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| 1 | +package org.opensearch.ml.engine.tools; |
| 2 | + |
| 3 | +public class QueryPlanningPromptTemplate { |
| 4 | + |
| 5 | + public static final String DEFAULT_QUERY = "{\"size\":10,\"query\":{\"match_all\":{}}}"; |
| 6 | + |
| 7 | + public static final String QUERY_TYPE_RULES = "\nChoose query types based on user intent and fields: \n" |
| 8 | + + "match: single-token full‑text searches on analyzed text fields, \n" |
| 9 | + + "match_phrase: multi-token phrases on analyzed text fields (search string contains a space, hyphen, comma, etc.), \n" |
| 10 | + + "term / terms:exact match on keyword, numeric, boolean, \n" |
| 11 | + + "range:numeric/date comparisons (gt, lt, gte, lte), \n" |
| 12 | + + "bool with must, should, must_not, filter: AND/OR/NOT logic, \n" |
| 13 | + + "wildcard / prefix on keyword:\"starts with\", \"contains\", \n" |
| 14 | + + "exists:field presence/absence, \n" |
| 15 | + + "nested query / nested agg:Never wrap a field in nested unless the mapping for that exact path (or one of its parents) explicitly says \"type\": \"nested\". \n" |
| 16 | + + "Otherwise use a normal query on the flattened field. \n"; |
| 17 | + |
| 18 | + public static final String AGGREGATION_RULES = "Aggregations (when asked for counts, averages, \"top N\", distributions): \n" |
| 19 | + + "terms on field.keyword or numeric for grouping / top N, \n" |
| 20 | + + "Metric aggs (avg, min, max, sum, stats, cardinality) on numeric fields, \n" |
| 21 | + + "date_histogram, histogram, range for distributions, \n" |
| 22 | + + "Always set \"size\": 0 when only aggregations are needed, \n" |
| 23 | + + "Use sub‑aggregations + order for \"top N by metric\", \n" |
| 24 | + + "If grouping by a text field, use its .keyword sub‑field.\n"; |
| 25 | + |
| 26 | + public static final String PROMPT_PREFIX = |
| 27 | + "You are an OpenSearch DSL expert. Your job is to convert natural‑language questions into strict JSON OpenSearch search query bodies. \n" |
| 28 | + + "Follow every rule: Use only the provided index mapping to decide which fields exist and their types, pay close attention to index mapping. \n" |
| 29 | + + "Do not use fields that not present in mapping. \n" |
| 30 | + + QUERY_TYPE_RULES |
| 31 | + + AGGREGATION_RULES; |
| 32 | + |
| 33 | + public static final String USE_QUERY_FIELDS_INSTRUCTION = |
| 34 | + "When Query Fields are provided, prioritize incorporating them into the generated query."; |
| 35 | + |
| 36 | + public static final String OUTPUT_FORMAT_INSTRUCTIONS = "Output format: Output only a valid escaped JSON string or the literal \n" |
| 37 | + + DEFAULT_QUERY |
| 38 | + + " \nReturn exactly one JSON object. " |
| 39 | + + "Output nothing before or after it — no code fences/backticks (`), angle brackets (< >), hash marks (#), asterisks (*), pipes (|), tildes (~), ellipses (… or ...), emojis, typographic quotes (\" \"), non-breaking spaces (U+00A0), zero-width characters (U+200B, U+FEFF), or any other markup/control characters. " |
| 40 | + + "Use valid JSON only (standard double quotes \"; no comments; no trailing commas). " |
| 41 | + + "This applies to formatting only, string values inside the JSON may contain any needed Unicode characters. \n" |
| 42 | + + "Follow the examples below. \n" |
| 43 | + + USE_QUERY_FIELDS_INSTRUCTION |
| 44 | + + "Fallback: If the request cannot be fulfilled with the mapping (missing field, unsupported feature, etc.), \n" |
