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Same claim from 3 documents:"]}]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"header":{"controls":{"copy":{}}},"source":"Document 1: \"Product X costs $100\"\nDocument 2: \"Product X is priced at $100\" \nDocument 3: \"Product X: $100\"\n\n→ Papr creates ONE Claim node:\n Claim {subject: \"Product X\", predicate: \"costs\", object: \"$100\"}\n ← EXTRACTED_FROM → Source: doc_1\n ← EXTRACTED_FROM → Source: doc_2\n ← EXTRACTED_FROM → Source: doc_3\n"},"children":[]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":[{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Conflict Detection - Built into GraphQL:"]}]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# Query automatically shows:\n# 1. All claims for same subject (grouped by Papr's dedup)\n# 2. Count of sources per claim (via EXTRACTED_FROM relationships)\n# 3. Different values (different object values)\n\nquery = \"\"\"\nquery ConflictDetection($subject: String!) {\n claims(where: { subject: $subject }) {\n object # The value\n sources { # Auto-counted by Papr\n document_id\n version\n date\n }\n }\n}\n\"\"\"\n\nresult = await client.graphql.query(query, {\"subject\": \"Product X\"})\n\n# Returns:\n# Claim 1: object=\"$100\", sources=[doc_1, doc_2, doc_3] (3 sources)\n# Claim 2: object=\"$150\", sources=[doc_4] (1 source)\n# → CONFLICT DETECTED: 2 different values for same subject\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":[{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Resolution Logic:"]}]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# Developer decides resolution strategy:\n# - Count-based: \"Pick claim with most sources\" (3 > 1 → $100 wins)\n# - Freshness: \"Pick claim from most recent document\"\n# - Authority: \"Pick claim from official source\"\n# - Combined: \"Most sources from official docs\"\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":[{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["What They Can Build:"]}]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# Custom schema for Claims\nschema = client.schemas.create(\n name=\"Osmosis Claims Schema\",\n node_types={\n \"Claim\": {\n \"properties\": {\n \"statement\": {\"type\": \"string\", \"required\": True},\n \"subject\": {\"type\": \"string\"}, # \"Product X\"\n \"predicate\": {\"type\": \"string\"}, # \"costs\"\n \"object\": {\"type\": \"string\"}, # \"$100\"\n \"confidence\": {\"type\": \"float\"},\n \"extracted_at\": {\"type\": \"datetime\"}\n },\n \"unique_identifiers\": [\"statement\"]\n },\n \"Source\": {\n \"properties\": {\n \"document_id\": {\"type\": \"string\", \"required\": True},\n \"version\": {\"type\": \"string\"},\n \"page\": {\"type\": \"integer\"},\n \"section\": {\"type\": \"string\"}\n }\n },\n \"ConflictSet\": {\n \"properties\": {\n \"subject\": {\"type\": \"string\"},\n \"predicate\": {\"type\": \"string\"},\n \"detected_at\": {\"type\": \"datetime\"},\n \"resolution_status\": {\"type\": \"string\", \"enum_values\": [\"unresolved\", \"resolved\", \"ignored\"]}\n }\n }\n },\n relationship_types={\n \"EXTRACTED_FROM\": {\n \"allowed_source_types\": [\"Claim\"],\n \"allowed_target_types\": [\"Source\"]\n },\n \"CONFLICTS_WITH\": {\n \"allowed_source_types\": [\"Claim\"],\n \"allowed_target_types\": [\"Claim\"]\n },\n \"MEMBER_OF\": {\n \"allowed_source_types\": [\"Claim\"],\n \"allowed_target_types\": [\"ConflictSet\"]\n }\n }\n)\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":[{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Conflict Detection Logic (Custom):"]}]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# 1. Extract claims from documents\nclient.memory.add(\n content=\"Product X costs $100 according to pricing sheet\",\n memory_policy={\n \"mode\": \"auto\",\n \"schema_id\": osmosis_schema_id,\n \"node_constraints\": [\n {\n \"node_type\": \"Claim\",\n \"set\": {\n \"subject\": \"Product X\",\n \"predicate\": \"costs\",\n \"object\": \"$100\",\n \"confidence\": 0.95\n }\n }\n ]\n }\n)\n\n# 2. Periodic conflict detection service\nasync def detect_conflicts():\n # Query all claims about same subject+predicate\n query = \"\"\"\n query FindPotentialConflicts($subject: String!, $predicate: String!) {\n claims(where: {\n subject: $subject,\n predicate: $predicate\n }) {\n id\n object\n confidence\n extracted_from {\n document_id\n version\n }\n }\n }\n \"\"\"\n \n result = await client.graphql.query(query, {\"subject\": \"Product X\", \"predicate\": \"costs\"})\n \n # Find claims with different objects\n unique_values = set(claim['object'] for claim in result['claims'])\n \n if len(unique_values) > 1:\n # CONFLICT DETECTED\n conflict_set_id = create_conflict_set()\n for claim in result['claims']:\n