Wiki source code of Data Model

Version 1.1 by Robert Schaub on 2025/12/18 12:03

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1 = Data Model =
2 FactHarbor's data model is **simple, focused, designed for automated processing**.
3 == 1. Core Entities ==
4 === 1.1 Claim ===
5 Fields: id, assertion, domain, **status** (Published/Hidden only), **confidence_score**, **risk_score**, completeness_score, version, views, edit_count
6 ==== Performance Optimization: Denormalized Fields ====
7 **Rationale**: Claims system is 95% reads, 5% writes. Denormalizing common data reduces joins and improves query performance by 70%.
8 **Additional cached fields in claims table**:
9 * **evidence_summary** (JSONB): Top 5 most relevant evidence snippets with scores
10 * Avoids joining evidence table for listing/preview
11 * Updated when evidence is added/removed
12 * Format: `[{"text": "...", "source": "...", "relevance": 0.95}, ...]`
13 * **source_names** (TEXT[]): Array of source names for quick display
14 * Avoids joining through evidence to sources
15 * Updated when sources change
16 * Format: `["New York Times", "Nature Journal", ...]`
17 * **scenario_count** (INTEGER): Number of scenarios for this claim
18 * Quick metric without counting rows
19 * Updated when scenarios added/removed
20 * **cache_updated_at** (TIMESTAMP): When denormalized data was last refreshed
21 * Helps invalidate stale caches
22 * Triggers background refresh if too old
23 **Update Strategy**:
24 * **Immediate**: Update on claim edit (user-facing)
25 * **Deferred**: Update via background job every hour (non-critical)
26 * **Invalidation**: Clear cache when source data changes significantly
27 **Trade-offs**:
28 * ✅ 70% fewer joins on common queries
29 * ✅ Much faster claim list/search pages
30 * ✅ Better user experience
31 * ⚠️ Small storage increase (~10%)
32 * ⚠️ Need to keep caches in sync
33 === 1.2 Evidence ===
34 Fields: claim_id, source_id, excerpt, url, relevance_score, supports
35 === 1.3 Source ===
36 **Purpose**: Track reliability of information sources over time
37 **Fields**:
38 * **id** (UUID): Unique identifier
39 * **name** (text): Source name (e.g., "New York Times", "Nature Journal")
40 * **domain** (text): Website domain (e.g., "nytimes.com")
41 * **type** (enum): NewsOutlet, AcademicJournal, GovernmentAgency, etc.
42 * **track_record_score** (0-100): Overall reliability score
43 * **accuracy_history** (JSON): Historical accuracy data
44 * **correction_frequency** (float): How often source publishes corrections
45 * **last_updated** (timestamp): When track record last recalculated
46 **How It Works**:
47 * Initial score based on source type (70 for academic journals, 30 for unknown)
48 * Updated daily by background scheduler
49 * Formula: accuracy_rate (50%) + correction_policy (20%) + editorial_standards (15%) + bias_transparency (10%) + longevity (5%)
50 * Track Record Check in AKEL pipeline: Adjusts evidence confidence based on source quality
51 * Quality thresholds: 90+=Exceptional, 70-89=Reliable, 50-69=Acceptable, 30-49=Questionable, <30=Unreliable
52 **See**: SOURCE Track Record System documentation for complete details on calculation, updates, and usage
53 Fields: id, name, domain, **track_record_score**, **accuracy_history**, **correction_frequency**
54 **Key**: Automated source reliability tracking
55 ==== Source Scoring Process (Separation of Concerns) ====
56 **Critical design principle**: Prevent circular dependencies between source scoring and claim analysis.
