Wiki source code of Data Model

Last modified by Robert Schaub on 2025/12/24 19:33

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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 * **created_at** (timestamp): When scenario was created
161 * **updated_at** (timestamp): Last modification
162 **How Found**: Evidence search → Extract context → Create scenario → Link to claim
163 **Example**:
164 For claim "Vaccines reduce hospitalization":
165 * Scenario 1: "Clinical trials (healthy adults 18-65, original strain)" from trial paper
166 * Scenario 2: "Real-world data (diverse population, Omicron variant)" from hospital data
167 * Scenario 3: "Immunocompromised patients" from specialist study
168 **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.
169
170 === 1.5 Verdict ===
171
172 **Purpose**: Assessment of a claim within a specific scenario context. Each verdict provides a conclusion about whether the claim is supported, refuted, or uncertain given the scenario's assumptions and available evidence.
173
174 **Core Fields**:
175 * **id** (UUID): Primary key
176 * **scenario_id** (UUID FK): The scenario being assessed
177 * **likelihood_range** (text): Probabilistic assessment (e.g., "0.40-0.65 (uncertain)", "0.75-0.85 (likely true)")
178 * **confidence** (decimal 0-1): How confident we are in this assessment
179 * **explanation_summary** (text): Human-readable reasoning explaining the verdict
180 * **uncertainty_factors** (text array): Specific factors limiting confidence (e.g., "Small sample sizes", "Lifestyle confounds", "Long-term effects unknown")
181 * **created_at** (timestamp): When verdict was created
182 * **updated_at** (timestamp): Last modification
183
184 **Change Tracking**: Like all entities, verdict changes are tracked through the Edit entity (section 1.7), not through separate version tables. Each edit records before/after states.
185
186 **Relationship**: Each Scenario has one Verdict. When understanding evolves, the verdict is updated and the change is logged in the Edit entity.
187
188 **Example**:
189 For claim "Exercise improves mental health" in scenario "Clinical trials (healthy adults, structured programs)":
190 * Initial state: likelihood_range="0.40-0.65 (uncertain)", uncertainty_factors=["Small sample sizes", "Short-term studies only"]
191 * After new evidence: likelihood_range="0.70-0.85 (likely true)", uncertainty_factors=["Lifestyle confounds remain"]
192 * Edit entity records the complete before/after change with timestamp and reason
193
194 **Key Design**: Verdicts are mutable entities tracked through the centralized Edit entity, consistent with Claims, Evidence, and Scenarios.
195
196 === 1.6 User ===
197 Fields: username, email, **role** (Reader/Contributor/Moderator), **reputation**, contributions_count
198 === User Reputation System ==
199 **V1.0 Approach**: Simple manual role assignment
200 **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.
201 === Roles (Manual Assignment) ===
202 **reader** (default):
203 * View published claims and evidence
204 * Browse and search content
205 * No editing permissions
206 **contributor**:
207 * Submit new claims
208 * Suggest edits to existing content
209 * Add evidence
210 * Requires manual promotion by moderator/admin
211 **moderator**:
212 * Approve/reject contributor suggestions
213 * Flag inappropriate content
214 * Handle abuse reports
215 * Assigned by admins based on trust
216 **admin**:
217 * Manage users and roles
218 * System configuration
219 * Access to all features
220 * Founder-appointed initially
221 === Contribution Tracking (Simple) ===
222 **Basic metrics only**:
223 * `contributions_count`: Total number of contributions
224 * `created_at`: Account age
225 * `last_active`: Recent activity
226 **No complex calculations**:
227 * No point systems
228 * No automated privilege escalation
229 * No reputation decay
230 * No threshold-based promotions
231 === Promotion Process ===
232 **Manual review by moderators/admins**:
233 1. User demonstrates value through contributions
234 2. Moderator reviews user's contribution history
235 3. Moderator promotes user to contributor role
236 4. Admin promotes trusted contributors to moderator
237 **Criteria** (guidelines, not automated):
238 * Quality of contributions
239 * Consistency over time
240 * Collaborative behavior
241 * Understanding of project goals
242 === V2.0+ Evolution ===
243 **Add complex reputation when**:
244 * 100+ active contributors
245 * Manual role management becomes bottleneck
246 * Clear patterns of abuse emerge requiring automation
247 **Future features may include**:
