Snowflake Cortex AI Connector Guide

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Snowflake Cortex AI connection actions and objects

When you publish a Snowflake Cortex AI connection in Application Integration, the

Actions

and

Objects

appear on the

Metadata

tab.

Consider the following information when you use specific actions and objects:

The Complete Inference
action creates a chat completion for the message.

Enter the following input field values as a sample request:

model: mistral -large

messages: hi

top_p: 1 (default)

temperature: 0 (default)

max_tokens: 4096 (default)

The following snippet is a sample response:

<result
    xmlns:aetgt="http://schemas.informatica.com/socrates/data-services/2014/05/business-connector-model.xsd"
    xmlns:bconn="http://schemas.informatica.com/socrates/data-services/2014/05/business-connector-model.xsd" xml="false">data: {"id":"0bc629cd-9967-42d8-b396-87efccc2799d","created":1745300398,"model":"mistral-large","choices":[{"delta":{"type":"text","content":" Hello","content_list":[{"type":"text","text":" Hello"}],"text":" Hello"}}],"usage":{"prompt_tokens":10,"completion_tokens":1,"total_tokens":11}} data: {"id":"0bc629cd-9967-42d8-b396-87efccc2799d","created":1745300398,"model":"mistral-large","choices":[{"delta":{"type":"text","content":"!","content_list":[{"type":"text","text":"!"}],"text":"!"}}],"usage":{"prompt_tokens":10,"completion_tokens":2,"total_tokens":12}} data: {"id":"0bc629cd-9967-42d8-b396-87efccc2799d","created":1745300398,"model":"mistral-large","choices":[{"delta":{"type":"text","content":" How","content_list":[{"type":"text","text":" How"}],"text":" How"}}],"usage":{"prompt_tokens":10,"completion_tokens":3,"total_tokens":13}} data: {"id":"0bc629cd-9967-42d8-b396-87efccc2799d","created":1745300398,"model":"mistral-large","choices":[{"delta":{"type":"text","content":" can","content_list":[{"type":"text","text":" can"}],"text":" can"}}],"usage":{"prompt_tokens":10,"completion_tokens":4,"total_tokens":14}} data: {"id":"0bc629cd-9967-42d8-b396-87efccc2799d","created":1745300398,"model":"mistral-large","choices":[{"delta":{"type":"text","content":" I","content_list":[{"type":"text","text":" I"}],"text":" I"}}],"usage":{"prompt_tokens":10,"completion_tokens":5,"total_tokens":15}} data: {"id":"0bc629cd-9967-42d8-b396-87efccc2799d","created":1745300398,"model":"mistral-large","choices":[{"delta":{"type":"text","content":" assist","content_list":[{"type":"text","text":" assist"}],"text":" assist"}}],"usage":{"prompt_tokens":10,"completion_tokens":6,"total_tokens":16}} data: {"id":"0bc629cd-9967-42d8-b396-87efccc2799d","created":1745300398,"model":"mistral-large","choices":[{"delta":{"type":"text","content":" you","content_list":[{"type":"text","text":" you"}],"text":" you"}}],"usage":{"prompt_tokens":10,"completion_tokens":7,"total_tokens":17}} data: {"id":"0bc629cd-9967-42d8-b396-87efccc2799d","created":1745300398,"model":"mistral-large","choices":[{"delta":{"type":"text","content":" today","content_list":[{"type":"text","text":" today"}],"text":" today"}}],"usage":{"prompt_tokens":10,"completion_tokens":8,"total_tokens":18}} data: {"id":"0bc629cd-9967-42d8-b396-87efccc2799d","created":1745300398,"model":"mistral-large","choices":[{"delta":{"type":"text","content":"?","content_list":[{"type":"text","text":"?"}],"text":"?"}}],"usage":{"prompt_tokens":10,"completion_tokens":9,"total_tokens":19}} data: {"id":"0bc629cd-9967-42d8-b396-87efccc2799d","created":1745300398,"model":"mistral-large","choices":[{"delta":{"type":"text","content_list":[{"type":"text","text":""}],"text":""},"finish_reason":"stop"}],"usage":{"prompt_tokens":10,"completion_tokens":10,"total_tokens":20}} 
</result>

The following snippet is a sample extracted response:

Hello! How can I assist you today?

The Cortex Analyst
action converts humanized text into an SQL query.

You must provide the input fields, such as message role, message content type, message content text, and path to the semantic model file. For example, @DEMO.MARKS.MARKS/mark.yaml

Semantic model path can be obtained from

Databases

Required database

Required schema

Required stage

. The list of YAML files appears. You can copy the file path of the required YAML file.

You can create a YAML file in Snowflake web UI using AI & ML studio.

The following image shows the YAML file to copy:

You can pass only the path of the YAML file.