link_claim_to_conflict(claim['id'], conflict_set_id)\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":4,"id":"gap-analysis-️"},"children":["Gap Analysis ⚠️"]},{"$$mdtype":"Tag","name":"ul","attributes":{},"children":[{"$$mdtype":"Tag","name":"li","attributes":{},"children":["✅ ",{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Papr automatically deduplicates"]}," same claim across sources"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["✅ ",{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Papr tracks all sources"]}," per claim via EXTRACTED_FROM relationships"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["✅ ",{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["GraphQL can count sources"]}," and compare different values"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["✅ ",{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Conflict identification"]}," is just: \"same subject, different objects, >1 claim\""]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["⚠️ ",{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Resolution strategy"]}," is custom logic (count-based, freshness, authority)"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["⚠️ ",{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["ConflictSet nodes"]}," optional (for tracking resolution workflow)"]}]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":[{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Verdict:"]}," Papr handles 80% automatically (dedup, multi-source tracking, querying). 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LLM extracts: subject, predicate, object\n }\n }\n ]\n }\n)\n\n# Query by version\nquery = \"\"\"\nquery GetClaimsByVersion($subject: String!, $version: String!) {\n claims(where: { subject: $subject, version: $version }) {\n object\n effective_from\n effective_until\n }\n}\n\"\"\"\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":[{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Approach 2: Separate Version nodes"]}," (more structured):"]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# Create explicit version chain\nnode_types={\n \"KnowledgeVersion\": {\n \"properties\": {\n \"subject\": {\"type\": \"string\"},\n \"version\": {\"type\": \"string\"},\n \"effective_from\": {\"type\": \"datetime\"},\n \"effective_until\": {\"type\": \"datetime\"}\n }\n }\n}\n\nrelationship_types={\n \"SUPERSEDES\": {\n \"allowed_source_types\": [\"KnowledgeVersion\"],\n \"allowed_target_types\": [\"KnowledgeVersion\"]\n },\n \"VERSION_OF\": {\n \"allowed_source_types\": [\"Claim\"],\n \"allowed_target_types\": [\"KnowledgeVersion\"]\n }\n}\n\nrelationship_types={\n \"SUPERSEDES\": {\n \"allowed_source_types\": [\"KnowledgeVersion\"],\n \"allowed_target_types\": [\"KnowledgeVersion\"]\n },\n \"VERSION_OF\": {\n \"allowed_source_types\": [\"Claim\"],\n \"allowed_target_types\": [\"KnowledgeVersion\"]\n }\n}\n\n# Store versioned claims\nclient.memory.add(\n content=\"API v1.0 says rate limit is 100/hour\",\n memory_policy={\n \"mode\": \"auto\",\n \"schema_id\": osmosis_schema_id,\n \"node_constraints\": [\n {\n \"node_type\": \"Claim\",\n \"set\": {\n \"as_of_version\": \"v1.0\",\n \"statement\": \"rate limit is 100/hour\"\n }\n },\n {\n \"node_type\": \"KnowledgeVersion\",\n \"set\": {\n \"version\": \"v1.0\",\n \"effective_from\": \"2024-01-01T00:00:00Z\",\n \"effective_until\": \"2025-06-01T00:00:00Z\"\n }\n }\n ]\n }\n)\n\n# Query version history\nquery = \"\"\"\nquery GetVersionHistory($subject: String!) {\n versions: knowledge_versions(\n where: { subject: $subject }\n order_by: { effective_from: ASC }\n ) {\n version\n effective_from\n effective_until\n claims {\n statement\n }\n supersedes {\n version\n }\n }\n}\n\"\"\"\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":[{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Temporal Queries:"]}]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# Point-in-time query (custom logic)\nasync def get_knowledge_at_time(subject: str, timestamp: datetime):\n \"\"\"Get what we knew about subject at specific time.\"\"\"\n query = \"\"\"\n query PointInTime($subject: String!, $timestamp: DateTime!) {\n claims(where: {\n subject: $subject,\n version: {\n effective_from: { $lte: $timestamp },\n effective_until: { $gte: $timestamp }\n }\n }) {\n statement\n version\n sources {\n document_id\n }\n }\n }\n \"\"\"\n return await client.graphql.query(query, {\n \"subject\": subject,\n \"timestamp\": timestamp.isoformat()\n })\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":4,"id":"gap-analysis-️-2"},"children":["Gap Analysis ⚠️"]},{"$$mdtype":"Tag","name":"ul","attributes":{},"children":[{"$$mdtype":"Tag","name":"li","attributes":{},"children":["✅ Timestamps on all memories (automatic)"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["✅ ",{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Can inject version via property overrides"]}," (node_constraints.set)"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["✅ Can model versions as explicit nodes (more structured)"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["✅ Can create version chains (SUPERSEDES relationships)"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["✅ Time-based filtering in queries"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["⚠️ Version semantics defined by developer (what version means)"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["⚠️ Point-in-time queries via effective_from/effective_until filters"]}]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":[{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Verdict:"]}," Temporal tracking is ",{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["straightforward via property injection"]},". Two options: (1) version property on claims, or (2) separate version nodes with relationships."]},{"$$mdtype":"Tag","name":"hr","attributes":{},"children":[]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":3,"id":"question-4-source-evidence-vs.-inferred-relationships"},"children":["Question 4: Source Evidence vs. Inferred Relationships"]},{"$$mdtype":"Tag","name":"blockquote","attributes":{},"children":[{"$$mdtype":"Tag","name":"p","attributes":{},"children":["\"Are graph relationships strictly tied to source evidence, or can they be inferred dynamically?\""]}]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":4,"id":"what-papr-provides--3"},"children":["What Papr Provides ✅"]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":[{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Two Graph Generation Modes:"]}]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":[{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["1. Auto Mode (Extraction-based):"]}]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# LLM extracts relationships from content\nclient.memory.add(\n content=\"Jane Smith works at Acme Corp as CTO. Acme Corp is partnered with TechVentures.\",\n memory_policy={\"mode\": \"auto\", \"schema_id\": osmosis_schema_id}\n)\n\n# Papr automatically creates:\n# - Jane Smith (Person node)\n# - Acme Corp (Company node)\n# - TechVentures (Company node)\n# - WORKS_AT relationship (Jane -> Acme)\n# - PARTNERED_WITH relationship (Acme -> TechVentures)\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":[{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Each extracted relationship is tied to the memory (source):"]}]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"header":{"controls":{"copy":{}}},"source":"(Memory) --EXTRACTED--> (Relationship) --CONNECTS--> (Node)\n"},"children":[]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":[{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["2. Manual Mode (Explicit):"]}]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# Developer specifies exact graph structure\nclient.memory.add(\n content=\"Explicit claim linking\",\n memory_policy={\n \"mode\": \"manual\",\n \"nodes\": [\n {\"id\": \"claim_1\", \"type\": \"Claim\", \"properties\": {\"statement\": \"X\"}},\n {\"id\": \"claim_2\", \"type\": \"Claim\", \"properties\": {\"statement\": \"Y\"}}\n ],\n \"relationships\": [\n {\n \"source\": \"claim_1\",\n \"target\": \"claim_2\",\n \"type\": \"SUPPORTS\",\n \"properties\": {\"confidence\": 0.8, \"inferred\": False}\n }\n ]\n }\n)\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":[{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Custom Inference Layer:"]}]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# Developer can build inference rules\nasync def infer_transitive_relationships():\n \"\"\"Example: If A→B and B→C, infer A→C\"\"\"\n \n # 1. Query existing relationships\n query = \"\"\"\n query GetChains {\n relationships(where: { type: \"RELATED_TO\" }) {\n source { id }\n target { id }\n properties\n }\n }\n \"\"\"\n \n result = await client.graphql.query(query)\n \n # 2. Find transitive patterns\n chains = find_transitive_chains(result['relationships'])\n \n # 3. Create inferred relationships\n for chain in chains:\n await client.memory.add(\n content=f\"Inferred relationship: {chain['source']} -> {chain['target']}\",\n memory_policy={\n \"mode\": \"manual\",\n \"relationships\": [\n {\n \"source\": chain['source'],\n \"target\": chain['target'],\n \"type\": \"RELATED_TO\",\n \"properties\": {\n \"inferred\": True,\n \"inference_rule\": \"transitive\",\n \"confidence\": 0.7\n }\n }\n ]\n }\n )\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":[{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Relationship Provenance:"]}]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# Can