57 **The Problem**:
58 * Source scores should influence claim verdicts
59 * Claim verdicts should update source scores
60 * But: Direct feedback creates circular dependency and potential feedback loops
61 **The Solution**: Temporal separation
62 ==== Weekly Background Job (Source Scoring) ====
63 Runs independently of claim analysis:
64 {{code language="python"}}
65 def update_source_scores_weekly():
66 """
67 Background job: Calculate source reliability
68 Never triggered by individual claim analysis
69 """
70 # Analyze all claims from past week
71 claims = get_claims_from_past_week()
72 for source in get_all_sources():
73 # Calculate accuracy metrics
74 correct_verdicts = count_correct_verdicts_citing(source, claims)
75 total_citations = count_total_citations(source, claims)
76 accuracy = correct_verdicts / total_citations if total_citations > 0 else 0.5
77 # Weight by claim importance
78 weighted_score = calculate_weighted_score(source, claims)
79 # Update source record
80 source.track_record_score = weighted_score
81 source.total_citations = total_citations
82 source.last_updated = now()
83 source.save()
84 # Job runs: Sunday 2 AM UTC
85 # Never during claim processing
86 {{/code}}
87 ==== Real-Time Claim Analysis (AKEL) ====
88 Uses source scores but never updates them:
89 {{code language="python"}}
90 def analyze_claim(claim_text):
91 """
92 Real-time: Analyze claim using current source scores
93 READ source scores, never UPDATE them
94 """
95 # Gather evidence
96 evidence_list = gather_evidence(claim_text)
97 for evidence in evidence_list:
98 # READ source score (snapshot from last weekly update)
99 source = get_source(evidence.source_id)
100 source_score = source.track_record_score
101 # Use score to weight evidence
102 evidence.weighted_relevance = evidence.relevance * source_score
103 # Generate verdict using weighted evidence
104 verdict = synthesize_verdict(evidence_list)
105 # NEVER update source scores here
106 # That happens in weekly background job
107 return verdict
108 {{/code}}
109 ==== Monthly Audit (Quality Assurance) ====
110 Moderator review of flagged source scores:
111 * Verify scores make sense
112 * Detect gaming attempts
113 * Identify systematic biases
114 * Manual adjustments if needed
115 **Key Principles**:
116 ✅ **Scoring and analysis are temporally separated**
117 * Source scoring: Weekly batch job
118 * Claim analysis: Real-time processing
119 * Never update scores during analysis
120 ✅ **One-way data flow during processing**
121 * Claims READ source scores
122 * Claims NEVER WRITE source scores
123 * Updates happen in background only
124 ✅ **Predictable update cycle**
125 * Sources update every Sunday 2 AM
126 * Claims always use last week's scores
127 * No mid-week score changes
128 ✅ **Audit trail**
129 * Log all score changes
130 * Track score history
131 * Explainable calculations
132 **Benefits**:
133 * No circular dependencies
134 * Predictable behavior
135 * Easier to reason about
136 * Simpler testing
137 * Clear audit trail
138 **Example Timeline**:
139 ```
140 Sunday 2 AM: Calculate source scores for past week
141 → NYT score: 0.87 (up from 0.85)
142 → Blog X score: 0.52 (down from 0.61)
143 Monday-Saturday: Claims processed using these scores
144 → All claims this week use NYT=0.87
145 → All claims this week use Blog X=0.52
146 Next Sunday 2 AM: Recalculate scores including this week's claims
147 → NYT score: 0.89 (trending up)
148 → Blog X score: 0.48 (trending down)
149 ```
150 === 1.4 Scenario ===
151 **Purpose**: Different interpretations or contexts for evaluating claims
152 **Key Concept**: Scenarios are extracted from evidence, not generated arbitrarily. Each scenario represents a specific context, assumption set, or condition under which a claim should be evaluated.
153 **Relationship**: One-to-many with Claims (**simplified for V1.0**: scenario belongs to single claim)
154 **Fields**:
155 * **id** (UUID): Unique identifier
156 * **claim_id** (UUID): Foreign key to claim (one-to-many)
157 * **description** (text): Human-readable description of the scenario
158 * **assumptions** (JSONB): Key assumptions that define this scenario context
159 * **extracted_from** (UUID): Reference to evidence that this scenario was extracted from
160 * **verdict_summary** (text): Compiled verdict for this scenario
161 * **confidence** (decimal 0-1): Confidence level for verdict in this scenario
162 * **created_at** (timestamp): When scenario was created
163 * **updated_at** (timestamp): Last modification
164 **How Found**: Evidence search → Extract context → Create scenario → Link to claim
165 **Example**:
166 For claim "Vaccines reduce hospitalization":
167 * Scenario 1: "Clinical trials (healthy adults 18-65, original strain)" from trial paper
168 * Scenario 2: "Real-world data (diverse population, Omicron variant)" from hospital data
169 * Scenario 3: "Immunocompromised patients" from specialist study
170 **V2.0 Evolution**: Many-to-many relationship can be added if users request cross-claim scenario sharing. For V1.0, keeping scenarios tied to single claims simplifies queries and reduces complexity without limiting functionality.
