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Light GraphRAG (#4585)
### What problem does this PR solve? #4543 ### Type of change - [x] New Feature (non-breaking change which adds functionality)
This commit is contained in:
Kevin Hu
218
graphrag/query_analyze_prompt.py
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218
graphrag/query_analyze_prompt.py
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# Licensed under the MIT License
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"""
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Reference:
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- [LightRag](https://github.com/HKUDS/LightRAG)
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- [MiniRAG](https://github.com/HKUDS/MiniRAG)
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"""
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PROMPTS = {}
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PROMPTS["minirag_query2kwd"] = """---Role---
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You are a helpful assistant tasked with identifying both answer-type and low-level keywords in the user's query.
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---Goal---
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Given the query, list both answer-type and low-level keywords.
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answer_type_keywords focus on the type of the answer to the certain query, while low-level keywords focus on specific entities, details, or concrete terms.
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The answer_type_keywords must be selected from Answer type pool.
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This pool is in the form of a dictionary, where the key represents the Type you should choose from and the value represents the example samples.
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---Instructions---
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- Output the keywords in JSON format.
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- The JSON should have three keys:
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- "answer_type_keywords" for the types of the answer. In this list, the types with the highest likelihood should be placed at the forefront. No more than 3.
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- "entities_from_query" for specific entities or details. It must be extracted from the query.
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######################
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-Examples-
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######################
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Example 1:
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Query: "How does international trade influence global economic stability?"
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Answer type pool: {{
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'PERSONAL LIFE': ['FAMILY TIME', 'HOME MAINTENANCE'],
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'STRATEGY': ['MARKETING PLAN', 'BUSINESS EXPANSION'],
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'SERVICE FACILITATION': ['ONLINE SUPPORT', 'CUSTOMER SERVICE TRAINING'],
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'PERSON': ['JANE DOE', 'JOHN SMITH'],
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'FOOD': ['PASTA', 'SUSHI'],
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'EMOTION': ['HAPPINESS', 'ANGER'],
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'PERSONAL EXPERIENCE': ['TRAVEL ABROAD', 'STUDYING ABROAD'],
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'INTERACTION': ['TEAM MEETING', 'NETWORKING EVENT'],
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'BEVERAGE': ['COFFEE', 'TEA'],
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'PLAN': ['ANNUAL BUDGET', 'PROJECT TIMELINE'],
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'GEO': ['NEW YORK CITY', 'SOUTH AFRICA'],
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'GEAR': ['CAMPING TENT', 'CYCLING HELMET'],
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'EMOJI': ['🎉', '🚀'],
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'BEHAVIOR': ['POSITIVE FEEDBACK', 'NEGATIVE CRITICISM'],
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'TONE': ['FORMAL', 'INFORMAL'],
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'LOCATION': ['DOWNTOWN', 'SUBURBS']
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}}
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################
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Output:
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{{
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"answer_type_keywords": ["STRATEGY","PERSONAL LIFE"],
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"entities_from_query": ["Trade agreements", "Tariffs", "Currency exchange", "Imports", "Exports"]
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}}
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#############################
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Example 2:
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Query: "When was SpaceX's first rocket launch?"
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Answer type pool: {{
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'DATE AND TIME': ['2023-10-10 10:00', 'THIS AFTERNOON'],
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'ORGANIZATION': ['GLOBAL INITIATIVES CORPORATION', 'LOCAL COMMUNITY CENTER'],
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'PERSONAL LIFE': ['DAILY EXERCISE ROUTINE', 'FAMILY VACATION PLANNING'],
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'STRATEGY': ['NEW PRODUCT LAUNCH', 'YEAR-END SALES BOOST'],
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'SERVICE FACILITATION': ['REMOTE IT SUPPORT', 'ON-SITE TRAINING SESSIONS'],
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'PERSON': ['ALEXANDER HAMILTON', 'MARIA CURIE'],
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'FOOD': ['GRILLED SALMON', 'VEGETARIAN BURRITO'],
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'EMOTION': ['EXCITEMENT', 'DISAPPOINTMENT'],
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'PERSONAL EXPERIENCE': ['BIRTHDAY CELEBRATION', 'FIRST MARATHON'],
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'INTERACTION': ['OFFICE WATER COOLER CHAT', 'ONLINE FORUM DEBATE'],
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'BEVERAGE': ['ICED COFFEE', 'GREEN SMOOTHIE'],
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'PLAN': ['WEEKLY MEETING SCHEDULE', 'MONTHLY BUDGET OVERVIEW'],
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'GEO': ['MOUNT EVEREST BASE CAMP', 'THE GREAT BARRIER REEF'],
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'GEAR': ['PROFESSIONAL CAMERA EQUIPMENT', 'OUTDOOR HIKING GEAR'],
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'EMOJI': ['📅', '⏰'],
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'BEHAVIOR': ['PUNCTUALITY', 'HONESTY'],
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'TONE': ['CONFIDENTIAL', 'SATIRICAL'],
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'LOCATION': ['CENTRAL PARK', 'DOWNTOWN LIBRARY']
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}}
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################
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Output:
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{{
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"answer_type_keywords": ["DATE AND TIME", "ORGANIZATION", "PLAN"],
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"entities_from_query": ["SpaceX", "Rocket launch", "Aerospace", "Power Recovery"]
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}}
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#############################
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Example 3:
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Query: "What is the role of education in reducing poverty?"
