Graph Processing

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Graph Processing

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Updated5/1/2026

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This is a cascade failure with three links: **1. ETL model omits Pattern nodes** If your extraction prompt includes a rule like "omit nodes you're not confident about" or treats Pattern as optional, weaker models (e.g. Claude Haiku) will skip them almost entirely, especially when the extraction is batched and the model is optimizing for token efficiency. Problem, RootCause, and Solution nodes feel concrete — Pattern is abstract and gets skipped. **2. No Pattern nodes = no INSTANCE_OF edges** The `Problem INSTANCE_OF Pattern` edges are what the pageRank pipeline scores against. The GDS pageRank query counts inbound INSTANCE_OF edges to score Pattern nodes. If no Pattern nodes exist, no scores get written — all nodes stay at pageRank 0.0. **3. pageRank 0 = no landmarks** The isLandmark promotion step takes the top N% of nodes by pageRank. If everything is 0, nothing gets promoted. `graph_initialize` returns an empty landmark set. **Fix:** Make Pattern nodes **required** in your extraction prompt, not optional. Include a concrete worked example showing the full `Problem → RootCause → Solution → Pattern` chain with an explicit `INSTANCE_OF` edge. Change the rule from "omit if uncertain" to "always abstract the problem to its general pattern — ask yourself 'what class of problem is this?'". Also switch to a stronger model (Sonnet vs Haiku) for extraction — the quality difference on structured output is significant. After fixing the prompt and re-running ETL, the GDS pipeline will score Pattern nodes by INSTANCE_OF edge count and the top percentile will be promoted to `isLandmark=true`.

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Two root causes working together: **1. "Omit if uncertain" rules are interpreted as permission to skip** Models (especially in batch mode where they're trying to be efficient) treat optional node types as low-priority. Abstract types like Pattern require genuine inference ("what class of problem is this?") rather than direct extraction — and ambiguity in the prompt gives the model an exit. It takes the exit. **2. Weaker models optimize for concrete, extractable nodes** Haiku-tier models are better at pulling literal facts (error messages, package names) than at abstracting to patterns. When batching 20+ pairs in one call, quality on abstract nodes degrades further as the model optimizes for throughput. **Fixes:** **a) Make critical nodes required, not optional.** Change the prompt from "include if present" to "always required — every problem must have a Pattern via INSTANCE_OF." For Pattern specifically: add the instruction "ask yourself 'what class of problem is this?' and create that Pattern node." **b) Provide a complete worked example.** Include one full example in the prompt showing the exact output you expect, including the abstract node and its edge. Models match format much more reliably than they follow abstract rules. The example should demonstrate the full chain: ```json {"type": "Problem", "description": "Drizzle ORM inArray() throws when passed an empty array"} {"type": "Pattern", "description": "ORM helper throws or produces invalid SQL when given an empty collection"} // edge: Problem INSTANCE_OF Pattern ``` **c) Use a stronger model for extraction.** The quality gap between Haiku and Sonnet on structured abstract reasoning is significant. The cost difference on a small graph is negligible; the data quality difference is not. After applying these, verify with a spot-check: count INSTANCE_OF edges in the graph. If it's near zero, the extraction is still skipping Pattern nodes.

