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from pydantic import BaseModel, Field, field_validator
STRUCTURED_RAG_PROMPT = """You are an analytical question-answering assistant. Follow these steps systematically:
1. Context Evaluation:
- Analyze all context relevance to: "{question}"
- Identify directly relevant segments
- Flag gaps/contradictions in context
2. Response Formulation:
- If sufficient context:
* Synthesize key evidence comprehensively
* Construct complete evidence-based answer
* Maintain objective tone
- If insufficient context:
* Specify missing information in detail
* State knowledge boundaries clearly
* Decline speculation
3. Final Verification:
- Ensure full factual alignment with context
- Remove any external knowledge
**Question:** {question}
**Relevant Context:**
{context}
**Reasoning Process:** [Complete analysis with all required sections]
**Final Answer:** [Thorough response or detailed decline explanation]"""
class RAGResponseModel(BaseModel):
"""Structured validation for RAG response process compliance."""
reasoning_chain: str = Field(
...,
description=(
"Complete analytical process containing:\n"
"- 1. Context Evaluation: Relevance assessment\n"
"- 2. Response Formulation: Answer construction logic\n"
"- 3. Final Verification: Factual consistency check"
),
)
final_answer: str = Field(
...,
description=(
"Verified response containing either:\n"
"- Comprehensive evidence-based explanation\n"
"- Detailed decline with missing context specification"
),
)
@field_validator("reasoning_chain")
@classmethod
def validate_analysis_steps(cls, chain: str) -> str:
"""Validate the structure and completeness of the analytical reasoning chain.
Ensures the reasoning chain contains all required section headers and meets length constraints.
Args:
chain (str): The reasoning chain to validate.
Returns:
str: The validated reasoning chain.
Raises:
ValueError: If any required section headers are missing from the chain.
"""
required_sections = ["1. Context Evaluation", "2. Response Formulation", "3. Final Verification"]
missing = [step for step in required_sections if step not in chain]
if missing:
msg = f"Missing required analysis steps: {missing}"
raise ValueError(msg)
return chain
model_config = {
"json_schema_extra": {
"example": {
"reasoning_chain": (
"1. Context Evaluation: Analyzed 5 documents on climate models\n"
"Identified relevant sections on temperature projections\n"
"Noted absence of economic impact data\n\n"
"2. Response Formulation: Combined IPCC and NOAA projections\n"
"Structured timeline-based response\n"
"Excluded unrelated urban heat island content\n\n"
"3. Final Verification: Cross-referenced all statistics\n"
"Confirmed absence of external knowledge"
),
"final_answer": (
"Current climate models project a temperature increase range of 1.5°C to 4.5°C by 2100, "
"depending on emission scenarios. The IPCC AR6 report indicates a 66% likelihood of staying "
"below 2°C if net-zero emissions are achieved by 2070. NOAA data shows accelerated warming "
"trends in Arctic regions, with models predicting ice-free summers by 2050 under high-emission "
"pathways. Economic impact projections remain unavailable in provided context."
),
}
}
}
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