETWEEN 35 AND 36 THEN '35-36'
WHEN images.salinity BETWEEN 36 AND 37 THEN '36-37'
WHEN images.salinity > 37 THEN '>37'
END AS salinity_level,
COUNT(bounding_boxes.concept) AS species_count
FROM
dbo.images
JOIN
dbo.bounding_boxes ON images.id = bounding_boxes.image_id