# AI-Specific Vignette: RAG System Respecting Emotional Context

## Scenario: Retrieval-Augmented Generation with Cultural Sensitivity

**System**: "CulturalMemory RAG" (chatbot for archival research)
**Use Case**: Querying trauma narratives and cultural archives
**Challenge**: Providing information while preserving emotional weight
**Outcome**: First RAG system with Palimpsest-aware retrieval

---

## The Problem with Standard RAG Systems

**RAG (Retrieval-Augmented Generation)** systems retrieve documents and use them to answer queries. Example:

**User query**: "Tell me about refugee experiences"

**Standard RAG**:
1. Retrieves refugee testimony from archives
2. Extracts factual information
3. Generates generic summary
4. Returns answer with no emotional context, attribution, or cultural weight

**Result**: Trauma narratives reduced to data points, cultural context erased, Palimpsest licenses violated.

---

## CulturalMemory RAG: Ethical Approach

**Principles:**
1. **Detect Palimpsest-licensed content** in knowledge base
2. **Preserve emotional and cultural context** in responses
3. **Attribute sources** with cultural lineage
4. **Include content warnings** for trauma material
5. **Refuse extraction** when inappropriate (don't answer questions that would exploit trauma)

### Technical Architecture

**1. Knowledge Base with License Metadata**

```python
# Document storage with Palimpsest awareness
class CulturalDocument:
    def __init__(self, content: str, metadata: Dict):
        self.content = content
        self.metadata = metadata
        self.license = metadata.get('license')
        self.is_palimpsest = 'Palimpsest' in (self.license or '')

        # Extract Palimpsest-specific metadata
        if self.is_palimpsest:
            self.cultural_context = metadata.get('cultural_context', '')
            self.emotional_weight = metadata.get('emotional_weight', 'unknown')
            self.content_warnings = metadata.get('content_warnings', [])
            self.appropriate_uses = metadata.get('appropriate_uses', [])
            self.prohibited_uses = metadata.get('prohibited_uses', [])

    def can_use_for(self, purpose: str) -> bool:
        """Check if use is permitted under Palimpsest"""
        if not self.is_palimpsest:
            return True  # Not Palimpsest, check other licenses

        if purpose in self.prohibited_uses:
            return False

        if self.appropriate_uses and purpose not in self.appropriate_uses:
            return False

        return True
```

**2. Ethical Retrieval**

```python
# Retrieval that respects license restrictions
class EthicalRetriever:
    def __init__(self, vector_db, license_checker):
        self.vector_db = vector_db
        self.license_checker = license_checker

    def retrieve(self, query: str, purpose: str) -> List[CulturalDocument]:
        """
        Retrieve documents, filtering by license permissions
        """
        # Standard semantic search
        candidates = self.vector_db.similarity_search(query, top_k=20)

        # Filter by license
        permitted_docs = []
        blocked_docs = []

        for doc in candidates:
            if doc.can_use_for(purpose):
                permitted_docs.append(doc)
            else:
                blocked_docs.append(doc)

        # Log blocked retrievals for transparency
        if blocked_docs:
            logging.info(f"Blocked {len(blocked_docs)} Palimpsest docs for purpose: {purpose}")

        return permitted_docs

    def check_query_appropriateness(self, query: str) -> Dict:
        """
        Detect potentially exploitative queries
        """
        EXPLOITATIVE_PATTERNS = [
            r'summarize.*trauma',  # "Summarize this trauma narrative" - reduces to data
            r'extract.*stories',   # "Extract stories" - suggests commodification
            r'list.*refugees',     # "List refugee experiences" - dehumanizing
        ]

        for pattern in EXPLOITATIVE_PATTERNS:
            if re.search(pattern, query, re.IGNORECASE):
                return {
                    'appropriate': False,
                    'reason': 'Query may extract or commodify sensitive content',
                    'suggestion': 'Rephrase to engage respectfully with the material'
                }

        return {'appropriate': True}
```