Enter the following input field values as a sample request:

messages.role: user

messages.content.type: text

messages.content.text: give me the average marks of students?

semantic_model_file: @DEMO.MARKS.MARKS/marks.yaml

The following snippet is a sample response:

<root
    xmlns:aetgt="http://schemas.informatica.com/socrates/data-services/2014/05/business-connector-model.xsd"
    xmlns:bconn="http://schemas.informatica.com/socrates/data-services/2014/05/business-connector-model.xsd">
    <warnings/>
    <message>
        <role>analyst</role>
        <content>
            <text>I can help you calculate the average marks across all subjects for all students. This will give you an overall view of student performance across math, Telugu, Hindi, English, physics and social studies.</text>
            <type>text</type>
        </content>
        <content>
            <text>This is our interpretation of your question: What is the average mark across all subjects (math, telugu, hindi, english, physics, social) for all students?</text>
            <type>text</type>
        </content>
        <content>
            <confidence>
                <verified_query_used/>
            </confidence>
            <statement>WITH __marks AS ( SELECT math_mark, tel_mark, hin_mark, eng_mark, phy_mark, soc_mark FROM demo.marks.marks ) SELECT AVG(math_mark) AS avg_math, AVG(tel_mark) AS avg_telugu, AVG(hin_mark) AS avg_hindi, AVG(eng_mark) AS avg_english, AVG(phy_mark) AS avg_physics, AVG(soc_mark) AS avg_social FROM __marks -- Generated by Cortex Analyst ;</statement>
            <type>sql</type>
        </content>
    </message>
    <request_id>0084220e-8203-46a9-83f6-ae6ffba2c371</request_id>
    <semantic_model_selection/>
</root>

The following snippet is a sample extracted response:

SELECT AVG(math_mark) AS avg_math, AVG(tel_mark) AS avg_telugu, AVG(hin_mark) AS avg_hindi, AVG(eng_mark) AS avg_english, AVG(phy_mark) AS avg_physics, AVG(soc_mark) AS avg_social FROM __marks

The Cortex Search
action searches the database based on a given category.

Enter the following input field values as a sample request:

database: PRODUCT

schema: SALES

service_name: exampl

queries: Personal Care

columns: name,price,review,category

limit: 5

filters:

The following snippet is a sample response:

<root
    xmlns:aetgt="http://schemas.informatica.com/socrates/data-services/2014/05/business-connector-model.xsd"
    xmlns:bconn="http://schemas.informatica.com/socrates/data-services/2014/05/business-connector-model.xsd">
    <results>
        <price>39.99</price>
        <review>A luxurious perfume.</review>
        <name>Perfume</name>
        <category>Gifts</category>
    </results>
    <results>
        <price>6.99</price>
        <review>Keeps your hands soft and moisturized.</review>
        <name>Hand Lotion</name>
        <category>Cosmetics</category>
    </results>
    <results>
        <price>7.99</price>
        <review>Cleans and nourishes your hair.</review>
        <name>Shampoo</name>
        <category>Personal Care</category>
    </results>
    <results>
        <price>4.99</price>
        <review>Protects and whitens your teeth.</review>
        <name>Toothpaste</name>
        <category>Personal Care</category>
    </results>
    <results>
        <review>Cleans and moisturizes your skin.</review>
        <price>2.99</price>
        <name>Soap</name>
        <category>Personal Care</category>
    </results>
    <request_id>a6f58cdb-84dd-42ce-be47-9a8ea172884f</request_id>
</root>

The following snippet is a sample extracted response:

39.99-A luxurious perfume.-Perfume-Gifts, 6.99-Keeps your hands soft and moisturized.-Hand Lotion-Cosmetics, 7.99-Cleans and nourishes your hair.-Shampoo-Personal Care, 4.99-Protects and whitens your teeth.-Toothpaste-Personal Care, Cleans and moisturizes your skin.-2.99-Soap-Personal Care

The Classify Text
action classifies the given text into one of the categories that you define.

Enter the following input field values as a sample request:

input: One day, I will see the world

label1: cooking

label2: travel

The following snippet is a sample response:

{
  "label": "travel"
}

The Translate Text
action translates the given input text from one supported language to another.

Enter the following input field values as a sample request:

text: Hello,how are you?

source_langauge: en

target_language: fr

The following snippet is a sample response:

Bonjour, comment allez-vous ?

The Create Embed Text
action creates a vector embedding for a given text.

Enter the following input field values as a sample request:

type: 768

model_for_1024: snowflake-arctic-embed-l-v2.0

text: hello world

model_for_768: snowflake-arctic-embed-m-v1.5

The following snippet is a sample response:

[0.085388,0.018173,0.097717,0.006729,0.137207,0.019424,0.040680,0.069519,-0.033325,-0.078613,-0.052246,0.045105,-0.002104,0.031311,0.010681,0.052155,0.027588,0.007805,-0.013657,-0.013649,-0.049896,-0.039429,0.060333,0.020248,0.004433,0.000884,-0.006615,0.025024,-0.079529,0.033356,-0.041779,0.007786,0.046997,-0.032166,-0.023453,0.003206,-0.065735,0.055176,-0.047272,-0.052643,-0.018295,-0.059631,-0.008286,0.054840,-0.028549,-0.016815,-0.298340,0.086365,-0.013359,-0.018478,-0.060272,0.022919,0.018661,-0.011650,0.035614,0.043060,0.028717,-0.067444,-0.029694,-0.047943,0.089111,0.000471,-0.024109,0.024109,0.066528,0.034271,0.017075,0.034912,-0.050598,0.016953,0.036224,-0.040802,-0.006416,-0.042603,0.049683,-0.079895,-0.005314,0.016663,0.060760,0.080750,0.024704,-0.015762,0.112366,0.083130,0.004971,-0.051544,0.046509,0.009407,-0.009552,0.016388,-0.007965,-0.110962,0.011780,0.047150,0.009705,-0.050232,0.022308,0.014397,0.059875,0.026199,-0.044800,-0.000631,0.036987,-0.045807,-0.009003,-0.009590,-0.026138,-0.075378,-0.028519,0.021347,-0.015656,0.024841,0.040985,-0.041565,-0.032532,0.080322,0.018707,-0.005756,0.008324,0.034912,0.001840,0.082825,0.044098,0.062561,0.047882,0.008629,-0.002594,0.068359,-0.042572,-0.041656,0.034424,-0.008965,0.045807,-0.014008,-0.036682,-0.046051,0.015587,-0.007328,0.018661,-0.025528,0.026840,0.027863,-0.016403,0.082275,0.024551,-0.014610,-0.040222,0.018875,-0.037872,-0.001452,-0.006348,0.045197,-0.025940,-0.064453,-0.025894,0.036652,0.042572,0.016357,-0.074280,-0.008736,0.008713,-0.016037,-0.012726,0.007427,0.049805,-0.036163,0.022324,-0.029510,0.010834,-0.116943,-0.029922,0.003778,0.060760,-0.034027,-0.043243,0.042816,0.084045,0.040497,0.007633,0.045258,-0.030457,0.032501,-0.008682,0.029678,0.013390,-0.034790,0.031647,-0.008278,-0.003540,0.051727,-0.108276,-0.059387,0.093994,0.001240,0.024185,-0.054199,-0.006302,0.087402,0.037079,0.096069,0.052979,0.027588,0.080444,-0.011162,-0.075562,0.027252,0.085754,0.012360,0.016846,0.014748,-0.005371,-0.075500,0.035858,-0.025803,-0.002466,-0.023300,-0.020859,-0.027847,0.034241,-0.023132,0.087952,-0.022720,-0.018661,0.036560,-0.030411,0.081848,0.111877,-0.005440,0.087585,-0.102478,-0.033783,-0.015808,0.004230,0.081909,0.018600,0.064026,0.051056,-0.051300,0.005936,-0.017654,0.044800,0.008904,-0.050659,0.031464,-0.031982,-0.057373,0.028671,0.007614,-0.061523,-0.030777,-0.030945,0.075562,0.031677,0.015297,0.090698,-0.008553,-0.010208,-0.011986,-0.007919,0.011536,0.020905,-0.027191,0.022949,-0.056335,-0.021179,0.002085,0.011192,0.010773,0.009338,0.022751,-0.000670,-0.032715,0.031555,-0.005886,-0.008301,0.024780,0.045685,-0.016464,0.001999,-0.022400,-0.005383,-0.017441,0.024567,0.015480,0.021683,-0.027878,0.021027,-0.002476,-0.038574,0.015572,-0.009003,0.024872,-0.012039,-0.013107,0.011902,-0.023270,-0.011749,0.002331,-0.037567,-0.022171,0.035950,0.003609,-0.040039,0.020279,-0.056519,0.032318,-0.005924,-0.019180,-0.170776,-0.040955,0.025116,0.011536,-0.012146,-0.022949,0.034515,-0.030594,-0.017532,0.000460,0.010323,0.031189,0.021027,-0.039886,0.002279,0.004719,-0.037659,0.031143,0.003456,-0.006149,-0.030365,0.050415,-0.009102,-0.009972,-0.008194,0.036407,0.040527,0.028122,0.021454,-0.023819,-0.019455,0.035767,-0.028595,0.022217,-0.027573,-0.009232,0.006905,-0.021378,-0.005974,0.034485,0.019379,-0.015930,0.027237,0.022980,-0.008690,0.006344,-0.010742,-0.009605,0.053833,-0.029221,-0.029449,-0.017014,0.004055,0.028198,-0.015457,-0.003500,-0.021133,0.023483,0.061707,-0.047943,-0.018341,0.024597,0.025391,-0.014458,-0.040070,0.027496,-0.007843,0.014374,-0.032410,0.041718,0.007477,0.001478,-0.007965,0.017609,-0.007240,0.033508,-0.010406,-0.024963,0.051727,-0.013634,0.012314,-0.021881,-0.007610,-0.021927,-0.018448,0.009018,-0.003357,0.002367,0.004089,0.067688,0.021591,-0.019562,0.006805,-0.007145,0.013657,0.034943,0.061401,-0.004280,-0.007652,-0.002182,0.031235,-0.014091,-0.005215,0.018997,0.007835,0.051636,-0.018661,-0.047302,-0.031830,0.024597,0.006218,-0.037567,0.005558,-0.053680,-0.017578,0.003426,-0.013435,-0.050385,-0.057800,0.002924,-0.005711,0.038422,0.012733,-0.012711,-0.021652,-0.010017,0.029022,-0.027039,-0.013100,0.013435,-0.022720,-0.018143,0.044067,-0.001525,0.033112,0.013618,-0.008057,0.018738,0.016464,-0.077393,-0.026550,0.006767,0.020645,-0.022903,0.025528,-0.016235,-0.018326,-0.002472,0.005085,0.013512,0.013977,-0.055939,-0.016312,0.004028,0.007473,0.004280,0.007591,-0.002211,-0.047424,0.057343,-0.004189,0.000339,-0.052307,0.031464,-0.029297,-0.000417,0.012299,0.004875,0.002394,-0.000421,0.005676,0.001891,0.028000,-0.038177,0.020340,0.031677,0.004345,0.029114,0.004047,0.018845,0.004826,-0.006493,0.033051,-0.001017,-0.027176,-0.021881,0.017441,0.007145,0.001649,0.042236,-0.028748,-0.001776,0.002434,0.000373,0.005981,-0.013687,0.020752,-0.014290,-0.020859,0.003181,0.012772,0.026352,-0.009773,-0.014320,0.018707,0.018387,0.012756,-0.011086,0.002312,-0.025513,0.007786,0.021301,-0.022095,-0.019363,-0.021179,-0.040649,-0.042999,0.032379,0.017303,0.027725,0.033234,0.010834,-0.003403,0.033539,0.047791,-0.004925,0.022461,0.033875,-0.001345,0.036865,0.019577,0.014015,-0.004128,0.007622,0.044098,0.017380,-0.080750,0.046661,0.006226,0.026169,-0.014450,-0.030457,-0.000729,-0.031311,-0.034515,0.013748,0.062347,-0.004906,0.017487,0.022369,0.014488,0.008942,0.009338,-0.013672,0.004784,0.008820,-0.013817,-0.021744,0.009415,-0.027222,0.010338,-0.020523,0.017502,0.031586,-0.004070,0.029434,-0.009842,-0.016205,0.002872,0.036621,-0.015503,0.028519,0.035431,-0.033875,0.013924,-0.030182,0.027328,0.029297,-0.002533,0.008743,0.022903,0.029602,0.026077,-0.006954,-0.024811,0.012093,0.030685,-0.005165,0.034393,0.015717,0.004738,-0.001149,-0.018677,0.035431,-0.023743,0.017487,0.017258,-0.050140,0.010941,-0.026566,0.052032,-0.001825,-0.001314,-0.015038,-0.002840,0.011810,0.029663,-0.013191,0.005634,-0.030045,0.015274,0.055420,-0.039215,-0.026062,-0.010803,-0.055176,0.044312,-0.013008,-0.017242,-0.030914,0.027222,-0.027557,-0.019562,-0.025467,-0.031708,-0.062042,0.020737,0.023788,0.011551,-0.025925,0.000571,0.007317,0.000923,0.002300,-0.028793,0.063965,0.016068,0.022751,-0.012321,-0.024734,-0.042389,-0.016312,-0.013474,-0.011086,0.025452,-0.023254,-0.017456,-0.004501,-0.016724,0.018906,0.019547,0.007561,-0.004951,0.021057,-0.045868,-0.026321,-0.022629,-0.005184,0.020233,-0.013199,-0.005470,0.016052,0.025436,-0.039398,-0.036774,0.013474,-0.006451,-0.017761,0.028473,0.007153,-0.024490,-0.019058,0.009392,0.024048,-0.045227,-0.004349,-0.028168,-0.030396,-0.036957,-0.022827,0.068359,0.021881,0.018875,0.014160,0.045746,-0.012283,-0.033173,0.000255,0.006733,0.024002,-0.022446,-0.030670,-0.037079,-0.082397,0.018265,-0.013573,-0.068359,-0.005608,-0.005150,0.015152,-0.040680,0.035034,0.044952,0.015823,0.017563,-0.018234,-0.002010,0.001966,0.019806,0.008347,0.031677,0.045197,0.000187,-0.060638,0.047913,-0.045807,-0.038910,-0.000497,0.061127,-0.020432,-0.046356,0.006203,0.004864,-0.010361,0.010796,0.012367,-0.042877,0.027740,0.010246,-0.030792,0.025909,-0.011032,0.010475,0.045105,0.029816,-0.012260,-0.006256,-0.032227,0.016861,-0.000732,-0.004185,-0.005306,0.009354,-0.018051,-0.028397,-0.049316,0.034515,-0.000303,-0.015160,-0.026871,-0.008514,-0.013870,0.001649,0.040253,-0.019455,0.061066,0.025818,-0.035797,0.021729,0.022705]