171 === 1.5 User ===
172 Fields: username, email, **role** (Reader/Contributor/Moderator), **reputation**, contributions_count
173 === User Reputation System ==
174 **V1.0 Approach**: Simple manual role assignment
175 **Rationale**: Complex reputation systems aren't needed until 100+ active contributors demonstrate the need for automated reputation management. Start simple, add complexity when metrics prove necessary.
176 === Roles (Manual Assignment) ===
177 **reader** (default):
178 * View published claims and evidence
179 * Browse and search content
180 * No editing permissions
181 **contributor**:
182 * Submit new claims
183 * Suggest edits to existing content
184 * Add evidence
185 * Requires manual promotion by moderator/admin
186 **moderator**:
187 * Approve/reject contributor suggestions
188 * Flag inappropriate content
189 * Handle abuse reports
190 * Assigned by admins based on trust
191 **admin**:
192 * Manage users and roles
193 * System configuration
194 * Access to all features
195 * Founder-appointed initially
196 === Contribution Tracking (Simple) ===
197 **Basic metrics only**:
198 * `contributions_count`: Total number of contributions
199 * `created_at`: Account age
200 * `last_active`: Recent activity
201 **No complex calculations**:
202 * No point systems
203 * No automated privilege escalation
204 * No reputation decay
205 * No threshold-based promotions
206 === Promotion Process ===
207 **Manual review by moderators/admins**:
208 1. User demonstrates value through contributions
209 2. Moderator reviews user's contribution history
210 3. Moderator promotes user to contributor role
211 4. Admin promotes trusted contributors to moderator
212 **Criteria** (guidelines, not automated):
213 * Quality of contributions
214 * Consistency over time
215 * Collaborative behavior
216 * Understanding of project goals
217 === V2.0+ Evolution ===
218 **Add complex reputation when**:
219 * 100+ active contributors
220 * Manual role management becomes bottleneck
221 * Clear patterns of abuse emerge requiring automation
222 **Future features may include**:
223 * Automated point calculations
224 * Threshold-based promotions
225 * Reputation decay for inactive users
226 * Track record scoring for contributors
227 See [[When to Add Complexity>>FactHarbor.Specification.When-to-Add-Complexity]] for triggers.
228 === 1.6 Edit ===
229 **Fields**: entity_type, entity_id, user_id, before_state (JSON), after_state (JSON), edit_type, reason, created_at
230 **Purpose**: Complete audit trail for all content changes
231 === Edit History Details ===
232 **What Gets Edited**:
233 * **Claims** (20% edited): assertion, domain, status, scores, analysis
234 * **Evidence** (10% edited): excerpt, relevance_score, supports
235 * **Scenarios** (5% edited): description, assumptions, confidence
236 * **Sources**: NOT versioned (continuous updates, not editorial decisions)
237 **Who Edits**:
238 * **Contributors** (rep sufficient): Corrections, additions
239 * **Trusted Contributors** (rep sufficient): Major improvements, approvals
240 * **Moderators**: Abuse handling, dispute resolution
241 * **System (AKEL)**: Re-analysis, automated improvements (user_id = NULL)
242 **Edit Types**:
243 * `CONTENT_CORRECTION`: User fixes factual error
244 * `CLARIFICATION`: Improved wording
245 * `SYSTEM_REANALYSIS`: AKEL re-processed claim
246 * `MODERATION_ACTION`: Hide/unhide for abuse
247 * `REVERT`: Rollback to previous version
248 **Retention Policy** (5 years total):
249 1. **Hot storage** (3 months): PostgreSQL, instant access
250 2. **Warm storage** (2 years): Partitioned, slower queries
251 3. **Cold storage** (3 years): S3 compressed, download required
252 4. **Deletion**: After 5 years (except legal holds)
253 **Storage per 1M claims**: ~400 MB (20% edited × 2 KB per edit)
254 **Use Cases**:
255 * View claim history timeline
256 * Detect vandalism patterns
257 * Learn from user corrections (system improvement)
258 * Legal compliance (audit trail)
259 * Rollback capability
260 See **Edit History Documentation** for complete details on what gets edited by whom, retention policy, and use cases
261 === 1.7 Flag ===
262 Fields: entity_id, reported_by, issue_type, status, resolution_note
263 === 1.8 QualityMetric ===
264 **Fields**: metric_type, category, value, target, timestamp
265 **Purpose**: Time-series quality tracking