248 * Automated point calculations
249 * Threshold-based promotions
250 * Reputation decay for inactive users
251 * Track record scoring for contributors
252 See [[When to Add Complexity>>FactHarbor.Specification.When-to-Add-Complexity]] for triggers.
253 === 1.7 Edit ===
254 **Fields**: entity_type, entity_id, user_id, before_state (JSON), after_state (JSON), edit_type, reason, created_at
255 **Purpose**: Complete audit trail for all content changes
256 === Edit History Details ===
257 **What Gets Edited**:
258 * **Claims** (20% edited): assertion, domain, status, scores, analysis
259 * **Evidence** (10% edited): excerpt, relevance_score, supports
260 * **Scenarios** (5% edited): description, assumptions, confidence
261 * **Sources**: NOT versioned (continuous updates, not editorial decisions)
262 **Who Edits**:
263 * **Contributors** (rep sufficient): Corrections, additions
264 * **Trusted Contributors** (rep sufficient): Major improvements, approvals
265 * **Moderators**: Abuse handling, dispute resolution
266 * **System (AKEL)**: Re-analysis, automated improvements (user_id = NULL)
267 **Edit Types**:
268 * `CONTENT_CORRECTION`: User fixes factual error
269 * `CLARIFICATION`: Improved wording
270 * `SYSTEM_REANALYSIS`: AKEL re-processed claim
271 * `MODERATION_ACTION`: Hide/unhide for abuse
272 * `REVERT`: Rollback to previous version
273 **Retention Policy** (5 years total):
274 1. **Hot storage** (3 months): PostgreSQL, instant access
275 2. **Warm storage** (2 years): Partitioned, slower queries
276 3. **Cold storage** (3 years): S3 compressed, download required
277 4. **Deletion**: After 5 years (except legal holds)
278 **Storage per 1M claims**: ~400 MB (20% edited × 2 KB per edit)
279 **Use Cases**:
280 * View claim history timeline
281 * Detect vandalism patterns
282 * Learn from user corrections (system improvement)
283 * Legal compliance (audit trail)
284 * Rollback capability
285 See **Edit History Documentation** for complete details on what gets edited by whom, retention policy, and use cases
286 === 1.8 Flag ===
287 Fields: entity_id, reported_by, issue_type, status, resolution_note
288 === 1.9 QualityMetric ===
289 **Fields**: metric_type, category, value, target, timestamp
290 **Purpose**: Time-series quality tracking
291 **Usage**:
292 * **Continuous monitoring**: Hourly calculation of error rates, confidence scores, processing times
293 * **Quality dashboard**: Real-time display with trend charts
294 * **Alerting**: Automatic alerts when metrics exceed thresholds
295 * **A/B testing**: Compare control vs treatment metrics
296 * **Improvement validation**: Measure before/after changes
297 **Example**: `{type: "ErrorRate", category: "Politics", value: 0.12, target: 0.10, timestamp: "2025-12-17"}`
298 === 1.10 ErrorPattern ===
299 **Fields**: error_category, claim_id, description, root_cause, frequency, status
300 **Purpose**: Capture errors to trigger system improvements
301 **Usage**:
302 * **Error capture**: When users flag issues or system detects problems
303 * **Pattern analysis**: Weekly grouping by category and frequency
304 * **Improvement workflow**: Analyze → Fix → Test → Deploy → Re-process → Monitor
305 * **Metrics**: Track error rate reduction over time
306 **Example**: `{category: "WrongSource", description: "Unreliable tabloid cited", root_cause: "No quality check", frequency: 23, status: "Fixed"}`
307
308 == 1.4 Core Data Model ERD ==
309
310 {{include reference="FactHarbor.Specification.Diagrams.Core Data Model ERD.WebHome"/}}
311
312 == 1.5 User Class Diagram ==
313 {{include reference="FactHarbor.Specification.Diagrams.User Class Diagram.WebHome"/}}
314 == 2. Versioning Strategy ==
315 **All Content Entities Are Versioned**:
316 * **Claim**: Every edit creates new version (V1→V2→V3...)
