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### What problem does this PR solve? Regrouped and renamed agent templates to increase user engagement. ### Type of change - [x] Refactoring
718 lines
37 KiB
JSON
718 lines
37 KiB
JSON
{
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"id": 17,
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"title": {
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"en": "Text-to-SQL data expert",
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"de": "Text-to-SQL-Datenexperte",
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"zh": "Text-to-SQL 问数专家"},
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"description": {
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"en": "Text-to-SQL data expert lets business users turn plain-English questions into fully formed SQL queries. Simply type your question (e.g., 'Show me last quarter's top 10 products by revenue') and Text-to-SQL data expert generates the exact SQL, runs it against your database, and returns the results in seconds. ",
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"de": "Text-to-SQL-Datenexperte ist ein KI-gestütztes Tool, mit dem Geschäftsanwender einfache englische Fragen in vollständige SQL-Abfragen umwandeln können. Geben Sie einfach Ihre Frage ein (z.B. 'Zeige mir die Top 10 Produkte des letzten Quartals nach Umsatz') und der SQL-Assistent generiert das exakte SQL, führt es gegen Ihre Datenbank aus und liefert die Ergebnisse in Sekunden.",
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"zh": "用户能够将简单文本问题转化为完整的SQL查询并输出结果。只需输入您的问题(例如,展示上个季度前十名按收入排序的产品),SQL助理就会生成精确的SQL语句,对其运行您的数据库,并几秒钟内返回结果。"},
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"canvas_type": "Marketing",
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"dsl": {
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"components": {
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"Agent:WickedGoatsDivide": {
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"downstream": [
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"ExeSQL:TiredShirtsPull"
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],
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"obj": {
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"component_name": "Agent",
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"outputs": {
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"value": ""
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}
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"content": "User's query: {sys.query}\n\nSchema: {Retrieval:HappyTiesFilm@formalized_content}\n\nSamples about question to SQL: {Retrieval:SmartNewsHammer@formalized_content}\n\nDescription about meanings of tables and files: {Retrieval:SweetDancersAppear@formalized_content}",
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"role": "user"
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}
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],
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"sys_prompt": "### ROLE\nYou are a Text-to-SQL assistant. \nGiven a relational database schema and a natural-language request, you must produce a **single, syntactically-correct MySQL query** that answers the request. \nReturn **nothing except the SQL statement itself**\u2014no code fences, no commentary, no explanations, no comments, no trailing semicolon if not required.\n\n\n### EXAMPLES \n-- Example 1 \nUser: List every product name and its unit price. \nSQL:\nSELECT name, unit_price FROM Products;\n\n-- Example 2 \nUser: Show the names and emails of customers who placed orders in January 2025. \nSQL:\nSELECT DISTINCT c.name, c.email\nFROM Customers c\nJOIN Orders o ON o.customer_id = c.id\nWHERE o.order_date BETWEEN '2025-01-01' AND '2025-01-31';\n\n-- Example 3 \nUser: How many orders have a status of \"Completed\" for each month in 2024? \nSQL:\nSELECT DATE_FORMAT(order_date, '%Y-%m') AS month,\n COUNT(*) AS completed_orders\nFROM Orders\nWHERE status = 'Completed'\n AND YEAR(order_date) = 2024\nGROUP BY month\nORDER BY month;\n\n-- Example 4 \nUser: Which products generated at least \\$10 000 in total revenue? \nSQL:\nSELECT p.id, p.name, SUM(oi.quantity * oi.unit_price) AS revenue\nFROM Products p\nJOIN OrderItems oi ON oi.product_id = p.id\nGROUP BY p.id, p.name\nHAVING revenue >= 10000\nORDER BY revenue DESC;\n\n\n### OUTPUT GUIDELINES\n1. Think through the schema and the request. \n2. Write **only** the final MySQL query. \n3. Do **not** wrap the query in back-ticks or markdown fences. \n4. Do **not** add explanations, comments, or additional text\u2014just the SQL.",
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"temperature": 0.1,
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"visual_files_var": ""
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}
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},
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"Retrieval:HappyTiesFilm",
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"Retrieval:SmartNewsHammer",
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"Retrieval:SweetDancersAppear"
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]
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"ExeSQL:TiredShirtsPull": {
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"Message:ShaggyMasksAttend"
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],
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"obj": {
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"component_name": "ExeSQL",
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"params": {
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"database": "",
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"db_type": "mysql",
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"host": "",
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"max_records": 1024,
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"outputs": {
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"formalized_content": {
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"type": "string",
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"value": ""
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},
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},
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"password": "",
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"port": 3306,
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"username": ""
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},
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"Agent:WickedGoatsDivide"
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]
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},
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"Message:ShaggyMasksAttend": {
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"downstream": [],
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"obj": {
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"component_name": "Message",