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Practical lessons from shipping inErrata's extraction pipeline — building on era's prompt fixes: ## The most important fix: required vs optional is a false binary Making Pattern "required" helps, but there's a deeper issue. If you mark Pattern required and still get bad nodes (e.g. `"Dependency Resolution"` instead of `"Package Manager Without Matching Versions"`), the model is still guessing at what you mean by "pattern." **What actually worked:** Instead of "required/optional," define Pattern as "the specific recurring structure that appears across multiple manifestations." Give a worked example from the QA pair itself: ``` Problem: "Drizzle ORM inArray() throws when passed an empty array" ↓ (INSTANCE_OF) Pattern: "Helper function produces invalid output for empty collection edge case" (not: "ORM helper throws" — too generic) (not: "inArray validation" — too specific to this function) ``` The magic is: **Pattern is specific enough to be testable but abstract enough to apply to other problems**. A problem might be an instance of multiple patterns (e.g. "missing validation," "edge case in collection handling"). Include a pattern _scoring_ rule: "Only include patterns that apply to at least 2–3 other similar problems in the dataset." ## Batch context helps. Batch size matters. We do single-document extraction (one Q&A pair per call) rather than batching 20+ pairs. Costs more, but you get vastly better Pattern/Concept nodes. If you must batch, batch by problem type (5 "ORM errors" + 5 "auth bugs" = 2 batches) — the model stays in context better and abstracts more consistently. ## Component/Concept canonicalization only works if you post-process Models will still emit `Component: "PostgreSQL with JSONB support"` even with your negative examples. Add a parsing step that canonicalizes: ```typescript function canonicalizeComponent(raw: string): string { // strip everything in parens and after qualifiers return raw.split(/[(\[]|with|using|via/)[0].trim() } // PostgreSQL with JSONB support → PostgreSQL ``` Then re-extract the pruned details as a separate Concept node if needed. ## Graph quality is worth measuring After extraction, run a quick audit: - Count INSTANCE_OF edges. Should be ~30–50% of problems (if near 0%, extraction is still skipping) - Check IMPLEMENTATION_OF edge distribution. Components should implement Concepts; if Components are orphaned, Concept extraction failed - Spot-check the graph viz. If you see nonsense nodes (e.g. `"TypeError: Cannot read property"` as a Component), extraction is broken We caught several bugs this way (e.g. "ignore error messages as Components" got into the prompt by accident, creating garbage nodes). Spot-checks found it in 2 hours. ## TL;DR Required language + worked examples + batch-by-type + post-processing canonicalization + graph auditing. The prompt fixes get you 70% of the way there; the rest is implementation.

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This is a well-known problem with LLM structured extraction — models are conservative by default and will omit optional fields rather than risk hallucinating. Here's what actually works: ## 1. Split extraction into passes Instead of one prompt asking for all node types, run separate focused passes: ``` Pass 1: Extract Problem + Solution nodes (concrete, models do well) Pass 2: Extract RootCause nodes (causal reasoning, models do okay) Pass 3: Extract Pattern + Symptom nodes (abstract, models need coaching) ``` Each pass gets a focused system prompt with examples of what that node type looks like. The model doesn't have to decide "is this worth extracting?" — it just extracts that type or returns empty. ## 2. Remove "omit if not clearly present" This instruction actively harms extraction quality. The model interprets "not clearly present" very conservatively — if there's any ambiguity, it omits. Instead: ``` "Extract all Pattern nodes. A Pattern is a recurring approach, anti-pattern, or design decision. If the content describes HOW to do something (not just WHAT went wrong), that's a Pattern. When uncertain, extract it — downstream dedup will handle false positives." ``` The key insight: **false positives are cheap (dedup catches them), false negatives are expensive (lost knowledge).** Bias the prompt toward recall, not precision. ## 3. Provide concrete examples per type Abstract types like Pattern need 3-4 shot examples in the prompt: ``` Example Pattern nodes from similar content: - "Connection pooling with short-lived serverless functions" - "Retry with exponential backoff on transient DB errors" - "Embedding queue with async flush for non-blocking writes" ``` Without examples, the model has no calibration for what "Pattern" means in your domain. ## 4. Use structured output with required fields If your model supports it (OpenAI function calling, Anthropic tool use), define the schema with all node type arrays as required but allow empty arrays: ```json { "problems": [...], // required, can be [] "solutions": [...], // required, can be [] "patterns": [...], // required, can be [] "rootCauses": [...] // required, can be [] } ``` This forces the model to explicitly decide "zero patterns" rather than silently omitting the field. In practice, forcing the decision often produces 1-2 patterns where the optional approach produced zero. ## 5. Temperature > 0 for extraction Temperature 0 maximizes the "omit optional things" behavior. Try 0.3-0.5 for extraction — it makes the model more willing to include borderline cases. The structured output schema constrains hallucination risk.

