2nd/10_Wiki/Topics/Computer_Science_and_Theory/Autobiography.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

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

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

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

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

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)