The Sentiment Analysis
action returns a sentiment score for the given English language input text.

Enter the following input field value as a sample request:

text: I love using Snowflake, it's simple and easy

The following snippet is a sample response:

0.8380476

The Summarize
action summarizes the given English-language input text.

The following snippet as a sample request:

Informatica is a leading company in the field of enterprise cloud data management, offering a range of software solution's designed to help organizations effectively manage and integrate their data. Their tools and platforms, such as PowerCenter and Cloud Data, provide functionalities for data integration, profiling, and cleaning, which are essential for ensuring data quality and governance. By leveraging these solutions, businesses can enhance their data management processes, maintain compliance, and facilitate seamless data integration across various systems. Informatica's offerings empower organizations to drive their digital transformation initiatives, enabling them to make informed decisions and stay competitive in a data-driven world.

The following snippet is a sample response:

Informatica is a leading enterprise cloud data management company, providing software solutions for effective data management and integration. Their offerings include PowerCenter and Cloud Data for data integration, profiling, and cleaning, ensuring data quality and governance. Businesses use these tools to enhance data management processes, maintain compliance, and facilitate seamless data integration. Informatica's solutions enable digital transformation, enabling informed decision-making and competitiveness in a data-driven world.

The Extract Answer
action extracts an answer to a given question from a text document. The document might be a plain English document or a string representation of a semi-structured JSON data object.

Enter the following input field values as a sample request:

source_document: Apple is a fruit which is nutritious and healthy. Banana is a fruit which is good for our health. Orange's are rich in Vitamin C, which is essential for immune function. Strawberries are high in antioxidants, promoting heart health and skin beauty. Grapes are packed with vitamins and minerals, aiding in healthy blood circulation. Mangoes are a source of Vitamin A, supporting eye health. Pineapples contain enzymes that aid digestion and reduce inflammation. Watermelons are hydrating and help in maintaining skin health.

question: What is pineapple?