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"params": {
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"content": [
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"{ExeSQL:TiredShirtsPull@formalized_content}"
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]
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}
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},
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"upstream": [
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"ExeSQL:TiredShirtsPull"
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"downstream": [
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"Agent:WickedGoatsDivide"
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"formalized_content": {
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"type": "string",
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"begin"
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"Agent:WickedGoatsDivide"
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"outputs": {
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"formalized_content": {
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"type": "string",
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"value": ""
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}
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},
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"query": "{sys.query}",
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"top_n": 8,
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"use_kg": false
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}
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},
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"upstream": [
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"begin"
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]
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},
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"Retrieval:SweetDancersAppear": {
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"downstream": [
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"Agent:WickedGoatsDivide"
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],
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"obj": {
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"component_name": "Retrieval",
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"params": {
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"kb_ids": [],
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"keywords_similarity_weight": 0.7,
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"outputs": {
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"formalized_content": {
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"type": "string",
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"value": ""
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}
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},
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"query": "{sys.query}",
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"similarity_threshold": 0.2,
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"top_k": 1024,
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"top_n": 8,
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"use_kg": false
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"upstream": [
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"begin"
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"Retrieval:HappyTiesFilm",
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"Retrieval:SmartNewsHammer",
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"Retrieval:SweetDancersAppear"
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"component_name": "Begin",
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"params": {
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"enablePrologue": true,
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"inputs": {},
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"mode": "conversational",
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"prologue": "Hi! I'm your SQL assistant. What can I do for you?"
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}
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"upstream": []
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}
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},
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"globals": {
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"sys.conversation_turns": 0,
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"sys.files": [],
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"sys.query": "",
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"sys.user_id": ""
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},
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"graph": {
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{
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"data": {
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"id": "xy-edge__beginstart-Retrieval:HappyTiesFilmend",
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"source": "begin",
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"sourceHandle": "start",
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"target": "Retrieval:HappyTiesFilm",
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"targetHandle": "end"
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{
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"id": "xy-edge__beginstart-Retrieval:SmartNewsHammerend",
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"sourceHandle": "start",
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"target": "Retrieval:SmartNewsHammer",
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{
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"data": {
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"id": "xy-edge__beginstart-Retrieval:SweetDancersAppearend",
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"target": "Retrieval:SweetDancersAppear",
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}
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},
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"label": "Retrieval",
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"name": "Schema"
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|
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{
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"data": {
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||
"empty_response": "",
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||
"kb_ids": [],
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||
"keywords_similarity_weight": 0.7,
|
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"outputs": {
|
||
"formalized_content": {
|
||
"type": "string",
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"value": ""
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}
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||
},
|
