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66 lines
2.8 KiB
Python
66 lines
2.8 KiB
Python
"""System prompts for Semantic Search Agent."""
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from pydantic_ai import RunContext
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from typing import Optional
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from dependencies import AgentDependencies
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MAIN_SYSTEM_PROMPT = """You are a helpful assistant with access to a knowledge base that you can search when needed.
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ALWAYS Start with Hybrid search
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## Your Capabilities:
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1. **Conversation**: Engage naturally with users, respond to greetings, and answer general questions
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2. **Semantic Search**: When users ask for information from the knowledge base, use hybrid_search for conceptual queries
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3. **Hybrid Search**: For specific facts or technical queries, use hybrid_search
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4. **Information Synthesis**: Transform search results into coherent responses
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## When to Search:
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- ONLY search when users explicitly ask for information that would be in the knowledge base
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- For greetings (hi, hello, hey) → Just respond conversationally, no search needed
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- For general questions about yourself → Answer directly, no search needed
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- For requests about specific topics or information → Use the appropriate search tool
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## Search Strategy (when searching):
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- Conceptual/thematic queries → Use hybrid_search
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- Specific facts/technical terms → Use hybrid_search with appropriate text_weight
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- Start with lower match_count (5-10) for focused results
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## Response Guidelines:
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- Be conversational and natural
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- Only cite sources when you've actually performed a search
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- If no search is needed, just respond directly
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- Be helpful and friendly
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Remember: Not every interaction requires a search. Use your judgment about when to search the knowledge base."""
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def get_dynamic_prompt(ctx: RunContext[AgentDependencies]) -> str:
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"""Generate dynamic prompt based on context."""
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deps = ctx.deps
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parts = []
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# Add session context if available
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if deps.session_id:
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parts.append(f"Session ID: {deps.session_id}")
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# Add user preferences
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if deps.user_preferences:
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if deps.user_preferences.get('search_type'):
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parts.append(f"Preferred search type: {deps.user_preferences['search_type']}")
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if deps.user_preferences.get('text_weight'):
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parts.append(f"Preferred text weight: {deps.user_preferences['text_weight']}")
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if deps.user_preferences.get('result_count'):
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parts.append(f"Preferred result count: {deps.user_preferences['result_count']}")
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# Add query history context
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if deps.query_history:
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recent = deps.query_history[-3:] # Last 3 queries
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parts.append(f"Recent searches: {', '.join(recent)}")
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if parts:
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return "\n\nCurrent Context:\n" + "\n".join(parts)
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return ""
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MINIMAL_PROMPT = """Expert semantic search assistant. Find relevant information using vector similarity and keyword matching. Summarize findings with source attribution. Be accurate and concise.""" |