The following snippet is a sample response:

[
  {
    "answer": "contain enzymes that aid digestion and reduce inflammation",
    "score": 0.9908955
  }
]

The Parse Document
action returns the extracted content from a document at a Snowflake stage as an OBJECT. This OBJECT contains JSON-encoded objects as strings.

While creating the stage, you must enable Snowflake server-side encryption. You must use the following query to enable Snowflake server-side encryption:

CREATE STAGE <name> DIRECTORY = ( ENABLE = true ) ENCRYPTION = ( TYPE = 'SNOWFLAKE_SSE' );

To add a document to stage, you must use the following query in the terminal window:

snowsql -a <your accountIdentifier> -u <username> -d<database> -s <schema>

Enter the password. After that use the following PUT command to add the file in the Snowflake stage:

PUT 'file:path' @stage AUTO_COMPRESS = FALSE;

Ensure that you use

AUTO_COMPRESS = FALSE

. Otherwise, the file will be compressed.

Enter the following input field values as a sample request:

stage: @DEMO.LOCATION.input_stage

path: collation.pdf

mode: OCR

The following snippet is a sample response:

{
  "content": "SnowFlake - Collate --> to combine we generally use where join, aggregate functions, order by, limit and offset, group by, window functions,\nscalar functions, clustering keys and clustered tables Collation defines how strings are compared, ordered, and stored based on certain\ncharacteristics. it defines rules for: 1.Case Sensitivity: Whether the uppercase and lowercase alphabets are considered as same 2.Accent\nSensitivity: whether the character like different accent are same or not 3.Locale: Different languages have different sorting and comparison\nvalues 4.Character Sorting: Defines the order of character in string comparison Types of collation in snowflake: 1.Binary Collation:(BINARY)\nThis is default collation, this is case-Sensitive and accent-senstive and character are compared based on its binary value(ASCII). 2.Case-\nInsensitive Collation:(CI) This ignores case sensitivity, it can be used where scenarios where case does not matter in string comparisons.\n3.Accent-Insensitive Collation:(AI) This ignores the accents, it's useful in scenarios where accents should not affect comparisons. 4.Case-\ninsensitive and Accent-Insensitive:(CI_AI) This ignores both case and accent, used in many multi-lingual scenarios where neither case nor\naccents should affect string matching 5.Locale-Specific Collations: It also supports locale-specific collations(eg.,en_US for English(united\nstates), fr_FR for French(france)) 6.Punctuation-Insensitivity:(PI) This ignore the punctuation that are used in the given string(i.e A-B-C and ABC\nboth are equal) Using COLLATE in Snowflake: It can be used in two primary ways: 1.On Columns when creating or Altering Table: example:\nCREATE TABLE users( username VARCHAR COLLATE 'en_US', email VARCHAR COLLATE 'ci' ); to modify the collate property we can use: ALTER\nTABLE users MODIFY COLUMN email SET COLLATION 'AI'; 2.On specific String Operations: example: SELECT * FROM users WHERE username =\n'john' COLLATE 'ci'; the output will be the name of john as JOHN or John or john we can also use the collation with ORDER BY SELECT * FROM\nusers ORDER BY username COLLATE 'fr_FR';",
  "metadata": {
    "pageCount": 1
  }
}

The Cortex Agent
action sends a user query to the Cortex Agent service provided in the request body and then generates a response.

Enter the following input field values as a sample request:

model: llama3.1-70b

prompt: give me the products whose catgeory is personal care

search_service_name: PRODUCT.SALES.EXAMPLE

semantic_model: @PRODUCT.SALES.PRODUCT/product.yaml

tool_specs: cortex_search:transcript_search,cortex_analyst_text_to_sql:data_model

type: text

queries: Personal care

columns: name

filter:

max_results: 4

The following snippet is a sample response:

<result xml="false">event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"tool_use","tool_use":{"tool_use_id":"toolu_708ed2d1","name":"transcript_search","input":{"filters":"{}","limit":4.0,"requestId":"7a40f028-2797-4dd8-9155-bef9d1310da3","scoringConfig":"\u003cnil\u003e","experimentalJson":"","query":"Give me the product\u0027\u0027s whose category is outdoor?","columns":[]}}},{"index":0,"type":"tool_results","tool_results":{"tool_use_id":"toolu_708ed2d1","content":[{"type":"json","json":{"searchResults":[{"source_id":1.0,"doc_id":"","doc_title":"","text":"Lawn Mower"},{"doc_id":"","doc_title":"","text":"Hat","source_id":2.0},{"text":"Soda","source_id":3.0,"doc_id":"","doc_title":""},{"doc_id":"","doc_title":"","text":"Binoculars","source_id":4.0}]}}],"status":"success","name":""}}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":""}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":""}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":"Her"}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":"e are the"}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":" prod"}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":"ucts th"}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":"at "}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":"belo"}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":"ng to th"}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":"e outdoor"}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":" ca"}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":"t"}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":"e"}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":"gory:"}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":"\n\n"}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":"1. L"}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":"aw"}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":"n"}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":" "}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":"M"}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":"ow"}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":"e"}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":"r"}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":" ã&#x80;&#x90;â&#x80; 1"}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":"â&#x80; ã&#x80;&#x91;\n2. "}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":"B"}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":"in"}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":"o"}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":"c"}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":"u"}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":"l"}]}}

event: message.delta
data: {"id":"msg_001","object":"message.delta","delta":{"content":[{"index":0,"type":"text","text":"ars ã&#x80;&#x90;â&#x80; 4â&#x80; ã&#x80;&#x91;"}]}}

event: done
data: [DONE]
</result>

The Execute Statement
action runs a database query.