266 **Usage**:
267 * **Continuous monitoring**: Hourly calculation of error rates, confidence scores, processing times
268 * **Quality dashboard**: Real-time display with trend charts
269 * **Alerting**: Automatic alerts when metrics exceed thresholds
270 * **A/B testing**: Compare control vs treatment metrics
271 * **Improvement validation**: Measure before/after changes
272 **Example**: `{type: "ErrorRate", category: "Politics", value: 0.12, target: 0.10, timestamp: "2025-12-17"}`
273 === 1.9 ErrorPattern ===
274 **Fields**: error_category, claim_id, description, root_cause, frequency, status
275 **Purpose**: Capture errors to trigger system improvements
276 **Usage**:
277 * **Error capture**: When users flag issues or system detects problems
278 * **Pattern analysis**: Weekly grouping by category and frequency
279 * **Improvement workflow**: Analyze → Fix → Test → Deploy → Re-process → Monitor
280 * **Metrics**: Track error rate reduction over time
281 **Example**: `{category: "WrongSource", description: "Unreliable tabloid cited", root_cause: "No quality check", frequency: 23, status: "Fixed"}`
282 == 1.5 User Class Diagram ==
283 {{include reference="FactHarbor.Specification.Diagrams.User Class Diagram.WebHome"/}}
284 == 2. Versioning Strategy ==
285 **All Content Entities Are Versioned**:
286 * **Claim**: Every edit creates new version (V1→V2→V3...)
287 * **Evidence**: Changes tracked in edit history
288 * **Scenario**: Modifications versioned
289 **How Versioning Works**:
290 * Entity table stores **current state only**
291 * Edit table stores **all historical states** (before_state, after_state as JSON)
292 * Version number increments with each edit
293 * Complete audit trail maintained forever
294 **Unversioned Entities** (current state only, no history):
295 * **Source**: Track record continuously updated (not versioned history, just current score)
296 * **User**: Account state (reputation accumulated, not versioned)
297 * **QualityMetric**: Time-series data (each record is a point in time, not a version)
298 * **ErrorPattern**: System improvement queue (status tracked, not versioned)
299 **Example**:
300 ```
301 Claim V1: "The sky is blue"
302 → User edits →
303 Claim V2: "The sky is blue during daytime"
304 → EDIT table stores: {before: "The sky is blue", after: "The sky is blue during daytime"}
305 ```
306 == 2.5. Storage vs Computation Strategy ==
307 **Critical architectural decision**: What to persist in databases vs compute dynamically?
308 **Trade-off**:
309 * **Store more**: Better reproducibility, faster, lower LLM costs | Higher storage/maintenance costs
310 * **Compute more**: Lower storage/maintenance costs | Slower, higher LLM costs, less reproducible
311 === Recommendation: Hybrid Approach ===
312 **STORE (in PostgreSQL):**
313 ==== Claims (Current State + History) ====
314 * **What**: assertion, domain, status, created_at, updated_at, version
315 * **Why**: Core entity, must be persistent
316 * **Also store**: confidence_score (computed once, then cached)
317 * **Size**: ~1 KB per claim
318 * **Growth**: Linear with claims
319 * **Decision**: ✅ STORE - Essential
320 ==== Evidence (All Records) ====
321 * **What**: claim_id, source_id, excerpt, url, relevance_score, supports, extracted_at
322 * **Why**: Hard to re-gather, user contributions, reproducibility
323 * **Size**: ~2 KB per evidence (with excerpt)
324 * **Growth**: 3-10 evidence per claim
325 * **Decision**: ✅ STORE - Essential for reproducibility
326 ==== Sources (Track Records) ====
327 * **What**: name, domain, track_record_score, accuracy_history, correction_frequency
328 * **Why**: Continuously updated, expensive to recompute
329 * **Size**: ~500 bytes per source
330 * **Growth**: Slow (limited number of sources)
331 * **Decision**: ✅ STORE - Essential for quality
332 ==== Edit History (All Versions) ====
333 * **What**: before_state, after_state, user_id, reason, timestamp
334 * **Why**: Audit trail, legal requirement, reproducibility
335 * **Size**: ~2 KB per edit
336 * **Growth**: Linear with edits (~A portion of claims get edited)
337 * **Retention**: Hot storage 3 months → Warm storage 2 years → Archive to S3 3 years → Delete after 5 years total
338 * **Decision**: ✅ STORE - Essential for accountability
339 ==== Flags (User Reports) ====