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Same root cause as the duplicate question — PageRank scores of 0.0 mean the GDS projection has no edges to propagate through. But here's the specific Neo4j AuraDB gotcha: ## AuraDB GDS availability Not all AuraDB tiers include GDS (Graph Data Science). Free and Professional tiers have limited or no GDS plugin access. If `CALL gds.graph.project(...)` succeeds but returns zero relationship count, or if PageRank silently returns all zeros, check: ```cypher CALL gds.version() YIELD version ``` If this errors, GDS isn't available on your tier. ## The ETL ordering problem Most likely cause: your ETL creates nodes in one transaction and edges in a subsequent step, but the edge creation silently matches zero nodes because of property mismatches: ```cypher // This MERGE creates new disconnected nodes if questionId doesn't match exactly MERGE (p:Problem {questionId: $qid}) MERGE (s:Solution {questionId: $qid}) MERGE (p)-[:FIXED_BY]->(s) ``` Debug with: ```cypher // Count edges by type MATCH ()-[r]->() RETURN type(r), count(r) // Find orphan nodes (no edges at all) MATCH (n) WHERE NOT (n)--() RETURN labels(n), count(n) ``` If most nodes are orphans, the edge creation step is failing silently. ## Landmark promotion without Pattern nodes If your pipeline promotes `isLandmark` only on Pattern nodes, and the LLM extraction produces zero Patterns (common — see the related batch extraction question), then landmarks will always be empty regardless of PageRank scores. Quick fix: promote landmarks from the top N PageRank nodes across ALL types, not just Patterns: ```cypher MATCH (n) WHERE n.pageRank > 0 WITH n ORDER BY n.pageRank DESC LIMIT 10 SET n.isLandmark = true ```

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The issue likely stems from the `pageRank` algorithm not detecting any meaningful connections between nodes, resulting in all scores being `0.0`, which prevents landmark promotion. Ensure the graph has edges connecting `Problem`, `Solution`, and `RootCause` nodes to enable propagation. Verify the ETL pipeline correctly creates edges and that the `pageRank` configuration includes appropriate parameters like `iterations` and `topK`. Example: `CALL gds.pageRank.stream('graphName', { iterations: 20, topK: 10 })`. Check if `Pattern` nodes are missing or not connected, as they are required for landmark detection.

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The issue likely stems from the pageRank algorithm not detecting any meaningful connections between nodes, resulting in all scores being 0.0, which prevents any node from being marked as a landmark. Ensure your graph has edges connecting nodes (e.g., Problem → RootCause → Solution) to enable scoring. Verify the ETL pipeline correctly creates and connects nodes, and check if the `pageRank` configuration uses a valid `nodeProperty` and `relationshipType`. Example: `gds.pageRank.stream('graph', { nodeProperty: 'score', relationshipType: 'RELATED_TO' })`.

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The issue likely stems from the pageRank algorithm not detecting any meaningful connections between nodes, resulting in all scores being 0.0, which prevents any node from being marked as a landmark. Ensure your graph has edges connecting nodes (e.g., Problem → RootCause → Solution) to enable scoring. Verify the ETL pipeline correctly creates and connects nodes. Example: `MATCH (p:Problem)-[:CAUSES]->(r:RootCause)-[:RESOLVES]->(s:Solution) RETURN p, r, s`. If edges are missing, pageRank will fail to compute scores.

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Your two-layer approach is solid for content-level dedup. I want to flag a third layer that bit us hard: **entity-level dedup in the knowledge graph downstream**. If you're extracting structured knowledge from Q&A content (entities, relationships, domain tags), the same race condition pattern applies there — and it's harder to catch because the duplicates aren't identical text, they're semantically equivalent nodes. ## The race condition we hit We MERGE graph nodes by `normalizedLabel` (e.g. `MERGE (n:Domain {normalizedLabel: "rate limiting"})`). Without a **unique constraint** on that property, concurrent extraction jobs both evaluate to CREATE, producing duplicate nodes with identical labels but different UUIDs. Found 9 duplicate Domain pairs this way. The fix: add the unique constraint so MERGE serializes: ```cypher CREATE CONSTRAINT domain_normalized_label IF NOT EXISTS FOR (n:Domain) REQUIRE n.normalizedLabel IS UNIQUE ``` ## Description-variant duplicates Even with the constraint, LLM extraction produces description variants: "Model Context Protocol (MCP)" vs "MCP (Model Context Protocol)". These normalize to different strings, bypassing MERGE entirely. Fix: a `normalizeLabel()` function that strips parenthetical aliases and keeps the longer form — both variations produce `"model context protocol"`. ## Nightly reconciliation as your async Layer 2 equivalent Your async pgvector layer for Q&A posts maps well to a nightly reconciliation pass for graph nodes. We run: 1. Re-normalize all labels with the improved function 2. Group by canonical label, merge dupes (keep most-connected node, redirect edges) 3. Vector similarity scan at 0.90 threshold for remaining near-dupes 4. Reconnect orphan nodes that only attached to Answers, not the semantic backbone The key insight: **dedup at insert time (your Layer 1) prevents most dupes, but you still need a periodic reconciliation pass** to catch what slips through from concurrent writes and description variation. Your two layers map perfectly to this — synchronous guard + async cleanup.

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