Enter the following input field value as a sample request:

statement: SELECT name FROM product WHERE category ='Electronics'
database: product
schema: sales

The following snippet is a sample response:

<root>
   <statementStatusUrl>/api/v2/statements/01bbda83-0304-fe9b-0006-e9df001edac6?requestId=a15dd6ed-cec1-4f0f-9a46-239824d9636a</statementStatusUrl>
   <code>090001</code>
   <data>
      <_1>Wireless Mouse</_1>
   </data>
   <data>
      <_1>Ergonomic Keyboard</_1>
   </data>
   <data>
      <_1>Bluetooth Headphones</_1>
   </data>
   <data>
      <_1>Webcam 1080p</_1>
   </data>
   <data>
      <_1>Monitor</_1>
   </data>
   <data>
      <_1>Printer</_1>
   </data>
   <data>
      <_1>Router</_1>
   </data>
   <data>
      <_1>External Hard Drive</_1>
   </data>
   <data>
      <_1>USB Flash Drive</_1>
   </data>
   <resultSetMetaData>
      <rowType>
         <schema>SALES</schema>
         <database>PRODUCT</database>
         <nullable>false</nullable>
         <precision/>
         <byteLength>1020</byteLength>
         <length>255</length>
         <name>NAME</name>
         <scale/>
         <collation/>
         <type>text</type>
         <table>PRODUCT</table>
      </rowType>
      <numRows>9</numRows>
      <format>jsonv2</format>
      <partitionInfo>
         <uncompressedSize>181</uncompressedSize>
         <rowCount>9</rowCount>
      </partitionInfo>
   </resultSetMetaData>
   <requestId>a15dd6ed-cec1-4f0f-9a46-239824d9636a</requestId>
   <statementHandle>01bbda83-0304-fe9b-0006-e9df001edac6</statementHandle>
   <sqlState>00000</sqlState>
   <message>Statement executed successfully.</message>
   <createdOn>1745303249621</createdOn>
</root>

The Structured Output
generates structured output in a specified format.

Enter the following input field value as a sample request:

model: mistral -large
messages: <messages>
<content>You are a planning agent as part of a supply chain copilot system in a computer hardware company for data centers. Your task is to analyze the user's input, determine the necessary steps to answer their question, and identify which data from the available systems is required for each step. Some user prompts require multiple systems, and your response should include all the systems included from the available systems. Always adhere to the responses in the JSON format as prescribed below.

Here is the list of available systems along with the information contained in each system:
* CDGC (Informatica Cloud Data Governance System): Contains metadata information about custom data models, including field or column names, data quality score, and business glossary explaining the business terms. ALWAYS ADD cdgc in planner output when orcl data is requested as cdgc contains the metadata for the oracle tables.
* Salesforce: Contains order information for computer hardware in the schema of the Salesforce order object with all standard fields without the order line items. Orders where the status is draft mean that the orders are pending review. The order details include order ID, product ordered, quantity ordered, order status, customer account name, and the date the order was created.
* MDM (Informatica MDM): Contains information across three different domains: products, the different components or parts for each product, and the supplier details for each component or part.
* ORCL: This is the Oracle database. Always include cdgc whenever orcl is present as a planning step as the metadata for oracle is contained in cdgc. Oracle database contains details of supplier performance with regards to delivery times for each component based on the airport of delivery. The airport codes are also available only through the Oracle database. When constructing the filter condition, only include component_id and supplier_name if relevant and nothing else. Make sure to execute cdgc before orcl to retrieve the oracle table names.
* TRR (Transportation Risk Report): Contains airport codes along with the current risk profile (low, medium, and high) and the current risk situation for the components to be delivered by the supplier using the airport for delivery from supplier performance analysis. So, supplier performance analysis information is needed before retrieving the data from the transportation risk report. Always execute orcl before executing trr.</content>
<role>system</role>
</messages>
<messages>
<content>You will be provided with a user prompt and conversation history.

User Prompt: Show me orders pending review

Conversation History:

To complete this task, follow these steps:

1. Analyze the available systems.
2. Validate the user prompt based on the criteria specified in the validation criteria. If the user prompt is very broad, do not proceed. The user input must have information that systems can provide. Add a question in the 'aiask' field of the JSON if the prompt is too broad.

Validation Criteria:

* User prompt should be specific to the supply chain and production scenario.
* User prompt should not be broad and generic and should not have the use of superlative terms like best order, highest price, lowest price, significant, worst laptop, cheaper laptop.
* User questions will contain the conversation history. Use the conversation history data while responding.

Sample list of allowed questions:

* Give order details
* Get order details for orders pending review
* Want to see order details for all orders
* Show product details
* What are the details for a product with a GPU?
* Show me supplier details for a product
* Do you have supplier details for the component?
* Who are the suppliers for the different components for the product?
* What is the ID for a product, supplier, or component with a name?
* What is the supplier lead time for the supplier?
* Show me alternate suppliers for the component along with lead times
* Show the low-risk option for delivery or transportation
* What is the delivery risk for the airport?
* Show me orders that need a review and show me product details
* Give me orders that need a review and product details for the order
* Show me the risk from pending orders and its suppliers?
3. Extract key information from the conversation history specified in the JSON element conversation\_history.
4. Reconstruct the user prompt based on the extracted information, especially from "aiask" and "User Question" in the previous conversation from conversation\_history.
5. Ensure the conditions within the prerequisites are met. If the conditions are not met, do not proceed and add only the information needed as a question within the 'aiask' field of the JSON response and be polite.