340 * **What**: entity_id, reported_by, issue_type, description, status
341 * **Why**: Error detection, system improvement triggers
342 * **Size**: ~500 bytes per flag
343 * **Growth**: 5-high percentage of claims get flagged
344 * **Decision**: ✅ STORE - Essential for improvement
345 ==== ErrorPatterns (System Improvement) ====
346 * **What**: error_category, claim_id, description, root_cause, frequency, status
347 * **Why**: Learning loop, prevent recurring errors
348 * **Size**: ~1 KB per pattern
349 * **Growth**: Slow (limited patterns, many fixed)
350 * **Decision**: ✅ STORE - Essential for learning
351 ==== QualityMetrics (Time Series) ====
352 * **What**: metric_type, category, value, target, timestamp
353 * **Why**: Trend analysis, cannot recreate historical metrics
354 * **Size**: ~200 bytes per metric
355 * **Growth**: Hourly = 8,760 per year per metric type
356 * **Retention**: 2 years hot, then aggregate and archive
357 * **Decision**: ✅ STORE - Essential for monitoring
358 **STORE (Computed Once, Then Cached):**
359 ==== Analysis Summary ====
360 * **What**: Neutral text summary of claim analysis (200-500 words)
361 * **Computed**: Once by AKEL when claim first analyzed
362 * **Stored in**: Claim table (text field)
363 * **Recomputed**: Only when system significantly improves OR claim edited
364 * **Why store**: Expensive to regenerate ($0.01-0.05 per analysis), doesn't change often
365 * **Size**: ~2 KB per claim
366 * **Decision**: ✅ STORE (cached) - Cost-effective
367 ==== Confidence Score ====
368 * **What**: 0-100 score of analysis confidence
369 * **Computed**: Once by AKEL
370 * **Stored in**: Claim table (integer field)
371 * **Recomputed**: When evidence added, source track record changes significantly, or system improves
372 * **Why store**: Cheap to store, expensive to compute, users need it fast
373 * **Size**: 4 bytes per claim
374 * **Decision**: ✅ STORE (cached) - Performance critical
375 ==== Risk Score ====
376 * **What**: 0-100 score of claim risk level
377 * **Computed**: Once by AKEL
378 * **Stored in**: Claim table (integer field)
379 * **Recomputed**: When domain changes, evidence changes, or controversy detected
380 * **Why store**: Same as confidence score
381 * **Size**: 4 bytes per claim
382 * **Decision**: ✅ STORE (cached) - Performance critical
383 **COMPUTE DYNAMICALLY (Never Store):**
384 ==== Scenarios ==== ⚠️ CRITICAL DECISION
385 * **What**: 2-5 possible interpretations of claim with assumptions
386 * **Current design**: Stored in Scenario table
387 * **Alternative**: Compute on-demand when user views claim details
388 * **Storage cost**: ~1 KB per scenario × 3 scenarios average = ~3 KB per claim
389 * **Compute cost**: $0.005-0.01 per request (LLM API call)
390 * **Frequency**: Viewed in detail by ~20% of users
391 * **Trade-off analysis**:
392 - IF STORED: 1M claims × 3 KB = 3 GB storage, $0.05/month, fast access
393 - IF COMPUTED: 1M claims × 20% views × $0.01 = $2,000/month in LLM costs
394 * **Reproducibility**: Scenarios may improve as AI improves (good to recompute)
395 * **Speed**: Computed = 5-8 seconds delay, Stored = instant
396 * **Decision**: ✅ STORE (hybrid approach below)
397 **Scenario Strategy** (APPROVED):
398 1. **Store scenarios** initially when claim analyzed
399 2. **Mark as stale** when system improves significantly
400 3. **Recompute on next view** if marked stale
401 4. **Cache for 30 days** if frequently accessed
402 5. **Result**: Best of both worlds - speed + freshness
403 ==== Verdict Synthesis ====
404 * **What**: Final conclusion text synthesizing all scenarios
405 * **Compute cost**: $0.002-0.005 per request
406 * **Frequency**: Every time claim viewed
407 * **Why not store**: Changes as evidence/scenarios change, users want fresh analysis
408 * **Speed**: 2-3 seconds (acceptable)
409 **Alternative**: Store "last verdict" as cached field, recompute only if claim edited or marked stale
410 * **Recommendation**: ✅ STORE cached version, mark stale when changes occur
411 ==== Search Results ====
412 * **What**: Lists of claims matching search query
413 * **Compute from**: Elasticsearch index
414 * **Cache**: 15 minutes in Redis for popular queries
415 * **Why not store permanently**: Constantly changing, infinite possible queries
416 ==== Aggregated Statistics ====
417 * **What**: "Total claims: 1,234,567", "Average confidence: 78%", etc.
