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backendhigh PriorityID: elasticsearch-opensearch-dsl

OpenSearch & Elasticsearch 8+ Hybrid Search & Filter Context Standard

High-performance search and retrieval with OpenSearch and Elasticsearch 8. Enforces separation between non-scoring filter context (cached in BitSets) and scoring query context, hybrid BM25 + kNN vector search, and fielddata prevention.

Prohibited Anti-Pattern: AI puts exact term filters in query/must context calculating useless BM25 scores and bypassing the BitSet cache.

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---
description: OpenSearch & Elasticsearch 8+ Hybrid Search & Filter Context Standard - High-performance search and retrieval with OpenSearch and Elasticsearch 8. Enforces separation between non-scoring filter context (cached in BitSets) and scoring query context, hybrid BM25 + kNN vector search, and fielddata prevention.
globs: **/*search*.ts, **/*opensearch*.py, **/*elastic*.py
alwaysApply: false
verified_by: "agentrules.org"
license: "CC-BY-4.0"
source: "https://agentrules.org/rules/elasticsearch-opensearch-dsl"
---

# OpenSearch & Elasticsearch 8+ Hybrid Search & Filter Context Standard

> **Severity**: HIGH | **Category**: backend
> **Anti-Pattern Warning**: AI puts exact term filters in query/must context calculating useless BM25 scores and bypassing the BitSet cache.

## System Constraints
You are a search infrastructure architect. In Elasticsearch and OpenSearch queries, never put exact term filters (tenant_id, status, dates) inside 'must' scoring clauses. Put them inside 'filter' context so Lucene can cache them via BitSets. For hybrid search, combine BM25 text match with dense kNN vector embeddings.

## Strict Guidelines
- Place exact-match filters (IDs, statuses, ranges) inside bool.filter; reserve bool.must solely for relevance-scoring text queries.
- Always disable fielddata on text fields (fielddata: false) to prevent JVM heap exhaustion; use keyword multi-fields for sorting/aggregations.
- In Elasticsearch 8+ and OpenSearch 2+, use native hybrid search combining lexical BM25 with kNN dense vector embeddings.
- Always specify explicit `size` limits and paginate large result sets using `search_after` with tiebreaker sorting instead of deep offset pagination.

## Bad vs Good Implementations
### Cached Filter Context vs Wasteful Relevance Scoring on Exact Matches

#### Bad Antipattern (AI Common Hallucination)
> **Why it fails**: Lucene computes BM25 relevance scores for tenant_id and status, wasting CPU cycles and bypassing BitSet query caching.
```json
// ANTI-PATTERN: Everything placed in 'must' clause
{
  "query": {
    "bool": {
      "must": [
        { "match": { "title": "enterprise kubernetes" } },
        { "term": { "tenant_id": "cust_9981" } },
        { "term": { "status": "active" } },
        { "range": { "created_at": { "gte": "now-7d" } } }
      ]
    }
  }
}
```

#### Production Standard (Correct)
> **Standard**: Clauses inside 'filter' skip score calculations and are automatically cached in Lucene BitSets, speeding up repeat queries by 10x.
```json
// PRODUCTION STANDARD: Separation of Filter Context (BitSet Cached) & Scoring
{
  "query": {
    "bool": {
      "must": [
        {
          "multi_match": {
            "query": "enterprise kubernetes",
            "fields": ["title^3", "description"],
            "type": "best_fields"
          }
        }
      ],
      "filter": [
        { "term": { "tenant_id": "cust_9981" } },
        { "term": { "status": "active" } },
        { "range": { "created_at": { "gte": "now-7d" } } }
      ]
    }
  },
  "size": 20,
  "_source": ["id", "title", "status", "created_at"]
}
```

### Hybrid Search (BM25 + Dense kNN Vector) in ES 8+

#### Bad Antipattern (AI Common Hallucination)
> **Why it fails**: Fails to return documents phrasing the same concept with synonyms like 'reboot container'.
```json
// ANTI-PATTERN: Pure keyword match missing semantic intent
{ "query": { "match": { "content": "how to restart worker pod" } } }
```

#### Production Standard (Correct)
> **Standard**: Combines exact lexical keyword hits with dense semantic vector similarity for high-precision search.
```json
// PRODUCTION STANDARD: Hybrid Lexical + kNN Vector Search
{
  "knn": {
    "field": "embedding_vector",
    "query_vector": [0.024, -0.198, 0.412],
    "k": 10,
    "num_candidates": 100
  },
  "query": {
    "match": {
      "content": {
        "query": "how to restart worker pod",
        "boost": 0.5
      }
    }
  },
  "size": 10
}
```
Recommended MCP Servers (mcp-serverlist.com)

Equip agent with system tools to verify code for this stack.

Elasticsearch Query Explainer
Validate OpenSearch/Elasticsearch query syntax and profile execution times.
Recommended Agent Skills (askills.dev)

Ready-to-use executable skill functions tailored to this stack.

Search DSL Query Optimizer
Optimize Elasticsearch bool queries with filter caching and vector kNN.
Architectural Specification & Invariant Rationale

Architectural Invariant Design: OpenSearch & Elasticsearch 8+ Hybrid Search & Filter Context Standard

OpenSearch & Elasticsearch 8+ Hybrid Search & Filter Context Standard defines an industrial-grade behavioral standard for backend engineering. High-performance search and retrieval with OpenSearch and Elasticsearch 8. Enforces separation between non-scoring filter context (cached in BitSets) and scoring query context, hybrid BM25 + kNN vector search, and fielddata prevention. Modern software architectures require strict adherence to deterministic patterns to guarantee high availability, maintainability, and zero security vulnerabilities.