Prerequisites:

* For orders, if nothing is specified, fetch all the records.
* For products, please ensure that product identity or product name is available.
* For component or part details, please ensure one of the following information: product identity or product name, component identity or component name is available.
* For supplier details, please ensure that one of the following information: product identity, product name, component identity, component name, supplier ID, or supplier name is available.
* For supplier lead time details, please ensure that component identity, component name, supplier ID, or supplier name is available.
* For transportation or delivery risk, please ensure that component identity, component name, supplier ID, supplier name, or airport code is available.
6. Identify which system(s) contain the required data.
7. Create a step-by-step plan in the JSON format to retrieve the associated metadata, if mentioned in the systems, and the information from each system.
8. Always include CDGC when ORCL information is requested. CDGC should come before ORCL. If CDGC is present in the output, keep CDGC before ORCL and after Salesforce and MDM.

Planner Optimization Paths:

* optimal\_path\_1: If both product and supplier details are requested, reduce the number of steps in the planner as both the product and supplier information can be queried in a single step.
* optimal\_path\_2: By default, the planner should retrieve the metadata for the most recently modified object from the CDGC system, then use this supplier information to get the lead times from the ORCL system. Only if the request specifies aggregate data quality, first retrieve the metadata for the object with the highest data quality from the CDGC system, then use this supplier information to fetch lead times from the ORCL system.
* optimal\_path\_3: Here are the guidelines for generating filter conditions for the ORCL system.

1. If the filter condition requires order details, add a step to the planner output to obtain it from Salesforce.
2. If the filter condition requires product details, component details, or supplier details, must add a step to the planner output to obtain product details, component details, or supplier details from MDM.
3. Always add a step to the planner to retrieve column names, table names necessary information from CDGC.
4. Ensure the filter condition only includes component\_id, component\_name, supplier\_id, and supplier\_name. No other columns are allowed.
5. The filter condition must not contain any SQL JOIN statements or subqueries. It should only contain SQL WHERE clause conditions. It should not contain any SQL SELECT statement or queries.
6. Avoid creating or referencing any database tables or columns outside of metadata information in the CDGC system.
* optimal\_path\_4: The table name for ORCL can be inferred from the object name in the CDGC metadata. If present, you can extract the object name from the conversation\_history in CDGC metadata.
9. Provide your final output in the JSON format as specified below. Ensure that your plan is comprehensive, logical, and directly addresses the user's input. Each step should be clear and actionable, with a specific description of the required data and the system it comes from. Explain the reasoning step-by-step within the 'reasoning' field of the JSON schema as one paragraph. The output should only contain the JSON adhering to the schema below. Always adhere to the JSON format for responses even when you are not able to retrieve the corresponding data.

JSON Output Format:

{
"reasoning": "The user is requesting information about orders that need review. Based on the available systems, this information can be found in the Salesforce system. Specifically, we need to query the order object in Salesforce and filter for orders with a 'draft' status, as these are the orders pending review according to the system description. This query will provide the necessary order details for review.",
"aiask": null,
"Planner": [
{
"app": "salesforce",
"action": "query",
"object": "order",
"filter": "status = 'review'",
"prompt_selector": "Get orders with status"
}
]
}
</content>
<role>user</role>
</messages>
top_p : 1 (default)
temperature : 0 (default)
max_tokens : 4096 (default)

The following snippet is a sample response:

<result
    xmlns:aetgt="http://schemas.informatica.com/socrates/data-services/2014/05/business-connector-model.xsd"
    xmlns:bconn="http://schemas.informatica.com/socrates/data-services/2014/05/business-connector-model.xsd" xml="false">data: {"id":"13e56dae-ea49-4dea-b617-207d0e3628d8","created":1747724294,"model":"mistral-large","choices":[{"delta":{"type":"text","content":"\n","content_list":[{"type":"text","text":"\n"}],"text":"\n"}}],"usage":{"prompt_tokens":1844,"completion_tokens":1,"total_tokens":1845}} data: {"id":"13e56dae-ea49-4dea-b617-207d0e3628d8","created":1747724294,"model":"mistral-large","choices":[{"delta":{"type":"text","content":"\n","content_list":[{"type":"text","text":"\n"}],"text":"\n"}}],"usage":{"prompt_tokens":1844,"completion_tokens":2,"total_tokens":1846}} data: {"id":"13e56dae-ea49-4dea-b617-207d0e3628d8","created":1747724294,"model":"mistral-large","choices":[{"delta":{"type":"text","content":"{","content_list":[{"type":"text","text":"{"}],"text":"{"}}],"usage":{"prompt_tokens":1844,"completion_tokens":3,"total_tokens":1847}} data: {"id":"13e56dae-ea49-4dea-b617-207d0e3628d8","created":1747724294,"model":"mistral-large","choices":[{"delta":{"type":"text","content":"\n","content_list":[{"type":"text","text":"\n"}],"text":"\n"}}],"usage":{"prompt_tokens":1844,"completion_tokens":4,"total_tokens":1848}} data: {"id":"13e56dae-ea49-4dea-b617-207d0e3628d8","created":1747724294,"model":"mistral-large","choices":[{"delta":{"type":"text","content":"\"","content_list":[{"type":"text","text":"\""}],"text":"\""}}],"usage":{"prompt_tokens":1844,"completion_tokens":5,"total_tokens":1849}} data: {"id":"13e56dae-ea49-4dea-b617-207d0e3628d8","created":1747724294,"model":"mistral-large","choices":[{"delta":{"type":"text","content":"reason","content_list":[{"type":"text","text":"reason"}],"text":"reason"}}],"usage":{"prompt_tokens":1844,"completion_tokens":6,"total_tokens":1850}} data: {"id":"13e56dae-ea49-4dea-b617-207d0e3628d8","created":1747724294,"model":"mistral-large","choices":[{"delta":{"type":"text","content":"ing","content_list":[{"type":"text","text":"ing"}],"text":"ing"}}],"usage":{"prompt_tokens":1844,"completion_tokens":7,"total_tokens":1851}} data: {"id":"13e56dae-ea49-4dea-b617-207d0e3628d8","created":1747724294,"model":"mistral-large","choices":[{"delta":{"type":"text","content":"\":","content_list":[{"type":"text","text":"\":"}],"text":"\":"}}],"usage":{"prompt_tokens":1844,"completion_tokens":8,"total_tokens":1852}} data: {"id":"13e56dae-ea49-4dea-b617-207d0e3628d8","created":1747724294,"model":"mistral-large","choices":[{"delta":{"type":"text","content":" \"","content_list":[{"type":"text","text":" \""}],"text":" \""}}],"usage":{"prompt_tokens":1844,"completion_tokens":9,"total_tokens":1853}} data: {"id":"13e56dae-ea49-4dea-b617-207d0e3628d8","created":1747724294,"model":"mistral-large","choices":[{"delta":{"type":"text","content":"The","content_list":[{"type":"text","text":"The"}],"text":"The"}}],"usage":{"prompt_tokens":1844,"completion_tokens":10,"total_tokens":1854}} data: {"id":"13e56dae-ea49-4dea-b617-207d0e3628d8","created":1747724294,"model":"mistral-large","choices":[{"delta":{"type":"text","content":" user","content_list":[{"type":"text","text":" user"}],"text":" user"}}],"usage":{"prompt_tokens":1844,"completion_tokens":11,"total_tokens":1855}} data: {"id":"13e56dae-ea49-4dea-b617-207d0e3628d8","created":1747724294,"model":"mistral-large","choices":[{"delta":{"type":"text","content":" is","content_list":[{"type":"text","text":" is"}],"text":" is"}}],"usage":{"prompt_tokens":1844,"completion_tokens":12,"total_tokens":1856}} data: {"id":"13e56dae-ea49-4dea-b617-207d0e3628d8","created":1747724294,"model":"mistral-large","choices":[{"delta":{"type":"text","content":" request","content_list":[{"type":"text","text":" request"}],"text":" request"}}],"usage":{"prompt_tokens":1844,"completion_tokens":13,"total_tokens":1857}} data: {"id":"13e56dae-ea49-4dea-b617-207d0e3628d8","created":1747724294,"model":"mistral-large","choices":[{"delta":{"type":"text","content":"ing","content_list":[{"type":"text","text":"ing"}],"text":"ing"}}],"usage":{"prompt_tokens":1844,"completion_tokens":14,"total_tokens":1858}} data: {"id":"13e56dae-ea49-4dea-b617-207d0e3628d8","created":1747724294,"model":"mistral-large","choices":[{"delta":{"type":"text","content":" information","content_list":[{"type":"text","text":" information"}],"text":" information"}}],"usage":{"prompt_tokens":1844,"completion_tokens":15,"total_tokens":1859}} data: {"id":"13e56dae-ea49-4dea-b617-207d0e3628d8","created":1747724294,"model":"mistral-large","choices":[{"delta":{"type":"text","content":" about","content_list":[{"type":"text","text":" about"}],"text":" about"}}],"usage":{"prompt_tokens":1844,"completion_tokens":16,"total_tokens":1860}} data: {"id":"13e56dae-ea49-4dea-b617-207d0e3628d8","created":1747724294,"model":"mistral-large","choices":[{"delta":{"type":"text","content":" orders","content_list":[{"type":"text","text":" orders"}],"text":" orders"}}],"usage":{"prompt_tokens":1844,"completion_tokens":17,"total_tokens":1861}} data: {"id":"13e56dae-ea49-4dea-b617-207d0e3628d8","created":1747724294,"model":"mistral-large","choices":[{"delta":{"type":"text","content":" that","content_list":[{"type":"text","text":" that"}],"text":" that"}}],"usage":{"prompt_tokens":1844,"completion_tokens":18,"total_tokens":1862}} data: {"id":"13e56dae-ea49-4dea-b617-207d0e3628d8","created":1747724294,"model":"mistral-large","choices":[{"delta":{"type":"text","content":" need","content_list":[{"type":"text","text":" need"}],"text":" need"}}],"usage":{"prompt_tokens":1844,"completion_tokens":19,"total_tokens":1863}} data: 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</result>

The following snippet is a sample extracted response:

{
  "reasoning": "The user is requesting information about orders that need review. Based on the available systems, this information can be found in the Salesforce system. Specifically, we need to query the order object in Salesforce and filter for orders with a 'draft' status, as these are the orders pending review according to the system description. This query will provide the necessary order details for review.",
  "aiask": null,
  "Planner": [
    {
      "app": "salesforce",
      "action": "query",
      "object": "order",
      "filter": "status = 'draft'",
      "prompt_selector": "Get orders with status"
    }
  ]
}

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