| 45 | + + "output the literal string: " |
| 46 | + + DEFAULT_QUERY; |
| 47 | + |
| 48 | + // Individual example constants for better maintainability |
| 49 | + public static final String EXAMPLE_1 = "Example 1 — numeric range \n" |
| 50 | + + "Input: Show all products that cost more than 50 dollars. \n" |
| 51 | + + "Mapping: { \"properties\": { \"price\": { \"type\": \"float\" }, \"cost\": { \"type\": \"float\" } } }\n" |
| 52 | + + "query_fields: [price]" |
| 53 | + + "Output: \"{ \"query\": { \"range\": { \"price\": { \"gt\": 50 } } } }\" \n"; |
| 54 | + |
| 55 | + public static final String EXAMPLE_2 = "Example 2 — text match + exact filter \n" |
| 56 | + + "Input: Find employees in London who are active. \n" |
| 57 | + + "Mapping: \"{ \"properties\": { \"city\": { \"type\": \"text\", \"fields\": { \"keyword\": { \"type\": \"keyword\" } } }, \"status\": { \"type\": \"keyword\" } } }\" \n" |
| 58 | + + "query_fields: [city, status]" |
| 59 | + + "Output: \"{ \"query\": { \"bool\": { \"must\": [ { \"match\": { \"city\": \"London\" } } ], \"filter\": [ { \"term\": { \"status\": \"active\" } } ] } } }\" \n"; |
| 60 | + |
| 61 | + public static final String EXAMPLE_3 = |
| 62 | + "Example 3 — match_phrase (use when search string contains a space, hyphen, comma, etc. here \"new york city\" has space) \n" |
| 63 | + + "Input: Find employees who are active and located in New York City \n" |
| 64 | + + "Mapping: \"{ \"properties\": { \"city\": { \"type\": \"text\", \"fields\": { \"keyword\": { \"type\": \"keyword\" } } }, \"status\": { \"type\": \"keyword\" } } }\" \n" |
| 65 | + + "Output: \"{ \"query\": { \"bool\": { \"must\": [ { \"match_phrase\": { \"city\": \"New York City\" } } ], \"filter\": [ { \"term\": { \"status\": \"active\" } } ] } } }\" \n"; |
| 66 | + |
| 67 | + public static final String EXAMPLE_4 = "Example 4 — bool with SHOULD \n" |
| 68 | + + "Input: Search articles about \"machine learning\" that are research papers or blogs. \n" |
| 69 | + + "Mapping: \"{ \"properties\": { \"content\": { \"type\": \"text\" }, \"type\": { \"type\": \"keyword\" } } }\" \n" |
| 70 | + + "Output: \"{ \"query\": { \"bool\": { \"must\": [ { \"match\": { \"content\": \"machine learning\" } } ], \"should\": [ { \"term\": { \"type\": \"research paper\" } }, { \"term\": { \"type\": \"blog\" } } ], \"minimum_should_match\": 1 } } }\" \n"; |
| 71 | + |
| 72 | + public static final String EXAMPLE_5 = "Example 5 — MUST NOT \n" |
| 73 | + + "Input: List customers who have not made a purchase in 2023. \n" |
| 74 | + + "Mapping: \"{ \"properties\": { \"last_purchase_date\": { \"type\": \"date\" } } }\" \n" |
| 75 | + + "Output: \"{ \"query\": { \"bool\": { \"must_not\": [ { \"range\": { \"last_purchase_date\": { \"gte\": \"2023-01-01\", \"lte\": \"2023-12-31\" } } } ] } } }\" \n"; |
| 76 | + |
| 77 | + public static final String EXAMPLE_6 = "Example 6 — wildcard \n" |
| 78 | + + "Input: Find files with names starting with \"report_\". \n" |
| 79 | + + "Mapping: \"{ \"properties\": { \"filename\": { \"type\": \"keyword\" } } }\" \n" |
| 80 | + + "Output: \"{ \"query\": { \"wildcard\": { \"filename\": \"report_*\" } } }\" \n"; |
| 81 | + |
| 82 | + public static final String EXAMPLE_7 = |
| 83 | + "Example 7 — nested query (note the index mapping says \"type\": \"nested\", do not use it for other types) \n" |