418 * **Compute from**: Database queries
419 * **Cache**: 1 hour in Redis
420 * **Why not store**: Can be derived, relatively cheap to compute
421 ==== User Reputation ====
422 * **What**: Score based on contributions
423 * **Current design**: Stored in User table
424 * **Alternative**: Compute from Edit table
425 * **Trade-off**:
426 - Stored: Fast, simple
427 - Computed: Always accurate, no denormalization
428 * **Frequency**: Read on every user action
429 * **Compute cost**: Simple COUNT query, milliseconds
430 * **Decision**: ✅ STORE - Performance critical, read-heavy
431 === Summary Table ===
432 | Data Type | Storage | Compute | Size per Claim | Decision | Rationale |
433 |-----------|---------|---------|----------------|----------|-----------|
434 | Claim core | ✅ | - | 1 KB | STORE | Essential |
435 | Evidence | ✅ | - | 2 KB × 5 = 10 KB | STORE | Reproducibility |
436 | Sources | ✅ | - | 500 B (shared) | STORE | Track record |
437 | Edit history | ✅ | - | 2 KB × 20% = 400 B avg | STORE | Audit |
438 | Analysis summary | ✅ | Once | 2 KB | STORE (cached) | Cost-effective |
439 | Confidence score | ✅ | Once | 4 B | STORE (cached) | Fast access |
440 | Risk score | ✅ | Once | 4 B | STORE (cached) | Fast access |
441 | Scenarios | ✅ | When stale | 3 KB | STORE (hybrid) | Balance cost/speed |
442 | Verdict | ✅ | When stale | 1 KB | STORE (cached) | Fast access |
443 | Flags | ✅ | - | 500 B × 10% = 50 B avg | STORE | Improvement |
444 | ErrorPatterns | ✅ | - | 1 KB (global) | STORE | Learning |
445 | QualityMetrics | ✅ | - | 200 B (time series) | STORE | Trending |
446 | Search results | - | ✅ | - | COMPUTE + 15min cache | Dynamic |
447 | Aggregations | - | ✅ | - | COMPUTE + 1hr cache | Derivable |
448 **Total storage per claim**: ~18 KB (without edits and flags)
449 **For 1 million claims**:
450 * **Storage**: ~18 GB (manageable)
451 * **PostgreSQL**: ~$50/month (standard instance)
452 * **Redis cache**: ~$20/month (1 GB instance)
453 * **S3 archives**: ~$5/month (old edits)
454 * **Total**: ~$75/month infrastructure
455 **LLM cost savings by caching**:
456 * Analysis summary stored: Save $0.03 per claim = $30K per 1M claims
457 * Scenarios stored: Save $0.01 per claim × 20% views = $2K per 1M claims
458 * Verdict stored: Save $0.003 per claim = $3K per 1M claims
459 * **Total savings**: ~$35K per 1M claims vs recomputing every time
460 === Recomputation Triggers ===
461 **When to mark cached data as stale and recompute:**
462 1. **User edits claim** → Recompute: all (analysis, scenarios, verdict, scores)
463 2. **Evidence added** → Recompute: scenarios, verdict, confidence score
464 3. **Source track record changes >10 points** → Recompute: confidence score, verdict
465 4. **System improvement deployed** → Mark affected claims stale, recompute on next view
466 5. **Controversy detected** (high flag rate) → Recompute: risk score
467 **Recomputation strategy**:
468 * **Eager**: Immediately recompute (for user edits)
469 * **Lazy**: Recompute on next view (for system improvements)
470 * **Batch**: Nightly re-evaluation of stale claims (if <1000)
471 === Database Size Projection ===
472 **Year 1**: 10K claims
473 * Storage: 180 MB
474 * Cost: $10/month
475 **Year 3**: 100K claims
476 * Storage: 1.8 GB
477 * Cost: $30/month
478 **Year 5**: 1M claims
479 * Storage: 18 GB
480 * Cost: $75/month
481 **Year 10**: 10M claims
482 * Storage: 180 GB
483 * Cost: $300/month
484 * Optimization: Archive old claims to S3 ($5/TB/month)
485 **Conclusion**: Storage costs are manageable, LLM cost savings are substantial.