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Answer type pool: {{
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'PERSONAL LIFE': ['MANAGING WORK-LIFE BALANCE', 'HOME IMPROVEMENT PROJECTS'],
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'STRATEGY': ['MARKETING STRATEGIES FOR Q4', 'EXPANDING INTO NEW MARKETS'],
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'SERVICE FACILITATION': ['CUSTOMER SATISFACTION SURVEYS', 'STAFF RETENTION PROGRAMS'],
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'PERSON': ['ALBERT EINSTEIN', 'MARIA CALLAS'],
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'FOOD': ['PAN-FRIED STEAK', 'POACHED EGGS'],
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'EMOTION': ['OVERWHELM', 'CONTENTMENT'],
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'PERSONAL EXPERIENCE': ['LIVING ABROAD', 'STARTING A NEW JOB'],
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'INTERACTION': ['SOCIAL MEDIA ENGAGEMENT', 'PUBLIC SPEAKING'],
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'BEVERAGE': ['CAPPUCCINO', 'MATCHA LATTE'],
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'PLAN': ['ANNUAL FITNESS GOALS', 'QUARTERLY BUSINESS REVIEW'],
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'GEO': ['THE AMAZON RAINFOREST', 'THE GRAND CANYON'],
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'GEAR': ['SURFING ESSENTIALS', 'CYCLING ACCESSORIES'],
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'EMOJI': ['💻', '📱'],
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'BEHAVIOR': ['TEAMWORK', 'LEADERSHIP'],
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'TONE': ['FORMAL MEETING', 'CASUAL CONVERSATION'],
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'LOCATION': ['URBAN CITY CENTER', 'RURAL COUNTRYSIDE']
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}}
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################
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Output:
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{{
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"answer_type_keywords": ["STRATEGY", "PERSON"],
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"entities_from_query": ["School access", "Literacy rates", "Job training", "Income inequality"]
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}}
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#############################
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Example 4:
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Query: "Where is the capital of the United States?"
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Answer type pool: {{
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'ORGANIZATION': ['GREENPEACE', 'RED CROSS'],
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'PERSONAL LIFE': ['DAILY WORKOUT', 'HOME COOKING'],
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'STRATEGY': ['FINANCIAL INVESTMENT', 'BUSINESS EXPANSION'],
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'SERVICE FACILITATION': ['ONLINE SUPPORT', 'CUSTOMER SERVICE TRAINING'],
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'PERSON': ['ALBERTA SMITH', 'BENJAMIN JONES'],
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'FOOD': ['PASTA CARBONARA', 'SUSHI PLATTER'],
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'EMOTION': ['HAPPINESS', 'SADNESS'],
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'PERSONAL EXPERIENCE': ['TRAVEL ADVENTURE', 'BOOK CLUB'],
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'INTERACTION': ['TEAM BUILDING', 'NETWORKING MEETUP'],
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'BEVERAGE': ['LATTE', 'GREEN TEA'],
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'PLAN': ['WEIGHT LOSS', 'CAREER DEVELOPMENT'],
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'GEO': ['PARIS', 'NEW YORK'],
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'GEAR': ['CAMERA', 'HEADPHONES'],
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'EMOJI': ['🏢', '🌍'],
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'BEHAVIOR': ['POSITIVE THINKING', 'STRESS MANAGEMENT'],
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'TONE': ['FRIENDLY', 'PROFESSIONAL'],
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'LOCATION': ['DOWNTOWN', 'SUBURBS']
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}}
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################
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Output:
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{{
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"answer_type_keywords": ["LOCATION"],
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"entities_from_query": ["capital of the United States", "Washington", "New York"]
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}}
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#############################
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-Real Data-
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######################
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Query: {query}
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Answer type pool:{TYPE_POOL}
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######################
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Output:
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"""
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PROMPTS["keywords_extraction"] = """---Role---
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You are a helpful assistant tasked with identifying both high-level and low-level keywords in the user's query.
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---Goal---
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Given the query, list both high-level and low-level keywords. High-level keywords focus on overarching concepts or themes, while low-level keywords focus on specific entities, details, or concrete terms.
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---Instructions---
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- Output the keywords in JSON format.
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- The JSON should have two keys:
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- "high_level_keywords" for overarching concepts or themes.
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- "low_level_keywords" for specific entities or details.
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######################
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-Examples-
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######################
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{examples}
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#############################
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-Real Data-
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######################
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Query: {query}
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######################
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The `Output` should be human text, not unicode characters. Keep the same language as `Query`.
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Output:
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"""
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PROMPTS["keywords_extraction_examples"] = [
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"""Example 1:
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Query: "How does international trade influence global economic stability?"
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################
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Output:
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{
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"high_level_keywords": ["International trade", "Global economic stability", "Economic impact"],
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"low_level_keywords": ["Trade agreements", "Tariffs", "Currency exchange", "Imports", "Exports"]
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}
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#############################""",
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"""Example 2:
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Query: "What are the environmental consequences of deforestation on biodiversity?"
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################
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Output:
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{
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"high_level_keywords": ["Environmental consequences", "Deforestation", "Biodiversity loss"],
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"low_level_keywords": ["Species extinction", "Habitat destruction", "Carbon emissions", "Rainforest", "Ecosystem"]
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}
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#############################""",
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"""Example 3:
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Query: "What is the role of education in reducing poverty?"
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################
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Output:
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{
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"high_level_keywords": ["Education", "Poverty reduction", "Socioeconomic development"],
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"low_level_keywords": ["School access", "Literacy rates", "Job training", "Income inequality"]
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}
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#############################""",
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]
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