| 84 | + + "Input: Find books where an authors first_name is John AND last_name is Doe. \n" |
| 85 | + + "Mapping: \"{ \"properties\": { \"author\": { \"type\": \"nested\", \"properties\": { \"first_name\": { \"type\": \"text\", \"fields\": { \"keyword\": { \"type\": \"keyword\" } } }, \"last_name\": { \"type\": \"text\", \"fields\": { \"keyword\": { \"type\": \"keyword\" } } } } } } }\" \n" |
| 86 | + + "Output: \"{ \"query\": { \"nested\": { \"path\": \"author\", \"query\": { \"bool\": { \"must\": [ { \"term\": { \"author.first_name.keyword\": \"John\" } }, { \"term\": { \"author.last_name.keyword\": \"Doe\" } } ] } } } } }\" \n"; |
| 87 | + |
| 88 | + public static final String EXAMPLE_8 = "Example 8 — terms aggregation \n" |
| 89 | + + "Input: Show the number of orders per status. \n" |
| 90 | + + "Mapping: \"{ \"properties\": { \"status\": { \"type\": \"keyword\" } } }\" \n" |
| 91 | + + "Output: \"{ \"size\": 0, \"aggs\": { \"orders_by_status\": { \"terms\": { \"field\": \"status\" } } } }\" \n"; |
| 92 | + |
| 93 | + public static final String EXAMPLE_9 = "Example 9 — metric aggregation with filter \n" |
| 94 | + + "Input: What is the average price of electronics products? \n" |
| 95 | + + "Mapping: \"{ \"properties\": { \"category\": { \"type\": \"keyword\" }, \"price\": { \"type\": \"float\" } } }\" \n" |
| 96 | + + "Output: \"{ \"size\": 0, \"query\": { \"term\": { \"category\": \"electronics\" } }, \"aggs\": { \"avg_price\": { \"avg\": { \"field\": \"price\" } } } }\" \n"; |
| 97 | + |
| 98 | + public static final String EXAMPLE_10 = "Example 10 — top N by metric \n" |
| 99 | + + "Input: List the top 3 categories by total sales volume. \n" |
| 100 | + + "Mapping: \"{ \"properties\": { \"category\": { \"type\": \"text\", \"fields\": { \"keyword\": { \"type\": \"keyword\" } } }, \"sales\": { \"type\": \"float\" } } }\" \n" |
| 101 | + + "Output: \"{ \"size\": 0, \"aggs\": { \"top_categories\": { \"terms\": { \"field\": \"category.keyword\", \"size\": 3, \"order\": { \"total_sales\": \"desc\" } }, \"aggs\": { \"total_sales\": { \"sum\": { \"field\": \"sales\" } } } } } }\" \n"; |
| 102 | + |
| 103 | + public static final String EXAMPLE_11 = "Example 11 — fallback \n" |
| 104 | + + "Input: Find employees who speak Klingon fluently. \n" |
| 105 | + + "Mapping: \"{ \"properties\": { \"name\": { \"type\": \"text\" }, \"role\": { \"type\": \"keyword\" } } }\" \n" |
| 106 | + + "Output: " |
| 107 | + + DEFAULT_QUERY |
| 108 | + + "\n"; |
| 109 | + |
| 110 | + public static final String EXAMPLES = "\nEXAMPLES: " |
| 111 | + + EXAMPLE_1 |
| 112 | + + EXAMPLE_2 |
| 113 | + + EXAMPLE_3 |
| 114 | + + EXAMPLE_4 |
| 115 | + + EXAMPLE_5 |
| 116 | + + EXAMPLE_6 |
| 117 | + + EXAMPLE_7 |
| 118 | + + EXAMPLE_8 |
| 119 | + + EXAMPLE_9 |
| 120 | + + EXAMPLE_10 |
| 121 | + + EXAMPLE_11; |
| 122 | + |
| 123 | + public static final String PROMPT_SUFFIX = "GIVE THE OUTPUT PART ONLY IN YOUR RESPONSE \n" |
| 124 | + + "Question: asked by user \n" |
| 125 | + + "Mapping :${parameters.index_mapping:-} \n" |
| 126 | + + "Query Fields: ${parameters.query_fields:-} " |
| 127 | + + "Output:"; |
| 128 | + |
| 129 | + public static final String DEFAULT_SYSTEM_PROMPT = PROMPT_PREFIX |
| 130 | + + " \n " |
| 131 | + + OUTPUT_FORMAT_INSTRUCTIONS |
| 132 | + + " \n " |
| 133 | + + EXAMPLES |
| 134 | + + " \n " |
| 135 | + + PROMPT_SUFFIX; |
| 136 | +} |
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