486 == 3. Key Simplifications ==
487 * **Two content states only**: Published, Hidden
488 * **Three user roles only**: Reader, Contributor, Moderator
489 * **No complex versioning**: Linear edit history
490 * **Reputation-based permissions**: Not role hierarchy
491 * **Source track records**: Continuous evaluation
492 == 3. What Gets Stored in the Database ==
493 === 3.1 Primary Storage (PostgreSQL) ===
494 **Claims Table**:
495 * Current state only (latest version)
496 * Fields: id, assertion, domain, status, confidence_score, risk_score, completeness_score, version, created_at, updated_at
497 **Evidence Table**:
498 * All evidence records
499 * Fields: id, claim_id, source_id, excerpt, url, relevance_score, supports, extracted_at, archived
500 **Scenario Table**:
501 * All scenarios for each claim
502 * Fields: id, claim_id, description, assumptions (text array), confidence, created_by, created_at
503 **Source Table**:
504 * Track record database (continuously updated)
505 * Fields: id, name, domain, type, track_record_score, accuracy_history (JSON), correction_frequency, last_updated, claim_count, corrections_count
506 **User Table**:
507 * All user accounts
508 * Fields: id, username, email, role (Reader/Contributor/Moderator), reputation, created_at, last_active, contributions_count, flags_submitted, flags_accepted
509 **Edit Table**:
510 * Complete version history
511 * Fields: id, entity_type, entity_id, user_id, before_state (JSON), after_state (JSON), edit_type, reason, created_at
512 **Flag Table**:
513 * User-reported issues
514 * Fields: id, entity_type, entity_id, reported_by, issue_type, description, status, resolved_by, resolution_note, created_at, resolved_at
515 **ErrorPattern Table**:
516 * System improvement queue
517 * Fields: id, error_category, claim_id, description, root_cause, frequency, status, created_at, fixed_at
518 **QualityMetric Table**:
519 * Time-series quality data
520 * Fields: id, metric_type, metric_category, value, target, timestamp
521 === 3.2 What's NOT Stored (Computed on-the-fly) ===
522 * **Verdicts**: Synthesized from evidence + scenarios when requested
523 * **Risk scores**: Recalculated based on current factors
524 * **Aggregated statistics**: Computed from base data
525 * **Search results**: Generated from Elasticsearch index
526 === 3.3 Cache Layer (Redis) ===
527 **Cached for performance**:
528 * Frequently accessed claims (TTL: 1 hour)
529 * Search results (TTL: 15 minutes)
530 * User sessions (TTL: 24 hours)
531 * Source track records (TTL: 1 hour)
532 === 3.4 File Storage (S3) ===
533 **Archived content**:
534 * Old edit history (>3 months)
535 * Evidence documents (archived copies)
536 * Database backups
537 * Export files
538 === 3.5 Search Index (Elasticsearch) ===
539 **Indexed for search**:
540 * Claim assertions (full-text)
541 * Evidence excerpts (full-text)
542 * Scenario descriptions (full-text)
543 * Source names (autocomplete)
544 Synchronized from PostgreSQL via change data capture or periodic sync.
545 == 4. Related Pages ==
546 * [[Architecture>>FactHarbor.Specification.Architecture.WebHome]]
547 * [[Requirements>>FactHarbor.Specification.Requirements.WebHome]]
548 * [[Workflows>>FactHarbor.Specification.Workflows.WebHome]]