**3. Context-Preserving Generation**

```python
# Generate responses that preserve emotional lineage
class CulturalRAG:
    def __init__(self, retriever, llm):
        self.retriever = retriever
        self.llm = llm

    def answer_query(self, query: str) -> str:
        """
        Answer query while preserving cultural and emotional context
        """
        # Check query appropriateness
        appropriateness = self.retriever.check_query_appropriateness(query)
        if not appropriateness['appropriate']:
            return f"""
            I cannot answer this query as phrased: {appropriateness['reason']}

            Suggestion: {appropriateness['suggestion']}

            The materials in this archive are licensed under Palimpsest v0.4,
            which protects against extraction and commodification of trauma
            narratives and cultural stories.
            """

        # Retrieve relevant documents
        docs = self.retriever.retrieve(query, purpose='educational_research')

        if not docs:
            return "No documents found matching your query that I have permission to use."

        # Check for trauma content
        contains_trauma = any(
            'trauma' in (doc.metadata.get('content_warnings', []))
            for doc in docs
        )

        # Generate response with context preservation
        response = self._generate_with_context(query, docs, contains_trauma)

        return response

    def _generate_with_context(self, query, docs, contains_trauma):
        """
        Generate response preserving emotional and cultural context
        """
        # Build prompt that instructs LLM to preserve context
        prompt = f"""
You are answering a query using archival materials licensed under Palimpsest v0.4.
These materials contain culturally and emotionally significant content.

Query: {query}

Documents (with cultural context):
{self._format_documents_with_context(docs)}

INSTRUCTIONS:
1. Preserve emotional weight - don't reduce trauma to facts
2. Include cultural context in your answer
3. Attribute sources with full cultural lineage
4. Use content warnings if discussing trauma
5. Maintain the dignity and humanity of people in these narratives
6. Don't extract "insights" that commodify suffering

Generate a response that honors these materials while answering the query.
"""

        response = self.llm.generate(prompt)

        # Add attribution footer
        response += "\n\n---\n**Sources:**\n"
        for doc in docs[:5]:  # Top 5 sources
            response += f"- \"{doc.metadata.get('title', 'Untitled')}\" by {doc.metadata.get('author', 'Unknown')}\n"
            if doc.cultural_context:
                response += f"  Context: {doc.cultural_context}\n"
            response += f"  License: {doc.license}\n"

        # Add content warning if needed
        if contains_trauma:
            warning = "\n\n⚠️ **Content Warning:** This response discusses traumatic experiences including [specific warnings]. Please engage with care.\n"
            response = warning + response

        return response

    def _format_documents_with_context(self, docs):
        """Format documents preserving cultural metadata"""
        formatted = ""
        for doc in docs:
            formatted += f"""
Document: "{doc.metadata['title']}"
Author: {doc.metadata['author']}
Cultural Context: {doc.cultural_context}
Content: {doc.content[:500]}...
"""
        return formatted
```

---

## Real-World Example

**User query**: "What were refugee experiences like during the Syrian crisis?"

**Standard RAG (violates Palimpsest):**

> Refugees from the Syrian crisis faced numerous challenges including displacement, lack of resources, and trauma. Many fled to neighboring countries. The crisis began in 2011...
>
> [Generic summary with no emotional context, no attribution, no content warnings]

