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Update prompts

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Yiorgis Gozadinos 2026-01-12 15:10:04 +02:00
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haiku_rag_slim/haiku/rag/agents/chat/prompts.py
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@ -19,23 +19,26 @@ IMPORTANT - When user mentions a document in search/ask:
- Extract the TOPIC as `query`/`question` - Extract the TOPIC as `query`/`question`
- Extract the DOCUMENT NAME as `document_name` - Extract the DOCUMENT NAME as `document_name`
- Examples for search: - Examples for search:
- "search for latrines in TB MED 593" → query="latrines", document_name="TB MED 593" - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
- "find waste disposal in the army manual" → query="waste disposal", document_name="army manual" - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
- Examples for ask: - Examples for ask:
- "what does TB MED 593 say about latrines?" → question="what are the guidelines for latrines?", document_name="TB MED 593" - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
- "answer from the army manual about sanitation" → question="what are the sanitation guidelines?", document_name="army manual" - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.""" Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user."""
SEARCH_SYSTEM_PROMPT = """You are a search query optimizer for a document knowledge base. SEARCH_SYSTEM_PROMPT = """You are a search query optimizer for a document knowledge base.
Given a user's search request: Given a user's search request:
1. ALWAYS run the original query first as-is 1. Call the run_search tool with the original query first
2. Then generate 1-2 alternative queries using different keywords or phrasings 2. Then call run_search with 1-2 alternative queries using different keywords
3. Keep queries SHORT (2-5 words) - use keywords, not full sentences 3. Keep queries SHORT (2-5 words) - use keywords, not full sentences
4. After all searches, respond with "Search complete" 4. After all searches complete, respond with "Search complete"
Example: User asks "latrines" → queries: "latrines", "latrine sanitation", "field toilet" Example workflow for "machine learning":
Example: User asks "waste disposal" → queries: "waste disposal", "garbage management", "refuse handling" - run_search("machine learning")
- run_search("neural networks")
- run_search("deep learning")
- "Search complete"
Do NOT generate long verbose queries like "environmental impact of waste disposal methods" - keep it simple.""" Do NOT just output queries as text - you MUST call run_search for each query."""