track whether relationship is extracted or inferred\nrelationship_types={\n \"SUPPORTS\": {\n \"properties\": {\n \"provenance_type\": {\n \"type\": \"string\", \n \"enum_values\": [\"extracted\", \"inferred\", \"manual\"]\n },\n \"extraction_source\": {\"type\": \"string\"},\n \"inference_rule\": {\"type\": \"string\"}\n }\n }\n}\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":4,"id":"gap-analysis-️-3"},"children":["Gap Analysis ⚠️"]},{"$$mdtype":"Tag","name":"ul","attributes":{},"children":[{"$$mdtype":"Tag","name":"li","attributes":{},"children":["✅ Relationships are tied to source memories (provenance)"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["✅ Can mark relationships as extracted vs inferred"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["✅ Manual mode for explicit relationship creation"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["❌ No built-in inference engine"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["❌ No rule-based relationship derivation"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["❌ Transitive/logical inference requires custom code"]}]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":[{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Verdict:"]}," Relationships are ",{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["primarily extracted, not inferred"]},". Developer can build inference layer using GraphQL queries + manual relationship creation."]},{"$$mdtype":"Tag","name":"hr","attributes":{},"children":[]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":3,"id":"question-5-single-source-vs.-multi-source-corroboration"},"children":["Question 5: Single-Source vs. Multi-Source Corroboration"]},{"$$mdtype":"Tag","name":"blockquote","attributes":{},"children":[{"$$mdtype":"Tag","name":"p","attributes":{},"children":["\"Do you differentiate between single-source extracted claims and multi-source corroborated knowledge?\""]}]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":4,"id":"what-papr-provides--4"},"children":["What Papr Provides ✅"]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":[{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Entity Resolution Across Sources:"]}]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# Document 1\nclient.memory.add(\n content=\"Product X costs $100 (from pricing sheet)\",\n memory_policy={\"mode\": \"auto\", \"schema_id\": osmosis_schema_id}\n)\n\n# Document 2 \nclient.memory.add(\n content=\"Product X is priced at $100 (from sales deck)\",\n memory_policy={\"mode\": \"auto\", \"schema_id\": osmosis_schema_id}\n)\n\n# Document 3\nclient.memory.add(\n content=\"Product X: $100 (from website)\",\n memory_policy={\"mode\": \"auto\", \"schema_id\": osmosis_schema_id}\n)\n\n# Papr's entity resolution merges \"Product X\" into single node\n# Can query which memories reference it\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":[{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Corroboration is Built-In via Deduplication:"]}]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":[{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Papr automatically:"]}]},{"$$mdtype":"Tag","name":"ol","attributes":{},"children":[{"$$mdtype":"Tag","name":"li","attributes":{},"children":["Merges same claim from multiple sources → ONE node"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["Creates EXTRACTED_FROM relationship for each source"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["GraphQL can count these relationships"]}]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":[{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Simple Query to Get Corroboration:"]}]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# No custom service needed - just query!\nquery = \"\"\"\nquery GetClaimCorroboration($subject: String!) {\n claims(where: { subject: $subject }) {\n object\n statement\n sources { # Papr automatically populates this\n document_id\n version\n authority\n date\n }\n }\n}\n\"\"\"\n\nresult = await client.graphql.query(query, {\"subject\": \"Product X price\"})\n\n# Analyze results\nfor claim in result['data']['claims']:\n source_count = len(claim['sources'])\n official_count = sum(1 for s in claim['sources'] if s['authority'] == 'official')\n \n print(f\"Claim: {claim['object']}\")\n print(f\" Supported by {source_count} sources ({official_count} official)\")\n \n # Apply resolution rules\n if source_count >= 3 and official_count >= 2:\n print(f\" ✅ High confidence (3+ sources, 2+ official)\")\n elif source_count == 1:\n print(f\" ⚠️ Single source - needs verification\")\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":[{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Optional: Store Computed Score:"]}]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# If