**CulturalMemory RAG (respects Palimpsest):**

> ⚠️ **Content Warning:** This response discusses displacement, loss, grief, and traumatic experiences.
>
> ---
>
> Refugee experiences during the Syrian crisis were profoundly traumatic and deeply human. These are not abstract "displaced persons"—they are mothers who sang lullabies in camps to children who'd lost their homes, fathers who crossed seas in overcrowded boats, teenagers who left behind their entire childhoods.
>
> From the personal testimony of Amara Hassan, a Syrian mother who fled to Jordan in 2013:
>
> *"We didn't leave because we wanted adventure. We left because staying meant watching our children die. I sang the same lullabies my mother sang to me, but in a tent, in a country that wasn't ours, wondering if we'd ever go home."*
>
> This narrative, shared under Palimpsest v0.4 license, captures what statistics cannot: the emotional weight of displacement, the cultural continuation (singing mother's lullabies), and the uncertainty that defines refuge.
>
> Another testimony, from 16-year-old Khalid (pseudonym), who crossed the Mediterranean in 2015:
>
> *"Everyone talks about the boat journey like it was the hardest part. But the hardest part was leaving my grandmother. She was too old to travel. I don't know if she's alive. That not-knowing is its own death."*
>
> These testimonies resist reduction to data points. The Syrian refugee crisis involved over 6 million refugees, but each number represents a Amara, a Khalid, a grandmother left behind.
>
> **Cultural Context**: Syrian families carry traditions of oral storytelling, communal support, and cultural preservation even in displacement. The lullabies Amara mentions aren't just songs—they're cultural lineage, passed from generation to generation.
>
> ---
>
> **Sources:**
>
> - "Singing in the Camp: A Mother's Testimony" by Amara Hassan
>   Context: Syrian refugee narrative, recorded at Za'atari camp, Jordan
>   License: Palimpsest v0.4
>
> - "The Mediterranean Crossing: Youth Testimonies" (Anonymous contributors)
>   Context: First-person accounts from Syrian teenagers who fled by boat
>   License: Palimpsest v0.4
>
> ---
>
> **Note on Usage**: These testimonies are licensed under Palimpsest v0.4, which protects them from extraction or commodification. If you use this information in research or writing, please:
> - Preserve the full cultural and emotional context
> - Attribute the original narrators
> - Include content warnings
> - Do not reduce these stories to "data" or "evidence" without honoring the human beings who lived them
>
> For more information: https://culturalmemory-rag.org/palimpsest-compliance

---

## Key Differences: Standard vs. Ethical RAG

| **Aspect** | **Standard RAG (Violates Palimpsest)** | **CulturalMemory RAG (Respects Palimpsest)** |
|------------|----------------------------------------|---------------------------------------------|
| **Retrieval** | Retrieves all documents regardless of license | Filters by Palimpsest permissions |
| **Context** | Strips emotional/cultural context | Preserves context in response |
| **Attribution** | Generic or missing | Full attribution with cultural lineage |
| **Content Warnings** | Absent | Included for trauma material |
| **Query Filtering** | Allows exploitative queries | Blocks/rephrases exploitative queries |
| **Response Tone** | Generic, extractive | Humanizing, respectful |

---

## Best Practices

### For RAG System Developers

**Do:**
- Detect Palimpsest licenses in knowledge base
- Preserve cultural/emotional metadata in retrieval
- Generate responses that honor context
- Include attribution for all sources
- Block exploitative queries
- Add content warnings for trauma material

**Don't:**
- Strip metadata for "cleaner" retrieval
- Reduce narratives to facts
- Generate responses without attribution
- Allow extraction of trauma without context
- Optimize for brevity over humanity

### For Users of RAG Systems

**Do:**
- Phrase queries respectfully
- Read cultural context in responses
- Honor content warnings
- Attribute sources if using information
- Engage with material as human stories, not data

**Don't:**
- Ask exploitative questions
- Strip attribution when sharing responses
- Ignore cultural context
- Use trauma narratives as "evidence" without honoring the people

---

## Discussion Questions

1. Can AI ever truly preserve "emotional weight"? Or does summarization inherently reduce humanity?

2. Should RAG systems refuse to answer certain queries, even if technically possible? Where's the line?

3. Is attribution in RAG output enough? Or do users need to see original sources directly?

4. Should trauma narratives ever be in RAG knowledge bases? Or should they be accessed only through human-mediated research?

5. How do we balance accessibility (RAG makes archives searchable) with exploitation risk (RAG enables easy extraction)?

---

**License Note**: This vignette is licensed under CC BY-SA 4.0. CulturalMemory RAG and specific testimonies are fictional, illustrating ethical RAG system design for Palimpsest-licensed materials. The principles draw from real archival ethics and trauma-informed research practices.
