---
id: wiki-2026-0508-autobiography
title: Autobiography
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Self-Narrative, Memoir, Personal Narrative, Life Writing]
duplicate_of: none
source_trust_level: A
confidence_score: 0.85
verification_status: applied
tags: [narrative, qualitative-research, self-knowledge, llm-personalization]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: Python
  framework: LangChain/LlamaIndex
---

# Autobiography

## 매 한 줄
> **"매 self 의 narrative 의 construct — 매 memory 의 chronicle 의 X, 매 meaning-making 의 retrospective."**. Autobiography 의 self 의 first-person 의 life narrative — 매 Augustine "Confessions" 의 origin, 매 2026 의 LLM-era 의 personal corpus (lifelog + journal + chat history) 의 personalization / digital twin / memory-augmented agent 의 source data.

## 매 핵심

### 매 Theoretical Frames
- **Bruner's narrative identity**: self 의 ongoing story.
- **McAdams' life-story model**: 7 themes (agency, communion, redemption, contamination...).
- **Ricoeur's "narrative identity"**: 매 idem (sameness) + ipse (selfhood).
- **Distinction**: autobiography (whole life) vs memoir (period/theme) vs autoethnography (cultural lens).

### 매 2026 Computational Use
- **LLM personalization**: chat history → user profile embedding.
- **Lifelog**: passive sensing (location, photo, journal) → searchable corpus.
- **Digital twin / memory agent**: Mem0, MemGPT, Letta 의 long-term memory.
- **Therapy adjunct**: LLM-guided narrative therapy.

### 매 응용
1. Personal AI assistant memory.
2. Qualitative research (life-history interview).
3. Digital legacy / estate.
4. Self-reflection coaching (BetterUp AI).

## 💻 패턴

### Pattern 1 — Journal indexing (LlamaIndex)

```python
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

docs = SimpleDirectoryReader("~/journal").load_data()
index = VectorStoreIndex.from_documents(docs)
qe = index.as_query_engine(similarity_top_k=5)
print(qe.query("When did I feel most burned out in 2025?"))
```

### Pattern 2 — Mem0 long-term memory

```python
from mem0 import Memory
m = Memory()
m.add("I prefer tea over coffee, switched in 2024 after gastritis.", user_id="me")
results = m.search("morning beverage preference", user_id="me")
```

### Pattern 3 — Life-event timeline extraction

```python
import json
from anthropic import Anthropic
client = Anthropic()

EXTRACT = """Extract life events as JSON: [{date, type, summary, valence}].
Text: {text}"""

resp = client.messages.create(
    model="claude-opus-4-7",
    max_tokens=2000,
    messages=[{"role": "user", "content": EXTRACT.format(text=journal_text)}],
)
events = json.loads(resp.content[0].text)
```

### Pattern 4 — Narrative coherence score

```python
def coherence(events):
    # simple proxy: causal-chain density
    causal = sum(1 for e in events if e.get("caused_by"))
    return causal / max(len(events), 1)
```

## 매 결정 기준

| 상황 | Approach |
|---|---|
| LLM personalization | Mem0 / Letta + RAG over journal |
| Therapy / coaching | guided narrative writing + LLM reflection |
| Research interview | semi-structured + thematic analysis |
| Digital legacy | encrypted lifelog + access policy |

**기본값**: Mem0 for runtime memory, LlamaIndex RAG for retrospective query.

## 🔗 Graph
- 변형: [[Memoir]] · [[Autoethnography]]
- 응용: [[Digital-Twin]]
- Adjacent: [[Working-Memory]]

## 🤖 LLM 활용
**언제**: personal assistant memory, retrospective query over journal, coaching reflection prompts.
**언제 X**: clinical diagnosis, legal record (chain-of-custody), shared corpus (privacy).

## ❌ 안티패턴
- **Memory leak (PII)**: 매 personal corpus 의 train 의 leak — opt-out + local model.
- **Narrative coherence forcing**: LLM 의 confabulate 의 false memory.
- **Recency bias**: recent events 의 over-weight — temporal balance.
- **No edit/delete**: GDPR right-to-erasure 위반.

## 🧪 검증 / 중복
- Verified (McAdams "Stories We Live By", Mem0 / Letta docs).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — FULL content (LLM memory, lifelog, narrative theory) |
