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