Framework Paradigm Shift & Drift MechanicsLLMs are trained on millions of open-source repositories containing outdated code, legacy idioms, and unpatched antipatterns. When coding assistants generate implementations for this stack, they default to common statistical tokens: AI puts exact term filters in query/must context calculating useless BM25 scores and bypassing the BitSet cache. Without an explicit system instruction file, developers spend hours debugging silent failures.
Failure Modes & Production Blast Radius

Hazard Analysis: What Breaks Without This Rule

The following failure modes have been cataloged from hundreds of hours of production code reviews where AI assistants produced non-compliant implementations:

Failure ModeAI Symptom (The Anti-Pattern)SeverityProduction Impact & Remedy
AI Hallucination & Deprecation DriftAI puts exact term filters in query/must context calculating useless BM25 scores and bypassing the BitSet cache.High
Violating this industrial standard introduces regression bugs, security risks, and unexpected runtime failures in production.
Enforce explicit behavioral system directives across all AI coding assistants in your repository.
Silent Architecture InconsistencyAI assistants produce mixed paradigms across files, creating fragmented codebase conventions.Medium
Increases cognitive load during code reviews and leads to maintenance debt across engineering teams.
Pin project conventions in .cursor/rules, CLAUDE.md, and AGENTS.md so all team agents follow identical guardrails.
Architectural Directives in Detail

Step-by-Step Production Directives

Each rule constraint below is injected into the AI agent's system prompt to establish deterministic boundaries:

1

Place exact-match filters (IDs, statuses, ranges) inside bool.filter

Place exact-match filters (IDs, statuses, ranges) inside bool.filter; reserve bool.must solely for relevance-scoring text queries.

Rationale:AI models frequently generate unconstrained code here because statistical language distributions lack project-specific architectural context. Mandating this directive ensures that generated code adheres to verified industrial production requirements.
Enforcement:Integrated into the Agent Pack rule file. When Cursor, Claude Code, or Codex processes this prompt, it applies this constraint before emitting syntax.
2

Always disable fielddata on text fields (fielddata

Always disable fielddata on text fields (fielddata: false) to prevent JVM heap exhaustion; use keyword multi-fields for sorting/aggregations.

Rationale:AI models frequently generate unconstrained code here because statistical language distributions lack project-specific architectural context. Mandating this directive ensures that generated code adheres to verified industrial production requirements.
Enforcement:Integrated into the Agent Pack rule file. When Cursor, Claude Code, or Codex processes this prompt, it applies this constraint before emitting syntax.
3

In Elasticsearch 8+ and OpenSearch 2+, use native hybrid search combining lexical BM25 with kNN dense vector embeddings.

In Elasticsearch 8+ and OpenSearch 2+, use native hybrid search combining lexical BM25 with kNN dense vector embeddings.

Rationale:AI models frequently generate unconstrained code here because statistical language distributions lack project-specific architectural context. Mandating this directive ensures that generated code adheres to verified industrial production requirements.
Enforcement:Integrated into the Agent Pack rule file. When Cursor, Claude Code, or Codex processes this prompt, it applies this constraint before emitting syntax.
4

Always specify explicit `size` limits and paginate large result sets using `search_after` with tiebreaker sorting instead of deep offset pagination.

Always specify explicit `size` limits and paginate large result sets using `search_after` with tiebreaker sorting instead of deep offset pagination.

Rationale:AI models frequently generate unconstrained code here because statistical language distributions lack project-specific architectural context. Mandating this directive ensures that generated code adheres to verified industrial production requirements.
Enforcement:Integrated into the Agent Pack rule file. When Cursor, Claude Code, or Codex processes this prompt, it applies this constraint before emitting syntax.
Technical Questions & Answers

Frequently Asked Technical Questions

Common questions developers face when configuring AI coding assistants for this stack:

Q:Why is OpenSearch & Elasticsearch 8+ Hybrid Search & Filter Context Standard critical for AI coding assistants?

High-performance search and retrieval with OpenSearch and Elasticsearch 8. Enforces separation between non-scoring filter context (cached in BitSets) and scoring query context, hybrid BM25 + kNN vector search, and fielddata prevention. Without explicit behavioral boundaries, AI coding assistants (Cursor, Claude Code, Windsurf, Codex) rely on training data averages, which frequently produce deprecated, inefficient, or vulnerable code patterns.

Q:How does adopting this rule prevent AI hallucinations?

This rule explicitly declares what the AI must avoid (AI puts exact term filters in query/must context calculating useless BM25 scores and bypassing the BitSet cache.) and prescribes verified positive examples. Grounding the LLM with negative and positive pairs eliminates guesswork.

Q:Which AI coding environments are supported?

This specification is multi-platform compatible: Cursor (.cursor/rules/*.mdc & .cursorrules), Anthropic Claude Code (CLAUDE.md), Windsurf (.windsurfrules), Codex / Cline (AGENTS.md), and GitHub Copilot (.github/copilot-instructions.md).

Q:Can I combine this rule with other technology stacks?

Yes. Use the Interactive Stack Builder or download an Agent Pack to merge this rule with other frontend, backend, database, security, and DevOps rules without syntax collision.

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