317 * **Evidence**: Changes tracked in edit history
318 * **Scenario**: Modifications versioned
319 **How Versioning Works**:
320 * Entity table stores **current state only**
321 * Edit table stores **all historical states** (before_state, after_state as JSON)
322 * Version number increments with each edit
323 * Complete audit trail maintained forever
324 **Unversioned Entities** (current state only, no history):
325 * **Source**: Track record continuously updated (not versioned history, just current score)
326 * **User**: Account state (reputation accumulated, not versioned)
327 * **QualityMetric**: Time-series data (each record is a point in time, not a version)
328 * **ErrorPattern**: System improvement queue (status tracked, not versioned)
329 **Example**:
330 ```
331 Claim V1: "The sky is blue"
332 → User edits →
333 Claim V2: "The sky is blue during daytime"
334 → EDIT table stores: {before: "The sky is blue", after: "The sky is blue during daytime"}
335 ```
336 == 2.5. Storage vs Computation Strategy ==
337 **Critical architectural decision**: What to persist in databases vs compute dynamically?
338 **Trade-off**:
339 * **Store more**: Better reproducibility, faster, lower LLM costs | Higher storage/maintenance costs
340 * **Compute more**: Lower storage/maintenance costs | Slower, higher LLM costs, less reproducible
341 === Recommendation: Hybrid Approach ===
342 **STORE (in PostgreSQL):**
343 ==== Claims (Current State + History) ====
344 * **What**: assertion, domain, status, created_at, updated_at, version
345 * **Why**: Core entity, must be persistent
346 * **Also store**: confidence_score (computed once, then cached)
347 * **Size**: ~1 KB per claim
348 * **Growth**: Linear with claims
349 * **Decision**: ✅ STORE - Essential
350 ==== Evidence (All Records) ====
351 * **What**: claim_id, source_id, excerpt, url, relevance_score, supports, extracted_at
352 * **Why**: Hard to re-gather, user contributions, reproducibility
353 * **Size**: ~2 KB per evidence (with excerpt)
354 * **Growth**: 3-10 evidence per claim
355 * **Decision**: ✅ STORE - Essential for reproducibility
356 ==== Sources (Track Records) ====
357 * **What**: name, domain, track_record_score, accuracy_history, correction_frequency
358 * **Why**: Continuously updated, expensive to recompute
359 * **Size**: ~500 bytes per source
360 * **Growth**: Slow (limited number of sources)
361 * **Decision**: ✅ STORE - Essential for quality
362 ==== Edit History (All Versions) ====
363 * **What**: before_state, after_state, user_id, reason, timestamp
364 * **Why**: Audit trail, legal requirement, reproducibility
365 * **Size**: ~2 KB per edit
366 * **Growth**: Linear with edits (~A portion of claims get edited)
367 * **Retention**: Hot storage 3 months → Warm storage 2 years → Archive to S3 3 years → Delete after 5 years total
368 * **Decision**: ✅ STORE - Essential for accountability
369 ==== Flags (User Reports) ====
370 * **What**: entity_id, reported_by, issue_type, description, status
371 * **Why**: Error detection, system improvement triggers
372 * **Size**: ~500 bytes per flag
373 * **Growth**: 5-high percentage of claims get flagged
374 * **Decision**: ✅ STORE - Essential for improvement
375 ==== ErrorPatterns (System Improvement) ====
376 * **What**: error_category, claim_id, description, root_cause, frequency, status
377 * **Why**: Learning loop, prevent recurring errors
378 * **Size**: ~1 KB per pattern
379 * **Growth**: Slow (limited patterns, many fixed)
380 * **Decision**: ✅ STORE - Essential for learning
381 ==== QualityMetrics (Time Series) ====
382 * **What**: metric_type, category, value, target, timestamp