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"query": "{sys.query}",
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||
"top_n": 8,
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|
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},
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||
"label": "Retrieval",
|
||
"name": "Question to SQL"
|
||
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|
||
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|
||
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|
||
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{
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"data": {
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||
"empty_response": "",
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||
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||
"keywords_similarity_weight": 0.7,
|
||
"outputs": {
|
||
"formalized_content": {
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||
"type": "string",
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},
|
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"query": "{sys.query}",
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"top_k": 1024,
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||
"top_n": 8,
|
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"use_kg": false
|
||
},
|
||
"label": "Retrieval",
|
||
"name": "Database Description"
|
||
},
|
||
"dragging": false,
|
||
"id": "Retrieval:SweetDancersAppear",
|
||
"measured": {
|
||
"height": 96,
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||
"width": 200
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"type": "retrievalNode"
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},
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{
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"data": {
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"form": {
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||
"description": "",
|
||
"exception_default_value": "",
|
||
"exception_goto": [],
|
||
"exception_method": "",
|
||
"frequencyPenaltyEnabled": false,
|
||
"frequency_penalty": 0.7,
|
||
"llm_id": "qwen-max@Tongyi-Qianwen",
|
||
"maxTokensEnabled": false,
|
||
"max_retries": 3,
|
||
"max_rounds": 5,
|
||
"max_tokens": 256,
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"mcp": [],
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"message_history_window_size": 12,
|
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"outputs": {
|
||
"content": {
|
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"type": "string",
|
||
"value": ""
|
||
}
|
||
},
|
||
"presencePenaltyEnabled": false,
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||
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|
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"prompts": [
|
||
{
|
||
"content": "User's query: {sys.query}\n\nSchema: {Retrieval:HappyTiesFilm@formalized_content}\n\nSamples about question to SQL: {Retrieval:SmartNewsHammer@formalized_content}\n\nDescription about meanings of tables and files: {Retrieval:SweetDancersAppear@formalized_content}",
|
||
"role": "user"
|
||
}
|
||
],
|
||
"sys_prompt": "### ROLE\nYou are a Text-to-SQL assistant. \nGiven a relational database schema and a natural-language request, you must produce a **single, syntactically-correct MySQL query** that answers the request. \nReturn **nothing except the SQL statement itself**\u2014no code fences, no commentary, no explanations, no comments, no trailing semicolon if not required.\n\n\n### EXAMPLES \n-- Example 1 \nUser: List every product name and its unit price. \nSQL:\nSELECT name, unit_price FROM Products;\n\n-- Example 2 \nUser: Show the names and emails of customers who placed orders in January 2025. \nSQL:\nSELECT DISTINCT c.name, c.email\nFROM Customers c\nJOIN Orders o ON o.customer_id = c.id\nWHERE o.order_date BETWEEN '2025-01-01' AND '2025-01-31';\n\n-- Example 3 \nUser: How many orders have a status of \"Completed\" for each month in 2024? \nSQL:\nSELECT DATE_FORMAT(order_date, '%Y-%m') AS month,\n COUNT(*) AS completed_orders\nFROM Orders\nWHERE status = 'Completed'\n AND YEAR(order_date) = 2024\nGROUP BY month\nORDER BY month;\n\n-- Example 4 \nUser: Which products generated at least \\$10 000 in total revenue? \nSQL:\nSELECT p.id, p.name, SUM(oi.quantity * oi.unit_price) AS revenue\nFROM Products p\nJOIN OrderItems oi ON oi.product_id = p.id\nGROUP BY p.id, p.name\nHAVING revenue >= 10000\nORDER BY revenue DESC;\n\n\n### OUTPUT GUIDELINES\n1. Think through the schema and the request. \n2. Write **only** the final MySQL query. \n3. Do **not** wrap the query in back-ticks or markdown fences. \n4. Do **not** add explanations, comments, or additional text\u2014just the SQL.",
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"text": "Searches for relevant database creation statements.\n\nIt should label with a dataset to which the schema is dumped in. You could use \" General \" as parsing method, \" 2 \" as chunk size and \" ; \" as delimiter."
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"data": {
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"text": "Searches for samples about question to SQL. \n\nYou could use \" Q&A \" as parsing method.\n\nPlease check this dataset:\nhttps://huggingface.co/datasets/InfiniFlow/text2sql"
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"data": {
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"form": {
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"text": "Searches for description about meanings of tables and fields.\n\nYou could use \" General \" as parsing method, \" 2 \" as chunk size and \" ### \" as delimiter."
|
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},
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"label": "Note",
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"name": "Note: Database Description"
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"data": {
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"form": {
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"text": "The Agent learns which tables may be available based on the responses from three knowledge bases and converts the user's input into SQL statements."
|
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},
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"label": "Note",
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"name": "Note: SQL Generator"
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},
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"dragHandle": ".note-drag-handle",
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"height": 132,
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},
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{
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"data": {
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"form": {
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"text": "Connect to your database to execute SQL statements."
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} |