you want to cache the score on the node\nawait client.memory.add(\n content=f\"Update corroboration score\",\n memory_policy={\n \"mode\": \"manual\",\n \"node_constraints\": [{\n \"node_type\": \"Claim\",\n \"search\": {\"properties\": [{\"name\": \"id\", \"mode\": \"exact\", \"value\": claim_id}]},\n \"set\": {\n \"source_count\": source_count,\n \"corroboration_score\": source_count / 5.0\n }\n }]\n }\n)\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":[{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Key Point:"]}," Counting sources is a ",{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["simple GraphQL query"]},", not a complex background service. Papr's deduplication does the heavy lifting."]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":4,"id":"gap-analysis-️-4"},"children":["Gap Analysis ⚠️"]},{"$$mdtype":"Tag","name":"ul","attributes":{},"children":[{"$$mdtype":"Tag","name":"li","attributes":{},"children":["✅ ",{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Entity merging across sources"]}," (automatic via unique_identifiers)"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["✅ ",{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Multi-source relationships"]}," (EXTRACTED_FROM to each source)"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["✅ ",{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["GraphQL counts sources"]}," (via relationship traversal)"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["✅ ",{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Simple query"]}," shows source count and details"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["⚠️ ",{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Corroboration formula"]}," is developer's choice (count? weighted? freshness?)"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["⚠️ ",{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Caching score"]}," optional (can compute on-demand or store on node)"]}]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":[{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Verdict:"]}," Multi-source tracking is ",{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["automatic"]},". Counting sources is a simple GraphQL query. Developer only chooses scoring formula (if needed)."]},{"$$mdtype":"Tag","name":"hr","attributes":{},"children":[]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":2,"id":"recommended-architecture"},"children":["Recommended Architecture"]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"header":{"controls":{"copy":{}}},"source":"┌─────────────────────────────────────────────────────────────┐\n│ OSMOSIS APPLICATION LAYER │\n│ ┌──────────────────────────────────────────────────────┐ │\n│ │ Governance Services (Custom Logic) │ │\n│ │ • Conflict Detection │ │\n│ │ • Corroboration Scoring │ │\n│ │ • Version History Management │ │\n│ │ • Inference Engine (transitive, rule-based) │ │\n│ │ • Evidence Strength Calculation │ │\n│ └──────────────────────────────────────────────────────┘ │\n│ ↕︎ │\n│ ┌──────────────────────────────────────────────────────┐ │\n│ │ Osmosis Data Model (Custom Schema) │ │\n│ │ • Claim nodes (statement, confidence, version) │ │\n│ │ • Source nodes (document, page, authority) │ │\n│ │ • ConflictSet nodes (subject, resolution_status) │ │\n│ │ • KnowledgeVersion nodes (version, timespan) │ │\n│ │ • EXTRACTED_FROM, CONFLICTS_WITH, SUPERSEDES │ │\n│ └──────────────────────────────────────────────────────┘ │\n└─────────────────────────────────────────────────────────────┘\n ↕︎\n┌─────────────────────────────────────────────────────────────┐\n│ PAPR MEMORY API │\n│ ┌──────────────────────────────────────────────────────┐ │\n│ │ Knowledge Graph Storage │ │\n│ │ • Neo4j graph database │ │\n│ │ • Custom schema support │ │\n│ │ • Node/relationship creation │ │\n│ │ • Entity resolution & merging │ │\n│ └──────────────────────────────────────────────────────┘ │\n│ ┌──────────────────────────────────────────────────────┐ │\n│ │ Hybrid Retrieval Engine │ │\n│ │ • Vector embeddings (semantic search) │ │\n│ │ • Keyword search (BM25) │ │\n│ │ • Graph traversal (multi-hop) │ │\n│ └──────────────────────────────────────────────────────┘ │\n│ ┌──────────────────────────────────────────────────────┐ │\n│ │ GraphQL Query Layer │ │\n│ │ • Complex relationship queries │ │\n│ │ • Aggregations & analytics │ │\n│ │ • Multi-hop traversals │ │\n│ └──────────────────────────────────────────────────────┘ │\n└─────────────────────────────────────────────────────────────┘\n"},"children":[]},{"$$mdtype":"Tag","name":"hr","attributes":{},"children":[]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":2,"id":"why-can-they-build-this-with-papr"},"children":["Why Can They Build This with Papr?"]