383 * **Why**: Trend analysis, cannot recreate historical metrics
384 * **Size**: ~200 bytes per metric
385 * **Growth**: Hourly = 8,760 per year per metric type
386 * **Retention**: 2 years hot, then aggregate and archive
387 * **Decision**: ✅ STORE - Essential for monitoring
388 **STORE (Computed Once, Then Cached):**
389 ==== Analysis Summary ====
390 * **What**: Neutral text summary of claim analysis (200-500 words)
391 * **Computed**: Once by AKEL when claim first analyzed
392 * **Stored in**: Claim table (text field)
393 * **Recomputed**: Only when system significantly improves OR claim edited
394 * **Why store**: Expensive to regenerate ($0.01-0.05 per analysis), doesn't change often
395 * **Size**: ~2 KB per claim
396 * **Decision**: ✅ STORE (cached) - Cost-effective
397 ==== Confidence Score ====
398 * **What**: 0-100 score of analysis confidence
399 * **Computed**: Once by AKEL
400 * **Stored in**: Claim table (integer field)
401 * **Recomputed**: When evidence added, source track record changes significantly, or system improves
402 * **Why store**: Cheap to store, expensive to compute, users need it fast
403 * **Size**: 4 bytes per claim
404 * **Decision**: ✅ STORE (cached) - Performance critical
405 ==== Risk Score ====
406 * **What**: 0-100 score of claim risk level
407 * **Computed**: Once by AKEL
408 * **Stored in**: Claim table (integer field)
409 * **Recomputed**: When domain changes, evidence changes, or controversy detected
410 * **Why store**: Same as confidence score
411 * **Size**: 4 bytes per claim
412 * **Decision**: ✅ STORE (cached) - Performance critical
413 **COMPUTE DYNAMICALLY (Never Store):**
414 ==== Scenarios ==== ⚠️ CRITICAL DECISION
415 * **What**: 2-5 possible interpretations of claim with assumptions
416 * **Current design**: Stored in Scenario table
417 * **Alternative**: Compute on-demand when user views claim details
418 * **Storage cost**: ~1 KB per scenario × 3 scenarios average = ~3 KB per claim
419 * **Compute cost**: $0.005-0.01 per request (LLM API call)
420 * **Frequency**: Viewed in detail by ~20% of users
421 * **Trade-off analysis**:
422 - IF STORED: 1M claims × 3 KB = 3 GB storage, $0.05/month, fast access
423 - IF COMPUTED: 1M claims × 20% views × $0.01 = $2,000/month in LLM costs
424 * **Reproducibility**: Scenarios may improve as AI improves (good to recompute)
425 * **Speed**: Computed = 5-8 seconds delay, Stored = instant
426 * **Decision**: ✅ STORE (hybrid approach below)
427 **Scenario Strategy** (APPROVED):
428 1. **Store scenarios** initially when claim analyzed
429 2. **Mark as stale** when system improves significantly
430 3. **Recompute on next view** if marked stale
431 4. **Cache for 30 days** if frequently accessed
432 5. **Result**: Best of both worlds - speed + freshness
433 ==== Verdict Synthesis ====
434 * **What**: Final conclusion text synthesizing all scenarios
435 * **Compute cost**: $0.002-0.005 per request
436 * **Frequency**: Every time claim viewed
437 * **Why not store**: Changes as evidence/scenarios change, users want fresh analysis
438 * **Speed**: 2-3 seconds (acceptable)
439 **Alternative**: Store "last verdict" as cached field, recompute only if claim edited or marked stale
440 * **Recommendation**: ✅ STORE cached version, mark stale when changes occur
441 ==== Search Results ====
442 * **What**: Lists of claims matching search query
443 * **Compute from**: Elasticsearch index
444 * **Cache**: 15 minutes in Redis for popular queries
445 * **Why not store permanently**: Constantly changing, infinite possible queries
446 ==== Aggregated Statistics ====
447 * **What**: "Total claims: 1,234,567", "Average confidence: 78%", etc.
