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""" |
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Response Parser |
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Handles response parsing and formatting functions for AI operations. |
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Provides methods for creating prompts and processing AI responses. |
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""" |
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from typing import List, Dict, Any, Optional |
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from app.core.exceptions import ProcessingError |
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class ResponseParser: |
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""" |
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Parser for AI responses and prompt generation. |
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This class provides methods for creating structured prompts |
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and processing AI responses for different analysis types. |
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""" |
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@staticmethod |
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def create_overview_prompt( |
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context_docs: List[str], |
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strategy_text: Optional[str], |
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checklist_results: Optional[Dict] |
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) -> str: |
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"""Create overview analysis prompt focused on target company perspective""" |
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prompt = "Analyze the following target company documents from an acquisition perspective:\n\n" |
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if context_docs: |
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prompt += "Target Company Documents:\n" + "\n\n".join(context_docs) + "\n\n" |
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if strategy_text: |
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prompt += f"Acquirer's Strategic Context (for reference):\n{strategy_text[:1000]}\n\n" |
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if checklist_results: |
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prompt += f"Due Diligence Findings:\n{str(checklist_results)[:1000]}\n\n" |
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prompt += """Please provide a comprehensive analysis of the TARGET COMPANY focusing on: |
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1. **Company Overview**: Business model, market position, and core operations of the target |
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2. **Strategic Value**: Why this target company would be attractive for acquisition |
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3. **Competitive Strengths**: Key assets, capabilities, and competitive advantages the target brings |
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4. **Risk Assessment**: Main operational, financial, and strategic risks associated with the target |
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5. **Financial Health**: Target company's financial position and performance indicators |
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6. **Acquisition Rationale**: How the target fits acquisition criteria and strategic objectives |
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Focus on analyzing the target company as a potential acquisition candidate. Be specific, factual, and highlight both opportunities and concerns from an acquirer's due diligence perspective.""" |
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return prompt |
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@staticmethod |
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def create_strategic_prompt( |
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context_docs: List[str], |
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strategy_text: Optional[str], |
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checklist_results: Optional[Dict] |
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) -> str: |
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"""Create strategic analysis prompt focused on target company from acquisition perspective""" |
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prompt = "Conduct a strategic analysis of the target company from an acquisition perspective:\n\n" |
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if strategy_text: |
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prompt += f"Acquirer's Strategic Framework (for context):\n{strategy_text[:1000]}\n\n" |
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if context_docs: |
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prompt += "Target Company Documents:\n" + "\n\n".join(context_docs) + "\n\n" |
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if checklist_results: |
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prompt += f"Due Diligence Findings:\n{str(checklist_results)[:1000]}\n\n" |
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prompt += """Please provide a strategic analysis of the TARGET COMPANY focusing on: |
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1. **Strategic Fit Assessment**: How well the target aligns with the acquirer's strategic objectives and portfolio |
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2. **Market Position Analysis**: Target's competitive position, market share, and industry dynamics |
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3. **Value Creation Opportunities**: Potential synergies, cross-selling opportunities, and operational improvements |
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4. **Integration Considerations**: Key challenges and opportunities for successful integration |
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5. **Risk-Adjusted Valuation**: Strategic risks, regulatory concerns, and market vulnerabilities |
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6. **Post-Acquisition Strategy**: Recommended approach for maximizing value creation after acquisition |
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Analyze the target company as an acquisition candidate, evaluating both strategic alignment and value creation potential. Consider the acquirer's strategic framework when assessing fit and synergy opportunities.""" |
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return prompt |
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@staticmethod |
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def create_checklist_prompt(context_docs: List[str]) -> str: |
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"""Create checklist analysis prompt""" |
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prompt = "Analyze the following documents against standard due diligence checklist items:\n\n" |
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if context_docs: |
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prompt += "Documents to Analyze:\n" + "\n\n".join(context_docs) + "\n\n" |
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prompt += """For each major due diligence category, identify: |
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1. What information is available in the documents |
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2. What information appears to be missing |
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3. Any red flags or concerns identified |
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4. Recommendations for further investigation |
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Be thorough and specific in your analysis.""" |
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return prompt |
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@staticmethod |
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def create_questions_prompt(context_docs: List[str]) -> str: |
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"""Create questions analysis prompt""" |
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prompt = "Answer due diligence questions based on the following documents:\n\n" |
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if context_docs: |
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prompt += "Reference Documents:\n" + "\n\n".join(context_docs) + "\n\n" |
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prompt += """For each question, provide: |
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1. Direct answer based on available information |
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2. Supporting evidence from the documents |
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3. Confidence level in the answer |
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4. Any additional context or caveats |
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If information is not available, clearly state this and suggest what additional information would be needed.""" |
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return prompt |
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@staticmethod |
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def create_question_answer_prompt(question: str, context_docs: List[str]) -> str: |
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"""Create prompt for answering a specific question""" |
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return f"""Based on the following document excerpts, please answer this question: |
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Question: {question} |
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Relevant Document Excerpts: |
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{"\n\n".join(context_docs[:5])} |
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Please provide a comprehensive, factual answer with specific references to the source documents. |
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If the information is not available in the provided context, clearly state this.""" |
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@staticmethod |
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def format_response(response: str, max_length: Optional[int] = None) -> str: |
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""" |
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Format and clean AI response. |
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Args: |
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response: Raw AI response |
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max_length: Optional maximum length for the response |
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Returns: |
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Formatted response |
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Raises: |
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ProcessingError: If response formatting fails |
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""" |
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try: |
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if not response: |
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raise ValueError("Response cannot be empty") |
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result = response.strip() |
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if max_length and len(result) > max_length: |
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result = result[:max_length] + "..." |
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return result |
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except Exception as e: |
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raise ProcessingError(f"Failed to format AI response: {e}") |
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@staticmethod |
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def prepare_context_documents(documents: Dict[str, Dict[str, Any]], max_docs: int = 5) -> List[str]: |
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""" |
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Prepare context documents for AI processing. |
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Args: |
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documents: Dictionary mapping document names to document data |
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max_docs: Maximum number of documents to process |
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Returns: |
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List of formatted document contexts |
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Raises: |
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ProcessingError: If document preparation fails |
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""" |
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try: |
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if not documents: |
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raise ValueError("No documents provided for context preparation") |
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context_docs = [] |
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for doc_key, doc_data in list(documents.items())[:max_docs]: |
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if isinstance(doc_data, dict) and 'content' in doc_data: |
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content = doc_data['content'][:1000] |
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context_docs.append(f"Document: {doc_data.get('name', doc_key)}\n{content}") |
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if not context_docs: |
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raise ValueError("No valid documents found with content") |
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return context_docs |
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except Exception as e: |
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raise ProcessingError(f"Failed to prepare context documents: {e}") |
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