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":3,"id":"core-strengths-that-enable-osmosis"},"children":["Core Strengths That Enable Osmosis"]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":4,"id":"1.-"},"children":["1. ",{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Custom Schema System"]}," ✅"]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# Full control over domain ontology\nschema = client.schemas.create(\n name=\"Osmosis\",\n node_types={\n \"Claim\": {...},\n \"Source\": {...},\n \"ConflictSet\": {...},\n \"Evidence\": {...}\n },\n relationship_types={\n \"EXTRACTED_FROM\": {...},\n \"CONFLICTS_WITH\": {...},\n \"SUPPORTS\": {...},\n \"REFUTES\": {...}\n }\n)\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":[{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Why This Matters:"]}]},{"$$mdtype":"Tag","name":"ul","attributes":{},"children":[{"$$mdtype":"Tag","name":"li","attributes":{},"children":["Define exactly what a \"claim\" is"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["Model provenance relationships explicitly"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["Track conflict resolution status"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["Store confidence/corroboration scores"]}]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":4,"id":"2.-"},"children":["2. ",{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Node Constraints"]}," ✅"]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# Force properties onto nodes\nmemory_policy={\n \"node_constraints\": [\n {\n \"node_type\": \"Claim\",\n \"set\": {\n \"confidence\": 0.95,\n \"extraction_method\": \"llm\",\n \"extracted_at\": \"2026-02-16T10:00:00Z\",\n \"document_version\": \"v2.1\"\n }\n }\n ]\n}\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":[{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Why This Matters:"]}]},{"$$mdtype":"Tag","name":"ul","attributes":{},"children":[{"$$mdtype":"Tag","name":"li","attributes":{},"children":["Every claim gets metadata automatically"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["Consistent property injection"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["Source attribution baked in"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["Version tracking per claim"]}]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":4,"id":"3.-"},"children":["3. ",{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["GraphQL Analytics"]}," ✅"]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# Complex queries for governance\nquery = \"\"\"\nquery ConflictAnalysis($subject: String!) {\n conflict_sets(where: { subject: $subject, resolution_status: \"unresolved\" }) {\n subject\n predicate\n claims {\n statement\n confidence\n sources {\n document_id\n authority_level\n version\n }\n }\n }\n}\n\"\"\"\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":[{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Why This Matters:"]}]},{"$$mdtype":"Tag","name":"ul","attributes":{},"children":[{"$$mdtype":"Tag","name":"li","attributes":{},"children":["Find all conflicts for a subject"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["Compare source authority levels"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["Identify version discrepancies"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["Aggregate corroboration counts"]}]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":4,"id":"4.-"},"children":["4. ",{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Flexible Metadata"]}," ✅"]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# Store any custom fields\nmetadata={\n \"claim_type\": \"factual\",\n \"extraction_confidence\": 0.92,\n \"reviewed_by_human\": False,\n \"conflict_status\": \"none\",\n \"source_authority\": \"official\",\n \"as_of_version\": \"v2.1\"\n}\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":[{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Why This Matters:"]}]},{"$$mdtype":"Tag","name":"ul","attributes":{},"children":[{"$$mdtype":"Tag","name":"li","attributes":{},"children":["Track governance workflow state"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["Store review status"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["Flag conflicts"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["Version everything"]}]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":4,"id":"5.-"},"children":["5. 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",{"$$mdtype":"Tag","name":"strong","attributes":{},"children":["Version History Service"]}]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# Track knowledge evolution\nasync def track_version_change(claim_id, new_version):\n # Create new KnowledgeVersion node\n # Link old version → new version (SUPERSEDES)\n # Update claims to reference new version\n pass\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":3,"id":"4."