448 * **Compute from**: Database queries
449 * **Cache**: 1 hour in Redis
450 * **Why not store**: Can be derived, relatively cheap to compute
451 ==== User Reputation ====
452 * **What**: Score based on contributions
453 * **Current design**: Stored in User table
454 * **Alternative**: Compute from Edit table
455 * **Trade-off**:
456 - Stored: Fast, simple
457 - Computed: Always accurate, no denormalization
458 * **Frequency**: Read on every user action
459 * **Compute cost**: Simple COUNT query, milliseconds
460 * **Decision**: ✅ STORE - Performance critical, read-heavy
461 === Summary Table ===
462 | Data Type | Storage | Compute | Size per Claim | Decision | Rationale |
463 |-----------|---------|---------|----------------|----------|-----------|
464 | Claim core | ✅ | - | 1 KB | STORE | Essential |
465 | Evidence | ✅ | - | 2 KB × 5 = 10 KB | STORE | Reproducibility |
466 | Sources | ✅ | - | 500 B (shared) | STORE | Track record |
467 | Edit history | ✅ | - | 2 KB × 20% = 400 B avg | STORE | Audit |
468 | Analysis summary | ✅ | Once | 2 KB | STORE (cached) | Cost-effective |
469 | Confidence score | ✅ | Once | 4 B | STORE (cached) | Fast access |
470 | Risk score | ✅ | Once | 4 B | STORE (cached) | Fast access |
471 | Scenarios | ✅ | When stale | 3 KB | STORE (hybrid) | Balance cost/speed |
472 | Verdict | ✅ | When stale | 1 KB | STORE (cached) | Fast access |
473 | Flags | ✅ | - | 500 B × 10% = 50 B avg | STORE | Improvement |
474 | ErrorPatterns | ✅ | - | 1 KB (global) | STORE | Learning |
475 | QualityMetrics | ✅ | - | 200 B (time series) | STORE | Trending |
476 | Search results | - | ✅ | - | COMPUTE + 15min cache | Dynamic |
477 | Aggregations | - | ✅ | - | COMPUTE + 1hr cache | Derivable |
478 **Total storage per claim**: ~18 KB (without edits and flags)
479 **For 1 million claims**:
480 * **Storage**: ~18 GB (manageable)
481 * **PostgreSQL**: ~$50/month (standard instance)
482 * **Redis cache**: ~$20/month (1 GB instance)
483 * **S3 archives**: ~$5/month (old edits)
484 * **Total**: ~$75/month infrastructure
485 **LLM cost savings by caching**:
486 * Analysis summary stored: Save $0.03 per claim = $30K per 1M claims
487 * Scenarios stored: Save $0.01 per claim × 20% views = $2K per 1M claims
488 * Verdict stored: Save $0.003 per claim = $3K per 1M claims
489 * **Total savings**: ~$35K per 1M claims vs recomputing every time
490 === Recomputation Triggers ===
491 **When to mark cached data as stale and recompute:**
492 1. **User edits claim** → Recompute: all (analysis, scenarios, verdict, scores)
493 2. **Evidence added** → Recompute: scenarios, verdict, confidence score
494 3. **Source track record changes >10 points** → Recompute: confidence score, verdict
495 4. **System improvement deployed** → Mark affected claims stale, recompute on next view
496 5. **Controversy detected** (high flag rate) → Recompute: risk score
497 **Recomputation strategy**:
498 * **Eager**: Immediately recompute (for user edits)
499 * **Lazy**: Recompute on next view (for system improvements)
500 * **Batch**: Nightly re-evaluation of stale claims (if <1000)
501 === Database Size Projection ===
502 **Year 1**: 10K claims
503 * Storage: 180 MB
504 * Cost: $10/month
505 **Year 3**: 100K claims
506 * Storage: 1.8 GB
507 * Cost: $30/month
508 **Year 5**: 1M claims
509 * Storage: 18 GB
510 * Cost: $75/month
511 **Year 10**: 10M claims
512 * Storage: 180 GB
513 * Cost: $300/month
514 * Optimization: Archive old claims to S3 ($5/TB/month)
515 **Conclusion**: Storage costs are manageable, LLM cost savings are substantial.
516 == 3. Key Simplifications ==
517 * **Two content states only**: Published, Hidden
518 * **Three user roles only**: Reader, Contributor, Moderator
519 * **No complex versioning**: Linear edit history
520 * **Reputation-based permissions**: Not role hierarchy
521 * **Source track records**: Continuous evaluation
522 == 3. What Gets Stored in the Database ==
523 === 3.1 Primary Storage (PostgreSQL) ===
524 **Claims Table**:
525 * Current state only (latest version)
526 * Fields: id, assertion, domain, status, confidence_score, risk_score, completeness_score, version, created_at, updated_at
527 **Evidence Table**:
528 * All evidence records
529 * Fields: id, claim_id, source_id, excerpt, url, relevance_score, supports, extracted_at, archived
530 **Scenario Table**:
531 * All scenarios for each claim
532 * Fields: id, claim_id, description, assumptions (text array), confidence, created_by, created_at
533 **Source Table**:
534 * Track record database (continuously updated)
535 * Fields: id, name, domain, type, track_record_score, accuracy_history (JSON), correction_frequency, last_updated, claim_count, corrections_count
536 **User Table**:
537 * All user accounts
538 * Fields: id, username, email, role (Reader/Contributor/Moderator), reputation, created_at, last_active, contributions_count, flags_submitted, flags_accepted
539 **Edit Table**:
540 * Complete version history
541 * Fields: id, entity_type, entity_id, user_id, before_state (JSON), after_state (JSON), edit_type, reason, created_at
542 **Flag Table**:
543 * User-reported issues
544 * Fields: id, entity_type, entity_id, reported_by, issue_type, description, status, resolved_by, resolution_note, created_at, resolved_at
545 **ErrorPattern Table**:
546 * System improvement queue
547 * Fields: id, error_category, claim_id, description, root_cause, frequency, status, created_at, fixed_at
548 **QualityMetric Table**:
549 * Time-series quality data
550 * Fields: id, metric_type, metric_category, value, target, timestamp
551 === 3.2 What's NOT Stored (Computed on-the-fly) ===
552 * **Verdicts**: Synthesized from evidence + scenarios when requested
553 * **Risk scores**: Recalculated based on current factors
554 * **Aggregated statistics**: Computed from base data
555 * **Search results**: Generated from Elasticsearch index
556 === 3.3 Cache Layer (Redis) ===
557 **Cached for performance**:
558 * Frequently accessed claims (TTL: 1 hour)
559 * Search results (TTL: 15 minutes)
560 * User sessions (TTL: 24 hours)
561 * Source track records (TTL: 1 hour)
562 === 3.4 File Storage (S3) ===
563 **Archived content**:
564 * Old edit history (>3 months)
565 * Evidence documents (archived copies)
566 * Database backups
567 * Export files
568 === 3.5 Search Index (Elasticsearch) ===
569 **Indexed for search**:
570 * Claim assertions (full-text)
571 * Evidence excerpts (full-text)
572 * Scenario descriptions (full-text)
573 * Source names (autocomplete)
574 Synchronized from PostgreSQL via change data capture or periodic sync.
575 == 4. Related Pages ==
576 * [[Architecture>>FactHarbor.Specification.Architecture.WebHome]]
577 * [[Requirements>>FactHarbor.Specification.Requirements.WebHome]]
578 * [[Workflows>>FactHarbor.Specification.Workflows.WebHome]]