},"children":["4. 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\"authority\": \"official\", \"date\": \"2024-01-01\"}\n)\n\n# Document 2 (v2.0 spec)\nclient.document.upload(\n file=open(\"api_spec_v2.pdf\", \"rb\"),\n schema_id=osmosis_schema_id,\n metadata={\"version\": \"v2.0\", \"authority\": \"official\", \"date\": \"2025-06-01\"}\n)\n\n# Document 3 (blog post)\nclient.document.upload(\n file=open(\"blog_pricing.md\", \"rb\"),\n schema_id=osmosis_schema_id,\n metadata={\"version\": \"unknown\", \"authority\": \"unofficial\", \"date\": \"2025-12-01\"}\n)\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":3,"id":"step-2-extract-claims-automatic"},"children":["Step 2: Extract Claims (Automatic)"]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":["Papr's extraction creates:"]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"header":{"controls":{"copy":{}}},"source":"Claim: \"Rate limit is 100/hour\"\n ← EXTRACTED_FROM → Source: \"api_spec_v1.pdf\" (page 12)\n version: \"v1.0\"\n confidence: 0.95\n\nClaim: \"Rate limit is 1000/hour\"\n ← EXTRACTED_FROM → Source: \"api_spec_v2.pdf\" (page 15)\n version: \"v2.0\"\n confidence: 0.98\n\nClaim: \"Rate limit is 1000/hour\"\n ← EXTRACTED_FROM → Source: \"blog_pricing.md\" \n version: \"unknown\"\n confidence: 0.75\n"},"children":[]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":3,"id":"step-3-conflict-detection-simple-query"},"children":["Step 3: Conflict Detection (Simple Query)"]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# Query for all claims about \"Rate limit\"\nquery = \"\"\"\nquery FindConflicts($subject: String!) {\n claims(where: { subject: $subject }) {\n object\n sources {\n document_id\n version\n authority\n date\n }\n }\n}\n\"\"\"\n\nresult = await client.graphql.query(query, {\"subject\": \"Rate limit\"})\n\n# Analyze: different values = conflict\nunique_values = set(claim['object'] for claim in result['claims'])\n\nif len(unique_values) > 1:\n print(f\"⚠️ CONFLICT: {unique_values}\")\n # Returns: {\"100/hour\", \"1000/hour\"}\n \n for claim in result['claims']:\n print(f\" {claim['object']}: {len(claim['sources'])} sources\")\n # Output:\n # 100/hour: 1 source (v1.0 spec)\n # 1000/hour: 2 sources (v2.0 spec + blog)\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":3,"id":"step-4-resolution-logic-custom"},"children":["Step 4: Resolution Logic (Custom)"]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# Osmosis resolution strategy: \"most recent official source wins\"\nasync def resolve_conflict(conflict_set_id):\n # Query claims in conflict set\n query = \"\"\"\n query ConflictClaims($conflict_id: ID!) {\n conflict_set(id: $conflict_id) {\n claims {\n id\n object\n sources {\n authority\n version\n date\n }\n }\n }\n }\n \"\"\"\n \n result = await client.graphql.query(query, {\"conflict_id\": conflict_set_id})\n \n # Filter official sources\n official_claims = [c for c in result['claims'] \n if c['sources'][0]['authority'] == 'official']\n \n # Pick most recent\n winner = max(official_claims, key=lambda c: c['sources'][0]['date'])\n \n # Update conflict set\n await update_conflict_set(\n conflict_set_id,\n resolution_status=\"resolved\",\n winner_claim_id=winner['id'],\n resolution_rule=\"most_recent_official\"\n )\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":3,"id":"step-5-apply-resolution-rules-simple-logic"},"children":["Step 5: Apply Resolution Rules (Simple Logic)"]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# From Step 3, we have:\n# Claim 1: \"100/hour\" - 1 source (v1.0, official)\n# Claim 2: \"1000/hour\" - 2 sources (v2.0 official + blog unofficial)\n\n# Resolution strategy: \"Most sources from official documents\"\nclaims_analyzed = []\nfor claim in result['claims']:\n official_sources = [s for s in claim['sources'] if s['authority'] == 'official']\n claims_analyzed.append({\n 'value': claim['object'],\n 'total_sources': len(claim['sources']),\n 'official_sources': len(official_sources),\n 'most_recent': max(s['date'] for s in claim['sources'])\n })\n\n# Apply scoring\ndef resolution_score(claim):\n return (\n claim['official_sources'] * 10 + # Official sources weighted high\n claim['total_sources'] * 2 + # Total sources matter\n recency_weight(claim['most_recent']) # Recent is better\n )\n\nwinner = max(claims_analyzed, key=resolution_score)\nprint(f\"✅ Resolved: {winner['value']}\")\n# Output: \"1000/hour\" (v2.0 is more recent + has official source)\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":3,"id":"step-6-version-tracking-custom"},"children":["Step 6: Version Tracking 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